diff --git a/.idea/runConfigurations/EVC_System_Laming_Validation_Test_Script__SPECIFY_ControlSignals__.xml b/.idea/runConfigurations/EVC_System_Laming_Validation_Test_Script__SPECIFY_ControlSignals__.xml
index 044a20175a6..001c85fccf0 100644
--- a/.idea/runConfigurations/EVC_System_Laming_Validation_Test_Script__SPECIFY_ControlSignals__.xml
+++ b/.idea/runConfigurations/EVC_System_Laming_Validation_Test_Script__SPECIFY_ControlSignals__.xml
@@ -6,13 +6,13 @@
       <env name="PYTHONUNBUFFERED" value="1" />
       <env name="PATH" value="~/anaconda/bin/:/users/jdc/bin:/Users/jdc/bin:/Users/jdc/anaconda/bin:/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin:/opt/X11/bin&#10;" />
     </envs>
-    <option name="SDK_HOME" value="$USER_HOME$/anaconda/envs/python35/bin/python" />
+    <option name="SDK_HOME" value="$USER_HOME$/anaconda/envs/python35/bin/python3.5" />
     <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/Scripts/TEST SCRIPTS" />
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
     <module name="PsyNeuLink" />
-    <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/TEST SCRIPTS/EVC System Laming Validation Test Script [SPECIFY ControlSignals]].py" />
+    <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/DEBUGGING SCRIPTS/EVC System Laming Validation Test Script [SPECIFY ControlSignals]].py" />
     <option name="PARAMETERS" value="" />
     <option name="SHOW_COMMAND_LINE" value="false" />
     <option name="EMULATE_TERMINAL" value="false" />
diff --git a/.idea/runConfigurations/Multilayer_Learning_Test_Script__WITH_GATING_.xml b/.idea/runConfigurations/Multilayer_Learning_Test_Script__WITH_GATING_.xml
index 835474c0ba4..17a4d45d47c 100644
--- a/.idea/runConfigurations/Multilayer_Learning_Test_Script__WITH_GATING_.xml
+++ b/.idea/runConfigurations/Multilayer_Learning_Test_Script__WITH_GATING_.xml
@@ -6,13 +6,13 @@
       <env name="PYTHONUNBUFFERED" value="1" />
       <env name="PATH" value="~/anaconda/bin/:/users/jdc/bin:/Users/jdc/bin:/Users/jdc/anaconda/bin:/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin:/opt/X11/bin&#10;" />
     </envs>
-    <option name="SDK_HOME" value="" />
+    <option name="SDK_HOME" value="$USER_HOME$/anaconda/envs/python35/bin/python" />
     <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/Scripts/TEST SCRIPTS" />
-    <option name="IS_MODULE_SDK" value="true" />
+    <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
-    <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/TEST SCRIPTS/Multilayer Learning Test Script [WITH GATING].py" />
+    <module name="PsyNeuLink (Dropbox)" />
+    <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/DEBUGGING SCRIPTS/Multilayer Learning Test Script [WITH GATING].py" />
     <option name="PARAMETERS" value="" />
     <option name="SHOW_COMMAND_LINE" value="false" />
     <option name="EMULATE_TERMINAL" value="false" />
diff --git a/.idea/runConfigurations/_EVC_System_Laming_Validation_Test_Script.xml b/.idea/runConfigurations/_EVC_System_Laming_Validation_Test_Script.xml
index 3d591cba49a..b91a9de1273 100644
--- a/.idea/runConfigurations/_EVC_System_Laming_Validation_Test_Script.xml
+++ b/.idea/runConfigurations/_EVC_System_Laming_Validation_Test_Script.xml
@@ -11,7 +11,7 @@
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="PsyNeuLink (Dropbox)" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/TEST SCRIPTS/EVC System Laming Validation Test Script.py" />
     <option name="PARAMETERS" value="" />
     <option name="SHOW_COMMAND_LINE" value="false" />
diff --git a/.idea/runConfigurations/_META_TEST.xml b/.idea/runConfigurations/_META_TEST.xml
index e31c4b4f241..45e56a57fbd 100644
--- a/.idea/runConfigurations/_META_TEST.xml
+++ b/.idea/runConfigurations/_META_TEST.xml
@@ -11,7 +11,7 @@
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="PsyNeuLink (Dropbox)" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/TEST SCRIPTS/META Test Script.py" />
     <option name="PARAMETERS" value="" />
     <option name="SHOW_COMMAND_LINE" value="false" />
diff --git a/.idea/runConfigurations/_Multilayer_Learning_Test_Script.xml b/.idea/runConfigurations/_Multilayer_Learning_Test_Script.xml
index d3bbb98be1e..43638df8227 100644
--- a/.idea/runConfigurations/_Multilayer_Learning_Test_Script.xml
+++ b/.idea/runConfigurations/_Multilayer_Learning_Test_Script.xml
@@ -6,7 +6,7 @@
       <env name="PYTHONUNBUFFERED" value="1" />
       <env name="PATH" value="~/anaconda/bin/:/users/jdc/bin:/Users/jdc/bin:/Users/jdc/anaconda/bin:/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin:/opt/X11/bin&#10;" />
     </envs>
-    <option name="SDK_HOME" value="$USER_HOME$/anaconda/envs/python35/bin/python" />
+    <option name="SDK_HOME" value="$USER_HOME$/anaconda/envs/python35/bin/python3.5" />
     <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/Scripts" />
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
diff --git a/.idea/runConfigurations/_Reinforcement_Learning_Test_Script.xml b/.idea/runConfigurations/_Reinforcement_Learning_Test_Script.xml
index abd86930ca1..fc84bda3df6 100644
--- a/.idea/runConfigurations/_Reinforcement_Learning_Test_Script.xml
+++ b/.idea/runConfigurations/_Reinforcement_Learning_Test_Script.xml
@@ -10,7 +10,7 @@
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="PsyNeuLink (Dropbox)" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/TEST SCRIPTS/Reinforcement Learning Test Script.py" />
     <option name="PARAMETERS" value="" />
     <option name="SHOW_COMMAND_LINE" value="false" />
diff --git a/.idea/runConfigurations/_Scratch_Pad.xml b/.idea/runConfigurations/_Scratch_Pad.xml
index d4abb3e0af1..297acc32aa5 100644
--- a/.idea/runConfigurations/_Scratch_Pad.xml
+++ b/.idea/runConfigurations/_Scratch_Pad.xml
@@ -5,12 +5,12 @@
     <envs>
       <env name="PYTHONUNBUFFERED" value="1" />
     </envs>
-    <option name="SDK_HOME" value="" />
+    <option name="SDK_HOME" value="$USER_HOME$/anaconda/envs/python35/bin/python" />
     <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/Scripts" />
-    <option name="IS_MODULE_SDK" value="true" />
+    <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="PsyNeuLink (Dropbox)" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" sample_coverage="true" runner="coverage.py" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/Scratch Pad.py" />
     <option name="PARAMETERS" value="" />
diff --git a/PsyNeuLink Tutorial.ipynb b/PsyNeuLink Tutorial.ipynb
index f6fcad5e590..960fa6c017f 100644
--- a/PsyNeuLink Tutorial.ipynb	
+++ b/PsyNeuLink Tutorial.ipynb	
@@ -74,9 +74,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from PsyNeuLink.Components.System import system\n",
@@ -86,7 +84,7 @@
     "from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism\n",
     "from PsyNeuLink.Components.Functions.Function import Linear, Logistic\n",
     "\n",
-    "from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection"
+    "from PsyNeuLink.Components.Projections.TransmissiveProjections.MappingProjection import MappingProjection"
    ]
   },
   {
@@ -113,13 +111,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "linear_transfer_mechanism = TransferMechanism(function=Linear(slope = 1, intercept = 0))"
+    "linear_transfer_mechanism = TransferMechanism(\n",
+    "    function=Linear(slope=1, intercept=0))"
    ]
   },
   {
@@ -131,24 +130,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 20,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[array([[ 1.]])]"
+       "array([[ 1.]])"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "linear_transfer_mechanism.run([1])"
+    "linear_transfer_mechanism.execute([1])"
    ]
   },
   {
@@ -173,7 +172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false
    },
@@ -191,41 +190,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 24,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "\n",
-      "****************************************\n",
-      "\n",
-      "\n",
-      "''Process-1' component' executing with:\n",
-      "- pathway: ['TransferMechanism-1']\n",
-      "- input: 4\n",
-      "\n",
-      "'TransferMechanism-1'  executed:\n",
-      "- input:  4.0\n",
-      "- params:\n",
-      "\tfunction: Linear\n",
-      "\t\tintercept: 0\n",
-      "\t\tslope: 1\n",
-      "\tinitial_value: None\n",
-      "\tnoise: 0.0\n",
-      "\ttime_scale: TimeScale.TRIAL\n",
-      "- output: 4.0\n",
-      "\n",
-      "''Process-1' component' completed:\n",
-      "- output: 4.0\n",
-      "\n",
-      "\n",
-      "****************************************\n",
-      "\n"
+      "\n\n****************************************\n\n\n'Process-1' executing with:\n- pathway: ['TransferMechanism-1']\n- input: [[4]]\n\n'TransferMechanism-1'  executed:\n- input:  4.0\n- params:\n\tfunction: Linear\n\t\tintercept: 0\n\t\tslope: 1\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 4.0\n\n'Process-1' completed:\n- output: 4.0\n\n\n****************************************\n\n"
      ]
     },
     {
@@ -234,7 +208,7 @@
        "array([ 4.])"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -254,7 +228,7 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [
     {
@@ -300,23 +274,149 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 25,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "---------------------------------------------------"
+      "\n'TransferMechanism-2'  executed:\n- input:  -3.0, -6.0\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.0474 0.00247\n-\n'TransferMechanism-2'  executed:\n- input:  -2.88, -5.76\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.0532 0.00314\n-\n'TransferMechanism-2'  executed:\n- input:  -2.76, -5.52\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.0595 0.00399\n-\n'TransferMechanism-2'  executed:\n- input:  -2.64, -5.28\n- params:\n\tfunction: Logistic\n\t\tbias: 0"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.0666 0.00507\n-\n'TransferMechanism-2'  executed:\n- input:  -2.52, -5.04\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.0745 0.00643\n-\n'TransferMechanism-2'  executed:\n- input:  -2.4, -4.8\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.0832 0.00816\n-\n'TransferMechanism-2'  executed:\n- input:  -2.28, -4.56\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.0928 0.0104\n-\n'TransferMechanism-2'  executed:\n- input:  -2.16, -4.32\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.103 0.0131\n-\n'TransferMechanism-2'  executed:\n- input:  -2.04, -4.08"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.115 0.0166\n-\n'TransferMechanism-2'  executed:\n- input:  -1.92, -3.84\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.128 0.021\n-\n'TransferMechanism-2'  executed:\n- input:  -1.8, -3.6\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.142 0.0266\n-\n'TransferMechanism-2'  executed:\n- input:  -1.68, -3.36\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.157 0.0336\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-2'  executed:\n- input:  -1.56, -3.12\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.174 0.0423\n-\n'TransferMechanism-2'  executed:\n- input:  -1.44, -2.88\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.192 0.0532\n-\n'TransferMechanism-2'  executed:\n- input:  -1.32, -2.64\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.211 0.0666\n-\n'TransferMechanism-2'  executed:\n- input:  -1.2, -2.4\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.231 0.0832\n-\n'TransferMechanism-2'  executed:\n- input:  -1.08, -2.16\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.254 0.103\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-2'  executed:\n- input:  -0.96, -1.92\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.277 0.128\n-\n'TransferMechanism-2'  executed:\n- input:  -0.84, -1.68\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.302 0.157\n-\n'TransferMechanism-2'  executed:\n- input:  -0.72, -1.44\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.327 0.192\n-\n'TransferMechanism-2'  executed:\n- input:  -0.6, -1.2\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.354 0.231\n-\n'TransferMechanism-2'  executed:\n- input:  -0.48, -0.96\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.382 0.277\n-\n'TransferMechanism-2'  executed:\n- input:  -0.36, -0.72\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.411 0.327\n-\n'TransferMechanism-2'  executed:\n- input:  -0.24, -0.48\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.44 0.382\n-\n'TransferMechanism-2'  executed:\n- input:  -0.12, -0.24\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.47 0.44\n-\n'TransferMechanism-2'  executed:\n- input:  0.0, 0.0\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.5 0.5\n-\n'TransferMechanism-2'  executed:\n- input:  0.12, 0.24\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.53 0.56\n-\n'TransferMechanism-2'  executed:\n- input:  0.24, 0.48\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.56 0.618\n-\n'TransferMechanism-2'  executed:\n- input:  0.36, 0.72\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.589 0.673\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-2'  executed:\n- input:  0.48, 0.96\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.618 0.723\n-\n'TransferMechanism-2'  executed:\n- input:  0.6, 1.2\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.646 0.769\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-2'  executed:\n- input:  0.72, 1.44\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.673 0.808\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-2'  executed:\n- input:  0.84, 1.68\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.698 0.843\n-\n'TransferMechanism-2'  executed:\n- input:  0.96, 1.92\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.723 0.872\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-2'  executed:\n- input:  1.08, 2.16\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.746 0.897\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-2'  executed:\n- input:  1.2, 2.4\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.769 0.917\n-\n'TransferMechanism-2'  executed:\n- input:  1.32, 2.64\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n- output: 0.789 0.933\n-\n'TransferMechanism-2'  executed:\n- input:  1.44, 2.88\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.808 0.947\n-\n'TransferMechanism-2'  executed:\n- input:  1.56, 3.12\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.826 0.958\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-2'  executed:\n- input:  1.68, 3.36\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.843 0.966\n-\n'TransferMechanism-2'  executed:\n- input:  1.8, 3.6\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.858 0.973\n-\n'TransferMechanism-2'  executed:\n- input:  1.92, 3.84\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.872 0.979\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-2'  executed:\n- input:  2.04, 4.08\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.885 0.983\n-\n'TransferMechanism-2'  executed:\n- input:  2.16, 4.32\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.897 0.987\n-\n'TransferMechanism-2'  executed:\n- input:  2.28, 4.56\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.907 0.99\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-2'  executed:\n- input:  2.4, 4.8\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.917 0.992\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-2'  executed:\n- input:  2.52, 5.04\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.926 0.994\n-\n'TransferMechanism-2'  executed:\n- input:  2.64, 5.28\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.933 0.995\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-2'  executed:\n- input:  2.76, 5.52\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.94 0.996\n-\n'TransferMechanism-2'  executed:\n- input:  2.88, 5.76\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.947 0.997\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-2'  executed:\n- input:  3.0, 6.0\n- params:\n\tfunction: Logistic\n\t\tbias: 0\n\t\tgain: 1\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.953 0.998\n-"
      ]
     },
     {
      "data": {
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eoU/9PpQNLpuu1zoOPPccjBkDb78N/ftnUZEiXiQsLIywsLDzjsXExGToWukN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\nIDAzMzNcEJiZmRkuCMzMzAz4B2GdC7hWChzBAAAAAElFTkSuQmCC\n",
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u/2InW49lMe2iSP55dXc8G/LFaXXF2fDTcxAxFPreapl9CuFiJNyFxR04lceM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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x111b14ac8>"
+       "<matplotlib.figure.Figure at 0x10a5077f0>"
       ]
      },
      "metadata": {},
@@ -325,17 +425,19 @@
    ],
    "source": [
     "# Create the mechanism\n",
-    "logistic_transfer_mechanism = TransferMechanism(default_input_value = [0, 0], function=Logistic(gain = 1, bias = 0))\n",
+    "logistic_transfer_mechanism = TransferMechanism(default_input_value=[0, 0],\n",
+    "                                                function=Logistic(gain=1,\n",
+    "                                                                  bias=0))\n",
     "\n",
     "# Package into a process\n",
-    "logistic_transfer_process = process(pathway = [logistic_transfer_mechanism])\n",
+    "logistic_transfer_process = process(pathway=[logistic_transfer_mechanism])\n",
     "\n",
     "# Iterate and plot\n",
     "xVals = np.linspace(-3, 3, num=51)\n",
     "y1Vals = np.zeros((51,))\n",
     "y2Vals = np.zeros((51,))\n",
     "for i in range(xVals.shape[0]):\n",
-    "    output = logistic_transfer_process.execute([xVals[i], xVals[i]*2])\n",
+    "    output = logistic_transfer_process.execute([xVals[i], xVals[i] * 2])\n",
     "    y1Vals[i] = output[0]\n",
     "    y2Vals[i] = output[1]\n",
     "    # Progress bar\n",
@@ -363,189 +465,704 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 26,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "---------------------------------------------------"
+      "\n'TransferMechanism-3'  executed:\n- input:  -3.0\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -4.0\n\n'TransferMechanism-4'  executed:\n- input:  -4.0\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.018\n-\n'TransferMechanism-3'  executed:\n- input:  -2.88\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -3.76\n\n'TransferMechanism-4'  executed:\n- input:  -3.76\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.0228\n-\n'TransferMechanism-3'  executed:\n- input:  -2.76\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -3.52\n\n'TransferMechanism-4'  executed:\n- input:  -3.52\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.0287\n-"
      ]
     },
     {
-     "data": {
-      "image/png": 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lkC2BW4CtgE2Bd4E/RsS3ywmsYr3xBhxxBBxyiAsXSVJXUk6HYCBwVUppeErp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\nwP8HzmYp/YovtV8AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x111c58eb8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "linear_input_unit = TransferMechanism(function = Linear(slope = 2, intercept = 2))\n",
-    "logistic_output_unit = TransferMechanism(function = Logistic())\n",
-    "mini_connected_network = process(pathway = [linear_input_unit, IDENTITY_MATRIX, logistic_output_unit])\n",
-    "\n",
-    "# Iterate and plot\n",
-    "xVals = np.linspace(-3, 3, num=51)\n",
-    "yVals = np.zeros((51,))\n",
-    "for i in range(xVals.shape[0]):\n",
-    "    yVals[i] = mini_connected_network.execute([xVals[i]])[0]\n",
-    "    # Progress bar\n",
-    "    print(\"-\", end=\"\")\n",
-    "plt.plot(xVals, yVals)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "IDENTITY_MATRIX is a keyword that provides a projection from the unit preceding it to the unit following that creates a one-to-one output to input projection between the two. Other useful projection keywords are...\n",
-    "\n",
-    "Now let's make our projection definition a bit more explicit."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-3'  executed:\n- input:  -2.64\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -3.28\n\n'TransferMechanism-4'  executed:\n- input:  -3.28\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.0363\n-\n'TransferMechanism-3'  executed:\n- input:  -2.52\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -3.04\n\n'TransferMechanism-4'  executed:\n- input:  -3.04\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.0457\n-"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "---------------------------------------------------"
+      "\n'TransferMechanism-3'  executed:\n- input:  -2.4\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -2.8\n\n'TransferMechanism-4'  executed:\n- input:  -2.8\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.0573\n-\n'TransferMechanism-3'  executed:\n- input:  -2.28\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -2.56\n\n'TransferMechanism-4'  executed:\n- input:  -2.56\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.0718\n-\n'TransferMechanism-3'  executed:\n- input:  -2.16"
      ]
     },
     {
-     "data": {
-      "image/png": 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lkC2BW4CtgE2Bd4E/RsS3ywmsYr3xBhxxBBxyiAsXSVJXUk6HYCBwVUppeErp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\nwP8HzmYp/YovtV8AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x111d86828>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "linear_input_unit = TransferMechanism(function = Linear(slope = 2, intercept = 2))\n",
-    "logistic_output_unit = TransferMechanism(function = Logistic())\n",
-    "\n",
-    "mapping_matrix = np.asarray([[1]])\n",
-    "unit_mapping_projection = MappingProjection(sender = linear_input_unit, receiver = logistic_output_unit, matrix = mapping_matrix)\n",
-    "mini_connected_network = process(pathway = [linear_input_unit, unit_mapping_projection, logistic_output_unit])\n",
-    "\n",
-    "# Iterate and plot\n",
-    "xVals = np.linspace(-3, 3, num=51)\n",
-    "yVals = np.zeros((51,))\n",
-    "for i in range(xVals.shape[0]):\n",
-    "    yVals[i] = mini_connected_network.execute([xVals[i]])[0]\n",
-    "    # Progress bar\n",
-    "    print(\"-\", end=\"\")\n",
-    "plt.plot(xVals, yVals)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This time we specified our mapping matrix (which is a 2-D numpy array) then explicitly initialized a *[MappingProjection](https://princetonuniversity.github.io/PsyNeuLink/MappingProjection.html)* with that matrix as well as its input and output mechanisms. Note: because we specified the input and output mechanisms in the projection itself, we didn't need to include it in the process pathway as it will infer its position from those parameters. Ultimately, however, this does the exact same thing as our keyword method above which is far less verbose for this common use case."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Systems\n",
-    "\n",
-    "The highest level at which models are considered in PsyNeuLink is that of the *[System](https://princetonuniversity.github.io/PsyNeuLink/System.html)*. A system is composed of one or more processes which can then run in unison. This allows system graphs to be more complex than the strictly linear ones of processes. Our first system will consist of two input nodes that converge on a single output mechanism. We will be modelling competition between color naming and word reading in the stroop task."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "colors = TransferMechanism(default_input_value=[0,0], function=Linear, name=\"Colors\")\n",
-    "words = TransferMechanism(default_input_value=[0,0], function=Linear(slope = 1.5), name=\"Words\")\n",
-    "response = TransferMechanism(default_input_value=[0,0], function=Logistic, name=\"Response\")\n",
-    "\n",
-    "color_naming_process = process(pathway = [colors, IDENTITY_MATRIX, response], name = \"Color Naming\")\n",
-    "word_reading_process = process(pathway = [words, IDENTITY_MATRIX, response], name = \"Word Reading\")\n",
-    "\n",
-    "mini_stroop = system(processes=[color_naming_process, word_reading_process], name='Stroop Model')\n",
-    "\n",
-    "# Note: Once again, make sure you do not run this cell multiple times. This would create multiple\n",
-    "# processes and systems that share the same variable name in the PsyNeuLink registry causing\n",
-    "# unexpected behavior."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The order of processes in the system initialization is important here as it is the order we will feed inputs to the system."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -2.32\n\n'TransferMechanism-4'  executed:\n- input:  -2.32\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.0895\n-\n'TransferMechanism-3'  executed:\n- input:  -2.04\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -2.08\n"
+     ]
+    },
     {
-     "data": {
-      "text/plain": [
-       "[array([ 0.73105858,  0.81757448]),\n",
-       " array(0.7743165274118242),\n",
-       " array(0.0018712501328105086)]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mini_stroop.execute([[1, 0], [0, 1]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-4'  executed:\n- input:  -2.08\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.111\n-\n'TransferMechanism-3'  executed:\n- input:  -1.92\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -1.84\n\n'TransferMechanism-4'  executed:\n- input:  -1.84\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.137\n-\n'TransferMechanism-3'  executed:\n- input:  -1.8\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -1.6\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-4'  executed:\n- input:  -1.6\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.168\n-\n'TransferMechanism-3'  executed:\n- input:  -1.68\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -1.36\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-4'  executed:\n- input:  -1.36\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.204\n-\n'TransferMechanism-3'  executed:\n- input:  -1.56\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -1.12\n\n'TransferMechanism-4'  executed:\n- input:  -1.12\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.246\n-\n'TransferMechanism-3'  executed:\n- input:  -1.44\n- params:\n\tfunction: Linear\n\t\tintercept: 2"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -0.88\n\n'TransferMechanism-4'  executed:\n- input:  -0.88\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.293\n-\n'TransferMechanism-3'  executed:\n- input:  -1.32\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -0.64\n\n'TransferMechanism-4'  executed:\n- input:  -0.64\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.345\n-\n'TransferMechanism-3'  executed:\n- input:  -1.2\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -0.4\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-4'  executed:\n- input:  -0.4\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.401\n-\n'TransferMechanism-3'  executed:\n- input:  -1.08\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -0.16\n\n'TransferMechanism-4'  executed:\n- input:  -0.16\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.46\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-3'  executed:\n- input:  -0.96\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.08\n\n'TransferMechanism-4'  executed:\n- input:  0.08\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.52\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-3'  executed:\n- input:  -0.84\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.32\n\n'TransferMechanism-4'  executed:\n- input:  0.32\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.579\n-\n'TransferMechanism-3'  executed:\n- input:  -0.72\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.56\n\n'TransferMechanism-4'  executed:\n- input:  0.56\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.636\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-3'  executed:\n- input:  -0.6\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.8\n\n'TransferMechanism-4'  executed:\n- input:  0.8\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n- output: 0.69\n-\n'TransferMechanism-3'  executed:\n- input:  -0.48\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 1.04\n\n'TransferMechanism-4'  executed:\n- input:  1.04\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.739\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-3'  executed:\n- input:  -0.36\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 1.28\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-4'  executed:\n- input:  1.28\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.782\n-\n'TransferMechanism-3'  executed:\n- input:  -0.24\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 1.52\n\n'TransferMechanism-4'  executed:\n- input:  1.52\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.821\n-\n'TransferMechanism-3'  executed:\n- input:  -0.12\n- params:\n\tfunction: Linear"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 1.76\n\n'TransferMechanism-4'  executed:\n- input:  1.76\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.853\n-\n'TransferMechanism-3'  executed:\n- input:  0.0\n- params:\n\tfunction: Linear"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 2.0\n\n'TransferMechanism-4'  executed:\n- input:  2.0\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.881\n-\n'TransferMechanism-3'  executed:\n- input:  0.12\n- params:\n\tfunction: Linear"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 2.24\n\n'TransferMechanism-4'  executed:\n- input:  2.24\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.904\n-\n'TransferMechanism-3'  executed:\n- input:  0.24\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 2.48\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-4'  executed:\n- input:  2.48\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.923\n-\n'TransferMechanism-3'  executed:\n- input:  0.36\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 2.72\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-4'  executed:\n- input:  2.72\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.938\n-\n'TransferMechanism-3'  executed:\n- input:  0.48\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 2.96\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-4'  executed:\n- input:  2.96\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.951\n-\n'TransferMechanism-3'  executed:\n- input:  0.6\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 3.2\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-4'  executed:\n- input:  3.2\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.961\n-\n'TransferMechanism-3'  executed:\n- input:  0.72\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 3.44\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-4'  executed:\n- input:  3.44\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.969\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-3'  executed:\n- input:  0.84\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 3.68\n\n'TransferMechanism-4'  executed:\n- input:  3.68\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.975\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-3'  executed:\n- input:  0.96\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 3.92\n\n'TransferMechanism-4'  executed:\n- input:  3.92\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.981\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-3'  executed:\n- input:  1.08\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 4.16\n\n'TransferMechanism-4'  executed:\n- input:  4.16\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.985\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-\n'TransferMechanism-3'  executed:\n- input:  1.2\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 4.4\n\n'TransferMechanism-4'  executed:\n- input:  4.4\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- output: 0.988\n-\n'TransferMechanism-3'  executed:\n- input:  1.32\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 4.64\n\n'TransferMechanism-4'  executed:\n- input:  4.64\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.99\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-\n'TransferMechanism-3'  executed:\n- input:  1.44\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 4.88\n\n'TransferMechanism-4'  executed:\n- input:  4.88\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.992\n-\n'TransferMechanism-3'  executed:\n- input:  1.56\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 5.12\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-4'  executed:\n- input:  5.12\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.994\n-\n'TransferMechanism-3'  executed:\n- input:  1.68\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 5.36"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n\n'TransferMechanism-4'  executed:\n- input:  5.36\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.995\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-3'  executed:\n- input:  1.8\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 5.6\n\n'TransferMechanism-4'  executed:\n- input:  5.6\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.996\n-\n'TransferMechanism-3'  executed:\n- input:  1.92\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 5.84\n\n'TransferMechanism-4'  executed:\n- input:  5.84\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n- output: 0.997\n-\n'TransferMechanism-3'  executed:\n- input:  2.04\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 6.08\n\n'TransferMechanism-4'  executed:\n- input:  6.08\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.998\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-3'  executed:\n- input:  2.16\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 6.32\n\n'TransferMechanism-4'  executed:\n- input:  6.32\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.998\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-\n'TransferMechanism-3'  executed:\n- input:  2.28\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 6.56\n\n'TransferMechanism-4'  executed:\n- input:  6.56\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.999\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-3'  executed:\n- input:  2.4\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 6.8\n\n'TransferMechanism-4'  executed:\n- input:  6.8\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.999\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-3'  executed:\n- input:  2.52\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 7.04\n\n'TransferMechanism-4'  executed:\n- input:  7.04\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.999\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-3'  executed:\n- input:  2.64\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 7.28\n\n'TransferMechanism-4'  executed:\n- input:  7.28\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.999\n-\n'TransferMechanism-3'  executed:\n- input:  2.76\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 7.52\n\n'TransferMechanism-4'  executed:\n- input:  7.52\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.999\n-\n'TransferMechanism-3'  executed:\n- input:  2.88\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 7.76\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-4'  executed:\n- input:  7.76\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 1.0\n-\n'TransferMechanism-3'  executed:\n- input:  3.0\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 8.0\n\n'TransferMechanism-4'  executed:\n- input:  8.0\n- params:\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 1.0\n-"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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GZKdzpS4OJtItIjrm7u4LgAWHrJvT7r4DN7d9ibzH4nU7WVlWxY8+OZH01OSg44j0CPqE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\nyl1EJAGp3EVEEpDKXUQkAf0fgjtDHUYfk3kAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10a5077f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "linear_input_unit = TransferMechanism(function=Linear(slope=2, intercept=2))\n",
+    "logistic_output_unit = TransferMechanism(function=Logistic())\n",
+    "mini_connected_network = process(\n",
+    "    pathway=[linear_input_unit, IDENTITY_MATRIX, logistic_output_unit])\n",
+    "\n",
+    "# Iterate and plot\n",
+    "xVals = np.linspace(-3, 3, num=51)\n",
+    "yVals = np.zeros((51,))\n",
+    "for i in range(xVals.shape[0]):\n",
+    "    yVals[i] = mini_connected_network.execute([xVals[i]])[0]\n",
+    "    # Progress bar\n",
+    "    print(\"-\", end=\"\")\n",
+    "plt.plot(xVals, yVals)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "IDENTITY_MATRIX is a keyword that provides a projection from the unit preceding it to the unit following that creates a one-to-one output to input projection between the two. Other useful projection keywords are...\n",
+    "\n",
+    "Now let's make our projection definition a bit more explicit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-7'  executed:\n- input:  -3.0\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -4.0\n\n'TransferMechanism-8'  executed:\n- input:  -4.0\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.018\n-\n'TransferMechanism-7'  executed:\n- input:  -2.88\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -3.76\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-8'  executed:\n- input:  -3.76\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.0228\n-\n'TransferMechanism-7'  executed:\n- input:  -2.76\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -3.52\n\n'TransferMechanism-8'  executed:\n- input:  -3.52\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.0287\n-\n'TransferMechanism-7'  executed:\n- input:  -2.64\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\ttime_scale: TimeScale.TRIAL\n- output: -3.28\n\n'TransferMechanism-8'  executed:\n- input:  -3.28\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.0363\n-\n'TransferMechanism-7'  executed:\n- input:  -2.52\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -3.04\n\n'TransferMechanism-8'  executed:\n- input:  -3.04\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.0457\n-\n'TransferMechanism-7'  executed:\n- input:  -2.4\n- params:\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -2.8\n\n'TransferMechanism-8'  executed:\n- input:  -2.8\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.0573\n-\n'TransferMechanism-7'  executed:\n- input:  -2.28\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -2.56\n\n'TransferMechanism-8'  executed:\n- input:  -2.56\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.0718\n-\n'TransferMechanism-7'  executed:\n- input:  -2.16\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -2.32\n\n'TransferMechanism-8'  executed:\n- input:  -2.32\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.0895\n-\n'TransferMechanism-7'  executed:\n- input:  -2.04\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -2.08\n\n'TransferMechanism-8'  executed:\n- input:  -2.08\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.111\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-7'  executed:\n- input:  -1.92\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -1.84\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-8'  executed:\n- input:  -1.84\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.137\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-7'  executed:\n- input:  -1.8\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -1.6\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-8'  executed:\n- input:  -1.6\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.168\n-\n'TransferMechanism-7'  executed:\n- input:  -1.68"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -1.36\n\n'TransferMechanism-8'  executed:\n- input:  -1.36\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.204\n-\n'TransferMechanism-7'  executed:\n- input:  -1.56\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -1.12\n\n'TransferMechanism-8'  executed:\n- input:  -1.12\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.246\n-\n'TransferMechanism-7'  executed:\n- input:  -1.44\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -0.88\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-8'  executed:\n- input:  -0.88\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.293\n-\n'TransferMechanism-7'  executed:\n- input:  -1.32\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -0.64\n\n'TransferMechanism-8'  executed:\n- input:  -0.64\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.345\n-\n'TransferMechanism-7'  executed:\n- input:  -1.2\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -0.4\n\n'TransferMechanism-8'  executed:\n- input:  -0.4\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.401\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-7'  executed:\n- input:  -1.08\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: -0.16\n\n'TransferMechanism-8'  executed:\n- input:  -0.16\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.46\n-\n'TransferMechanism-7'  executed:\n- input:  -0.96\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.08\n\n'TransferMechanism-8'  executed:\n- input:  0.08\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.52\n-\n'TransferMechanism-7'  executed:\n- input:  -0.84\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.32\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-8'  executed:\n- input:  0.32\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.579\n-\n'TransferMechanism-7'  executed:\n- input:  -0.72\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.56\n\n'TransferMechanism-8'  executed:\n- input:  0.56\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.636\n-\n'TransferMechanism-7'  executed:\n- input:  -0.6\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.8\n\n'TransferMechanism-8'  executed:\n- input:  0.8\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.69\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-7'  executed:\n- input:  -0.48\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 1.04\n\n'TransferMechanism-8'  executed:\n- input:  1.04\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.739\n-\n'TransferMechanism-7'  executed:\n- input:  -0.36\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 1.28\n\n'TransferMechanism-8'  executed:\n- input:  1.28\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.782\n-\n'TransferMechanism-7'  executed:\n- input:  -0.24\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 1.52\n\n'TransferMechanism-8'  executed:\n- input:  1.52\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.821\n-\n'TransferMechanism-7'  executed:\n- input:  -0.12\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 1.76\n\n'TransferMechanism-8'  executed:\n- input:  1.76\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.853\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-7'  executed:\n- input:  0.0\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 2.0\n\n'TransferMechanism-8'  executed:\n- input:  2.0\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.881\n-\n'TransferMechanism-7'  executed:\n- input:  0.12\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 2.24\n\n'TransferMechanism-8'  executed:\n- input:  2.24\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.904\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-7'  executed:\n- input:  0.24\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 2.48\n\n'TransferMechanism-8'  executed:\n- input:  2.48\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.923\n-\n'TransferMechanism-7'  executed:\n- input:  0.36\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 2.72\n\n'TransferMechanism-8'  executed:\n- input:  2.72\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.938\n-\n'TransferMechanism-7'  executed:\n- input:  0.48\n- params:\n\tfunction: Linear\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 2.96\n\n'TransferMechanism-8'  executed:\n- input:  2.96\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.951\n-\n'TransferMechanism-7'  executed:\n- input:  0.6\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 3.2\n\n'TransferMechanism-8'  executed:\n- input:  3.2\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.961\n-\n'TransferMechanism-7'  executed:\n- input:  0.72\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 3.44\n\n'TransferMechanism-8'  executed:\n- input:  3.44\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.969\n-\n'TransferMechanism-7'  executed:\n- input:  0.84\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 3.68\n\n'TransferMechanism-8'  executed:\n- input:  3.68\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.975\n-\n'TransferMechanism-7'  executed:\n- input:  0.96\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 3.92\n\n'TransferMechanism-8'  executed:\n- input:  3.92\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.981\n-\n'TransferMechanism-7'  executed:\n- input:  1.08\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 4.16\n\n'TransferMechanism-8'  executed:\n- input:  4.16\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.985\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-7'  executed:\n- input:  1.2\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 4.4\n\n'TransferMechanism-8'  executed:\n- input:  4.4\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.988\n-\n'TransferMechanism-7'  executed:\n- input:  1.32\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 4.64\n\n'TransferMechanism-8'  executed:\n- input:  4.64\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.99\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-7'  executed:\n- input:  1.44\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 4.88\n\n'TransferMechanism-8'  executed:\n- input:  4.88\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.992\n-\n'TransferMechanism-7'  executed:\n- input:  1.56\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 5.12\n\n'TransferMechanism-8'  executed:\n- input:  5.12"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.994\n-\n'TransferMechanism-7'  executed:\n- input:  1.68\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 5.36\n\n'TransferMechanism-8'  executed:\n- input:  5.36\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.995\n-\n'TransferMechanism-7'  executed:\n- input:  1.8\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 5.6\n\n'TransferMechanism-8'  executed:\n- input:  5.6\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.996\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-7'  executed:\n- input:  1.92\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 5.84\n\n'TransferMechanism-8'  executed:\n- input:  5.84\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.997\n-\n'TransferMechanism-7'  executed:\n- input:  2.04\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 6.08\n\n'TransferMechanism-8'  executed:\n- input:  6.08\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.998\n-"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n'TransferMechanism-7'  executed:\n- input:  2.16\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 6.32\n\n'TransferMechanism-8'  executed:\n- input:  6.32\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.998\n-\n'TransferMechanism-7'  executed:\n- input:  2.28\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 6.56\n\n'TransferMechanism-8'  executed:\n- input:  6.56\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.999\n-\n'TransferMechanism-7'  executed:\n- input:  2.4\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 6.8\n\n'TransferMechanism-8'  executed:\n- input:  6.8\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.999\n-\n'TransferMechanism-7'  executed:\n- input:  2.52\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 7.04\n\n'TransferMechanism-8'  executed:\n- input:  7.04\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.999\n-\n'TransferMechanism-7'  executed:\n- input:  2.64\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 7.28\n\n'TransferMechanism-8'  executed:\n- input:  7.28"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.999\n-\n'TransferMechanism-7'  executed:\n- input:  2.76\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 7.52\n\n'TransferMechanism-8'  executed:\n- input:  7.52\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.999\n-\n'TransferMechanism-7'  executed:\n- input:  2.88\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 7.76\n\n'TransferMechanism-8'  executed:\n- input:  7.76\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 1.0\n-\n'TransferMechanism-7'  executed:\n- input:  3.0\n- params:\n\tfunction: Linear\n\t\tintercept: 2\n\t\tslope: 2\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 8.0\n\n'TransferMechanism-8'  executed:\n- input:  8.0\n- params:\n\tfunction: Logistic\n\t\tbias: 0.0\n\t\tgain: 1.0\n\tinitial_value: 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 1.0\n-"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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GZKdzpS4OJtItIjrm7u4LgAWHrJvT7r4DN7d9ibzH4nU7WVlWxY8+OZH01OSg44j0CPqE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\nyl1EJAGp3EVEEpDKXUQkAf0fgjtDHUYfk3kAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10a69eac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "linear_input_unit = TransferMechanism(function=Linear(slope=2, intercept=2))\n",
+    "logistic_output_unit = TransferMechanism(function=Logistic())\n",
+    "\n",
+    "mapping_matrix = np.asarray([[1]])\n",
+    "unit_mapping_projection = MappingProjection(sender=linear_input_unit,\n",
+    "                                            receiver=logistic_output_unit,\n",
+    "                                            matrix=mapping_matrix)\n",
+    "mini_connected_network = process(\n",
+    "    pathway=[linear_input_unit, unit_mapping_projection, logistic_output_unit])\n",
+    "\n",
+    "# Iterate and plot\n",
+    "xVals = np.linspace(-3, 3, num=51)\n",
+    "yVals = np.zeros((51,))\n",
+    "for i in range(xVals.shape[0]):\n",
+    "    yVals[i] = mini_connected_network.execute([xVals[i]])[0]\n",
+    "    # Progress bar\n",
+    "    print(\"-\", end=\"\")\n",
+    "plt.plot(xVals, yVals)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This time we specified our mapping matrix (which is a 2-D numpy array) then explicitly initialized a *[MappingProjection](https://princetonuniversity.github.io/PsyNeuLink/MappingProjection.html)* with that matrix as well as its input and output mechanisms. Note: because we specified the input and output mechanisms in the projection itself, we didn't need to include it in the process pathway as it will infer its position from those parameters. Ultimately, however, this does the exact same thing as our keyword method above which is far less verbose for this common use case."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Systems\n",
+    "\n",
+    "The highest level at which models are considered in PsyNeuLink is that of the *[System](https://princetonuniversity.github.io/PsyNeuLink/System.html)*. A system is composed of one or more processes which can then run in unison. This allows system graphs to be more complex than the strictly linear ones of processes. Our first system will consist of two input nodes that converge on a single output mechanism. We will be modelling competition between color naming and word reading in the stroop task."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "colors = TransferMechanism(default_input_value=[0, 0], function=Linear,\n",
+    "                           name=\"Colors\")\n",
+    "words = TransferMechanism(default_input_value=[0, 0],\n",
+    "                          function=Linear(slope=1.5), name=\"Words\")\n",
+    "response = TransferMechanism(default_input_value=[0, 0], function=Logistic,\n",
+    "                             name=\"Response\")\n",
+    "\n",
+    "color_naming_process = process(pathway=[colors, IDENTITY_MATRIX, response],\n",
+    "                               name=\"Color Naming\")\n",
+    "word_reading_process = process(pathway=[words, IDENTITY_MATRIX, response],\n",
+    "                               name=\"Word Reading\")\n",
+    "\n",
+    "# Should pass in scheduler or have system create new scheduler if scheduler=None\n",
+    "mini_stroop = system(processes=[color_naming_process, word_reading_process],\n",
+    "                     name='Stroop Model')\n",
+    "\n",
+    "# Note: Once again, make sure you do not run this cell multiple times. This would create multiple\n",
+    "# processes and systems that share the same variable name in the PsyNeuLink registry causing\n",
+    "# unexpected behavior."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The order of processes in the system initialization is important here as it is the order we will feed inputs to the system."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[array([ 0.73105858,  0.81757448])], [array([ 0.73105858,  0.81757448])]]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "input = {colors: [1, 0], \n",
+    "         words: [0, 1]}\n",
+    "mini_stroop.run(input)3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[array([ 0.5       ,  0.92414182]),\n",
-       " array(0.7120709099893783),\n",
-       " array(0.04497407086372298)]"
+       "[[array([ 0.73105858,  0.81757448])],\n [array([ 0.73105858,  0.81757448])],\n [array([ 0.5       ,  0.92414182])]]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "mini_stroop.execute([[0, 1], [0, 1]])"
+    "input_dict = {colors: [0, 1],\n",
+    "              words: [0, 1]}\n",
+    "mini_stroop.run(input_dict)"
    ]
   },
   {
@@ -566,34 +1183,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "ink_color = TransferMechanism(default_input_value = [0, 0], function = Linear())\n",
-    "word = TransferMechanism(default_input_value = [0, 0], function = Linear())\n",
-    "task_demand = TransferMechanism(default_input_value = [0, 0], function = Linear())\n",
+    "ink_color = TransferMechanism(default_input_value=[0, 0], function=Linear())\n",
+    "word = TransferMechanism(default_input_value=[0, 0], function=Linear())\n",
+    "task_demand = TransferMechanism(default_input_value=[0, 0], function=Linear())\n",
     "\n",
-    "hidden_layer = TransferMechanism(default_input_value = [0, 0, 0, 0], function = Logistic(bias = -4))\n",
+    "hidden_layer = TransferMechanism(default_input_value=[0, 0, 0, 0],\n",
+    "                                 function=Logistic(bias=-4))\n",
     "\n",
-    "output_layer = TransferMechanism(default_input_value = [0, 0], function = Linear())\n",
+    "output_layer = TransferMechanism(default_input_value=[0, 0], function=Linear())\n",
     "\n",
     "color_mapping_matrix = np.asarray([[2.2, -2.2, 0, 0], [-2.2, 2.2, 0, 0]])\n",
-    "color_projection = MappingProjection(sender = ink_color, receiver = hidden_layer, matrix = color_mapping_matrix)\n",
+    "color_projection = MappingProjection(sender=ink_color, receiver=hidden_layer,\n",
+    "                                     matrix=color_mapping_matrix)\n",
     "word_mapping_matrix = np.asarray([[0, 0, 2.6, -2.6], [0, 0, -2.6, 2.6]])\n",
-    "word_projection = MappingProjection(sender = word, receiver = hidden_layer, matrix = word_mapping_matrix)\n",
+    "word_projection = MappingProjection(sender=word, receiver=hidden_layer,\n",
+    "                                    matrix=word_mapping_matrix)\n",
     "task_mapping_matrix = np.asarray([[4, 4, 0, 0], [0, 0, 4, 4]])\n",
-    "task_projection = MappingProjection(sender = task_demand, receiver = hidden_layer, matrix = task_mapping_matrix)\n",
-    "output_mapping_matrix = np.asarray([[1.3, -1.3], [-1.3, 1.3], [2.5, -2.5], [-2.5, 2.5]])\n",
-    "MappingProjection(sender = hidden_layer, receiver = output_layer, matrix = output_mapping_matrix)\n",
+    "task_projection = MappingProjection(sender=task_demand, receiver=hidden_layer,\n",
+    "                                    matrix=task_mapping_matrix)\n",
+    "output_mapping_matrix = np.asarray(\n",
+    "    [[1.3, -1.3], [-1.3, 1.3], [2.5, -2.5], [-2.5, 2.5]])\n",
+    "MappingProjection(sender=hidden_layer, receiver=output_layer,\n",
+    "                  matrix=output_mapping_matrix)\n",
     "\n",
-    "color_naming_process = process(pathway = [ink_color, hidden_layer, output_layer])\n",
-    "word_reading_process = process(pathway = [word, hidden_layer, output_layer])\n",
-    "task_process = process(pathway = [task_demand, hidden_layer, output_layer])\n",
+    "color_naming_process = process(pathway=[ink_color, hidden_layer, output_layer])\n",
+    "word_reading_process = process(pathway=[word, hidden_layer, output_layer])\n",
+    "task_process = process(pathway=[task_demand, hidden_layer, output_layer])\n",
     "\n",
-    "stroop_model = system(processes = [color_naming_process, word_reading_process, task_process])\n",
+    "stroop_model = system(\n",
+    "    processes=[color_naming_process, word_reading_process, task_process])\n",
     "\n",
     "ink_color.reportOutputPref = True\n",
     "word.reportOutputPref = True\n",
@@ -607,7 +1231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
@@ -616,65 +1240,25 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "'TransferMechanism-7'  executed:\n",
-      "- input:  1.0, 0.0\n",
-      "- params:\n",
-      "\tfunction: Linear\n",
-      "\t\tintercept: 0.0\n",
-      "\t\tslope: 1.0\n",
-      "\tinitial_value: None\n",
-      "\tnoise: 0.0\n",
-      "\ttime_scale: TimeScale.TRIAL\n",
-      "- output: 1.0 0.0\n",
-      "\n",
-      "'TransferMechanism-8'  executed:\n",
-      "- input:  1.0, 0.0\n",
-      "- params:\n",
-      "\tfunction: Linear\n",
-      "\t\tintercept: 0.0\n",
-      "\t\tslope: 1.0\n",
-      "\tinitial_value: None\n",
-      "\tnoise: 0.0\n",
-      "\ttime_scale: TimeScale.TRIAL\n",
-      "- output: 1.0 0.0\n",
-      "\n",
-      "'TransferMechanism-9'  executed:\n",
-      "- input:  0.0, 1.0\n",
-      "- params:\n",
-      "\tfunction: Linear\n",
-      "\t\tintercept: 0.0\n",
-      "\t\tslope: 1.0\n",
-      "\tinitial_value: None\n",
-      "\tnoise: 0.0\n",
-      "\ttime_scale: TimeScale.TRIAL\n",
-      "- output: 0.0 1.0\n",
-      "\n",
-      "'TransferMechanism-10'  executed:\n",
-      "- input:  2.2, -2.2, 6.6, 1.4\n",
-      "- params:\n",
-      "\tfunction: Logistic\n",
-      "\t\tbias: -4\n",
-      "\t\tgain: 1.0\n",
-      "\tinitial_value: None\n",
-      "\tnoise: 0.0\n",
-      "\ttime_scale: TimeScale.TRIAL\n",
-      "- output: 0.9979746796109501 0.8581489350995123 0.9999750846110607 0.9955037268390589\n"
+      "\n'TransferMechanism-1'  executed:\n- input:  1.0, 0.0\n- params:\n\tfunction: Linear\n\t\tintercept: 0.0\n\t\tslope: 1.0\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 1.0 0.0\n\n'TransferMechanism-3'  executed:\n- input:  0.0, 1.0\n- params:\n\tfunction: Linear\n\t\tintercept: 0.0\n\t\tslope: 1.0\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.0 1.0\n\n'TransferMechanism-2'  executed:\n- input:  1.0, 0.0\n- params:\n\tfunction: Linear\n\t\tintercept: 0.0\n\t\tslope: 1.0\n\tinitial_value: 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 1.0 0.0\n\n'TransferMechanism-4'  executed:\n- input:  2.2, -2.2, 6.6, 1.4\n- params:\n\tfunction: Logistic\n\t\tbias: -4\n\t\tgain: 1.0\n\tinitial_value: 0 0 0 0\n\tnoise: 0.0\n\ttime_scale: TimeScale.TRIAL\n- output: 0.998 0.858 1.0 0.996\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[array([ 0.19295186, -0.19295186]), array(0.0), array(0.037230421163059885)]"
+       "[[array([ 0.19295186, -0.19295186])]]"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "stroop_model.execute([[1, 0], [1, 0], [0, 1]])"
+    "input_dict = {ink_color: [1, 0],\n",
+    "              word: [1, 0],\n",
+    "              task_demand: [0, 1]}\n",
+    "stroop_model.run(input_dict)"
    ]
   },
   {
@@ -707,4 +1291,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
\ No newline at end of file
+}
diff --git a/PsyNeuLink/Components/Component.py b/PsyNeuLink/Components/Component.py
index 3208bd80290..235d0bf84fe 100644
--- a/PsyNeuLink/Components/Component.py
+++ b/PsyNeuLink/Components/Component.py
@@ -16,8 +16,8 @@
 Overview
 --------
 
-Component is the base class for all of the objects used to create compositions (`processes <Process>` or 
-`systems <System>`) in PsyNeuLink.  It defines a common set of attributes possessed, and methods used by all 
+Component is the base class for all of the objects used to create compositions (`processes <Process>` or
+`systems <System>`) in PsyNeuLink.  It defines a common set of attributes possessed, and methods used by all
 component objects.
 
 .. _Component_Creation:
@@ -26,7 +26,7 @@
 --------------------
 
 A Component is never created directly.  However, its __init__() method is always called when a subclass is instantiated;
-that, in turn, calls a standard set of methods (listed `below <Component_Methods>`) as part of the initialization 
+that, in turn, calls a standard set of methods (listed `below <Component_Methods>`) as part of the initialization
 procedure.
 
 .. _Component_Structure:
@@ -43,125 +43,125 @@
 
 .. _Component_Variable:
 
-* **variable** - the value of the `variable <Component.variable>` attribute is used as the input to its 
-  `function <Component.function>`.  Specification of the variable in the constructor for a component determines both 
-  its format (e.g., whether it's value is numeric, its dimensionality and shape if it is an array, etc.) as well as 
-  its default value (the value used when the component is executed and no input is provided). 
+* **variable** - the value of the `variable <Component.variable>` attribute is used as the input to its
+  `function <Component.function>`.  Specification of the variable in the constructor for a component determines both
+  its format (e.g., whether it's value is numeric, its dimensionality and shape if it is an array, etc.) as well as
+  its default value (the value used when the component is executed and no input is provided).
 
 .. _Component_Function:
 
-* **function** - the `function <Component.function>` attribute determines the computation that a component carries out.  
+* **function** - the `function <Component.function>` attribute determines the computation that a component carries out.
   It is always a PsyNeuLink `Function <Function>` object (itself a PsyNeuLink component).
-  
+
   .. note::
      The `function <Component.function>` of a component can be assigned either a `Function <Function>` object or any
-     other callable object in python.  If the latter is assigned, it will be "wrapped" in a `UserDefinedFunction`.  
-  
-  All components have a default `function <Component.function>` (with a default set of parameters), that is used if it 
-  is not otherwise specified.  The `function <Component.function>` can be specified in the 
-  function argument of the constructor for the component, using one of the following: 
+     other callable object in python.  If the latter is assigned, it will be "wrapped" in a `UserDefinedFunction`.
+
+  All components have a default `function <Component.function>` (with a default set of parameters), that is used if it
+  is not otherwise specified.  The `function <Component.function>` can be specified in the
+  function argument of the constructor for the component, using one of the following:
 
-    * **class** - this must be a subclass of `Function <Function>`, as in the following example::   
+    * **class** - this must be a subclass of `Function <Function>`, as in the following example::
 
         my_component = SomeComponent(function=SomeFunction)
-      
-      This will create a default instance of the specified subclass, using default values for its parameters. 
+
+      This will create a default instance of the specified subclass, using default values for its parameters.
     |
     * **Function** - this can be either an existing `Function <Function>` object or the constructor for one, as in the
       following examples:
-            
+
         my_component = SomeComponent(function=SomeFunction)
-        
+
         or
-        
+
         some_function = SomeFunction(some_param=1)
         my_component = SomeComponent(some_function)
-        
-      The specified Function will be used as a template to create a new Function object that is assigned to the 
-      `function_object` attribute of the component, the `function <Function.function>` of which will be assigned as  
+
+      The specified Function will be used as a template to create a new Function object that is assigned to the
+      `function_object` attribute of the component, the `function <Function.function>` of which will be assigned as
       the 'function <Component.function>` attribute of the component.
-    
+
       .. note::
-      
-        In the current implementation of PsyNeuLink, if a `Function <Function>` object (or the constructor for one) is 
-        used to specify the `function <Component.function>` attribute of a component, the Function object specified (or 
-        created) is used to determine attributes of the Function object created for and assigned to the component, but  
-        is not *itself* assigned to the component.  This is so that `Functions <Function>` can be used as templates for 
+
+        In the current implementation of PsyNeuLink, if a `Function <Function>` object (or the constructor for one) is
+        used to specify the `function <Component.function>` attribute of a component, the Function object specified (or
+        created) is used to determine attributes of the Function object created for and assigned to the component, but
+        is not *itself* assigned to the component.  This is so that `Functions <Function>` can be used as templates for
         more than one component, without being assigned simultaneously to multiple components.
 
-  A `function <Component.function>` can also be specified in an entry of a 
-  `parameter specification dictionary <ParameterState_Specifying_Parameters>` assigned to the 
-  **params** argument of the constructor for the component, with the keyword FUNCTION as its key, and one of the 
-  specifications above as its value, as in the following example::  
+  A `function <Component.function>` can also be specified in an entry of a
+  `parameter specification dictionary <ParameterState_Specifying_Parameters>` assigned to the
+  **params** argument of the constructor for the component, with the keyword FUNCTION as its key, and one of the
+  specifications above as its value, as in the following example::
 
         my_component = SomeComponent(params={FUNCTION:SomeFunction(some_param=1)})
 
-* **function_params** - the `function_params <Component.function>` attribute contains a dictionary of the parameters 
+* **function_params** - the `function_params <Component.function>` attribute contains a dictionary of the parameters
   for the component's `function <Component.function>` and their values.  Each entry is the name of a parameter, and
-  its value the value of that parameter.  This dictionary is read-only. Changes to the value of the function's 
-  parameters must be made by assigning a value to the corresponding attribute of the component's 
-  `function_object <Component.function_object>` attribute (e.g., myMechanism.function_object.my_parameter), 
-  or in a FUNCTION_PARAMS dict using its `assign_params` method.  The parameters for the function can be specified 
+  its value the value of that parameter.  This dictionary is read-only. Changes to the value of the function's
+  parameters must be made by assigning a value to the corresponding attribute of the component's
+  `function_object <Component.function_object>` attribute (e.g., myMechanism.function_object.my_parameter),
+  or in a FUNCTION_PARAMS dict using its `assign_params` method.  The parameters for the function can be specified
   when the component is created in one of the following ways:
-  
+
   * in the **constructor** for a Function -- if that is used to specify the `function <Component.function>` argument,
     as in the following example::
 
         my_component = SomeComponent(function=SomeFunction(some_param=1, some_param=2)
 
   * in an argument of the **component's constructor** -- if all of the allowable functions for a component's
-    `function <Component.function>` share some or all of their parameters in common, the shared paramters may appear 
+    `function <Component.function>` share some or all of their parameters in common, the shared paramters may appear
     as arguments in the constructor of the component itself, which can be used to set their values.
 
-  * in an entry of a `parameter specification dictionary <ParameterState_Specifying_Parameters>` assigned to the 
-    **params** argument of the constructor for the component.  The entry must use the keyword 
+  * in an entry of a `parameter specification dictionary <ParameterState_Specifying_Parameters>` assigned to the
+    **params** argument of the constructor for the component.  The entry must use the keyword
     FUNCTION_PARAMS as its key, and its value must be a dictionary containing the parameters and their values.
-    The key for each entry in the FUNCTION_PARAMS dictionary must be the name of a parameter, and its value the 
+    The key for each entry in the FUNCTION_PARAMS dictionary must be the name of a parameter, and its value the
     parameter's value, as in the example below::
-    
+
         my_component = SomeComponent(function=SomeFunction
                                      params={FUNCTION_PARAMS:{SOME_PARAM=1, SOME_OTHER_PARAM=2}})
-  
+
   See `ParameterState_Specifying_Parameters` for details concerning different ways in which the value of a parameter
   can be specified.
 
 .. _Component_Function_Object:
 
-* **function_object** - the `function_object` attribute refers to the `Function <Function>` assigned to the component; 
-  The Function's `function <Function.function>` is assigned to the `function <Component>` attribute of the 
-  component. The  parameters of the Function can be modified by assigning values to the attributes corresponding to 
+* **function_object** - the `function_object` attribute refers to the `Function <Function>` assigned to the component;
+  The Function's `function <Function.function>` is assigned to the `function <Component>` attribute of the
+  component. The  parameters of the Function can be modified by assigning values to the attributes corresponding to
   those parameters (see `function_params <Component.function_params>` above).
 
 .. _Component_User_Params:
 
 * **user_params** - the `user_params` attribute contains a dictionary of all of the user-modifiable attributes for the
-  the component.  This dictionary is read-only.  Changes to the value of an attribute must be made by assigning a 
+  the component.  This dictionary is read-only.  Changes to the value of an attribute must be made by assigning a
   value to the attribute directly, or using the component's `assign_params` method.
-..  
+..
 COMMENT:
   INCLUDE IN DEVELOPERS' MANUAL
     * **paramClassDefaults**
-    
+
     * **paramInstanceDefaults**
 COMMENT
 
-* **value** - the `value <Component.value>` attribute contains the result (return value) of the component's 
-  `function <Component.function>` after the function is called.     
+* **value** - the `value <Component.value>` attribute contains the result (return value) of the component's
+  `function <Component.function>` after the function is called.
 ..
-* **name** - the `name <Component.name>` attribute contains the name assigned to the component when it was created.  
-  If it was not specified, a default is assigned by the registry for subclass (see :doc:`Registry <LINK>` for 
+* **name** - the `name <Component.name>` attribute contains the name assigned to the component when it was created.
+  If it was not specified, a default is assigned by the registry for subclass (see :doc:`Registry <LINK>` for
   conventions used in assigning default names and handling of duplicate names).
 ..
 * **prefs** - the `prefs <Components.prefs>` attribute contains the `PreferenceSet` assigned to the component when
   it was created.  If it was not specified, a default is assigned using `classPreferences` defined in __init__.py
   Each individual preference is accessible as an attribute of the component, the name of which is the name of the
-  preference (see `PreferenceSet <LINK>` for details).  
+  preference (see `PreferenceSet <LINK>` for details).
 
 COMMENT:
 * **log**
 COMMENT
 
-      
+
 .. _Component_Methods:
 
 Component Methods
@@ -169,69 +169,69 @@
 
 COMMENT:
    INCLUDE IN DEVELOPERS' MANUAL
-    
-    There are two sets of methods that belong to every component: one set that is called when it is initialized; and 
-    another set that can be called to perform various operations common to all components.  Each of these is described 
-    briefly below.  All of these methods can be overridden by subclasses to implement customized operations, however   
-    it is strongly recommended that the method be called on super() at some point, so that the standard operations are 
-    carried out.  Whether customization operations should be performed before or after the call to super is discussed in 
-    the descriptions below where relevant. 
-    
+
+    There are two sets of methods that belong to every component: one set that is called when it is initialized; and
+    another set that can be called to perform various operations common to all components.  Each of these is described
+    briefly below.  All of these methods can be overridden by subclasses to implement customized operations, however
+    it is strongly recommended that the method be called on super() at some point, so that the standard operations are
+    carried out.  Whether customization operations should be performed before or after the call to super is discussed in
+    the descriptions below where relevant.
+
     .. _Component_Initialization_Methods:
-    
+
     Initialization Methods
     ^^^^^^^^^^^^^^^^^^^^^^
-    
+
     These methods can be overridden by the subclass to customize the initialization process, but should always call the
     corresponding method of the Component base class (using ``super``) to insure full initialization.  There are two
-    categories of initializion methods:  validation and instantiation.  
-    
-    
+    categories of initializion methods:  validation and instantiation.
+
+
     .. _Component_Validation_Methods:
-    
-    * **Validation methods** perform a strictly *syntactic* check, to determine if a value being validated conforms 
-    to the format expected for it by the component (i.e., the type of the value and, if it is iterable, the type its 
-    elements and/or its length).  The value itself is not checked in any other way (e.g., whether it equals a particular 
-    value or falls in a specified range).  If the validation fails, and exception is raised.  Validation methods never 
+
+    * **Validation methods** perform a strictly *syntactic* check, to determine if a value being validated conforms
+    to the format expected for it by the component (i.e., the type of the value and, if it is iterable, the type its
+    elements and/or its length).  The value itself is not checked in any other way (e.g., whether it equals a particular
+    value or falls in a specified range).  If the validation fails, and exception is raised.  Validation methods never
     make changes the actual value of an attribute, but they may change its format (e.g., from a list to an ndarray) to
     comply with requirements of the component.
-    
-      * `_validate_variable <Component._validate_variable>` validates the value provided to the keyword:`variable` 
-        argument in the constructor for the component.  If it is overridden, customized validation should generally 
-        performed *prior* to the call to super(), to allow final processing by the Component base class. 
-        
-      * `_validate_params <Component._validate_params>` validates the value of any parameters specified in the 
-        constructor for the component (whether they are made directly in the argument for a parameter, or in a 
-        `parameter specification dictionary <ParameterState_Specifying_Parameters>`.  If it is overridden by a subclass, 
+
+      * `_validate_variable <Component._validate_variable>` validates the value provided to the keyword:`variable`
+        argument in the constructor for the component.  If it is overridden, customized validation should generally
+        performed *prior* to the call to super(), to allow final processing by the Component base class.
+
+      * `_validate_params <Component._validate_params>` validates the value of any parameters specified in the
+        constructor for the component (whether they are made directly in the argument for a parameter, or in a
+        `parameter specification dictionary <ParameterState_Specifying_Parameters>`.  If it is overridden by a subclass,
         customized validation should generally be performed *after* the call to super().
-    
-    * **Instantiation methods** create, assign, and/or perform *semantic* checks on the values of component attributes.  
-      Semantic checks may include value and/or range checks, as well as checks of formatting and/or value 
+
+    * **Instantiation methods** create, assign, and/or perform *semantic* checks on the values of component attributes.
+      Semantic checks may include value and/or range checks, as well as checks of formatting and/or value
       compatibility with other attributes of the component and/or the attributes of other components (for example, the
-      _instantiate_function method checks that the input of the component's `function <Comonent.function>` is compatible 
+      _instantiate_function method checks that the input of the component's `function <Comonent.function>` is compatible
       with its `variable <Component.variable>`).
-    
-      * `_instantiate_defaults <Component._instantiate_defaults>` first calls the validation methods, and then  
+
+      * `_instantiate_defaults <Component._instantiate_defaults>` first calls the validation methods, and then
         assigns the default values for all of the attributes of the instance of the component being created.
-        
+
         _instantiate_attributes_before_function
         _instantiate_function
         _instantiate_attributes_after_function
-    
+
     .. _Component_Callable_Methods:
-    
+
     Callable Methods
     ^^^^^^^^^^^^^^^^
-    
+
     initialize
 COMMENT
 
 .. _Component_Assign_Params:
 
-* **assign_params** - the `assign_params` method is used to assign the value of one or more parameters of a 
-  component.  Each parameter is specified as an entry in a dict in the **request_set** argument;  
-  parameters for the component's function are specified as entries in a FUNCTION_PARAMS dict within 
-  **request_set** dict.   
+* **assign_params** - the `assign_params` method is used to assign the value of one or more parameters of a
+  component.  Each parameter is specified as an entry in a dict in the **request_set** argument;
+  parameters for the component's function are specified as entries in a FUNCTION_PARAMS dict within
+  **request_set** dict.
 
 * **reset_params** - the `reset_params` method is used to reset the value of all user_params to their default
   (paramClass
@@ -248,7 +248,7 @@
 COMMENT:
    INCLUDE IN DEVELOPERS' MANUAL
     .. _Component_Class_Reference:
-    
+
     Class Reference
     ---------------
 COMMENT
@@ -275,10 +275,12 @@
 
 """
 
-from collections import OrderedDict
+from collections import OrderedDict, Iterable
 from PsyNeuLink.Globals.Utilities import *
 from PsyNeuLink.Globals.Preferences.ComponentPreferenceSet import *
 
+component_keywords = {NAME, VARIABLE, VALUE, FUNCTION, FUNCTION_PARAMS, PARAMS, PREFS_ARG, CONTEXT}
+
 class ResetMode(Enum):
     CURRENT_TO_INSTANCE_DEFAULTS = 0
     INSTANCE_TO_CLASS = 1
@@ -296,17 +298,17 @@ class ResetMode(Enum):
 from collections import UserDict
 class ParamsDict(UserDict):
     """Create, set and get attribute of owner for each key in dict
-    
+
     Creates and maintains an interface to attributes of a component via a dict:
         - any assignment to an entry of the dict creates or updates the value of the attribute with the name of the key
         - any query retrieves the value of the attribute with the name of the key
     Dict itself is maintained in self.data
-    
-    Notes:  
-    * This provides functionality similar to the __dict__ attribute of a python object, 
+
+    Notes:
+    * This provides functionality similar to the __dict__ attribute of a python object,
         but is restricted to the attributes relevant to its role as a PsyNeuLink component.
     * It insures that any instantiation of a function_params attribute is a ReadOnlyOrderedDict
-     
+
     """
 
     def __init__(self, owner, dict=None):
@@ -399,7 +401,7 @@ class Component(object):
              it can be referenced either as self.function, self.params[FUNCTION] or self.paramsCurrent[FUNCTION]
          - function_object (Function): the object to which function belongs (and that defines it's parameters)
          - output (value: self.value)
-         - outputValue (return from self.execute: concatenated set of values of outputStates)
+         - output_values (return from self.execute: concatenated set of values of outputStates)
          - class and instance variable defaults
          - class and instance param defaults
         The components's execute method (<subclass>.execute is the component's primary method
@@ -466,8 +468,13 @@ class Component(object):
         + componentCategory - category of Component (i.e., process, mechanism, projection, learning, function)
         + componentType - type of component within a category
                              (e.g., TransferMechanism, MappingProjection, ControlProjection, etc.)
-        + requiredParamClassDefaultTypes - dict of param names and types that all subclasses of Component must
-        implement;
+        + requiredParamClassDefaultTypes - dict of param names & types that all subclasses of Component must implement;
+        + prev_context - str (primarily used to track and prevent recursive calls to assign_params from setters)
+
+        # Prevent recursive calls from setters
+        if self.prev_context == context:
+            return
+        
 
     Class methods:
         - _validate_variable(variable)
@@ -502,7 +509,7 @@ class Component(object):
         + function (implementation is optional; aliased to params[FUNCTION] by default)
     """
 
-    #region CLASS ATTRIBUTES
+    #CLASS ATTRIBUTES
     className = "COMPONENT"
     suffix = " " + className
 # IMPLEMENTATION NOTE:  *** CHECK THAT THIS DOES NOT CAUSE ANY CHANGES AT SUBORDNIATE LEVELS TO PROPOGATE EVERYWHERE
@@ -528,8 +535,18 @@ class Component(object):
     # * kwComponentCategory (below) is used as placemarker for Component.Function class; replaced in __init__ below
     #              (can't reference own class directly class block)
     requiredParamClassDefaultTypes = {}
+
     paramClassDefaults = {}
-    #endregion
+
+    # IMPLEMENTATION NOTE: This is needed so that the State class can be used with ContentAddressableList,
+    #                      which requires that the attribute used for addressing is on the class;
+    #                      it is also declared as a property, so that any assignments are validated to be strings,
+    #                      insuring that assignment by one instance will not affect the value of others.
+    name = None
+
+
+    # IMPLEMENTATION NOTE: Primarily used to track and prevent recursive calls to assign_params from setters.
+    prev_context = None
 
     def __init__(self,
                  variable_default,
@@ -544,11 +561,6 @@ def __init__(self,
         - params_default (dict): assigned as paramInstanceDefaults
         Note: if parameter_validation is off, validation is suppressed (for efficiency) (Component class default = on)
 
-        :param variable_default: (anything but a dict) - value to assign as variableInstanceDefault
-        :param param_defaults: (dict) - params to be assigned to paramInstanceDefaults
-        :param log: (ComponentLog enum) - log entry types set in self.componentLog
-        :param name: (string) - optional, overrides assignment of default (componentName of subclass)
-        :return:
         """
 
         # # MODIFIED 8/14/16 NEW:
@@ -578,18 +590,16 @@ def __init__(self,
             self.componentName = self.componentName or self.componentType
         except AttributeError:
             self.componentName = self.componentType
-        #endregion
 
-        #region ENFORCE REGISRY
+        # ENFORCE REGISRY
         if self.__class__.__bases__[0].__bases__[0].__bases__[0].__name__ is 'ShellClass':
             try:
                 self.__class__.__bases__[0].registry
             except AttributeError:
                 raise ComponentError("{0} is a category class and so must implement a registry".
                                     format(self.__class__.__bases__[0].__name__))
-        #endregion
 
-        #region ASSIGN PREFS
+        # ASSIGN PREFS
 
         # If a PreferenceSet was provided, assign to instance
         if isinstance(prefs, PreferenceSet):
@@ -598,45 +608,16 @@ def __init__(self,
         # Otherwise, if prefs is a specification dict instantiate it, or if it is None assign defaults
         else:
             self.prefs = ComponentPreferenceSet(owner=self, prefs=prefs, context=context)
-        #endregion
-
-        # MODIFIED 9/11/16 NEW:
-        # IMPLEMENTATION NOTE:  This is nice and all, but:
-        #                       - property version only works for getter, and for class (can't access instance values)
-        #                       - attribute version works for getter, but setter sets the attribute and not the pref
-        #                       So, for now, hard coding property setters and getters for each preference (see below)
-        # Assign prefs to attributes on object
-        # for pref in self.prefs.prefsList:
-            # # Generate attribute for each pref that returns value of the pref
-            # PROBLEM: MAKING AN ASSIGNMENT TO THE ATTRIBUTE WILL NOT AFFECT THE PREFERENCE, JUST THIS ATTRIBUTE
-            # setattr(self,
-            #         underscore_to_camelCase(pref),
-            #         getattr(getattr(self, 'prefs'), underscore_to_camelCase(pref)))
-            # PROBLEM: THIS REQUIRES THAT THE PROPERTY IS PUT ON THE CLASS, WHICH GENERATES UNDESIRABLE BEHAVIORS
-            #          ALSO, SETTER WON'T WORK PROPERLY HERE EITHER
-            # # IMPLEMENT: WITHOUT SETTER:
-            # setattr(type(self),
-            #         underscore_to_camelCase(pref),
-            #         property(lambda self: getattr(getattr(self, 'prefs'), underscore_to_camelCase(pref))))
-            # # IMPLEMENT: WITH SETTER:
-            # pref_name = underscore_to_camelCase(pref)
-            # setattr(type(self),
-            #         pref_name,
-            #         property(lambda self: getattr(getattr(self, 'prefs'), pref_name),
-            #                  lambda self, value: setattr(getattr(getattr(self, 'prefs'), pref_name),
-            #                                              pref_name,
-            #                                              value)))
-        # MODIFIED 9/11/16 END
-
-        #region ASSIGN LOG
+
+        # ASSIGN LOG
+
         self.log = Log(owner=self)
         self.recording = False
         # Used by run to store return value of execute
         self.results = []
-        #endregion
 
 
-        #region ENFORCE REQUIRED CLASS DEFAULTS
+        # ENFORCE REQUIRED CLASS DEFAULTS
 
         # All subclasses must implement variableClassDefault
         # Do this here, as _validate_variable might be overridden by subclass
@@ -647,9 +628,8 @@ def __init__(self,
         except AttributeError:
             raise ComponentError("variableClassDefault must be defined for {} or its base class".
                                 format(self.componentName))
-        #endregion
 
-        #region CHECK FOR REQUIRED PARAMS
+        # CHECK FOR REQUIRED PARAMS
 
         # All subclasses must implement, in their paramClassDefaults, params of types specified in
         #     requiredClassParams (either above or in subclass defintion)
@@ -665,7 +645,7 @@ def __init__(self,
                    type(PsyNeuLink.Components.Functions.Function.Function_Base)
 
             if required_param not in self.paramClassDefaults.keys():
-                raise ComponentError("Param {} must be in paramClassDefaults for {}".
+                raise ComponentError("Param \'{}\' must be in paramClassDefaults for {}".
                                     format(required_param, self.name))
 
             # If the param does not match any of the types specified for it in type_requirements
@@ -683,9 +663,10 @@ def __init__(self,
                                         format(required_param_value.__name__, required_param, self.name, type_names))
             except TypeError:
                 pass
-        #endregion
 
-        #region ASSIGN DEFAULTS
+
+        # VALIDATE VARIABLE AND PARAMS, AND ASSIGN DEFAULTS
+
         # Validate the set passed in and assign to paramInstanceDefaults
         # By calling with assign_missing, this also populates any missing params with ones from paramClassDefaults
         self._instantiate_defaults(variable=variable_default,
@@ -694,9 +675,9 @@ def __init__(self,
                target_set=self.paramInstanceDefaults, # destination set to which params are being assigned
                default_set=self.paramClassDefaults,   # source set from which missing params are assigned
                context=context)
-        #endregion
 
-        #region SET CURRENT VALUES OF VARIABLE AND PARAMS
+        # SET CURRENT VALUES OF VARIABLE AND PARAMS
+
         self.variable = self.variableInstanceDefault
         # self.variable = self.variableInstanceDefault.copy()
 
@@ -704,29 +685,28 @@ def __init__(self,
         self.paramsCurrent = self.paramInstanceDefaults.copy()
 
         self.runtime_params_in_use = False
-        #endregion
 
-        #region VALIDATE FUNCTION (self.function and/or self.params[function, FUNCTION_PARAMS])
+        # VALIDATE FUNCTION (self.function and/or self.params[function, FUNCTION_PARAMS])
         self._validate_function(context=context)
-        #endregion
 
-        #region INSTANTIATE ATTRIBUTES BEFORE FUNCTION
+        # INSTANTIATE ATTRIBUTES BEFORE FUNCTION
         # Stub for methods that need to be executed before instantiating function
         #    (e.g., _instantiate_sender and _instantiate_receiver in Projection)
         self._instantiate_attributes_before_function(context=context)
-        #endregion
 
-        #region INSTANTIATE FUNCTION and assign output (by way of self.execute) to self.value
+        # INSTANTIATE FUNCTION
+        #    - assign initial function parameter values from ParameterStates,
+        #    - assign function's output to self.value (based on call of self.execute)
         self._instantiate_function(context=context)
-        #endregion
 
-        #region INSTANTIATE ATTRIBUTES AFTER FUNCTION
+        # INSTANTIATE ATTRIBUTES AFTER FUNCTION
         # Stub for methods that need to be executed after instantiating function
         #    (e.g., instantiate_output_state in Mechanism)
         self._instantiate_attributes_after_function(context=context)
-        #endregion
 
-#endregion
+    def __repr__(self):
+        return '({0} {1})'.format(type(self).__name__, self.name)
+        #return '{1}'.format(type(self).__name__, self.name)
 
     def _deferred_init(self, context=None):
         """Use in subclasses that require deferred initialization
@@ -761,8 +741,9 @@ def _deferred_init(self, context=None):
 
             # If name is None, mark as deferred so that name can be customized
             #    using info that has become available at time of deferred init
-            self.init_args['name'] = self.init_args['name'] or ('deferred_init_' + self.className) or \
-                                     DEFERRED_DEFAULT_NAME
+            self.init_args['name'] = (self.init_args['name'] or
+                                      ('deferred_init_' + self.className) or
+                                      DEFERRED_DEFAULT_NAME)
 
             # Complete initialization
             super(self.__class__,self).__init__(**self.init_args)
@@ -790,6 +771,34 @@ def parse_arg(arg):
                 name = arg
             return name
 
+        def _convert_function_to_class(function, source):
+            from PsyNeuLink.Components.Functions.Function import Function
+            from inspect import isfunction
+            fct_cls = None
+            fct_params = None
+            # It is a PsyNeuLink Function class
+            if inspect.isclass(function) and issubclass(function, Function):
+                fct_cls = function
+            # It is an instantiateed PsyNeuLink Function class
+            elif isinstance(function, Function):
+                # Set it to the class (for compatibility with current implementation of _instantiate_function()
+                # and put its params in FUNCTION_PARAMS
+                fct_cls = function.__class__
+                fct_params = function.user_params.copy()
+            # It is a generic function
+            elif isfunction(function):
+                # Assign to paramClassDefaults as is (i.e., don't convert to class), since class is generic
+                # (_instantiate_function also tests for this and leaves it as is)
+                fct_cls = function
+            else:
+                if hasattr(self, 'name'):
+                    name = self.name
+                else:
+                    name = self.__class__.__name__
+                raise ComponentError("Unrecognized object ({}) specified in {} for {}".
+                                     format(function, source, name))
+            return (fct_cls, fct_params)
+
         # ASSIGN DEFAULTS TO paramClassDefaults
         # Check if defaults have been assigned to paramClassDefaults, and if not do so
         for arg in kwargs:
@@ -797,8 +806,8 @@ def parse_arg(arg):
             arg_name = parse_arg(arg)
 
 
-            # The params arg (nor anything in it) is never a default
-            if arg_name is kwParams:
+            # The params arg is never a default (nor is anything in it)
+            if arg_name is PARAMS:
                 continue
 
             # Check if param exists in paramClassDefaults
@@ -809,28 +818,11 @@ def parse_arg(arg):
             except:
                 # If arg is FUNCTION and it's default is an instance (i.e., not a class)
                 if arg_name is FUNCTION and not inspect.isclass(default(arg)):
-
                     # FIX: REFACTOR Component._instantiate_function TO USE COPY OF INSTANTIATED function
-
-                    function = default(arg)
-                    from PsyNeuLink.Components.Functions.Function import Function
-                    from inspect import isfunction
-
-                    # It is a PsyNeuLink Function
-                    if isinstance(function, Function):
-                        # Set it to the class (for compatibility with current implementation of _instantiate_function()
-                        # and put its params in FUNCTION_PARAMS
-                        self.paramClassDefaults[arg] = default(arg).__class__
-                        self.paramClassDefaults[FUNCTION_PARAMS] = default(arg).user_params.copy()
-
-                    # It is a generic function
-                    elif isfunction(function):
-                        # Assign to paramClassDefaults as is (i.e., don't convert to class), since class is generic
-                        # (_instantiate_function also tests for this and leaves it as is)
-                        self.paramClassDefaults[arg] = function
-                    else:
-                        raise ComponentError("Unrecognized object ({}) specified as function for {}".
-                                             format(function, self.name))
+                    fct_cls, fct_params = _convert_function_to_class(default(arg), 'function arg')
+                    self.paramClassDefaults[arg] = fct_cls
+                    if fct_params:
+                        self.paramClassDefaults[FUNCTION_PARAMS] = fct_params
 
                 # Get defaults values for args listed in FUNCTION_PARAMS
                 # Note:  is not an arg, but rather used to package args that belong to a non-instantiated function
@@ -839,11 +831,12 @@ def parse_arg(arg):
                     for item in kwargs[arg]:
                         self.paramClassDefaults[FUNCTION_PARAMS][item] = default(item)
                 else:
-                    if inspect.isclass(default(arg)) and issubclass(default(arg),inspect._empty):
+                    default_arg = default(arg)
+                    if inspect.isclass(default_arg) and issubclass(default_arg,inspect._empty):
                         raise ComponentError("PROGRAM ERROR: \'{}\' parameter of {} must be assigned a default value "
                                              "in its constructor or in paramClassDefaults (it can be \'None\')".
                                              format(arg, self.__class__.__name__))
-                    self.paramClassDefaults[arg] = default(arg)
+                    self.paramClassDefaults[arg] = default_arg
 
             # param corresponding to arg IS already in paramClassDefaults, so ignore
             else:
@@ -861,7 +854,7 @@ def parse_arg(arg):
         for arg in sorted(list(kwargs.keys())):
 
             # Put any values (presumably in a dict) passed in the "params" arg in params_arg
-            if arg is kwParams:
+            if arg is PARAMS:
                 params_arg = kwargs[arg]
                 continue
 
@@ -918,10 +911,9 @@ def parse_arg(arg):
                         # Assign as is (i.e., don't convert to class), since class is generic
                         # (_instantiate_function also tests for this and leaves it as is)
                         params[FUNCTION] = function
-                        # FIX: UNCOMMENT WHEN EVC IS GIVEN A PREF SET
-                        # if self.verbosePref:
-                        #     warnings.warn("{} is not a PsyNeuLink Function, "
-                        #                   "therefore runtime_params cannot be used".format(default(arg).__name__))
+                        if self.verbosePref:
+                            warnings.warn("{} is not a PsyNeuLink Function, "
+                                          "therefore runtime_params cannot be used".format(default(arg).__name__))
                     else:
                         raise ComponentError("Unrecognized object ({}) specified as function for {}".
                                              format(function, self.name))
@@ -933,23 +925,49 @@ def parse_arg(arg):
                 # If function was instantiated object, FUNCTION_PARAMS came from it, so ignore additional specification
                 if ignore_FUNCTION_PARAMS:
                     continue
-                # # MODIFIED 4/9/17 OLD:
-                # params[FUNCTION_PARAMS] = kwargs[arg]
-                # MODIFIED 4/9/17 NEW:
                 params[FUNCTION_PARAMS] = ReadOnlyOrderedDict(name=FUNCTION_PARAMS)
                 for param_name in sorted(list(kwargs[arg].keys())):
                     params[FUNCTION_PARAMS].__additem__(param_name,kwargs[arg][param_name])
-                # MODIFIED 4/9/17 END
 
-            # For standard params, assign arg and its default value to paramClassDefaults
+            # If no input_states or output_states are specified, ignore
+            #   (ones in paramClassDefaults will be assigned to paramsCurrent below (in params_class_defaults_only)
+            elif arg in {INPUT_STATES, OUTPUT_STATES} and kwargs[arg] is None:
+                continue
+
+            # For all other params, assign arg and its default value to paramClassDefaults
             else:
                 params[arg] = kwargs[arg]
 
-        # Add or override arg values with any specified in params dict (including FUNCTION_PARAMS)
+        # Add or override arg values with any specified in params dict (including FUNCTION and FUNCTION_PARAMS)
         if params_arg:
 
+            # If function was specified in the function arg of the constructor
+            #    and also in the FUNCTION entry of a params dict in params arg of constructor:
+            if params and FUNCTION in params and FUNCTION in params_arg:
+                # Check if it is the same as the default or the one assigned in the function arg of the constructor
+                if not is_same_function_spec(params[FUNCTION], params_arg[FUNCTION]):
+                    fct_cls, fct_params = _convert_function_to_class(params_arg[FUNCTION],
+                                                                     '{} entry of params dict'.format(FUNCTION))
+                    params_arg[FUNCTION] = fct_cls
+                    if fct_params:
+                        params_arg[FUNCTION_PARAMS] = fct_params
+                    # If it is not the same, delete any function params that have already been assigned
+                    #    in params[] for the function specified in the function arg of the constructor
+                    if FUNCTION_PARAMS in params:
+                        for param in list(params[FUNCTION_PARAMS].keys()):
+                            params[FUNCTION_PARAMS].__deleteitem__(param)
             try:
-                # Update params[FUNCTION_PARAMS] with any from param_arg[FUNCTION_PARAMS] (specified in the constructor)
+                # Replace any parameters for function specified in function arg of constructor
+                #    with those specified either in FUNCTION_PARAMS entry of params dict
+                #    or for an instantiated function specified in FUNCTION entry of params dict
+
+                # First, if the function is instantiated, get the parameters from its user_params dict
+                from PsyNeuLink.Components.Functions.Function import Function
+                if FUNCTION in params_arg and isinstance(params_arg[FUNCTION], Function):
+                    for param_name in params_arg[FUNCTION].user_params:
+                        params[FUNCTION_PARAMS].__additem__(param_name, params_arg[FUNCTION].user_params[param_name])
+                # Then get any specified in FUNCTION_PARAMS entry of the params dict
+                #    (these will override any specified in the constructor for the function)
                 for param_name in params_arg[FUNCTION_PARAMS].keys():
                     params[FUNCTION_PARAMS].__additem__(param_name, params_arg[FUNCTION_PARAMS][param_name])
                 # Convert params_arg[FUNCTION_PARAMS] to ReadOnlyOrderedDict and update it with params[FUNCTION_PARAMS];
@@ -985,7 +1003,6 @@ def parse_arg(arg):
         #    • therefore, assignments of paramInstance defaults to paramsCurrent in __init__ overwrites the
         #         the user-specified vaules (from the constructor args) in user_params
         self.user_params_for_instantiation = OrderedDict()
-        from collections import Iterable
         for param_name in sorted(list(self.user_params.keys())):
             param_value = self.user_params[param_name]
             if isinstance(param_value, (str, np.ndarray, tuple)):
@@ -996,8 +1013,8 @@ def parse_arg(arg):
                 if isinstance(param_value, dict):
                     for k, v in param_value.items():
                         self.user_params_for_instantiation[param_name][k] = v
-                elif isinstance(param_value, ReadOnlyOrderedDict):
-                    for k in sorted(list(param_value.keys())):
+                elif isinstance(param_value, (ReadOnlyOrderedDict, ContentAddressableList)):
+                    for k in sorted(list(param_value)):
                         self.user_params_for_instantiation[param_name].__additem__(k,param_value[k])
                 # SET
                 elif isinstance(param_value, set):
@@ -1010,12 +1027,15 @@ def parse_arg(arg):
             else:
                 self.user_params_for_instantiation[param_name] = param_value
 
+        # FIX: 6/1/17 - MAKE SURE FUNCTIONS DON'T GET ASSIGNED AS PROPERTIES, SINCE THEY DON'T HAVE ParameterStates
+        #                AND SO CAN'T RETURN A ParameterState.value AS THEIR VALUE
+
         # Provide opportunity for subclasses to filter final set of params in class-specific way
         # Note:  this is done here to preserve identity of user-specified params assigned to user_params above
         self._filter_params(params)
 
         # Create property on self for each parameter in user_params:
-        #    these will be validated whenever they are assigned a new value
+        #    these WILL be validated whenever they are assigned a new value
         self._create_attributes_for_params(make_as_properties=True, **self.user_params)
 
         # Create attribute on self for each parameter in paramClassDefaults not in user_params:
@@ -1044,12 +1064,11 @@ def _filter_params(self, params):
 
     def _create_attributes_for_params(self, make_as_properties=False, **kwargs):
         """Create property on parent class of object for all attributes passed in kwargs dict.
-         
+
         If attribute or property already exists, do nothing.
-        Create backing field for attribute with "_" prefixed to attribute name, 
-            and assign value provided in kwargs as its default value. 
+        Create backing field for attribute with "_" prefixed to attribute name,
+            and assign value provided in kwargs as its default value.
         """
-
         if make_as_properties:
             for arg_name, arg_value in kwargs.items():
                 if not any(hasattr(parent_class, arg_name) for parent_class in self.__class__.mro()):
@@ -1079,6 +1098,7 @@ def _check_args(self, variable, params=None, target_set=None, context=None):
         :target_set: (dict) - set to which params should be assigned (default: self.paramsCurrent)
         :return:
         """
+        # VARIABLE ------------------------------------------------------------
 
         # If function is called without any arguments, get default for variable
         if variable is None:
@@ -1098,6 +1118,8 @@ def _check_args(self, variable, params=None, target_set=None, context=None):
         else:
             self.variable = variable
 
+        # PARAMS ------------------------------------------------------------
+
         # If target_set is not specified, use paramsCurrent
         if target_set is None:
             target_set = self.paramsCurrent
@@ -1114,11 +1136,13 @@ def _check_args(self, variable, params=None, target_set=None, context=None):
         runtime_params = params
         if runtime_params and runtime_params is not None:
             for param_name in self.user_params:
-                # IMPLEMENTATION NOTE: FUNCTION_RUNTIME_PARAM_NOT_SUPPORTED
-                #    At present, assignment of ``function`` as runtime param is not supported
+                # Ignore input_states and output_states -- they should not be modified during run
+                # IMPLEMENTATION NOTE:
+                #    FUNCTION_RUNTIME_PARAM_NOT_SUPPORTED:
+                #        At present, assignment of ``function`` as runtime param is not supported
                 #        (this is because paramInstanceDefaults[FUNCTION] could be a class rather than an bound method;
                 #        i.e., not yet instantiated;  could be rectified by assignment in _instantiate_function)
-                if param_name is FUNCTION:
+                if param_name in {FUNCTION, INPUT_STATES, OUTPUT_STATES}:
                     continue
                 # If param is specified in runtime_params, then assign it
                 if param_name in runtime_params:
@@ -1217,28 +1241,45 @@ def _instantiate_defaults(self,
             if not isinstance(default_set, dict):
                 raise ComponentError("default parameter set must be a dictionary")
 
-        # IMPLEMENTATION NOTE:  REMOVE
-        # # Enforce implementation of variableEncodingDim and valueEncodingDim:
-        # try:
-        #     self.variableEncodingDim
-        # except AttributeError:
-        #     raise ComponentError("{0} or its base class must implement variableEncodingDim".
-        #                         format(self.__class__.__name__))
-        # try:
-        #     self.valueEncodingDim
-        # except AttributeError:
-        #     raise ComponentError("{0} or its base class must implement valueEncodingDim".
-        #                         format(self.__class__.__name__))
 
+        # # GET VARIABLE FROM PARAM DICT IF SPECIFIED
+        # #    (give precedence to that over variable arg specificadtion)
+        # if VARIABLE in request_set and request_set[VARIABLE] is not None:
+        #     variable = request_set[VARIABLE]
+
+        # ASSIGN SIZE OR SHAPE TO VARIABLE if specified
+
+        # If size has been specified, make sure it doesn't conflict with variable arg or param specification
+        if hasattr(self, 'size') and self.size is not None:
+            # Both variable and size are specified
+            if variable is not None:
+                # If they confict, raise exception, otherwise use variable (it specifies both size and content).
+                if self.size != len(variable):
+                    raise ComponentError("The size arg of {} ({}) conflicts with the length of its variable arg ({})".
+                                         format(self.name, self.size, len(variable)))
+            # Variable is not specified, so set to array of zero with length = size
+            else:
+                variable = np.zeros(self.size)
+
+        elif hasattr(self, 'shape') and self.shape is not None:
+            # Both variable and shape are specified
+            if variable is not None:
+                # If they conflict, raise exception, otherwise use variable (it specifies both shape and content)
+                if self.shape != np.array(variable).shape:
+                    raise ComponentError("The shape arg of {} ({}) conflicts the shape of its variable arg ({})".
+                                         format(self.name, self.size, np.array(variable).shape))
+            # Variable is not specified, so set to array of zeros with specified shape
+            else:
+                variable = np.zeros(self.shape)
 
         # VALIDATE VARIABLE (if not called from assign_params)
-        if not any(context_string in context for context_string in {COMMAND_LINE, 'ATTRIBUTE_SETTER'}):
+
+        if not any(context_string in context for context_string in {COMMAND_LINE, SET_ATTRIBUTE}):
             # if variable has been passed then validate and, if OK, assign as variableInstanceDefault
             self._validate_variable(variable, context=context)
             if variable is None:
                 self.variableInstanceDefault = self.variableClassDefault
             else:
-                # MODIFIED 6/9/16 (CONVERT TO np.ndarray)
                 self.variableInstanceDefault = self.variable
 
         # If no params were passed, then done
@@ -1254,7 +1295,7 @@ def _instantiate_defaults(self,
             raise ComponentError("Altering paramClassDefaults not permitted")
 
         if default_set is None:
-            if any(context_string in context for context_string in {COMMAND_LINE, 'ATTRIBUTE_SETTER'}):
+            if any(context_string in context for context_string in {COMMAND_LINE, SET_ATTRIBUTE}):
                 default_set = {}
                 for param_name in request_set:
                     default_set[param_name] = self.paramInstanceDefaults[param_name]
@@ -1310,33 +1351,21 @@ def _instantiate_defaults(self,
 
             self.assign_default_FUNCTION_PARAMS = True
 
-            try:
-                # # MODIFIED 11/30/16 OLD:
-                # function = request_set[FUNCTION]
-                # MODIFIED 11/30/16 NEW:
-                # Copy to keep record of request_set function for comparison below, after request_set has been updated
-                import copy
-                function = copy.deepcopy(request_set[FUNCTION])
-                # MODIFIED 11/30/16 END
-            except KeyError:
-                # If there is no function specified, then allow functionParams
-                # Note: this occurs for objects that have "hard-coded" functions
-                self.assign_default_FUNCTION_PARAMS = True
-            else:
+            if FUNCTION in request_set:
                 # Get function class:
+                function = request_set[FUNCTION]
                 if inspect.isclass(function):
                     function_class = function
                 else:
                     function_class = function.__class__
                 # Get default function (from ParamClassDefaults)
-                try:
-                    default_function = default_set[FUNCTION]
-                except KeyError:
+                if not FUNCTION in default_set:
                     # This occurs if a function has been specified as an arg in the call to __init__()
                     #     but there is no function spec in paramClassDefaults;
                     # This will be caught, and an exception raised, in _validate_params()
                     pass
                 else:
+                    default_function = default_set[FUNCTION]
                     # Get default function class
                     if inspect.isclass(function):
                         default_function_class = default_function
@@ -1392,15 +1421,14 @@ def _instantiate_defaults(self,
         # if request_set has been passed or created then validate and, if OK, assign params to target_set
         if request_set:
             # MODIFIED 4/18/17 NEW:
-            # For params that are a ParamValueProjection or 2-item tuple, extract the value
+            # For params that are a 2-item tuple, extract the value
             #    both for validation and assignment (tuples are left intact in user_params_for_instantiation dict
-            #    which is used it instantiate the specified projections)
+            #    which is used it instantiate the specified components in the 2nd item of the tuple)
             # IMPLEMENTATION NOTE:  Do this here rather than in _validate_params, as it needs to be done before
             #                       any override of _validate_params, which (should not, but) may process params
             #                       before calling super()._validate_params
-            from PsyNeuLink.Components.ShellClasses import ParamValueProjection
             for param_name, param_value in request_set.items():
-                if isinstance(param_value, (ParamValueProjection, tuple)):
+                if isinstance(param_value, tuple):
                     param_value = self._get_param_value_from_tuple(param_value)
                     request_set[param_name] = param_value
             # MODIFIED 4/18/17 END NEW
@@ -1424,6 +1452,21 @@ def _assign_params(self, request_set:tc.optional(dict)=None, context=None):
 
         from PsyNeuLink.Components.Functions.Function import Function
 
+        # FIX: Hack to prevent recursion in calls to setter and assign_params
+        # MODIFIED 5/6/17 NEW:
+        # Prevent recursive calls from setters
+        if self.prev_context == context:
+            return
+        self.prev_context = context
+        # MODIFIED 5/6/17 END
+        # import uuid
+        # try:
+        #     if self.prev_id == self.curr_id:
+        #         return
+        # except AttributeError:
+        #     pass
+        # self.curr_id = uuid.uuid4()
+
         if not request_set:
             if self.verbosePref:
                 warnings.warn("No params specified")
@@ -1449,17 +1492,13 @@ def _assign_params(self, request_set:tc.optional(dict)=None, context=None):
 
         pref_buffer = self.prefs._param_validation_pref
         self.paramValidationPref = PreferenceEntry(False, PreferenceLevel.INSTANCE)
-        # pref_buffer = self.prefs
-        # self.prefs = suppress_validation_preference_set
-        # self.prefs = ComponentPreferenceSet(prefs = {
-        #     kpParamValidationPref: PreferenceEntry(False,PreferenceLevel.INSTANCE),
-        #     kpVerbosePref: PreferenceEntry(False,PreferenceLevel.INSTANCE),
-        #     kpReportOutputPref: PreferenceEntry(False,PreferenceLevel.INSTANCE)})
-
         self.paramsCurrent.update(validated_set)
-
+        # The following is so that:
+        #    if the component is a function and it is passed as an argument to a component,
+        #    then the parameters are available in self.user_params_for_instantiation
+        #    (which is needed when the function is recreated from its class in _assign_args_to_params_dicts)
+        self.user_params_for_instantiation.update(self.user_params)
         self.paramValidationPref = pref_buffer
-        # self.prefs = pref_buffer
 
         # FIX: THIS NEEDS TO BE HANDLED BETTER:
         # FIX: DEAL WITH INPUT_STATES AND PARAMETER_STATES DIRECTLY (RATHER THAN VIA instantiate_attributes_before...)
@@ -1471,8 +1510,13 @@ def _assign_params(self, request_set:tc.optional(dict)=None, context=None):
 
         validated_set_param_names = list(validated_set.keys())
 
-        if INPUT_STATES in validated_set_param_names:
-            self._instantiate_attributes_before_function()
+        # If an input_state is being added from the command line,
+        #    must _instantiate_attributes_before_function to parse input_states specification
+        # Otherwise, should not be run,
+        #    as it induces an unecessary call to _instantatiate_parameter_states (during instantiate_input_states),
+        #    that causes name-repetition problems when it is called as part of the standard init procedure
+        if INPUT_STATES in validated_set_param_names and COMMAND_LINE in context:
+            self._instantiate_attributes_before_function(context=COMMAND_LINE)
 
         # Give owner a chance to instantiate function and/or function params
         # (e.g., wrap in UserDefineFunction, as per EVCMechanism)
@@ -1481,32 +1525,17 @@ def _assign_params(self, request_set:tc.optional(dict)=None, context=None):
                  for param_value in validated_set.values()):
             self._instantiate_attributes_before_function()
 
-        # MODIFIED 4/17/17 OLD:
-        # # If object is a Mechanism or MappingProjection, instantiate ParameterState for param if required
-        # #    (States don't have ParameterStates)
-        # from PsyNeuLink.Components.Mechanisms.Mechanism import Mechanism
-        # from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
-        # if isinstance(self, (Mechanism, MappingProjection)):
-        #     # NEED TO DO THIS NO MATTER WHAT, SINCE NEED PARAMETER STATES FOR ALL NEW PARAMS
-        #     # AS IT IS NOW, _instantiate_parameter_states ignores existing parameterStates
-        #     #               but this may cause it to ignore FUNCTION_PARAMS when FUNCTION has changed
-        #     from PsyNeuLink.Components.States.ParameterState import _instantiate_parameter_state
-        #     for param_name in validated_set_param_names:
-        #         if not param_name in self.user_params_for_instantiation:
-        #             _instantiate_parameter_state(owner=self,
-        #                                          param_name=param_name,
-        #                                          param_value=validated_set[param_name],
-        #                                          context=context)
-        # MODIFIED 4/17/17 END
-
         # If the object's function is being assigned, and it is a class, instantiate it as a Function object
         if FUNCTION in validated_set and inspect.isclass(self.function):
             self._instantiate_function(context=COMMAND_LINE)
 
-        if OUTPUT_STATES in validated_set:
-            self._instantiate_attributes_after_function()
-
-
+        # FIX: WHY SHOULD IT BE CALLED DURING STANDRD INIT PROCEDURE?
+        # # MODIFIED 5/5/17 OLD:
+        # if OUTPUT_STATES in validated_set:
+        # MODIFIED 5/5/17 NEW:  [THIS FAILS WITH A SPECIFICATION IN output_states ARG OF CONSTRUCTOR]
+        if OUTPUT_STATES in validated_set and COMMAND_LINE in context:
+        # MODIFIED 5/5/17 END
+            self._instantiate_attributes_after_function(context=COMMAND_LINE)
 
     def reset_params(self, mode=ResetMode.INSTANCE_TO_CLASS):
         """Reset current and/or instance defaults
@@ -1589,11 +1618,6 @@ def _validate_variable(self, variable, context=None):
 
         # Otherwise, do some checking on variable before converting to np.ndarray
 
-        # If variable is a ParamValueProjection tuple, get value:
-        from PsyNeuLink.Components.Mechanisms.Mechanism import ParamValueProjection
-        if isinstance(variable, ParamValueProjection):
-            variable = variable.value
-
         # If variable is callable (function or object reference), call it and assign return to value to variable
         # Note: check for list is necessary since function references must be passed wrapped in a list so that they are
         #       not called before being passed
@@ -1644,14 +1668,13 @@ def _validate_params(self, request_set, target_set=None, context=None):
         for param_name, param_value in request_set.items():
 
             # Check that param is in paramClassDefaults (if not, it is assumed to be invalid for this object)
-            try:
-                self.paramClassDefaults[param_name]
-            except KeyError:
-                # MODIFIED 11/30/16 NEW:
+            if not param_name in self.paramClassDefaults:
+                # these are always allowable since they are attribs of every component
+                if param_name in {VARIABLE, NAME, VALUE, PARAMS}:
+                    continue
                 # function is a class, so function_params has not yet been implemented
                 if param_name is FUNCTION_PARAMS and inspect.isclass(self.function):
                     continue
-                # MODIFIED 11/30/16 END
                 raise ComponentError("{0} is not a valid parameter for {1}".format(param_name, self.__class__.__name__))
 
             # The value of the param is None in paramClassDefaults: suppress type checking
@@ -1694,8 +1717,8 @@ def _validate_params(self, request_set, target_set=None, context=None):
             #    then simply assign value to paramClassDefault (implication of not specifying it explicitly);
             #    this also allows it to pass the test below and function execution to occur for initialization;
             from PsyNeuLink.Components.Projections.Projection import Projection, ProjectionRegistry
-            # from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection
-            # from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection
+            # from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
+            # from PsyNeuLink.Components.Projections.ModulatoryProjections.LearningProjection import LearningProjection
             if (((isinstance(param_value, str) and
                           param_value in {CONTROL_PROJECTION, LEARNING_PROJECTION, LEARNING}) or
                 isinstance(param_value, Projection) or  # These should be just ControlProjection or LearningProjection
@@ -1705,7 +1728,6 @@ def _validate_params(self, request_set, target_set=None, context=None):
 
             # If self is a Function and param is a class ref for function, instantiate it as the function
             from PsyNeuLink.Components.Functions.Function import Function_Base
-            from PsyNeuLink.Components.ShellClasses import ParamValueProjection
             if (isinstance(self, Function_Base) and
                     inspect.isclass(param_value) and
                     issubclass(param_value, self.paramClassDefaults[param_name])):
@@ -1714,12 +1736,6 @@ def _validate_params(self, request_set, target_set=None, context=None):
                     target_set[param_name] = param_value()
                     continue
 
-            # # MODIFIED 4/18/17 OLD:
-            # # Value is a ParamValueProjection or 2-item tuple extract its value for validation below
-            # if isinstance(param_value, (ParamValueProjection, tuple)):
-            #     param_value = self._get_param_value_from_tuple(param_value)
-            # MODIFIED 4/18/17 END
-
             # Check if param value is of same type as one with the same name in paramClassDefaults;
             #    don't worry about length
 
@@ -1796,13 +1812,11 @@ def _validate_params(self, request_set, target_set=None, context=None):
                         target_set[param_name] = param_value
                     else:
                         target_set[param_name] = param_value.copy()
-            # MODIFIED 4/3/17 KM adding list/array parameter for noise
 
-
-            # If param is a function_type, allow any other function_type
+            # If param is a function_type (or it has a function attribute that is one), allow any other function_type
             elif callable(param_value):
                 target_set[param_name] = param_value
-            elif callable(param_value.function):
+            elif hasattr(param_value, FUNCTION) and callable(param_value.function):
                 target_set[param_name] = param_value
 
             # Parameter is not a valid type
@@ -1810,27 +1824,33 @@ def _validate_params(self, request_set, target_set=None, context=None):
                 if type(self.paramClassDefaults[param_name]) is type:
                     type_name = 'the name of a subclass of ' + self.paramClassDefaults[param_name].__base__.__name__
                 else:
-                    type_name = 'an instance of  ' + self.paramClassDefaults[param_name].__name__
-                raise ComponentError("Value of {0} param ({1}) must be {2} ".
-                                    format(param_name, param_value, type_name))
+                    type_name = self.paramClassDefaults[param_name].__class__.__name__
+                raise ComponentError("Value of {} param for {} ({}) must be a {}".
+                                    format(param_name, self.name, param_value, type_name))
 
     def _get_param_value_from_tuple(self, param_spec):
-        """Returns param value (first item) of either a ParamValueProjection or an unnamed (value, projection) tuple
+        """Returns param value (first item) of a (value, projection) tuple
         """
-        from PsyNeuLink.Components.Mechanisms.Mechanism import ParamValueProjection
         from PsyNeuLink.Components.Projections.Projection import Projection
-        # from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection
-        # from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection
-
-        if isinstance(param_spec, ParamValueProjection):
-            value =  param_spec.value
+        # from PsyNeuLink.Components.Projections.Modulatory.ControlProjection import ControlProjection
+        # from PsyNeuLink.Components.Projections.Modulatory.LearningProjection import LearningProjection
+        from PsyNeuLink.Components.Projections.ModulatoryProjections.ModulatoryProjection import ModulatoryProjection_Base
+        from PsyNeuLink.Components.States.ModulatorySignals.ModulatorySignal import ModulatorySignal
+        ALLOWABLE_TUPLE_SPEC_KEYWORDS = {CONTROL_PROJECTION, LEARNING_PROJECTION, CONTROL, LEARNING}
+        ALLOWABLE_TUPLE_SPEC_CLASSES = (ModulatoryProjection_Base, ModulatorySignal)
 
         # If the 2nd item is a CONTROL or LEARNING SPEC, return the first item as the value
-        elif (isinstance(param_spec, tuple) and len(param_spec) is 2 and
-                (param_spec[1] in {CONTROL_PROJECTION, LEARNING_PROJECTION, CONTROL, LEARNING} or
-                     isinstance(param_spec[1], Projection) or
-                     (inspect.isclass(param_spec[1]) and issubclass(param_spec[1], Projection)))
-              ):
+        if (isinstance(param_spec, tuple) and len(param_spec) is 2 and
+                # # MODIFIED 6/19/17 OLD:
+                # (param_spec[1] in {CONTROL_PROJECTION, LEARNING_PROJECTION, CONTROL, LEARNING} or
+                #      isinstance(param_spec[1], Projection) or
+                #      (inspect.isclass(param_spec[1]) and issubclass(param_spec[1], Projection)))
+                # MODIFIED 6/19/17 NEW:
+                (param_spec[1] in ALLOWABLE_TUPLE_SPEC_KEYWORDS or
+                     isinstance(param_spec[1], ALLOWABLE_TUPLE_SPEC_CLASSES) or
+                         (inspect.isclass(param_spec[1]) and issubclass(param_spec[1], ALLOWABLE_TUPLE_SPEC_CLASSES)))
+                # MODIFIED 6/19/17 END
+            ):
             value =  param_spec[0]
 
         # Otherwise, just return the tuple
@@ -2006,15 +2026,11 @@ def _instantiate_function(self, context=None):
         :return:
         """
         try:
-
-            function = self.paramsCurrent[FUNCTION]
-
-        # params[FUNCTION] is NOT implemented
+            function = self.function
+        # self.function is NOT implemented
         except KeyError:
-
             function = None
-
-        # params[FUNCTION] IS implemented
+        # self.function IS implemented
         else:
             # If FUNCTION is an already instantiated method:
             if isinstance(function, method_type):
@@ -2027,7 +2043,7 @@ def _instantiate_function(self, context=None):
                     if self.prefs.verbosePref:
                         warnings.warn("{0} ({1}) is not a subclass of Function".
                                       format(FUNCTION,
-                                             self.paramsCurrent[FUNCTION].__class__.__name__,
+                                             self.function.__class__.__name__,
                                              self.name))
 
                     function = None
@@ -2045,7 +2061,7 @@ def _instantiate_function(self, context=None):
             elif inspect.isclass(function) and issubclass(function, COMPONENT_BASE_CLASS):
                 #  Check if params[FUNCTION_PARAMS] is specified
                 try:
-                    function_param_specs = self.paramsCurrent[FUNCTION_PARAMS].copy()
+                    function_param_specs = self.function_params.copy()
                 except (KeyError, AttributeError):
                     # FUNCTION_PARAMS not specified, so nullify
                     function_param_specs = {}
@@ -2060,13 +2076,10 @@ def _instantiate_function(self, context=None):
                                           format(FUNCTION_PARAMS, self.name, function_param_specs))
                     # parse entries of FUNCTION_PARAMS dict
                     else:
-                        # Get param value from any params specified as ParamValueProjection or (param, projection) tuple
+                        # Get param value from any params specified in a tuple or a dict
                         from PsyNeuLink.Components.Projections.Projection import Projection
-                        from PsyNeuLink.Components.Mechanisms.Mechanism import ParamValueProjection
                         for param_name, param_spec in function_param_specs.items():
-                            if isinstance(param_spec, ParamValueProjection):
-                                from PsyNeuLink.Components.States.ParameterState import ParameterState
-                                function_param_specs[param_name] =  param_spec.value
+                            # Get param value from (param, projection) tuple
                             if (isinstance(param_spec, tuple) and len(param_spec) is 2 and
                                     (param_spec[1] in {MAPPING_PROJECTION, CONTROL_PROJECTION, LEARNING_PROJECTION} or
                                          isinstance(param_spec[1], Projection) or
@@ -2074,6 +2087,9 @@ def _instantiate_function(self, context=None):
                                 ):
                                 from PsyNeuLink.Components.States.ParameterState import ParameterState
                                 function_param_specs[param_name] =  param_spec[0]
+                            # Get param value from VALUE entry of a parameter specification dictionary
+                            elif isinstance(param_spec, dict) and VALUE in param_spec:
+                                function_param_specs[param_name] =  param_spec[VALUE]
 
                 # Instantiate function from class specification
                 function_instance = function(variable_default=self.variable,
@@ -2084,7 +2100,7 @@ def _instantiate_function(self, context=None):
                                              #    and would require every function to have the owner arg in its __init__
                                              owner=self,
                                              context=context)
-                self.paramsCurrent[FUNCTION] = function_instance.function
+                self.function = function_instance.function
 
                 # If in VERBOSE mode, report assignment
                 if self.prefs.verbosePref:
@@ -2096,16 +2112,16 @@ def _instantiate_function(self, context=None):
                     except AttributeError:
                         pass
                     warnings.warn("{0} assigned as function for {1}".
-                                  format(self.paramsCurrent[FUNCTION].__self__.componentName,
+                                  format(self.function.__self__.componentName,
                                          object_name))
 
             # FUNCTION is a generic function (presumably user-defined), so "wrap" it in UserDefinedFunction:
             #   Note: calling UserDefinedFunction.function will call FUNCTION
             elif inspect.isfunction(function):
-                
+
 
                 from PsyNeuLink.Components.Functions.Function import UserDefinedFunction
-                self.paramsCurrent[FUNCTION] = UserDefinedFunction(function=function, context=context).function
+                self.function = UserDefinedFunction(function=function, context=context).function
 
             # If FUNCTION is NOT a Function class reference:
             # - issue warning if in VERBOSE mode
@@ -2114,7 +2130,7 @@ def _instantiate_function(self, context=None):
                 if self.prefs.verbosePref:
                     warnings.warn("{0} ({1}) is not a subclass of Function".
                                   format(FUNCTION,
-                                         self.paramsCurrent[FUNCTION].__class__.__name__,
+                                         self.function.__class__.__name__,
                                          self.name))
                 function = None
 
@@ -2122,13 +2138,13 @@ def _instantiate_function(self, context=None):
         if not function:
             # Try to assign to self.function
             try:
-                self.paramsCurrent[FUNCTION] = self.function
+                self.function = self.function
             # If self.function is also not implemented, raise exception
             # Note: this is a "sanity check," as this should have been checked in _validate_function (above)
             except AttributeError:
                 raise ComponentError("{0} ({1}) is not a Function object or class, "
                                     "and {2}.function is not implemented".
-                                    format(FUNCTION, self.paramsCurrent[FUNCTION],
+                                    format(FUNCTION, self.function,
                                            self.__class__.__name__))
             # If self.function is implemented, warn if in VERBOSE mode
             else:
@@ -2136,27 +2152,18 @@ def _instantiate_function(self, context=None):
                     warnings.warn("{0} ({1}) is not a Function object or a specification for one; "
                                   "{1}.function ({}) will be used instead".
                                   format(FUNCTION,
-                                         self.paramsCurrent[FUNCTION].__self__.componentName,
+                                         self.function.__self__.componentName,
                                          self.name,
                                          self.function.__self__.name))
 
-        # Now that function has been instantiated, call self.function
-        # to assign its output (and type of output) to self.value
-        if not context:
-            context = "DIRECT CALL"
-        # MODIFIED 8/29/16:  QUESTION:
-        # FIX: ?? SHOULD THIS CALL self.execute SO THAT function IS EVALUATED IN CONTEXT,
-        # FIX:    AS WELL AS HOW IT HANDLES RETURN VALUES (RE: outputStates AND self.value??
-        # ANSWER: MUST BE self.execute AS THE VALUE OF AN OBJECT IS THE OUTPUT OF ITS EXECUTE METHOD, NOT ITS FUNCTION
-        # self.value = self.function(context=context+kwSeparator+COMPONENT_INIT)
-        self.value = self.execute(context=context)
-        if self.value is None:
-            raise ComponentError("PROGRAM ERROR: Execute method for {} must return a value".format(self.name))
-        self._value_template = self.value
+        # MAKE ASSIGNMENTS
+        # Now that function has been instantiated:
 
+        #  - assign to paramInstanceDefaults
         self.paramInstanceDefaults[FUNCTION] = self.function
 
-        # For all components other than a Function itself, assign function_object and function_params
+        #  - for all components other than a Function itself,
+        #    assign function_object, function_params dict, and function's parameters from any ParameterStates
         from PsyNeuLink.Components.Functions.Function import Function
         if not isinstance(self, Function):
             self.function_object = self.function.__self__
@@ -2165,12 +2172,30 @@ def _instantiate_function(self, context=None):
             elif self.function_object.owner != self:
                 raise ComponentError("Function being assigned to {} ({}) belongs to another component: {}".
                                      format(self.name, self.function_object.name, self.function_object.owner.name))
-
+            # sort to maintain alphabetical order of function_params
             for param_name in sorted(list(self.function_object.user_params_for_instantiation.keys())):
+                # assign to param to function_params dict
                 self.function_params.__additem__(param_name,
                                                  self.function_object.user_params_for_instantiation[param_name])
+                # # assign values from any ParameterStates the Component may (which it should) have
+                # try:
+                #     value_type = type(getattr(self.function_object, '_'+param_name))
+                #     param_value = type_match(self._parameter_states[param_name].value, value_type)
+                # except:
+                #     pass
+                # else:
+                #     setattr(self.function_object, '_'+param_name, param_value)
             self.paramInstanceDefaults[FUNCTION_PARAMS] = self.function_params
 
+        #  - call self.execute to get value, since the value of a Component is defined as what is returned by its
+        #    execute method, not its function
+        if not context:
+            context = "DIRECT CALL"
+        self.value = self.execute(context=context)
+        if self.value is None:
+            raise ComponentError("PROGRAM ERROR: Execute method for {} must return a value".format(self.name))
+        self._value_template = self.value
+
     def _instantiate_attributes_after_function(self, context=None):
         pass
 
@@ -2193,6 +2218,21 @@ def _update_value(self, context=None):
     # def variable(self, value):
     #     self._variable = value
 
+    def _change_function(self, to_function):
+        pass
+
+    @property
+    def name(self):
+        return self._name
+
+    @name.setter
+    def name(self, value):
+        if not isinstance(value, str):
+            raise ComponentError("Name assigned to {} ({}) must be a string constant".
+                                 format(self.__class__.__name__, value))
+
+        self._name = value
+
     @property
     def prefs(self):
         # Whenever pref is accessed, use current owner as context (for level checking)
@@ -2330,57 +2370,57 @@ def auto_dependent(self, value):
 COMPONENT_BASE_CLASS = Component
 
 
-# Autoprop
-# per Bryn Keller
-
-docs = {'foo': 'Foo controls the fooness, as modulated by the the bar',
-        'bar': 'Bar none, the most important property'}
-
 def make_property(name, default_value):
     backing_field = '_' + name
 
     def getter(self):
-        return getattr(self, backing_field)
+        try:
+            # Get value of function param from ParameterState.value of owner
+            #    case: request is for the value of a Function parameter for which the owner has a ParameterState
+            #    example: slope or intercept parameter of a Linear Function)
+            #    rationale: most common and therefore requires the greatest efficiency
+            #    note: use backing_field[1:] to get name of parameter as index into _parameter_states)
+            return self.owner._parameter_states[backing_field[1:]].value
+        except (AttributeError, TypeError):
+            try:
+                # Get value of param from component's own ParameterState.value
+                #    case: request is for the value of a parameter of a Mechanism or Project that has a ParameterState
+                #    example: matrix parameter of a MappingProjection)
+                #    rationale: next most common case
+                #    note: use backing_field[1:] to get name of parameter as index into _parameter_states)
+                return self._parameter_states[backing_field[1:]].value
+            except (AttributeError, TypeError):
+                # Get value of param from component's attribute
+                #    case: request is for the value of an attribute for which the component has no ParameterState
+                #    rationale: least common case
+                #    example: parameter of a Function belonging to a state (which don't themselves have ParameterStates)
+                #    note: use backing_field since referencing property rather than item in _parameter_states)
+                return getattr(self, backing_field)
 
     def setter(self, val):
 
-        # if self.paramValidationPref and hasattr(self, backing_field):
         if self.paramValidationPref and hasattr(self, PARAMS_CURRENT):
-            self._assign_params(request_set={backing_field[1:]:val},
-                                context='ATTRIBUTE_SETTER')
+            val_str = val.__class__.__name__
+            curr_context = SET_ATTRIBUTE + ': ' + val_str + ' for ' + backing_field[1:] + ' of ' + self.name
+            self._assign_params(request_set={backing_field[1:]:val}, context=curr_context)
         else:
             setattr(self, backing_field, val)
 
-        # setattr(self, backing_field, val)
-
         # Update user_params dict with new value
         self.user_params.__additem__(name, val)
 
-        # # MODIFIED 4/20/17 NEW:
-        # # Update parameterState.value if there is one
-        # try:
-        #     if name in self.parameterStates:
-        #         self.parameterStates[name].baseValue = val
-        #         # self.parameterStates[name].value = val
-        # except AttributeError:
-        #     pass
-        # # MODIFIED 4/20/17 END
-
         # If component is a Function and has an owner, update function_params dict for owner
+        #    also, get parameter_state_owner if one exists
         from PsyNeuLink.Components.Functions.Function import Function_Base
         if isinstance(self, Function_Base) and self.owner:
+            param_state_owner = self.owner
             self.owner.function_params.__additem__(name, val)
+        else:
+            param_state_owner = self
 
-            # # MODIFIED 4/20/17 NEW:
-            # # Update value of owner's parameterState
-            # try:
-            #     if name in self.owner.parameterStates:
-            #         self.owner.parameterStates[name].baseValue = val
-            #         # self.owner.parameterStates[name].value = val
-            # except AttributeError:
-            #     pass
-            # # MODIFIED 4/20/17 END
-
+        # If the parameter is associated with a ParameterState, assign the value to the ParameterState's variable
+        if hasattr(param_state_owner, '_parameter_states') and name in param_state_owner._parameter_states:
+            param_state_owner._parameter_states[name].variable = val
 
     # Create the property
     prop = property(getter).setter(setter)
diff --git a/PsyNeuLink/Components/Functions/Function.py b/PsyNeuLink/Components/Functions/Function.py
index 46cb8fc7a96..ad23a370546 100644
--- a/PsyNeuLink/Components/Functions/Function.py
+++ b/PsyNeuLink/Components/Functions/Function.py
@@ -36,6 +36,10 @@
   * `GammaDist`
   * `WaldDist`
 
+Objective Functions:
+  * `Stability`
+  * `Distance`
+
 Learning Functions:
   * `Reinforcement`
   * `BackPropagation`
@@ -46,25 +50,25 @@
 --------
 
 A Function is a `component <Component>` that "packages" a function (in its `function <Function_Base.function>` method)
-for use by other PsyNeuLink components.  Every `component <Component>` in PsyNeuLink is assigned a Function; when thata 
+for use by other PsyNeuLink components.  Every `component <Component>` in PsyNeuLink is assigned a Function; when thata
 component is executed, its Function's `function <Function_Base.function>` is executed.  The
 `function <Function_Base.function>` can be any callable operation, although most commonly it is a mathematical operation
-(and, for those, almost always uses a call to one or more numpy functions).  There are two reasons PsyNeuLink 
+(and, for those, almost always uses a call to one or more numpy functions).  There are two reasons PsyNeuLink
 packages functions in a Function component: to *manage parameters*, and for *modularity*.
 
 **Manage parameters**. Parameters are attributes of a function that either remain stable over multiple calls to the
-function (e.g., the `gain <Logistic.gain>` or `bias <Logistic.bias>` of a `Logistic` function, or the learning rate 
+function (e.g., the `gain <Logistic.gain>` or `bias <Logistic.bias>` of a `Logistic` function, or the learning rate
 of a learning function); or, if they change, they do so less frequently or under the control of different factors
-than the function's variable (i.e., its input).  As a consequence, it is useful to manage these separately from the 
-function's variable, and not have to provide them every time the function is called.  To address this, every 
-PsyNeuLink Function has a set of attributes corresponding to the parameters of the function, that can be specified at 
-the time the Function is created (in arguments to its constructor), and can be modified independently 
-of a call to its :keyword:`function`. Modifications can be directly (e.g., in a script), or by the operation of other 
-PsyNeuLink components (e.g., `AdaptiveMechanisms`) by way of `ControlProjections <ControlProjection>`.  
+than the function's variable (i.e., its input).  As a consequence, it is useful to manage these separately from the
+function's variable, and not have to provide them every time the function is called.  To address this, every
+PsyNeuLink Function has a set of attributes corresponding to the parameters of the function, that can be specified at
+the time the Function is created (in arguments to its constructor), and can be modified independently
+of a call to its :keyword:`function`. Modifications can be directly (e.g., in a script), or by the operation of other
+PsyNeuLink components (e.g., `AdaptiveMechanisms`) by way of `ControlProjections <ControlProjection>`.
 
-**Modularity**. By providing a standard interface, any Function assigned to a components in PsyNeuLink can be replaced 
-with other PsyNeuLink Functions, or with user-written custom functions so long as they adhere to certain standards 
-(the PsyNeuLink :ref:`Function API <LINK>`). 
+**Modularity**. By providing a standard interface, any Function assigned to a components in PsyNeuLink can be replaced
+with other PsyNeuLink Functions, or with user-written custom functions so long as they adhere to certain standards
+(the PsyNeuLink :ref:`Function API <LINK>`).
 
 .. _Function_Creation:
 
@@ -86,15 +90,15 @@
 
 Every Function has a `variable <Function_Base.variable>` that provides the input to its
 `function <Function_Base.function>` method.  It's core attribute is its `function <Function_Base.function>` attribute,
-that determines the computation that it carries out.  Ths must be a callable object (that is, a python function or 
-method of some kind). Unlike other PsyNeuLink `Components`, it *cannot* be (another) Function object (it can't be 
+that determines the computation that it carries out.  Ths must be a callable object (that is, a python function or
+method of some kind). Unlike other PsyNeuLink `Components`, it *cannot* be (another) Function object (it can't be
 "turtles" all the way down!).  A Function also has an attribute for each of the parameters of its `function
 <Function_Base.function>`.   If a Function has been assigned to another component, then it also has an `owner
 <Function_Base.owner>` attribute that refers to that component.  The Function itself is assigned as the component's
 `function_object <Component.function_object>` attribute.  Each of the Function's attributes is also assigned
 as an attribute of the `owner <Function_Base.owner>`, and those are each associated with with a
 `parameterState <ParameterState>` of the `owner <Function_Base.owner>`.  Projections to those parameterStates can be
-used by `ControlProjections <ControlProjection>` to modify the Function's parameters. 
+used by `ControlProjections <ControlProjection>` to modify the Function's parameters.
 
 COMMENT:
 .. _Function_Output_Type_Conversion:
@@ -106,7 +110,7 @@
     * FunctionOutputType.NP_1D_ARRAY: return 1d np.array
     * FunctionOutputType.NP_2D_ARRAY: return 2d np.array.
 
-To implement FunctionOutputTypeConversion, the Function's kwFunctionOutputTypeConversion parameter must set to True,
+To implement FunctionOutputTypeConversion, the Function's FUNCTION_OUTPUT_TYPE_CONVERSION parameter must set to True,
 and function type conversion must be implemented by its `function <Function_Base.function>` method
 (see `Linear` for an example).
 COMMENT
@@ -119,8 +123,8 @@
 Functions are not executable objects, but their `function <Function_Base.function>` can be called.   This can be done
 directly.  More commonly, however, they are called when their `owner <Function_Base.owner>` is executed.  The parameters
 of the `function <Function_Base.function>` can be modified when it is executed, by assigning a
-`parameter specification dictionary <ParameterState_Specifying_Parameters>` to the **params** argument in the 
-call to the `function <Function_Base.function>`.  For `mechanisms <Mechanism>`, this can also be done by specifying 
+`parameter specification dictionary <ParameterState_Specifying_Parameters>` to the **params** argument in the
+call to the `function <Function_Base.function>`.  For `mechanisms <Mechanism>`, this can also be done by specifying
 `runtime_params <Mechanism_Runtime_Parameters>` for the mechanism when it is `executed <Mechanism_Base.execute>`.
 
 Class Reference
@@ -141,6 +145,8 @@
 #            'UniformDist`',
 #            'GammaDist',
 #            'WaldDist',
+#            'Stability`,
+#            'Distance`,
 #            'Reinforcement',
 #            'BackPropagation',
 #            'FunctionError',
@@ -149,18 +155,19 @@
 from functools import reduce
 from operator import *
 from random import randint
-from numpy import sqrt, abs, tanh, exp
-import numpy as np
 
-import typecheck as tc
+from numpy import abs, tanh, exp
 
 from PsyNeuLink.Components.ShellClasses import *
-from PsyNeuLink.Globals.Registry import register_category
 from PsyNeuLink.Globals.Keywords import *
-from PsyNeuLink.Globals.Utilities import random_matrix
+from PsyNeuLink.Globals.Registry import register_category
+from PsyNeuLink.Globals.Utilities import is_matrix
+
+EPSILON = np.finfo(float).eps
 
 FunctionRegistry = {}
 
+function_keywords = {FUNCTION_OUTPUT_TYPE, FUNCTION_OUTPUT_TYPE_CONVERSION}
 
 class FunctionError(Exception):
     def __init__(self, error_value):
@@ -201,6 +208,114 @@ def is_function_type(x):
         return False
 
 
+# Modulatory Parameters ************************************************************************************************
+
+ADDITIVE_PARAM = 'additive_param'
+MULTIPLICATIVE_PARAM = 'multiplicative_param'
+OVERRIDE_PARAM = 'OVERRIDE'
+DISABLE_PARAM = 'DISABLE'
+
+
+class MultiplicativeParam():
+    attrib_name = MULTIPLICATIVE_PARAM
+    name = 'MULTIPLICATIVE'
+    init_val = 1
+    reduce = lambda x : np.product(np.array(x), axis=0)
+
+
+class AdditiveParam():
+    attrib_name = ADDITIVE_PARAM
+    name = 'ADDITIVE_PARAM'
+    init_val = 0
+    reduce = lambda x : np.sum(np.array(x), axis=0)
+
+# IMPLEMENTATION NOTE:  USING A namedtuple DOESN'T WORK, AS CAN'T COPY PARAM IN Component._validate_param
+# ModulationType = namedtuple('ModulationType', 'attrib_name, name, init_val, reduce')
+
+
+class ModulationParam():
+    """Specify parameter of a `Function <Function>` for `modulation <ModulatorySignal_Modulation>` by a ModulatorySignal
+
+    COMMENT:
+        Each term specifies a different type of modulation used by a `ModulatorySignal`.  The first two refer to classes
+        that define the following terms:
+            * attrib_name (*ADDITIVE_PARAM* or *MULTIPLICATIVE_PARAM*):  specifies which meta-parameter of the function
+              to use for modulation;
+            * name (str): name of the meta-parameter
+            * init_val (int or float): value with which to initialize the parameter being modulated if it is not otherwise
+              specified
+            * reduce (function): the manner by which to aggregate multiple ModulatorySignals of that type, if the
+              `ParameterState` receives more than one `ModulatoryProjection` of that type.
+    COMMENT
+
+    Attributes
+    ----------
+
+    MULTIPLICATIVE
+        assign the `value <ModulatorySignal.value>` of the ModulatorySignal to the *MULTIPLICATIVE_PARAM*
+        of the State's `function <State.function>`;
+
+    ADDITIVE
+        assign the `value <ModulatorySignal.value>` of the ModulatorySignal to the *ADDITIVE_PARAM*
+        of the State's `function <State.function>`;
+
+    OVERRIDE
+        assign the `value <ModulatorySignal.value>` of the ModulatorySignal directly to the State's
+        `value <State.value>` (ignoring its `variable <State.variable>` and `function <State.function>`);
+
+    DISABLE
+        ignore the ModulatorySignal when calculating the State's `value <State.value>`.
+    """
+    MULTIPLICATIVE = MultiplicativeParam
+    # MULTIPLICATIVE = ModulationType(MULTIPLICATIVE_PARAM,
+    #                                 'MULTIPLICATIVE',
+    #                                 1,
+    #                                 lambda x : np.product(np.array(x), axis=0))
+    ADDITIVE = AdditiveParam
+    # ADDITIVE = ModulationType(ADDITIVE_PARAM,
+    #                           'ADDITIVE_PARAM',
+    #                           0,
+    #                           lambda x : np.sum(np.array(x), axis=0))
+    OVERRIDE = OVERRIDE_PARAM
+    DISABLE = DISABLE_PARAM
+
+MULTIPLICATIVE = ModulationParam.MULTIPLICATIVE
+ADDITIVE = ModulationParam.ADDITIVE
+OVERRIDE = ModulationParam.OVERRIDE
+DISABLE = ModulationParam.DISABLE
+
+
+def _is_modulation_param(val):
+    if val in ModulationParam.__dict__.values():
+        return True
+    else:
+        return False
+
+ModulatedParam = namedtuple('ModulatedParam', 'meta_param, function_param, function_param_val')
+
+from PsyNeuLink.Components.Projections.ModulatoryProjections.ModulatoryProjection import ModulatoryProjection_Base
+@tc.typecheck
+def _get_modulated_param(owner, mod_proj:ModulatoryProjection_Base):
+    """Return ModulationParam object, function param name and value of param modulated by ModulatoryProjection
+    """
+
+    # Get function "meta-parameter" object specified in the projection sender's modulation attribute
+    function_mod_meta_param_obj = mod_proj.sender.modulation
+
+    # Get the actual parameter of owner.function_object to be modulated
+    function_param_name = owner.function_object.params[function_mod_meta_param_obj.attrib_name]
+
+    # Get the function parameter's value
+    function_param_value = owner.function_object.params[function_param_name]
+    # MODIFIED 6/9/17 OLD:
+    # if function_param_value is None:
+    #     function_param_value = function_mod_meta_param_obj.init_val
+    # MODIFIED 6/9/17 END
+
+    # Return the meta_parameter object, function_param name, and function_param_value
+    return ModulatedParam(function_mod_meta_param_obj, function_param_name, function_param_value)
+
+
 # *******************************   get_param_value_for_keyword ********************************************************
 
 def get_param_value_for_keyword(owner, keyword):
@@ -258,7 +373,7 @@ class Function_Base(Function):
             Functions are used to "wrap" functions used used by other components;
             They are defined here (on top of standard libraries) to provide a uniform interface for managing parameters
              (including defaults)
-            NOTE:   the Function category definition serves primarily as a shell, and as an interface to the Function 
+            NOTE:   the Function category definition serves primarily as a shell, and as an interface to the Function
                        class, to maintain consistency of structure with the other function categories;
                     it also insures implementation of .function for all Function Components
                     (as distinct from other Function subclasses, which can use a FUNCTION param
@@ -285,7 +400,7 @@ class Function_Base(Function):
                 FunctionOutputType.RAW_NUMBER, return value is "exposed" as a number
                 FunctionOutputType.NP_1D_ARRAY, return value is 1d np.array
                 FunctionOutputType.NP_2D_ARRAY, return value is 2d np.array
-            - it must be enabled for a subclass by setting params[kwFunctionOutputTypeConversion] = True
+            - it must be enabled for a subclass by setting params[FUNCTION_OUTPUT_TYPE_CONVERSION] = True
             - it must be implemented in the execute method of the subclass
             - see Linear for an example
 
@@ -303,7 +418,7 @@ class Function_Base(Function):
             + registry (dict): FunctionRegistry
             + classPreference (PreferenceSet): ComponentPreferenceSet, instantiated in __init__()
             + classPreferenceLevel (PreferenceLevel): PreferenceLevel.CATEGORY
-            + paramClassDefaults (dict): {kwFunctionOutputTypeConversion: :keyword:`False`}
+            + paramClassDefaults (dict): {FUNCTION_OUTPUT_TYPE_CONVERSION: :keyword:`False`}
 
         Class methods:
             none
@@ -369,7 +484,7 @@ class Function_Base(Function):
         `component <Component>` to which the Function has been assigned.
 
     prefs : PreferenceSet or specification dict : Projection.classPreferences
-        the `PreferenceSet` for function. Specified in the `prefs` argument of the constructor for the function;
+        the `PreferenceSet` for function. Specified in the **prefs** argument of the constructor for the function;
         if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
         (see :doc:`PreferenceSet <LINK>` for details).
 
@@ -390,7 +505,10 @@ class Function_Base(Function):
     variableEncodingDim = 1
 
     paramClassDefaults = Component.paramClassDefaults.copy()
-    paramClassDefaults.update({kwFunctionOutputTypeConversion: False})  # Enable/disable output type conversion
+    paramClassDefaults.update({
+        FUNCTION_OUTPUT_TYPE_CONVERSION: False,  # Enable/disable output type conversion
+        FUNCTION_OUTPUT_TYPE:None                # Default is to not convert
+    })
 
     def __init__(self,
                  variable_default,
@@ -438,19 +556,32 @@ def execute(self, variable=None, params=None, context=None):
 
     @property
     def functionOutputType(self):
-        if self.paramsCurrent[kwFunctionOutputTypeConversion]:
+        # # MODIFIED 6/11/17 OLD:
+        # if self.paramsCurrent[FUNCTION_OUTPUT_TYPE_CONVERSION]:
+        # MODIFIED 6/11/17 NEW:
+        if hasattr(self, FUNCTION_OUTPUT_TYPE_CONVERSION):
+        # MODIFIED 6/11/17 END
             return self._functionOutputType
         return None
 
     @functionOutputType.setter
     def functionOutputType(self, value):
 
-        if not value and not self.paramsCurrent[kwFunctionOutputTypeConversion]:
+        # Initialize backing field if it has not yet been set
+        #    ??or if FunctionOutputTypeConversion is False?? <- FIX: WHY?? [IS THAT A SIDE EFFECT OR PREVIOUSLY USING
+        #                                                       FIX: self.paramsCurrent[FUNCTION_OUTPUT_TYPE_CONVERSION]
+        #                                                       FIX: TO DECIDE IF ATTRIBUTE EXISTS?
+        # # MODIFIED 6/11/17 OLD:
+        # if not value and not self.paramsCurrent[FUNCTION_OUTPUT_TYPE_CONVERSION]:
+        # MODIFIED 6/11/17 NEW:
+        if value is None and (not hasattr(self, FUNCTION_OUTPUT_TYPE_CONVERSION)
+                              or not self.FunctionOutputTypeConversion):
+        # MODIFIED 6/11/17 END
             self._functionOutputType = value
             return
 
         # Attempt to set outputType but conversion not enabled
-        if value and not self.paramsCurrent[kwFunctionOutputTypeConversion]:
+        if value and not self.paramsCurrent[FUNCTION_OUTPUT_TYPE_CONVERSION]:
             raise FunctionError("output conversion is not enabled for {0}".format(self.__class__.__name__))
 
         # Bad outputType specification
@@ -530,7 +661,7 @@ class ArgumentTherapy(Function_Base):
         `component <Component>` to which the Function has been assigned.
 
     prefs : PreferenceSet or specification dict : Projection.classPreferences
-        the `PreferenceSet` for function. Specified in the `prefs` argument of the constructor for the function;
+        the `PreferenceSet` for function. Specified in the **prefs** argument of the constructor for the function;
         if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
         (see :doc:`PreferenceSet <LINK>` for details).
 
@@ -561,7 +692,7 @@ class Manner(Enum):
     #  in the initialization call or later (using either _instantiate_defaults or during a function call)
 
     paramClassDefaults = Function_Base.paramClassDefaults.copy()
-    paramClassDefaults.update({kwFunctionOutputTypeConversion: True,
+    paramClassDefaults.update({FUNCTION_OUTPUT_TYPE_CONVERSION: True,
                                PARAMETER_STATE_PARAMS: None
                                # PROPENSITY: Manner.CONTRARIAN,
                                # PERTINACITY:  10
@@ -781,7 +912,7 @@ class UserDefinedFunction(Function_Base):
         `component <Component>` to which the Function has been assigned.
 
     prefs : PreferenceSet or specification dict : Projection.classPreferences
-        the `PreferenceSet` for function. Specified in the `prefs` argument of the constructor for the function;
+        the `PreferenceSet` for function. Specified in the **prefs** argument of the constructor for the function;
         if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
         (see :doc:`PreferenceSet <LINK>` for details).
 
@@ -793,7 +924,7 @@ class UserDefinedFunction(Function_Base):
 
     paramClassDefaults = Function_Base.paramClassDefaults.copy()
     paramClassDefaults.update({
-        kwFunctionOutputTypeConversion: False,
+        FUNCTION_OUTPUT_TYPE_CONVERSION: False,
         PARAMETER_STATE_PARAMS: None
     })
 
@@ -829,8 +960,53 @@ def function(self,
 
 
 class CombinationFunction(Function_Base):
+    """Function that combines multiple items, yielding a result with the same shape as its operands
+
+    All CombinationFunctions must have two attributes - multiplicative_param and additive_param - 
+        each of which is assigned the name of one of the function's parameters;  
+        this is for use by ModulatoryProjections (and, in particular, GatingProjections, 
+        when the CombinationFunction is used as the function of an InputState or OutputState). 
+     
+    """
     componentType = COMBINATION_FUNCTION_TYPE
 
+    # IMPLEMENTATION NOTE: THESE SHOULD SHOULD BE REPLACED WITH ABC WHEN IMPLEMENTED
+    def __init__(self, variable_default,
+                         params,
+                         owner,
+                         prefs,
+                         context):
+
+        if not hasattr(self, MULTIPLICATIVE_PARAM):
+            raise FunctionError("PROGRAM ERROR: {} must implement a {} attribute".
+                                format(self.__class__.__name__, MULTIPLICATIVE_PARAM))
+
+        if not hasattr(self, ADDITIVE_PARAM):
+            raise FunctionError("PROGRAM ERROR: {} must implement an {} attribute".
+                                format(self.__class__.__name__, ADDITIVE_PARAM))
+
+        super().__init__(variable_default=variable_default,
+                         params=params,
+                         owner=owner,
+                         prefs=prefs,
+                         context=context)
+
+    @property
+    def multiplicative(self):
+        return getattr(self, self.multiplicative_param)
+
+    @multiplicative.setter
+    def multiplicative(self, val):
+        setattr(self, self.multiplicative_param, val)
+
+    @property
+    def additive(self):
+        return getattr(self, self.additive_param)
+
+    @additive.setter
+    def additive(self, val):
+        setattr(self, self.additive_param, val)
+
 
 class Reduce(CombinationFunction):  # ------------------------------------------------------------------------
     # FIX: CONFIRM THAT 1D KWEIGHTS USES EACH ELEMENT TO SCALE CORRESPONDING VECTOR IN VARIABLE
@@ -840,6 +1016,8 @@ class Reduce(CombinationFunction):  # ------------------------------------------
     Reduce(                                     \
          variable_default=variableClassDefault, \
          operation=SUM,                         \
+         scale=1.0,                             \
+         offset=0.0,                            \
          params=None,                           \
          owner=None,                            \
          prefs=None,                            \
@@ -848,6 +1026,8 @@ class Reduce(CombinationFunction):  # ------------------------------------------
     .. _Reduce:
 
     Combine values in each of one or more arrays into a single value for each array.
+    Use optional SCALE and OFFSET parameters to linearly transform the resulting value for each array.
+    Returns a scalar value for each array of the input. 
 
     COMMENT:
         IMPLEMENTATION NOTE: EXTEND TO MULTIDIMENSIONAL ARRAY ALONG ARBITRARY AXIS
@@ -863,6 +1043,14 @@ class Reduce(CombinationFunction):  # ------------------------------------------
         specifies whether to sum or multiply the elements in `variable <Reduce.function.variable>` of
         `function <Reduce.function>`.
 
+    scale : float
+        specifies a value by which to multiply each element of the output of `function <Reduce.function>`
+        (see `scale <Reduce.scale>` for details)
+
+    offset : float
+        specifies a value to add to each element of the output of `function <Reduce.function>`
+        (see `offset <Reduce.offset>` for details)
+
     params : Optional[Dict[param keyword, param value]]
         a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
         function.  Values specified for parameters in the dictionary override any assigned to those parameters in
@@ -886,17 +1074,29 @@ class Reduce(CombinationFunction):  # ------------------------------------------
         determines whether elements of each array in `variable <Reduce.function.variable>` of
         `function <Reduce.function>` are summmed or multiplied.
 
+    scale : float
+        value is applied multiplicatively to each element of the array after applying the `operation <Reduce.operation>`
+        (see `scale <Reduce.scale>` for details);  this done before applying the `offset <Reduce.offset>` 
+        (if it is specified).
+
+    offset : float
+        value is added to each element of the array after applying the `operation <Reduce.operation>`
+        and `scale <Reduce.scale>` (if it is specified).
+
     owner : Mechanism
         `component <Component>` to which the Function has been assigned.
 
     prefs : PreferenceSet or specification dict : Projection.classPreferences
-        the `PreferenceSet` for function. Specified in the `prefs` argument of the constructor for the function;
+        the `PreferenceSet` for function. Specified in the **prefs** argument of the constructor for the function;
         if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
         (see :doc:`PreferenceSet <LINK>` for details).
 
     """
     componentName = REDUCE_FUNCTION
 
+    multiplicative_param = SCALE
+    additive_param = OFFSET
+
     variableClassDefault = [0, 0]
     # variableClassDefault_locked = True
 
@@ -906,6 +1106,8 @@ class Reduce(CombinationFunction):  # ------------------------------------------
     def __init__(self,
                  variable_default=variableClassDefault,
                  operation: tc.enum(SUM, PRODUCT) = SUM,
+                 scale: parameter_spec = 1.0,
+                 offset: parameter_spec = 0.0,
                  params=None,
                  owner=None,
                  prefs: is_pref_set = None,
@@ -913,6 +1115,8 @@ def __init__(self,
 
         # Assign args to params and functionParams dicts (kwConstants must == arg names)
         params = self._assign_args_to_param_dicts(operation=operation,
+                                                  scale=scale,
+                                                  offset=offset,
                                                   params=params)
 
         super().__init__(variable_default=variable_default,
@@ -939,8 +1143,8 @@ def function(self,
                  time_scale=TimeScale.TRIAL,
                  context=None):
         """
-        Returns a scalar value for each array in `variable <Reduce.variable>` that is either the sum or
-        product of the elements in that array.
+        Calculate sum or product of the elements for each array in `variable <Reduce.variable>`, 
+        apply `scale <Reduce.scale>` and/or `offset <Reduce.offset>`, and return array of resulting values.        
 
         Arguments
         ---------
@@ -969,12 +1173,14 @@ def function(self,
         self._check_args(variable=variable, params=params, context=context)
 
         operation = self.paramsCurrent[OPERATION]
+        scale = self.paramsCurrent[SCALE]
+        offset = self.paramsCurrent[OFFSET]
 
         # Calculate using relevant aggregation operation and return
         if (operation is SUM):
-            result = np.sum(self.variable)
+            result = np.sum(self.variable) * scale + offset
         elif operation is PRODUCT:
-            result = np.product(self.variable)
+            result = np.product(self.variable) * scale + offset
         else:
             raise FunctionError("Unrecognized operator ({0}) for Reduce function".
                                 format(self.paramsCurrent[OPERATION].self.Operation.SUM))
@@ -991,9 +1197,9 @@ class LinearCombination(
          variable_default, \
          weights=None,     \
          exponents=None,   \
-         scale=1.0,        \
-         offset=0.0,       \
          operation=SUM,    \
+         scale=None,       \
+         offset=None,      \
          params=None,      \
          owner=None,       \
          name=None,        \
@@ -1047,32 +1253,32 @@ class LinearCombination(
     variable : 1d or 2d np.array : default variableClassDefault
         specifies a template for the arrays to be combined.  If it is 2d, all items must have the same length.
 
-    weights : 1d or 2d np.array
+    weights : 1d or 2d np.array : default None
         specifies values used to multiply the elements of each array in `variable  <LinearCombination.variable>`.
         If it is 1d, its length must equal the number of items in `variable <LinearCombination.variable>`;
         if it is 2d, the length of each item must be the same as those in `variable <LinearCombination.variable>`,
         and there must be the same number of items as there are in `variable <LinearCombination.variable>`
         (see `weights <LinearCombination.weights>` for details)
 
-    exponents : 1d or 2d np.array
+    exponents : 1d or 2d np.array : default None
         specifies values used to exponentiate the elements of each array in `variable  <LinearCombination.variable>`.
         If it is 1d, its length must equal the number of items in `variable <LinearCombination.variable>`;
         if it is 2d, the length of each item must be the same as those in `variable <LinearCombination.variable>`,
         and there must be the same number of items as there are in `variable <LinearCombination.variable>`
         (see `exponents <LinearCombination.exponents>` for details)
 
-    scale : float
+    operation : SUM or PRODUCT
+        specifies whether the `function <LinearCombination.function>` takes the elementwise (Hadamarad)
+        sum or product of the arrays in `variable  <LinearCombination.variable>`.
+
+    scale : float or np.ndarray : default None
         specifies a value by which to multiply each element of the output of `function <LinearCombination.function>`
         (see `scale <LinearCombination.scale>` for details)
 
-    offset : float
+    offset : float or np.ndarray : default None
         specifies a value to add to each element of the output of `function <LinearCombination.function>`
         (see `offset <LinearCombination.offset>` for details)
 
-    operation : SUM or PRODUCT
-        specifies whether the `function <LinearCombination.function>` takes the elementwise (Hadamarad)
-        sum or product of the arrays in `variable  <LinearCombination.variable>`.
-
     params : Optional[Dict[param keyword, param value]]
         a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
         function.  Values specified for parameters in the dictionary override any assigned to those parameters in
@@ -1109,19 +1315,19 @@ class LinearCombination(
         In either case, exponentiating is applied after application of the `weights <LinearCombination.weights>`
         (if any are specified).
 
-    scale : float
-        value is multiplied by each element of the array after applying the `operation <LinearCombination.operation>`
-        (see `scale <LinearCombination.scale>` for details);  this done before applying the
-        `offset <LinearCombination.offset>` (if it is specified).
-
-    offset : float
-        value is added to each element of the array after applying the `operation <LinearCombination.operation>`
-        and `scale <LinearCombination.scale>` (if it is specified).
-
     operation : SUM or PRODUCT
         determines whether the `function <LinearCombination.function>` takes the elementwise (Hadamard) sum or
         product of the arrays in `variable  <LinearCombination.variable>`.
 
+    scale : float or np.ndarray
+        value is applied multiplicatively to each element of the array after applying the 
+        `operation <LinearCombination.operation>` (see `scale <LinearCombination.scale>` for details);  
+        this done before applying the `offset <LinearCombination.offset>` (if it is specified).
+
+    offset : float or np.ndarray
+        value is added to each element of the array after applying the `operation <LinearCombination.operation>`
+        and `scale <LinearCombination.scale>` (if it is specified).
+
     COMMENT:
     function : function
         applies the `weights <LinearCombination.weights>` and/or `exponents <LinearCombinations.weights>` to the
@@ -1142,7 +1348,7 @@ class LinearCombination(
         `component <Component>` to which the Function has been assigned.
 
     prefs : PreferenceSet or specification dict : Projection.classPreferences
-        the `PreferenceSet` for function. Specified in the `prefs` argument of the constructor for the function;
+        the `PreferenceSet` for function. Specified in the **prefs** argument of the constructor for the function;
         if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
         (see :doc:`PreferenceSet <LINK>` for details).
 
@@ -1156,6 +1362,9 @@ class LinearCombination(
         kpRuntimeParamStickyAssignmentPref: PreferenceEntry(False, PreferenceLevel.INSTANCE)
     }
 
+    multiplicative_param = SCALE
+    additive_param = OFFSET
+
     variableClassDefault = [2, 2]
     # variableClassDefault_locked = True
 
@@ -1170,24 +1379,30 @@ def __init__(self,
                  # MODIFIED 2/10/17 OLD: [CAUSING CRASHING FOR SOME REASON]
                  # # weights:is_numeric_or_none=None,
                  # # exponents:is_numeric_or_none=None,
-                 # MODIFIED 2/10/17 NEW:
-                 weights=None,
-                 exponents=None,
-                 scale: parameter_spec = 1.0,
-                 offset: parameter_spec = 0.0,
-                 # MODIFIED 2/10/17 END
-                 operation: tc.enum(SUM, PRODUCT) = SUM,
+                 # weights=None,
+                 # exponents=None,
+                 weights:tc.optional(parameter_spec)=None,
+                 exponents:tc.optional(parameter_spec)=None,
+                 operation: tc.enum(SUM, PRODUCT)=SUM,
+                 # scale=1.0,
+                 # offset=0.0,
+                 # scale:tc.optional(parameter_spec)=1.0,
+                 # offset:tc.optional(parameter_spec)=0.0,
+                 # scale:is_numeric_or_none=None,
+                 # offset:is_numeric_or_none=None,
+                 scale=None,
+                 offset=None,
                  params=None,
                  owner=None,
                  prefs: is_pref_set = None,
                  context=componentName + INITIALIZING):
 
         # Assign args to params and functionParams dicts (kwConstants must == arg names)
-        params = self._assign_args_to_param_dicts(scale=scale,
-                                                  offset=offset,
-                                                  weights=weights,
+        params = self._assign_args_to_param_dicts(weights=weights,
                                                   exponents=exponents,
                                                   operation=operation,
+                                                  scale=scale,
+                                                  offset=offset,
                                                   params=params)
 
         super().__init__(variable_default=variable_default,
@@ -1213,6 +1428,8 @@ def _validate_variable(self, variable, context=None):
         # FIX: THEN TEST THAT SHAPES OF EVERY ELEMENT ALONG AXIS 0 ARE THE SAME
         # FIX; PUT THIS IN DOCUMENTATION
         if isinstance(variable, (list, np.ndarray)):
+            if isinstance(variable, np.ndarray) and not variable.ndim:
+                return
             length = 0
             for i in range(len(variable)):
                 if i == 0:
@@ -1230,15 +1447,14 @@ def _validate_variable(self, variable, context=None):
                                         format(variable, self.__class__.__name__))
 
     def _validate_params(self, request_set, target_set=None, context=None):
-        """Insure that WEIGHTS and EXPONENTS are lists or np.arrays of numbers with length equal to variable
-
-        Args:
-            request_set:
-            target_set:
-            context:
-
-        Returns:
-
+        """Validate weghts, exponents, scale and offset parameters
+        
+        Check that WEIGHTS and EXPONENTS are lists or np.arrays of numbers with length equal to variable
+        Check that SCALE and OFFSET are either scalars or np.arrays of numbers with length and shape equal to variable
+        
+        Note: the checks of compatiability with variable are only performed for validation calls during execution
+              (i.e., from check_args(), since during initialization or COMMAND_LINE assignment,
+              a parameter may be re-assigned before variable assigned during is known
         """
 
         # FIX: MAKE SURE THAT IF OPERATION IS SUBTRACT OR DIVIDE, THERE ARE ONLY TWO VECTORS
@@ -1249,8 +1465,53 @@ def _validate_params(self, request_set, target_set=None, context=None):
 
         if WEIGHTS in target_set and target_set[WEIGHTS] is not None:
             target_set[WEIGHTS] = np.atleast_2d(target_set[WEIGHTS]).reshape(-1, 1)
+            if EXECUTING in context:
+                if len(target_set[WEIGHTS]) != len(self.variable):
+                    raise FunctionError("Number of weights ({0}) is not equal to number of items in variable ({1})".
+                                        format(len(target_set[WEIGHTS]), len(self.variable.shape)))
+
         if EXPONENTS in target_set and target_set[EXPONENTS] is not None:
             target_set[EXPONENTS] = np.atleast_2d(target_set[EXPONENTS]).reshape(-1, 1)
+            if EXECUTING in context:
+                if len(target_set[EXPONENTS]) != len(self.variable):
+                    raise FunctionError("Number of exponents ({0}) does not equal number of items in variable ({1})".
+                                        format(len(target_set[EXPONENTS]), len(self.variable.shape)))
+
+        if SCALE in target_set and target_set[SCALE] is not None:
+            scale = target_set[SCALE]
+            if isinstance(scale, numbers.Number):
+                pass
+            elif isinstance(scale, np.ndarray):
+                target_set[SCALE] = np.array(scale)
+            else:
+                raise FunctionError("{} param of {} ({}) must be a scalar or an np.ndarray".
+                                    format(SCALE, self.name, scale))
+            if EXECUTING in context:
+                if (isinstance(scale, np.ndarray) and
+                        (scale.size != self.variable.size or 
+                         scale.shape != self.variable.shape)):
+                    raise FunctionError("Scale is using Hadamard modulation "
+                                        "but its shape and/or size (shape: {}, size:{}) "
+                                        "do not match the variable being modulated (shape: {}, size: {})".
+                                        format(scale.shape, scale.size, self.variable.shape, self.variable.size))
+
+        if OFFSET in target_set and target_set[OFFSET] is not None:
+            offset = target_set[OFFSET]
+            if isinstance(offset, numbers.Number):
+                pass
+            elif isinstance(offset, np.ndarray):
+                target_set[OFFSET] = np.array(offset)
+            else:
+                raise FunctionError("{} param of {} ({}) must be a scalar or an np.ndarray".
+                                    format(OFFSET, self.name, offset))
+            if EXECUTING in context:
+                if (isinstance(offset, np.ndarray) and
+                        (offset.size != self.variable.size or 
+                         offset.shape != self.variable.shape)):
+                    raise FunctionError("Offset is using Hadamard modulation "
+                                        "but its shape and/or size (shape: {}, size:{}) "
+                                        "do not match the variable being modulated (shape: {}, size: {})".
+                                        format(offset.shape, offset.size, self.variable.shape, self.variable.size))
 
             # if not operation:
             #     raise FunctionError("Operation param missing")
@@ -1258,6 +1519,7 @@ def _validate_params(self, request_set, target_set=None, context=None):
             #     raise FunctionError("Operation param ({0}) must be Operation.SUM or Operation.PRODUCT".
             #     format(operation))
 
+
     def function(self,
                  variable=None,
                  params=None,
@@ -1299,11 +1561,22 @@ def function(self,
         # Validate variable and assign to self.variable, and validate params
         self._check_args(variable=variable, params=params, context=context)
 
-        exponents = self.paramsCurrent[EXPONENTS]
-        weights = self.paramsCurrent[WEIGHTS]
-        operation = self.paramsCurrent[OPERATION]
-        offset = self.paramsCurrent[OFFSET]
-        scale = self.paramsCurrent[SCALE]
+        exponents = self.exponents
+        weights = self.weights
+        operation = self.operation
+        # QUESTION:  WHICH IS LESS EFFICIENT:
+        #                A) UNECESSARY ARITHMETIC OPERATIONS IF SCALE AND/OR OFFSET ARE 1.0 AND 0, RESPECTIVELY?
+        #                   (DOES THE COMPILER KNOW NOT TO BOTHER WITH MULT BY 1 AND/OR ADD 0?)
+        #                B) EVALUATION OF IF STATEMENTS TO DETERMINE THE ABOVE?
+        # IMPLEMENTATION NOTE:  FOR NOW, ASSUME B) ABOVE, AND ASSIGN DEFAULT "NULL" VALUES TO offset AND scale
+        if self.offset is None:
+            offset = 0.0
+        else:
+            offset = self.offset
+        if self.scale is None:
+            scale = 1.0
+        else:
+            scale = self.scale
 
         # IMPLEMENTATION NOTE: CONFIRM: SHOULD NEVER OCCUR, AS _validate_variable NOW ENFORCES 2D np.ndarray
         # If variable is 0D or 1D:
@@ -1313,9 +1586,6 @@ def function(self,
         # FIX FOR EFFICIENCY: CHANGE THIS AND WEIGHTS TO TRY/EXCEPT // OR IS IT EVEN NECESSARY, GIVEN VALIDATION ABOVE??
         # Apply exponents if they were specified
         if exponents is not None:
-            if len(exponents) != len(self.variable):
-                raise FunctionError("Number of exponents ({0}) does not equal number of items in variable ({1})".
-                                    format(len(exponents), len(self.variable.shape)))
             # Avoid divide by zero warning:
             #    make sure there are no zeros for an element that is assigned a negative exponent
             if INITIALIZING in context and any(not any(i) and j < 0 for i, j in zip(self.variable, exponents)):
@@ -1325,33 +1595,140 @@ def function(self,
 
         # Apply weights if they were specified
         if weights is not None:
-            if len(weights) != len(self.variable):
-                raise FunctionError("Number of weights ({0}) is not equal to number of items in variable ({1})".
-                                    format(len(weights), len(self.variable.shape)))
-            else:
-                self.variable = self.variable * weights
+            self.variable = self.variable * weights
+
+        # CALCULATE RESULT USING RELEVANT COMBINATION OPERATION AND MODULATION
 
-        # Calculate using relevant aggregation operation and return
         if (operation is SUM):
-            result = sum(self.variable) * scale + offset
-        elif operation is PRODUCT:
-            result = reduce(mul, self.variable, 1)
+            if isinstance(scale, numbers.Number):
+                # Scalar scale and offset
+                if isinstance(offset, numbers.Number):
+                    result = np.sum(self.variable, axis=0) * scale + offset
+                # Scalar scale and Hadamard offset
+                else:
+                    result = np.sum(np.append([self.variable * scale], [offset], axis=0), axis=0)
+            else:
+                # Hadamard scale, scalar offset
+                if isinstance(offset, numbers.Number):
+                    result = np.product([np.sum([self.variable], axis=0), scale], axis=0)
+                # Hadamard scale and offset
+                else:
+                    hadamard_product = np.product([np.sum([self.variable], axis=0), scale], axis=0)
+                    result = np.sum(np.append([hadamard_product], [offset], axis=0), axis=0)
+
+        elif (operation is PRODUCT):
+            product = np.product(self.variable, axis=0)
+            if isinstance(scale, numbers.Number):
+                # Scalar scale and offset
+                if isinstance(offset, numbers.Number):
+                    result = product * scale + offset
+                # Scalar scale and Hadamard offset
+                else:
+                    result = np.sum(np.append([product], [offset], axis=0), axis=0) + offset
+            else:
+                # Hadamard scale, scalar offset
+                if isinstance(offset, numbers.Number):
+                    result = np.product(np.append([product], [scale], axis=0), axis=0) + offset
+                # Hadamard scale and offset
+                else:
+                    hadamard_product = np.product(np.append([product], [scale], axis=0), axis=0)
+                    result = np.sum(np.append([hadamard_product], [offset], axis=0), axis=0)
+
+        # elif operation is PRODUCT:
+        #     result = reduce(mul, self.variable, 1)
+
         else:
             raise FunctionError("Unrecognized operator ({0}) for LinearCombination function".
                                 format(self.paramsCurrent[OPERATION].self.Operation.SUM))
-        # FIX: CONFIRM THAT RETURNS LIST IF GIVEN A LIST
         return result
 
+    @property
+    def offset(self):
+        if not hasattr(self, '_offset'):
+            return None
+        else:
+            return self._offset
+
+    @offset.setter
+    def offset(self, val):
+        self._offset = val
+
+    @property
+    def scale(self):
+        if not hasattr(self, '_scale'):
+            return None
+        else:
+            return self._scale
+
+    @scale.setter
+    def scale(self, val):
+        self._scale = val
+
+
 
 # region ***********************************  TRANSFER FUNCTIONS  ***********************************************
 # endregion
 
+BOUNDS = 'bounds'
+
 class TransferFunction(Function_Base):
-    componentType = TRANFER_FUNCTION_TYPE
+    """Function that transforms variable but maintains its shape
+
+    All TransferFunctions must have the attribute `bounds` that specifies the lower and upper limits of the result;
+        if there are none, the attribute is set to `None`;  if it has at least one bound, the attribute is set to a
+        tuple specifying the lower and upper bounds, respectively, with `None` as the entry for no bound.
+        
+    All TransferFunctions must also have two attributes - multiplicative_param and additive_param - 
+        each of which is assigned the name of one of the function's parameters;  
+        this is for use by ModulatoryProjections (and, in particular, GatingProjections, 
+        when the TransferFunction is used as the function of an InputState or OutputState). 
+     
+    """
+    componentType = TRANSFER_FUNCTION_TYPE
+    
+    # IMPLEMENTATION NOTE: THESE SHOULD SHOULD BE REPLACED WITH ABC WHEN IMPLEMENTED
+    def __init__(self, variable_default,
+                         params,
+                         owner,
+                         prefs,
+                         context):
+
+        if not hasattr(self, BOUNDS):
+            raise FunctionError("PROGRAM ERROR: {} must implement a {} attribute".
+                                format(self.__class__.__name__, BOUNDS))
+
+        if not hasattr(self, MULTIPLICATIVE_PARAM):
+            raise FunctionError("PROGRAM ERROR: {} must implement a {} attribute".
+                                format(self.__class__.__name__, MULTIPLICATIVE_PARAM))
+
+        if not hasattr(self, ADDITIVE_PARAM):
+            raise FunctionError("PROGRAM ERROR: {} must implement an {} attribute".
+                                format(self.__class__.__name__, ADDITIVE_PARAM))
+
+        super().__init__(variable_default=variable_default,
+                         params=params,
+                         owner=owner,
+                         prefs=prefs,
+                         context=context)
+
+    @property
+    def multiplicative(self):
+        return getattr(self, self.multiplicative_param)
+    
+    @multiplicative.setter
+    def multiplicative(self, val):
+        setattr(self, self.multiplicative_param, val)
+        
+    @property
+    def additive(self):
+        return getattr(self, self.additive_param)
 
+    @additive.setter
+    def additive(self, val):
+        setattr(self, self.additive_param, val)
 
-class Linear(
-    TransferFunction):  # --------------------------------------------------------------------------------------
+
+class Linear(TransferFunction):  # -------------------------------------------------------------------------------------
     """
     Linear(                \
          variable_default, \
@@ -1408,11 +1785,13 @@ class Linear(
         value added to each element of `variable <Linear.variable>` after applying the `slope <Linear.slope>`
         (if it is specified).
 
+    bounds : None
+
     owner : Mechanism
         `component <Component>` to which the Function has been assigned.
 
     prefs : PreferenceSet or specification dict : Projection.classPreferences
-        the `PreferenceSet` for function. Specified in the `prefs` argument of the constructor for the function;
+        the `PreferenceSet` for function. Specified in the **prefs** argument of the constructor for the function;
         if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
         (see :doc:`PreferenceSet <LINK>` for details).
 
@@ -1420,6 +1799,10 @@ class Linear(
 
     componentName = LINEAR_FUNCTION
 
+    bounds = None
+    multiplicative_param = SLOPE
+    additive_param = INTERCEPT
+
     classPreferences = {
         kwPreferenceSetName: 'LinearClassPreferences',
         kpReportOutputPref: PreferenceEntry(False, PreferenceLevel.INSTANCE),
@@ -1430,7 +1813,7 @@ class Linear(
 
     paramClassDefaults = Function_Base.paramClassDefaults.copy()
     paramClassDefaults.update({
-        kwFunctionOutputTypeConversion: True,
+        FUNCTION_OUTPUT_TYPE_CONVERSION: True,
         PARAMETER_STATE_PARAMS: None
     })
 
@@ -1455,7 +1838,7 @@ def __init__(self,
                          prefs=prefs,
                          context=context)
 
-        self.functionOutputType = None
+        # self.functionOutputType = None
 
     def function(self,
                  variable=None,
@@ -1557,8 +1940,7 @@ def derivative(self, input=None, output=None):
         return self.slope
 
 
-class Exponential(
-    TransferFunction):  # ---------------------------------------------------------------------------------
+class Exponential(TransferFunction):  # --------------------------------------------------------------------------------
     """
     Exponential(           \
          variable_default, \
@@ -1609,13 +1991,15 @@ class Exponential(
         value by which `variable <Exponential.variable>` is multiplied before exponentiation.
 
     scale : float
-        value by which the exponentiated value is multipled.
+        value by which the exponentiated value is multiplied.
+
+    bounds : (0, None)
 
     owner : Mechanism
         `component <Component>` to which the Function has been assigned.
 
     prefs : PreferenceSet or specification dict : Projection.classPreferences
-        the `PreferenceSet` for function. Specified in the `prefs` argument of the constructor for the function;
+        the `PreferenceSet` for function. Specified in the **prefs** argument of the constructor for the function;
         if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
         (see :doc:`PreferenceSet <LINK>` for details).
 
@@ -1623,6 +2007,11 @@ class Exponential(
 
     componentName = EXPONENTIAL_FUNCTION
 
+    bounds = (0, None)
+    multiplicative_param = RATE
+    additive_param = SCALE
+
+
     variableClassDefault = 0
 
     paramClassDefaults = Function_Base.paramClassDefaults.copy()
@@ -1757,11 +2146,13 @@ class Logistic(
         value added to each element of `variable <Logistic.variable>` after applying the `gain <Logistic.gain>`
         (if it is specified).
 
+    bounds : (0,1)
+
     owner : Mechanism
         `component <Component>` to which the Function has been assigned.
 
     prefs : PreferenceSet or specification dict : Projection.classPreferences
-        the `PreferenceSet` for function. Specified in the `prefs` argument of the constructor for the function;
+        the `PreferenceSet` for function. Specified in the **prefs** argument of the constructor for the function;
         if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
         (see :doc:`PreferenceSet <LINK>` for details).
 
@@ -1770,6 +2161,10 @@ class Logistic(
     componentName = LOGISTIC_FUNCTION
     parameter_keywords.update({GAIN, BIAS})
 
+    bounds = (0,1)
+    multiplicative_param = GAIN
+    additive_param = BIAS
+
     variableClassDefault = 0
 
     paramClassDefaults = Function_Base.paramClassDefaults.copy()
@@ -1860,7 +2255,7 @@ class SoftMax(
 
     .. _SoftMax:
 
-    SoftMax transform of variable (see `The Softmax function and its derivative 
+    SoftMax transform of variable (see `The Softmax function and its derivative
     <http://eli.thegreenplace.net/2016/the-softmax-function-and-its-derivative/>`_ for a nice discussion).
 
     Arguments
@@ -1908,11 +2303,14 @@ class SoftMax(
             * **PROB**: probabilistically chosen element based on softmax-transformed values after normalizing sum of
               values to 1, 0 for all others.
 
+    bounds : None if `output <SoftMax.output>`==MAX_VAL, else (0,1) : default (0,1)
+
+
     owner : Mechanism
         `component <Component>` to which the Function has been assigned.
 
     prefs : PreferenceSet or specification dict : Projection.classPreferences
-        the `PreferenceSet` for function. Specified in the `prefs` argument of the constructor for the function;
+        the `PreferenceSet` for function. Specified in the **prefs** argument of the constructor for the function;
         if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
         (see :doc:`PreferenceSet <LINK>` for details).
 
@@ -1920,6 +2318,10 @@ class SoftMax(
 
     componentName = SOFTMAX_FUNCTION
 
+    bounds = (0,1)
+    multiplicative_param = GAIN
+    additive_param = None
+
     variableClassDefault = 0
 
     paramClassDefaults = Function_Base.paramClassDefaults.copy()
@@ -1938,6 +2340,8 @@ def __init__(self,
         params = self._assign_args_to_param_dicts(gain=gain,
                                                   output=output,
                                                   params=params)
+        if output is MAX_VAL:
+            bounds = None
 
         super().__init__(variable_default=variable_default,
                          params=params,
@@ -1954,7 +2358,7 @@ def function(self,
         Return: e**(`gain <SoftMax.gain>` * `variable <SoftMax.variable>`) /
         sum(e**(`gain <SoftMax.gain>` * `variable <SoftMax.variable>`)),
         filtered by `ouptput <SoftMax.output>` specification.
-        
+
         Arguments
         ---------
 
@@ -2017,14 +2421,14 @@ def derivative(self, output, input=None):
         """
         derivative(output)
 
-        Calculate the derivative of `function <SoftMax.function>`.  If OUTPUT_TYPE for the SoftMax Function is ALL, 
+        Calculate the derivative of `function <SoftMax.function>`.  If OUTPUT_TYPE for the SoftMax Function is ALL,
         return Jacobian matrix (derivative for each element of the output array with respect to each of the others):
             COMMENT:
                 D[j]/S[i] = S[i](d[i,j] - S[j]) where d[i,j]=1 if i==j; d[i,j]=0 if i!=j.
             COMMENT
-            D\ :sub:`j`\ S\ :sub:`i` = S\ :sub:`i`\ (𝜹\ :sub:`i,j` - S\ :sub:`j`), 
-            where 𝜹\ :sub:`i,j`\ =1 if i=j and 𝜹\ :sub:`i,j`\ =0 if i≠j.
-        If OUTPUT_TYPE is MAX_VAL or MAX_INDICATOR, return 1d array of the derivatives of the maximum 
+            D\\ :sub:`j`\\ S\\ :sub:`i` = S\\ :sub:`i`\\ (𝜹\\ :sub:`i,j` - S\\ :sub:`j`),
+            where 𝜹\\ :sub:`i,j`\\ =1 if i=j and 𝜹\\ :sub:`i,j`\\ =0 if i≠j.
+        If OUTPUT_TYPE is MAX_VAL or MAX_INDICATOR, return 1d array of the derivatives of the maximum
         value with respect to the others (calculated as above). If OUTPUT_TYPE is PROB, raise an exception
         (since it is ambiguous as to which element would have been chosen by the SoftMax function)
 
@@ -2091,13 +2495,13 @@ class LinearMatrix(TransferFunction):  # ---------------------------------------
 
     COMMENT:  [CONVERT TO FIGURE]
         ----------------------------------------------------------------------------------------------------------
-        MATRIX FORMAT
+        MATRIX FORMAT <shape: (3,5)>
                                          INDICES:
                                      Output elements:
-                              0       1       2       3
-                         0  [0,0]   [0,1]   [0,2]   [0,3]
-        Input elements:  1  [1,0]   [1,1]   [1,2]   [1,3]
-                         2  [2,0]   [2,1]   [2,2]   [2,3]
+                              0       1       2       3       4
+                         0  [0,0]   [0,1]   [0,2]   [0,3]   [0,4]
+        Input elements:  1  [1,0]   [1,1]   [1,2]   [1,3]   [1,4]
+                         2  [2,0]   [2,1]   [2,2]   [2,3]   [2,4]
 
         matrix.shape => (input/rows, output/cols)
 
@@ -2125,6 +2529,8 @@ class LinearMatrix(TransferFunction):  # ---------------------------------------
         specifies matrix used to transform `variable <LinearMatrix.variable>`
         (see `matrix <LinearMatrix.matrix>` for specification details).
 
+    bounds : None
+
     params : Optional[Dict[param keyword, param value]]
         a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
         function.  Values specified for parameters in the dictionary override any assigned to those parameters in
@@ -2157,7 +2563,7 @@ class LinearMatrix(TransferFunction):  # ---------------------------------------
         `component <Component>` to which the Function has been assigned.
 
     prefs : PreferenceSet or specification dict : Projection.classPreferences
-        the `PreferenceSet` for function. Specified in the `prefs` argument of the constructor for the function;
+        the `PreferenceSet` for function. Specified in the **prefs** argument of the constructor for the function;
         if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
         (see :doc:`PreferenceSet <LINK>` for details).
 
@@ -2165,25 +2571,29 @@ class LinearMatrix(TransferFunction):  # ---------------------------------------
 
     componentName = LINEAR_MATRIX_FUNCTION
 
+    bounds = None
+    multiplicative_param = None
+    additive_param = None
+
     DEFAULT_FILLER_VALUE = 0
 
     variableClassDefault = [DEFAULT_FILLER_VALUE]  # Sender vector
 
     paramClassDefaults = Function_Base.paramClassDefaults.copy()
 
-    def matrix_spec(m):
-        if m is None:
-            return True
-        if m in MATRIX_KEYWORD_VALUES:
-            return True
-        if isinstance(m, (list, np.ndarray, np.matrix, function_type)):
-            return True
-        return False
+    # def is_matrix_spec(m):
+    #     if m is None:
+    #         return True
+    #     if m in MATRIX_KEYWORD_VALUES:
+    #         return True
+    #     if isinstance(m, (list, np.ndarray, np.matrix, function_type)):
+    #         return True
+    #     return False
 
     @tc.typecheck
     def __init__(self,
                  variable_default=variableClassDefault,
-                 matrix: matrix_spec = None,
+                 matrix:tc.optional(is_matrix) = None,
                  params=None,
                  owner=None,
                  prefs: is_pref_set = None,
@@ -2202,29 +2612,26 @@ def __init__(self,
                          prefs=prefs,
                          context=context)
 
-        self.matrix = self.instantiate_matrix(self.paramsCurrent[MATRIX])
+        self._matrix = self.instantiate_matrix(self.paramsCurrent[MATRIX])
 
     def _validate_variable(self, variable, context=None):
-        """Insure that variable passed to LinearMatrix is a 1D np.array
+        """Insure that variable passed to LinearMatrix is a max 2D np.array
 
-        :param variable: (1D np.array)
+        :param variable: (max 2D np.array)
         :param context:
         :return:
         """
         super()._validate_variable(variable, context)
 
-        # Check that self.variable == 1D
+        # Check that self.variable <= 2D
         try:
-            is_not_1D = not self.variable.ndim is 1
-
+            if not self.variable.ndim <= 2:
+                raise FunctionError("variable ({0}) for {1} must be a numpy.ndarray of dimension at most 2".format(self.variable, self.__class__.__name__))
         except AttributeError:
-            raise FunctionError("PROGRAM ERROR: variable ({0}) for {1} should be an np.ndarray".
-                                format(self.variable, self.__class__.__name__))
-        else:
-            if is_not_1D:
-                raise FunctionError("variable ({0}) for {1} must be a 1D np.ndarray".
+            raise FunctionError("PROGRAM ERROR: variable ({0}) for {1} should be a numpy.ndarray".
                                     format(self.variable, self.__class__.__name__))
 
+
     def _validate_params(self, request_set, target_set=None, context=None):
         """Validate params and assign to targets
 
@@ -2256,8 +2663,8 @@ def _validate_params(self, request_set, target_set=None, context=None):
         # MODIFIED 3/26/17 OLD:
         # Check for and validate kwReceiver first, since it may be needed to validate and/or construct the matrix
         # First try to get receiver from specification in params
-        if kwReceiver in param_set:
-            self.receiver = param_set[kwReceiver]
+        if RECEIVER in param_set:
+            self.receiver = param_set[RECEIVER]
             # Check that specification is a list of numbers or an np.array
             if ((isinstance(self.receiver, list) and all(isinstance(elem, numbers.Number) for elem in self.receiver)) or
                     isinstance(self.receiver, np.ndarray)):
@@ -2270,7 +2677,7 @@ def _validate_params(self, request_set, target_set=None, context=None):
             if (self.owner and self.owner.prefs.verbosePref) or self.prefs.verbosePref:
                 print("Identity matrix requested but kwReceiver not specified; sender length ({0}) will be used".
                       format(sender_len))
-            self.receiver = param_set[kwReceiver] = sender
+            self.receiver = param_set[RECEIVER] = sender
         # # MODIFIED 3/26/17 NEW:
         # self.receiver = param_set[kwReceiver] = sender
         # MODIFIED 3/26/17 END
@@ -2282,11 +2689,11 @@ def _validate_params(self, request_set, target_set=None, context=None):
         for param_name, param_value in param_set.items():
 
             # Receiver param already checked above
-            if param_name is kwReceiver:
+            if param_name is RECEIVER:
                 continue
 
             # Not currently used here
-            if param_name is kwFunctionOutputTypeConversion:
+            if param_name in function_keywords:
                 continue
 
             if param_name is AUTO_DEPENDENT:
@@ -2300,7 +2707,7 @@ def _validate_params(self, request_set, target_set=None, context=None):
                     continue
 
                 # np.matrix or np.ndarray provided, so validate that it is numeric and check dimensions
-                elif isinstance(param_value, (np.ndarray, np.matrix)):
+                elif isinstance(param_value, (list, np.ndarray, np.matrix)):
                     # get dimensions specified by:
                     #   variable (sender): width/cols/outer index
                     #   kwReceiver param: height/rows/inner index
@@ -2330,7 +2737,7 @@ def _validate_params(self, request_set, target_set=None, context=None):
                     continue
 
                 # Identity matrix requested (using keyword), so check send_len == receiver_len
-                elif param_value in {IDENTITY_MATRIX, OFF_DIAGNOAL_MATRIX}:
+                elif param_value in {IDENTITY_MATRIX, HOLLOW_MATRIX}:
                     # Receiver length doesn't equal sender length
                     if not (self.receiver.shape == sender.shape and self.receiver.size == sender.size):
                         # if self.owner.prefs.verbosePref:
@@ -2385,7 +2792,7 @@ def _validate_params(self, request_set, target_set=None, context=None):
                                                MATRIX_KEYWORD_NAMES))
             else:
                 message += "Unrecognized param ({}) specified for the {} function of {}".format(param_name,
-                                                                                                self.name,
+                                                                                                self.componentName,
                                                                                                 self.owner.name)
                 continue
 
@@ -2393,7 +2800,7 @@ def _validate_params(self, request_set, target_set=None, context=None):
             raise FunctionError(message)
 
     def _instantiate_attributes_before_function(self, context=None):
-        self.matrix = self.instantiate_matrix(self.matrix)
+        self._matrix = self.instantiate_matrix(self.matrix)
 
     def instantiate_matrix(self, specification, context=None):
         """Implements matrix indicated by specification
@@ -2469,7 +2876,7 @@ def function(self,
 
     def keyword(self, keyword):
 
-        from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
+        from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
         rows = None
         cols = None
         if isinstance(self, MappingProjection):
@@ -2489,6 +2896,16 @@ def param_function(owner, function):
         return function(sender_len, receiver_len)
 
 
+# def is_matrix_spec(m):
+#     if m is None:
+#         return True
+#     if isinstance(m, (list, np.ndarray, np.matrix, function_type)):
+#         return True
+#     if m in MATRIX_KEYWORD_VALUES:
+#         return True
+#     return False
+
+
 def get_matrix(specification, rows=1, cols=1, context=None):
     """Returns matrix conforming to specification with dimensions = rows x cols or None
 
@@ -2499,7 +2916,7 @@ def get_matrix(specification, rows=1, cols=1, context=None):
         + matrix keyword:
             + AUTO_ASSIGN_MATRIX: IDENTITY_MATRIX if it is square, othwerwise FULL_CONNECTIVITY_MATRIX
             + IDENTITY_MATRIX: 1's on diagonal, 0's elsewhere (must be square matrix), otherwise generates error
-            + OFF_DIAGONAL_MATRIX: 0's on diagonal, 1's elsewhere (must be square matrix), otherwise generates error
+            + HOLLOW_MATRIX: 0's on diagonal, 1's elsewhere (must be square matrix), otherwise generates error
             + FULL_CONNECTIVITY_MATRIX: all 1's
             + RANDOM_CONNECTIVITY_MATRIX (random floats uniformly distributed between 0 and 1)
         + 2D list or np.ndarray of numbers
@@ -2507,6 +2924,10 @@ def get_matrix(specification, rows=1, cols=1, context=None):
      Returns 2D np.array with length=rows in dim 0 and length=cols in dim 1, or none if specification is not recognized
     """
 
+
+    if isinstance(specification, list):
+        specification = np.array(specification)
+
     # Matrix provided (and validated in _validate_params); convert to np.array
     if isinstance(specification, np.matrix):
         return np.array(specification)
@@ -2536,7 +2957,7 @@ def get_matrix(specification, rows=1, cols=1, context=None):
                                 format(rows, cols, specification))
         return np.identity(rows)
 
-    if specification is OFF_DIAGNOAL_MATRIX:
+    if specification is HOLLOW_MATRIX:
         if rows != cols:
             raise FunctionError("Sender length ({0}) must equal receiver length ({1}) to use {}".
                                 format(rows, cols, specification))
@@ -2562,165 +2983,130 @@ def get_matrix(specification, rows=1, cols=1, context=None):
 class IntegratorFunction(Function_Base):
     componentType = INTEGRATOR_FUNCTION_TYPE
 
-
-# FIX: IF RATE HAS TO BE BETWEEN 0 AND 1, VALIDATE_VARIABLE ACCORDINGLY
-# SEARCH & REPLACE: previous_value -> previous_value
-
 # • why does integrator return a 2d array?
-# • does rate have to be between 0 and 1 (if so, validate_variable)
-# • does rate = 0 and rate = 1 have the same meaning for all integration_types?
-# • should we change "integration_type" to "type"??
 # • are rate and noise converted to 1d np.array?  If not, correct docstring
 # • can noise and initializer be an array?  If so, validated in validate_param?
-# • time_step_size?? (vs rate??)
-# • can noise be a function now?
-
 
 class Integrator(
     IntegratorFunction):  # --------------------------------------------------------------------------------
-    """
-    Integrator(                 \
-        variable_default=None,  \
-        rate=1.0,               \
-        integration_type=CONSTANT,     \
-        noise=0.0,              \
-        time_step_size=1.0,     \
-        initializer,     \
-        params=None,            \
-        owner=None,             \
-        prefs=None,             \
-        )
-
-    .. _Integrator:
-
-    Integrate current value of `variable <Integrator.variable>` with its prior value.
-
-    Arguments
-    ---------
-
-    variable_default : number, list or np.array : default variableClassDefault
-        specifies a template for the value to be integrated;  if it is a list or array, each element is independently
-        integrated.
-
-    rate : float, list or 1d np.array : default 1.0
-        specifies the rate of integration.  If it is a list or array, it must be the same length as
-        `variable <Integrator.variable_default>` (see `rate <Integrator.rate>` for details).
-
-    integration_type : CONSTANT, SIMPLE, ADAPTIVE, DIFFUSION : default CONSTANT
-        specifies type of integration (see `integration_type <Integrator.integration_type>` for details).
-
-    noise : float, PsyNeuLink Function, list or 1d np.array : default 0.0
-        specifies random value to be added in each call to `function <Integrator.function>`. (see
-        `noise <Integrator.noise>` for details).
-
-    time_step_size : float : default 0.0
-        determines the timing precision of the integration process when `integration_type <Integrator.integration_type>` 
-        is set to DIFFUSION (see `time_step_size <Integrator.time_step_size>` for details.
-
-    initializer float, list or 1d np.array : default 0.0
-        specifies starting value for integration.  If it is a list or array, it must be the same length as
-        `variable_default <Integrator.variable_default>` (see `initializer <Integrator.initializer>` for details).
-
-    params : Optional[Dict[param keyword, param value]]
-        a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
-        function.  Values specified for parameters in the dictionary override any assigned to those parameters in
-        arguments of the constructor.
-
-    owner : Component
-        `component <Component>` to which to assign the Function.
-
-    prefs : Optional[PreferenceSet or specification dict : Function.classPreferences]
-        the `PreferenceSet` for the Function. If it is not specified, a default is assigned using `classPreferences`
-        defined in __init__.py (see :doc:`PreferenceSet <LINK>` for details).
-
-
-    Attributes
-    ----------
-
-    variable : number or np.array
-        current input value some portion of which (determined by `rate <Integrator.rate>`) that will be
-        added to the prior value;  if it is an array, each element is independently integrated.
-
-    rate : float or 1d np.array
-        determines the rate of integration based on current and prior values.  If integration_type is set to ADAPTIVE,
-        all elements must be between 0 and 1 (0 = no change; 1 = instantaneous change). If it has a single element, it
-        applies to all elements of `variable <Integrator.variable>`;  if it has more than one element, each element
-        applies to the corresponding element of `variable <Integrator.variable>`.
-
-    integration_type : CONSTANT, SIMPLE, ADAPTIVE, DIFFUSION
-        specifies type of integration:
-            * **CONSTANT**: `previous_value <Integrator.previous_value>` + `rate <Integrator.rate>` + 
-              `noise <Integrator.noise>` (ignores `variable <Integrator.variable>`);
-            * **SIMPLE**: `previous_value <Integrator.previous_value>` + `rate <Integrator.rate>` *
-              `variable <variable.Integrator.variable>` + `noise <Integrator.noise>`;
-            * **ADAPTIVE**: (1-`rate <Integrator.rate>`) * `variable <Integrator.variable>` +
-              (`rate <Integrator.rate>` * `previous_value <Integrator.previous_value>`) + `noise <Integrator.noise>`
-              (`Weiner filter <https://en.wikipedia.org/wiki/Wiener_filter>`_ or
-              `Delta rule <https://en.wikipedia.org/wiki/Delta_rule>`_);
-            * **DIFFUSION**: `previous_value <Integrator.previous_value>` +
-              (`rate <Integrator.rate>` * `previous_value` * `time_step_size <Integrator.time_step_size>`) +
-              √(`time_step_size <Integrator.time_step_size>` * `noise <Integrator.noise>` * Gaussian(0,1))
-              (`Drift Diffusion Model
-              <https://en.wikipedia.org/wiki/Two-alternative_forced_choice#Drift-diffusion_model>`_).
-
-    noise : float, function, list, or 1d np.array
-        specifies random value to be added in each call to `function <Integrator.function>`.
-
-        If noise is a list or array, it must be the same length as `variable <Integrator.variable_default>`. If noise is
-        specified as a single float or function, while `variable <Integrator.variable>` is a list or array,
-        noise will be applied to each variable element. In the case of a noise function, this means that the function
-        will be executed separately for each variable element.
-
-        Note that in the case of DIFFUSION, noise must be specified as a float (or list or array of floats) because this
-        value will be used to construct the standard DDM probability distribution. For all other types of integration,
-        in order to generate random noise, we recommend that you instead select a probability distribution function
-        (see `Distribution Functions <DistributionFunction>` for details), which will generate a new noise value from
-        its distribution on each execution. If noise is specified as a float or as a function with a fixed output (or a
-        list or array of these), then the noise will simply be an offset that remains the same across all executions.
-
-    time_step_size : float
-        determines the timing precision of the integration process when `integration_type <Integrator.integration_type>`
-        is set to DIFFUSION (and used to scale the `noise <Integrator.noise>` parameter appropriately).
-
-    initializer : 1d np.array or list
-        determines the starting value for integration (i.e., the value to which 
-        `previous_value <Integrator.previous_value>` is set.
-
-        If initializer is a list or array, it must be the same length as `variable <Integrator.variable_default>`. If
-        initializer is specified as a single float or function, while `variable <Integrator.variable>` is a list or
-        array, initializer will be applied to each variable element. In the case of an initializer function, this means
-        that the function will be executed separately for each variable element.
-
-    previous_value : 1d np.array : default variableClassDefault
-        stores previous value with which `variable <Integrator.variable>` is integrated.
-
-    owner : Mechanism
-        `component <Component>` to which the Function has been assigned.
-
-    prefs : PreferenceSet or specification dict : Projection.classPreferences
-        the `PreferenceSet` for function. Specified in the `prefs` argument of the constructor for the function;
-        if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
-        (see :doc:`PreferenceSet <LINK>` for details).
-
-    """
+    # """
+    # Integrator(                 \
+    #     variable_default=None,  \
+    #     rate=1.0,               \
+    #
+    #     noise=0.0,              \
+    #     time_step_size=1.0,     \
+    #     initializer,     \
+    #     params=None,            \
+    #     owner=None,             \
+    #     prefs=None,             \
+    #     )
+    #
+    # .. _Integrator:
+    #
+    # Integrate current value of `variable <Integrator.variable>` with its prior value.
+    #
+    # Arguments
+    # ---------
+    #
+    # variable_default : number, list or np.array : default variableClassDefault
+    #     specifies a template for the value to be integrated;  if it is a list or array, each element is independently
+    #     integrated.
+    #
+    # rate : float, list or 1d np.array : default 1.0
+    #     specifies the rate of integration.  If it is a list or array, it must be the same length as
+    #     `variable <Integrator.variable_default>` (see `rate <Integrator.rate>` for details).
+    #
+    # noise : float, PsyNeuLink Function, list or 1d np.array : default 0.0
+    #     specifies random value to be added in each call to `function <Integrator.function>`. (see
+    #     `noise <Integrator.noise>` for details).
+    #
+    # time_step_size : float : default 0.0
+    #     determines the timing precision of the integration process when `integration_type <Integrator.integration_type>`
+    #     is set to DIFFUSION (see `time_step_size <Integrator.time_step_size>` for details.
+    #
+    # initializer float, list or 1d np.array : default 0.0
+    #     specifies starting value for integration.  If it is a list or array, it must be the same length as
+    #     `variable_default <Integrator.variable_default>` (see `initializer <Integrator.initializer>` for details).
+    #
+    # params : Optional[Dict[param keyword, param value]]
+    #     a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
+    #     function.  Values specified for parameters in the dictionary override any assigned to those parameters in
+    #     arguments of the constructor.
+    #
+    # owner : Component
+    #     `component <Component>` to which to assign the Function.
+    #
+    # prefs : Optional[PreferenceSet or specification dict : Function.classPreferences]
+    #     the `PreferenceSet` for the Function. If it is not specified, a default is assigned using `classPreferences`
+    #     defined in __init__.py (see :doc:`PreferenceSet <LINK>` for details).
+    #
+    #
+    # Attributes
+    # ----------
+    #
+    # variable : number or np.array
+    #     current input value some portion of which (determined by `rate <Integrator.rate>`) that will be
+    #     added to the prior value;  if it is an array, each element is independently integrated.
+    #
+    # rate : float or 1d np.array
+    #     determines the rate of integration based on current and prior values.  If integration_type is set to ADAPTIVE,
+    #     all elements must be between 0 and 1 (0 = no change; 1 = instantaneous change). If it has a single element, it
+    #     applies to all elements of `variable <Integrator.variable>`;  if it has more than one element, each element
+    #     applies to the corresponding element of `variable <Integrator.variable>`.
+    #
+    # noise : float, function, list, or 1d np.array
+    #     specifies random value to be added in each call to `function <Integrator.function>`.
+    #
+    #     If noise is a list or array, it must be the same length as `variable <Integrator.variable_default>`. If noise is
+    #     specified as a single float or function, while `variable <Integrator.variable>` is a list or array,
+    #     noise will be applied to each variable element. In the case of a noise function, this means that the function
+    #     will be executed separately for each variable element.
+    #
+    #     Note that in the case of DIFFUSION, noise must be specified as a float (or list or array of floats) because this
+    #     value will be used to construct the standard DDM probability distribution. For all other types of integration,
+    #     in order to generate random noise, we recommend that you instead select a probability distribution function
+    #     (see `Distribution Functions <DistributionFunction>` for details), which will generate a new noise value from
+    #     its distribution on each execution. If noise is specified as a float or as a function with a fixed output (or a
+    #     list or array of these), then the noise will simply be an offset that remains the same across all executions.
+    #
+    # initializer : 1d np.array or list
+    #     determines the starting value for integration (i.e., the value to which
+    #     `previous_value <Integrator.previous_value>` is set.
+    #
+    #     If initializer is a list or array, it must be the same length as `variable <Integrator.variable_default>`. If
+    #     initializer is specified as a single float or function, while `variable <Integrator.variable>` is a list or
+    #     array, initializer will be applied to each variable element. In the case of an initializer function, this means
+    #     that the function will be executed separately for each variable element.
+    #
+    # previous_value : 1d np.array : default variableClassDefault
+    #     stores previous value with which `variable <Integrator.variable>` is integrated.
+    #
+    # owner : Mechanism
+    #     `component <Component>` to which the Function has been assigned.
+    #
+    # prefs : PreferenceSet or specification dict : Projection.classPreferences
+    #     the `PreferenceSet` for function. Specified in the **prefs** argument of the constructor for the function;
+    #     if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
+    #     (see :doc:`PreferenceSet <LINK>` for details).
+    #
+    # """
 
     componentName = INTEGRATOR_FUNCTION
-
     variableClassDefault = [[0]]
 
     paramClassDefaults = Function_Base.paramClassDefaults.copy()
     # paramClassDefaults.update({INITIALIZER: variableClassDefault})
     paramClassDefaults.update({
-        NOISE: None
+        NOISE: None,
+        RATE: None
     })
 
     @tc.typecheck
     def __init__(self,
                  variable_default=None,
                  rate: parameter_spec = 1.0,
-                 integration_type: tc.enum(CONSTANT, SIMPLE, ADAPTIVE, DIFFUSION) = CONSTANT,
                  noise=0.0,
-                 time_step_size=1.0,
                  initializer=variableClassDefault,
                  params: tc.optional(dict) = None,
                  owner=None,
@@ -2729,12 +3115,9 @@ def __init__(self,
 
         # Assign args to params and functionParams dicts (kwConstants must == arg names)
         params = self._assign_args_to_param_dicts(rate=rate,
-                                                  integration_type=integration_type,
-                                                  time_step_size=time_step_size,
                                                   initializer=initializer,
-                                                  params=params,
                                                   noise=noise,
-                                                  )
+                                                  params=params)
 
 
         # Assign here as default, for use in initialization of function
@@ -2747,16 +3130,17 @@ def __init__(self,
                          context=context)
 
         # Reassign to kWInitializer in case default value was overridden
-        self.previous_value = self.initializer
+        # self.previous_value = self.initializer
 
         self.auto_dependent = True
 
+
     def _validate_params(self, request_set, target_set=None, context=None):
 
         # Handle list or array for rate specification
         if RATE in request_set:
             rate = request_set[RATE]
-            if isinstance(rate, (list, np.ndarray)):
+            if isinstance(rate, (list, np.ndarray)) and not iscompatible(rate, self.variable):
                 if len(rate) != np.array(self.variable).size:
                     # If the variable was not specified, then reformat it to match rate specification
                     #    and assign variableClassDefault accordingly
@@ -2785,129 +3169,282 @@ def _validate_params(self, request_set, target_set=None, context=None):
                                                    self.name,
                                                    np.array(self.variable).size,
                                                    self.variable))
+                # OLD:
+                # self.paramClassDefaults[RATE] = np.zeros_like(np.array(rate))
 
-                self.paramClassDefaults[RATE] = np.zeros_like(np.array(rate))
+                # KAM changed 5/15 b/c paramClassDefaults were being updated and *requiring* future integrator functions
+                # to have a rate parameter of type ndarray/list
 
         super()._validate_params(request_set=request_set,
                                  target_set=target_set,
                                  context=context)
 
-        if RATE in target_set:
-            if self.integration_type is ADAPTIVE:
-                if isinstance(target_set[RATE], (list, np.ndarray)):
-                    for r in target_set[RATE]:
-                        if r < 0.0 or r > 1.0:
-                            raise FunctionError("The rate parameter ({}) (or all of its elements) of {} must be "
-                                                "between 0.0 and 1.0 when integration_type is set to ADAPTIVE.".
-                                                format(target_set[RATE], self.name))
-                else:
-                    if target_set[RATE] < 0.0 or target_set[RATE] > 1.0:
-                        raise FunctionError(
-                            "The rate parameter ({}) (or all of its elements) of {} must be between 0.0 and "
-                            "1.0 when integration_type is set to ADAPTIVE.".format(target_set[RATE], self.name))
-
-        # self._validate_initializer()
+        # if INITIALIZER in target_set:
+        #     print(target_set)
+        #     self._validate_initializer(target_set[INITIALIZER])
 
         if NOISE in target_set:
-            # FIX: SHOULDN'T THIS PASS target_set[NOISE] to _validate_noise() rather than use self.noise?
             self._validate_noise(target_set[NOISE])
 
-        if TIME_STEP_SIZE in target_set:
-            time_step_size = target_set[TIME_STEP_SIZE]
-
     # Ensure that the noise parameter makes sense with the input type and shape; flag any noise functions that will
     # need to be executed
+
     def _validate_noise(self, noise):
+        self.noise_function = False
         # Noise is a list or array
         if isinstance(noise, (np.ndarray, list)):
             # Variable is a list/array
             if isinstance(self.variable, (np.ndarray, list)):
                 if len(noise) != np.array(self.variable).size:
+                    # Formatting noise for proper display in error message
                     try:
                         formatted_noise = list(map(lambda x: x.__qualname__, noise))
                     except AttributeError:
                         formatted_noise = noise
-                    raise FunctionError("The length ({}) of the array specified for the noise parameter ({}) of {} "
-                                        "must match the length ({}) of the default input ({}). If noise is specified as"
-                                        " an array or list, it must be of the same size as the input."
-                                        .format(len(noise), formatted_noise, self.name, np.array(self.variable).size,
-                                                self.variable))
+                    raise FunctionError(
+                        "The length ({}) of the array specified for the noise parameter ({}) of {} "
+                        "must match the length ({}) of the default input ({}). If noise is specified as"
+                        " an array or list, it must be of the same size as the input."
+                        .format(len(noise), formatted_noise, self.name, np.array(self.variable).size,
+                                self.variable))
                 else:
                     # Noise is a list or array of functions
                     if callable(noise[0]):
                         self.noise_function = True
-                    # Noise is a list or array of floats
-                    elif isinstance(noise[0], float):
+                        # Noise is a list or array of invalid elements
+                    elif not isinstance(noise[0], (float, int)):
                         self.noise_function = False
-                    # Noise is a list or array of invalid elements
-                    else:
-                        raise FunctionError("The elements of a noise list or array must be floats or functions.")
+                        raise FunctionError(
+                            "The elements of a noise list or array must be floats or functions.")
+
+
             # Variable is not a list/array
             else:
                 raise FunctionError("The noise parameter ({}) for {} may only be a list or array if the "
                                     "default input value is also a list or array.".format(noise, self.name))
 
+            # Elements of list/array have different types
+            if not all(isinstance(x, type(noise[0])) for x in noise):
+                raise FunctionError("All elements of noise list/array ({}) for {} must be of the same type. "
+                                    .format(noise, self.name))
+
+
         elif callable(noise):
             self.noise_function = True
-            if isinstance(self.variable, (np.ndarray, list)):
-                new_noise = []
-                for i in self.variable:
-                    new_noise.append(self.noise)
-                noise = new_noise
-        elif isinstance(noise, float):
-            self.noise_function = False
-        else:
-            raise FunctionError("noise parameter ({}) for {} must be a float, function, array or list of floats, or "
-                                "array or list of functions.".format(noise, self.name))
-
-        if self.noise_function and self.integration_type == "diffusion":
 
-            raise FunctionError("Invalid noise parameter for {}. When integration type is DIFFUSION, noise must be a"
-                                " float. Noise parameter is used to construct the standard DDM noise distribution"
-                                .format(self.name))
+        elif not isinstance(noise, (float, int)):
+            raise FunctionError(
+                "Noise parameter ({}) for {} must be a float, function, array or list of floats, or "
+                "array or list of functions.".format(noise, self.name))
 
     def _validate_initializer(self, initializer):
+        self.initializer_function = False
         # Initializer is a list or array
-        if isinstance(initializer[0], (np.ndarray, list)):
+
+        if isinstance(initializer, (np.ndarray, list)):
+            if len(initializer) == 1 and isinstance(initializer[0], (list, np.ndarray)):
+                initializer = initializer[0]
             # Variable is a list/array
             if isinstance(self.variable, (np.ndarray, list)):
-                if len(initializer[0]) != np.array(self.variable).size:
+                if len(initializer) != np.array(self.variable).size:
+                    # Formatting initializer for proper display in error message
                     try:
-                        formatted_initializer = list(map(lambda x: x.__qualname__, initializer[0]))
+                        formatted_initializer = list(map(lambda x: x.__qualname__, initializer))
                     except AttributeError:
-                        formatted_initializer = initializer[0]
-                    raise FunctionError("The length ({}) of the array specified for the initializer parameter "
-                                        "({}) of {} must match the length ({}) of the default input ({}). "
-                                        "If initializer is specified as an array or list, "
-                                        "it must be of the same size as the input."
-                                        .format(len(initializer[0]),
-                                                formatted_initializer,
-                                                self.name, np.array(self.variable).size,
-                                                self.variable))
+                        formatted_initializer = initializer
+                    raise FunctionError(
+                        "The length ({}) of the array specified for the initializer parameter ({}) of {} "
+                        "must match the length ({}) of the default input ({}). If initializer is specified as"
+                        " an array or list, it must be of the same size as the input."
+                            .format(len(initializer), formatted_initializer, self.name, np.array(self.variable).size,
+                                    self.variable))
                 else:
-                    if callable(initializer[0][0]):
+                    # Initializer is a list or array of functions
+                    if callable(initializer[0]):
                         self.initializer_function = True
-                    # Initializer is a list or array of floats
+                    # Initializer is a list or array of invalid elements
+                    elif not isinstance(initializer[0], (float, int)):
+                        raise FunctionError(
+                            "The elements of the initializer list/array ({}) for [] must be floats or functions."
+                                .format(initializer, self.name))
 
-       # Variable is not a list/array
+            # Variable is not a list/array
             else:
                 raise FunctionError("The initializer parameter ({}) for {} may only be a list or array if the "
-                                    "default input value is also a list or array.".
-                                    format(self.initializer[0], self.name))
+                                    "default input value is also a list or array.".format(initializer, self.name))
+            # elements of initializer list/array are of different types
+            if not all(isinstance(x, type(initializer[0])) for x in initializer):
+                raise FunctionError("All elements of initializer list/array ({}) for {} must be of the same type. "
+                                    .format(initializer, self.name))
 
-        elif callable(initializer[0]):
+        elif callable(initializer):
             self.initializer_function = True
-            if isinstance(self.variable, (np.ndarray, list)):
-                new_initializer = []
-                for i in self.variable:
-                    new_initializer.append(initializer[0])
-                initializer[0] = new_initializer
-        elif isinstance(initializer[0], float):
-            initializer_function = False
-        else:
-            raise FunctionError("initializer parameter ({}) for {} must be a number, function, "
-                                "array or list of floats, or array or list of functions.".
-                                format(initializer[0], self.name))
+
+        elif not isinstance(initializer, (float, int)):
+            raise FunctionError(
+                "Initializer parameter ({}) for {} must be a float, function, array or list of floats, or "
+                "array or list of functions.".format(initializer, self.name))
+
+    def function(self, *args, **kwargs):
+        raise FunctionError("Integrator is not meant to be called explicitly")
+
+    @property
+    def reset_initializer(self):
+        return self._initializer
+
+    @reset_initializer.setter
+    def reset_initializer(self, val):
+        self._initializer = val
+        self.previous_value = val
+
+
+class SimpleIntegrator(
+    Integrator):  # --------------------------------------------------------------------------------
+    """
+    SimpleIntegrator(                 \
+        variable_default=None,  \
+        rate=1.0,               \
+        noise=0.0,              \
+        initializer,            \
+        params=None,            \
+        owner=None,             \
+        prefs=None,             \
+        )
+
+    .. _SimpleIntegrator:
+
+    Integrate current value of `variable <SimpleIntegrator.variable>` with its prior value:
+
+    `previous_value <SimpleIntegrator.previous_value>` + `rate <SimpleIntegrator.rate>` *`variable <variable.SimpleIntegrator.variable>` +
+    `noise <SimpleIntegrator.noise>`;
+
+    Arguments
+    ---------
+
+    variable_default : number, list or np.array : default variableClassDefault
+        specifies a template for the value to be integrated;  if it is a list or array, each element is independently
+        integrated.
+
+    rate : float, list or 1d np.array : default 1.0
+        specifies the rate of integration.  If it is a list or array, it must be the same length as
+        `variable <SimpleIntegrator.variable_default>` (see `rate <SimpleIntegrator.rate>` for details).
+
+    noise : float, PsyNeuLink Function, list or 1d np.array : default 0.0
+        specifies random value to be added in each call to `function <SimpleIntegrator.function>`. (see
+        `noise <SimpleIntegrator.noise>` for details).
+
+    initializer float, list or 1d np.array : default 0.0
+        specifies starting value for integration.  If it is a list or array, it must be the same length as
+        `variable_default <SimpleIntegrator.variable_default>` (see `initializer <SimpleIntegrator.initializer>` for details).
+
+    params : Optional[Dict[param keyword, param value]]
+        a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
+        function.  Values specified for parameters in the dictionary override any assigned to those parameters in
+        arguments of the constructor.
+
+    owner : Component
+        `component <Component>` to which to assign the Function.
+
+    prefs : Optional[PreferenceSet or specification dict : Function.classPreferences]
+        the `PreferenceSet` for the Function. If it is not specified, a default is assigned using `classPreferences`
+        defined in __init__.py (see :doc:`PreferenceSet <LINK>` for details).
+
+
+    Attributes
+    ----------
+
+    variable : number or np.array
+        current input value some portion of which (determined by `rate <SimpleIntegrator.rate>`) will be
+        added to the prior value;  if it is an array, each element is independently integrated.
+
+    rate : float or 1d np.array
+        determines the rate of integration based on current and prior values. If it has a single element, it
+        applies to all elements of `variable <SimpleIntegrator.variable>`;  if it has more than one element, each element
+        applies to the corresponding element of `variable <SimpleIntegrator.variable>`.
+
+    noise : float, function, list, or 1d np.array
+        specifies random value to be added in each call to `function <SimpleIntegrator.function>`.
+
+        If noise is a list or array, it must be the same length as `variable <SimpleIntegrator.variable_default>`.
+
+        If noise is specified as a single float or function, while `variable <SimpleIntegrator.variable>` is a list or array,
+        noise will be applied to each variable element. In the case of a noise function, this means that the function
+        will be executed separately for each variable element.
+
+        **Note:**
+        In order to generate random noise, we recommend selecting a probability distribution function
+        (see `Distribution Functions <DistributionFunction>` for details), which will generate a new noise value from
+        its distribution on each execution. If noise is specified as a float or as a function with a fixed output, then
+        the noise will simply be an offset that remains the same across all executions.
+
+    initializer : float, 1d np.array or list
+        determines the starting value for integration (i.e., the value to which
+        `previous_value <SimpleIntegrator.previous_value>` is set.
+
+        If initializer is a list or array, it must be the same length as `variable <SimpleIntegrator.variable_default>`.
+
+        TBI:
+
+        Initializer may be a function or list/array of functions.
+
+        If initializer is specified as a single float or function, while `variable <SimpleIntegrator.variable>` is
+        a list or array, initializer will be applied to each variable element. In the case of an initializer function,
+        this means that the function will be executed separately for each variable element.
+
+    previous_value : 1d np.array : default variableClassDefault
+        stores previous value with which `variable <SimpleIntegrator.variable>` is integrated.
+
+    owner : Mechanism
+        `component <Component>` to which the Function has been assigned.
+
+    prefs : PreferenceSet or specification dict : Projection.classPreferences
+        the `PreferenceSet` for function. Specified in the **prefs** argument of the constructor for the function;
+        if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
+        (see :doc:`PreferenceSet <LINK>` for details).
+
+    """
+
+    componentName = SIMPLE_INTEGRATOR_FUNCTION
+
+    variableClassDefault = [[0]]
+
+    paramClassDefaults = Function_Base.paramClassDefaults.copy()
+    # paramClassDefaults.update({INITIALIZER: variableClassDefault})
+    paramClassDefaults.update({
+        NOISE: None,
+        RATE: None
+    })
+
+    multiplicative_param = RATE
+    additive_param = OFFSET
+
+    @tc.typecheck
+    def __init__(self,
+                 variable_default=None,
+                 rate: parameter_spec=1.0,
+                 noise=0.0,
+                 offset=None,
+                 initializer=variableClassDefault,
+                 params: tc.optional(dict)=None,
+                 owner=None,
+                 prefs: is_pref_set = None,
+                 context="SimpleIntegrator Init"):
+
+        # Assign args to params and functionParams dicts (kwConstants must == arg names)
+        params = self._assign_args_to_param_dicts(rate=rate,
+                                                  initializer=initializer,
+                                                  noise=noise,
+                                                  offset=offset,
+                                                  params=params)
+
+        super().__init__(variable_default=variable_default,
+                         params=params,
+                         owner=owner,
+                         prefs=prefs,
+                         context=context)
+
+        self.previous_value = self.initializer
+        self.auto_dependent = True
 
     def function(self,
                  variable=None,
@@ -2915,8 +3452,9 @@ def function(self,
                  time_scale=TimeScale.TRIAL,
                  context=None):
         """
-        Return: some fraction of `variable <Linear.slope>` combined with some fraction of `previous_value
-        <Integrator.previous_value>` (see `integration_type <Integrator.integration_type>`).
+        Return: `variable <Linear.slope>` combined with `previous_value <SimpleIntegrator.previous_value>`
+        according to `previous_value <SimpleIntegrator.previous_value>` + `rate <SimpleIntegrator.rate>` *`variable
+        <variable.SimpleIntegrator.variable>` + `noise <SimpleIntegrator.noise>`;
 
         Arguments
         ---------
@@ -2939,16 +3477,1304 @@ def function(self,
 
         """
 
-        # FIX:  CONVERT TO NP?
-        # FIX:  NEED TO CONVERT OLD_VALUE TO NP ARRAY
-
         self._check_args(variable=variable, params=params, context=context)
 
         rate = np.array(self.paramsCurrent[RATE]).astype(float)
-        integration_type = self.paramsCurrent[INTEGRATION_TYPE]
 
-        time_step_size = self.paramsCurrent[TIME_STEP_SIZE]
+        if self.offset is None:
+            offset = 0.0
+        else:
+            offset = self.offset
+
+        # if noise is a function, execute it
+        if self.noise_function:
+            if isinstance(self.noise, (np.ndarray, list)):
+                noise = list(map(lambda x: x(), self.noise))
+            else:
+                noise = self.noise()
+        else:
+            noise = self.noise
+
+        # TBI: execute initializer function if self.initializer_function == True
+
+        # try:
+        #     previous_value = self._initializer
+        # except (TypeError, KeyError):
+        previous_value = self.previous_value
+
+        # previous_value = np.atleast_2d(previous_value)
+        new_value = self.variable
+
+
+        # if params and VARIABLE in params:
+        #     new_value = params[VARIABLE]
+
+        # Compute function based on integration_type param
+
+        value = previous_value + (new_value * rate) + noise
+
+        adjusted_value = value + offset
+        # If this NOT an initialization run, update the old value
+        # If it IS an initialization run, leave as is
+        #    (don't want to count it as an execution step)
+        if not context or not INITIALIZING in context:
+            self.previous_value = adjusted_value
+
+        return adjusted_value
+
+class ConstantIntegrator(
+    Integrator):  # --------------------------------------------------------------------------------
+    """
+    ConstantIntegrator(                 \
+        variable_default=None,          \
+        rate=1.0,                       \
+        noise=0.0,                      \
+        scale: parameter_spec = 1.0,    \
+        offset: parameter_spec = 0.0,   \
+        initializer,                    \
+        params=None,                    \
+        owner=None,                     \
+        prefs=None,                     \
+        )
+
+    .. _ConstantIntegrator:
+
+    Integrates prior value by adding `rate <Integrator.rate>` and `noise <Integrator.noise>`. Ignores
+    `variable <Integrator.variable>`).
+
+    `previous_value <Integrator.previous_value>` + `rate <Integrator.rate>` +`noise <Integrator.noise>`
+
+    Arguments
+    ---------
+
+    variable_default : number, list or np.array : default variableClassDefault
+        specifies a template for the value to be integrated;  if it is a list or array, each element is independently
+        integrated.
+
+    rate : float, list or 1d np.array : default 1.0
+        specifies the rate of integration.  If it is a list or array, it must be the same length as
+        `variable <ConstantIntegrator.variable_default>` (see `rate <ConstantIntegrator.rate>` for details).
+
+    noise : float, PsyNeuLink Function, list or 1d np.array : default 0.0
+        specifies random value to be added in each call to `function <ConstantIntegrator.function>`. (see
+        `noise <ConstantIntegrator.noise>` for details).
+
+    initializer float, list or 1d np.array : default 0.0
+        specifies starting value for integration.  If it is a list or array, it must be the same length as
+        `variable_default <ConstantIntegrator.variable_default>` (see `initializer <ConstantIntegrator.initializer>` for details).
+
+    params : Optional[Dict[param keyword, param value]]
+        a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
+        function.  Values specified for parameters in the dictionary override any assigned to those parameters in
+        arguments of the constructor.
+
+    owner : Component
+        `component <Component>` to which to assign the Function.
+
+    prefs : Optional[PreferenceSet or specification dict : Function.classPreferences]
+        the `PreferenceSet` for the Function. If it is not specified, a default is assigned using `classPreferences`
+        defined in __init__.py (see :doc:`PreferenceSet <LINK>` for details).
+
+
+    Attributes
+    ----------
+
+    variable : number or np.array
+        **Ignored** by the ConstantIntegrator function. Refer to SimpleIntegrator or AdaptiveIntegrator for integrator
+         functions that depend on both a prior value and a new value (variable).
+
+    rate : float or 1d np.array
+        determines the rate of integration.
+
+        If it has a single element, that element is added to each element of
+        `previous_value <ConstantIntegrator.previous_value>`.
+
+        If it has more than one element, each element is added to the corresponding element of
+        `previous_value <ConstantIntegrator.previous_value>`.
+
+    noise : float, function, list, or 1d np.array
+        specifies random value to be added in each call to `function <ConstantIntegrator.function>`.
+
+        If noise is a list or array, it must be the same length as `variable <ConstantIntegrator.variable_default>`.
+
+        If noise is specified as a single float or function, while `variable <ConstantIntegrator.variable>` is a list or array,
+        noise will be applied to each variable element. In the case of a noise function, this means that the function
+        will be executed separately for each variable element.
+
+        **Note:**
+        In order to generate random noise, we recommend selecting a probability distribution function
+        (see `Distribution Functions <DistributionFunction>` for details), which will generate a new noise value from
+        its distribution on each execution. If noise is specified as a float or as a function with a fixed output, then
+        the noise will simply be an offset that remains the same across all executions.
+
+    initializer : float, 1d np.array or list
+        determines the starting value for integration (i.e., the value to which
+        `previous_value <ConstantIntegrator.previous_value>` is set.
+
+        If initializer is a list or array, it must be the same length as `variable <ConstantIntegrator.variable_default>`.
+
+        TBI:
+
+        Initializer may be a function or list/array of functions.
+
+        If initializer is specified as a single float or function, while `variable <ConstantIntegrator.variable>` is
+        a list or array, initializer will be applied to each variable element. In the case of an initializer function,
+        this means that the function will be executed separately for each variable element.
+
+    previous_value : 1d np.array : default variableClassDefault
+        stores previous value to which `rate <ConstantIntegrator.rate>` and `noise <ConstantIntegrator.noise>` will be
+        added.
+
+    owner : Mechanism
+        `component <Component>` to which the Function has been assigned.
+
+    prefs : PreferenceSet or specification dict : Projection.classPreferences
+        the `PreferenceSet` for function. Specified in the **prefs** argument of the constructor for the function;
+        if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
+        (see :doc:`PreferenceSet <LINK>` for details).
+
+    """
+
+    componentName = CONSTANT_INTEGRATOR_FUNCTION
+
+    variableClassDefault = [[0]]
+
+    paramClassDefaults = Function_Base.paramClassDefaults.copy()
+    # paramClassDefaults.update({INITIALIZER: variableClassDefault})
+    paramClassDefaults.update({
+        NOISE: None,
+        RATE: None,
+        OFFSET: None,
+        SCALE: None,
+    })
+
+    multiplicative_param = SCALE
+    additive_param = RATE
+
+    @tc.typecheck
+    def __init__(self,
+                 variable_default=None,
+                 # rate: parameter_spec = 1.0,
+                 rate=0.0,
+                 noise=0.0,
+                 offset=0.0,
+                 scale = 1.0,
+                 initializer=variableClassDefault,
+                 params: tc.optional(dict) = None,
+                 owner=None,
+                 prefs: is_pref_set = None,
+                 context="ConstantIntegrator Init"):
+
+        # Assign args to params and functionParams dicts (kwConstants must == arg names)
+        params = self._assign_args_to_param_dicts(rate=rate,
+                                                  initializer=initializer,
+                                                  noise=noise,
+                                                  scale = scale,
+                                                  offset=offset,
+                                                  params=params)
+
+        # Assign here as default, for use in initialization of function
+        self.previous_value = self.paramClassDefaults[INITIALIZER]
+
+        super().__init__(variable_default=variable_default,
+                         params=params,
+                         owner=owner,
+                         prefs=prefs,
+                         context=context)
+
+        # Reassign to initializer in case default value was overridden
+        self.previous_value = self.initializer
+
+        self.auto_dependent = True
+
+    def function(self,
+                 variable=None,
+                 params=None,
+                 time_scale=TimeScale.TRIAL,
+                 context=None):
+        """
+        Return: `previous_value <ConstantIntegrator.previous_value>` combined with `rate <ConstantIntegrator.rate>` and
+        `noise <ConstantIntegrator.noise>`.
+
+        Arguments
+        ---------
+
+        params : Optional[Dict[param keyword, param value]]
+            a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
+            function.  Values specified for parameters in the dictionary override any assigned to those parameters in
+            arguments of the constructor.
+
+        time_scale :  TimeScale : default TimeScale.TRIAL
+            specifies whether the function is executed on the time_step or trial time scale.
+
+        Returns
+        -------
+
+        updated value of integral : 2d np.array
+
+        """
+        self._check_args(variable=variable, params=params, context=context)
+
+        rate = np.array(self.rate).astype(float)
+        offset = self.offset
+        scale = self.scale
+
+        # if noise is a function, execute it
+        if self.noise_function:
+            if isinstance(self.noise, (np.ndarray, list)):
+                noise = list(map(lambda x: x(), self.noise))
+            else:
+                noise = self.noise()
+        else:
+            noise = self.noise
+
+        # TBI: execute initializer function if self.initializer_function == True
+
+        # try:
+        #     previous_value = params[INITIALIZER]
+        # except (TypeError, KeyError):
+        previous_value = self.previous_value
+
+        previous_value = np.atleast_2d(previous_value)
+
+        value = previous_value + rate + noise
+
+
+        adjusted_value = value*scale + offset
+        # If this NOT an initialization run, update the old value
+        # If it IS an initialization run, leave as is
+        #    (don't want to count it as an execution step)
+        if not context or not INITIALIZING in context:
+            self.previous_value = adjusted_value
+
+        return adjusted_value
+
+class AdaptiveIntegrator(
+    Integrator):  # --------------------------------------------------------------------------------
+    """
+    AdaptiveIntegrator(                 \
+        variable_default=None,          \
+        rate=1.0,                       \
+        noise=0.0,                      \
+        scale: parameter_spec = 1.0,    \
+        offset: parameter_spec = 0.0,   \
+        initializer,                    \
+        params=None,                    \
+        owner=None,                     \
+        prefs=None,                     \
+        )
+
+    .. _AdaptiveIntegrator:
+
+    Integrate current value of `variable <AdaptiveIntegrator.variable>` with its prior value.
+
+    Arguments
+    ---------
+
+    variable_default : number, list or np.array : default variableClassDefault
+        specifies a template for the value to be integrated;  if it is a list or array, each element is independently
+        integrated.
+
+    rate : float, list or 1d np.array : default 1.0
+        specifies the rate of integration.  If it is a list or array, it must be the same length as
+        `variable <AdaptiveIntegrator.variable_default>` (see `rate <AdaptiveIntegrator.rate>` for details).
+
+    noise : float, PsyNeuLink Function, list or 1d np.array : default 0.0
+        specifies random value to be added in each call to `function <AdaptiveIntegrator.function>`. (see
+        `noise <AdaptiveIntegrator.noise>` for details).
+
+    initializer float, list or 1d np.array : default 0.0
+        specifies starting value for integration.  If it is a list or array, it must be the same length as
+        `variable_default <AdaptiveIntegrator.variable_default>` (see `initializer <AdaptiveIntegrator.initializer>` for details).
+
+    params : Optional[Dict[param keyword, param value]]
+        a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
+        function.  Values specified for parameters in the dictionary override any assigned to those parameters in
+        arguments of the constructor.
+
+    owner : Component
+        `component <Component>` to which to assign the Function.
+
+    prefs : Optional[PreferenceSet or specification dict : Function.classPreferences]
+        the `PreferenceSet` for the Function. If it is not specified, a default is assigned using `classPreferences`
+        defined in __init__.py (see :doc:`PreferenceSet <LINK>` for details).
+
+
+    Attributes
+    ----------
+
+    variable : number or np.array
+        current input value some portion of which (determined by `rate <AdaptiveIntegrator.rate>`) that will be
+        added to the prior value;  if it is an array, each element is independently integrated.
+
+    rate : float or 1d np.array
+        determines the rate of integration based on current and prior values.  All rate elements must be between 0 and 1
+        (0 = no change; 1 = instantaneous change).
+
+        If rate is a float, it is applied to all elements of `variable <AdaptiveAdaptiveIntegrator.variable>`;
+        if it has more than one element, each element is applied to the corresponding element of
+        `variable <AdaptiveAdaptiveIntegrator.variable>`.
+
+    noise : float, function, list, or 1d np.array
+        specifies random value to be added in each call to `function <AdaptiveIntegrator.function>`.
+
+        If noise is a list or array, it must be the same length as `variable <AdaptiveIntegrator.variable_default>`.
+
+        If noise is specified as a single float or function, while `variable <AdaptiveIntegrator.variable>` is a list or array,
+        noise will be applied to each variable element. In the case of a noise function, this means that the function
+        will be executed separately for each variable element.
+
+        **Note:**
+        In order to generate random noise, we recommend selecting a probability distribution function
+        (see `Distribution Functions <DistributionFunction>` for details), which will generate a new noise value from
+        its distribution on each execution. If noise is specified as a float or as a function with a fixed output, then
+        the noise will simply be an offset that remains the same across all executions.
+
+    initializer : float, 1d np.array or list
+        determines the starting value for integration (i.e., the value to which
+        `previous_value <AdaptiveIntegrator.previous_value>` is set.
+
+        If initializer is a list or array, it must be the same length as `variable <AdaptiveIntegrator.variable_default>`.
+
+        TBI:
+
+        Initializer may be a function or list/array of functions.
+
+        If initializer is specified as a single float or function, while `variable <AdaptiveIntegrator.variable>` is
+        a list or array, initializer will be applied to each variable element. In the case of an initializer function,
+        this means that the function will be executed separately for each variable element.
+
+    previous_value : 1d np.array : default variableClassDefault
+        stores previous value with which `variable <AdaptiveIntegrator.variable>` is integrated.
+
+    owner : Mechanism
+        `component <Component>` to which the Function has been assigned.
+
+    prefs : PreferenceSet or specification dict : Projection.classPreferences
+        the `PreferenceSet` for function. Specified in the **prefs** argument of the constructor for the function;
+        if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
+        (see :doc:`PreferenceSet <LINK>` for details).
+
+    """
+
+    componentName = ADAPTIVE_INTEGRATOR_FUNCTION
+
+    variableClassDefault = [[0]]
+
+    multiplicative_param = RATE
+    additive_param = OFFSET
+
+    paramClassDefaults = Function_Base.paramClassDefaults.copy()
+    # paramClassDefaults.update({INITIALIZER: variableClassDefault})
+    paramClassDefaults.update({
+        NOISE: None,
+        RATE: None
+    })
+
+    @tc.typecheck
+    def __init__(self,
+                 variable_default=None,
+                 rate: parameter_spec = 1.0,
+                 noise=0.0,
+                 offset= 0.0,
+                 initializer=variableClassDefault,
+                 params: tc.optional(dict) = None,
+                 owner=None,
+                 prefs: is_pref_set = None,
+                 context="AdaptiveIntegrator Init"):
+
+        # Assign args to params and functionParams dicts
+        params = self._assign_args_to_param_dicts(rate=rate,
+                                                  initializer=initializer,
+                                                  noise=noise,
+                                                  offset=offset,
+                                                  params=params)
+
+        super().__init__(variable_default=variable_default,
+                         params=params,
+                         owner=owner,
+                         prefs=prefs,
+                         context=context)
+
+        self.previous_value = self.initializer
+
+        self.auto_dependent = True
+
+    def _validate_params(self, request_set, target_set=None, context=None):
+
+        # Handle list or array for rate specification
+        if RATE in request_set:
+            rate = request_set[RATE]
+            if isinstance(rate, (list, np.ndarray)):
+                if len(rate) != np.array(self.variable).size:
+                    # If the variable was not specified, then reformat it to match rate specification
+                    #    and assign variableClassDefault accordingly
+                    # Note: this situation can arise when the rate is parameterized (e.g., as an array) in the
+                    #       AdaptiveIntegrator's constructor, where that is used as a specification for a function parameter
+                    #       (e.g., for an IntegratorMechanism), whereas the input is specified as part of the
+                    #       object to which the function parameter belongs (e.g., the IntegratorMechanism);
+                    #       in that case, the Integrator gets instantiated using its variableClassDefault ([[0]]) before
+                    #       the object itself, thus does not see the array specification for the input.
+                    if self._variable_not_specified:
+                        self._instantiate_defaults(variable=np.zeros_like(np.array(rate)), context=context)
+                        if self.verbosePref:
+                            warnings.warn(
+                                "The length ({}) of the array specified for the rate parameter ({}) of {} "
+                                "must match the length ({}) of the default input ({});  "
+                                "the default input has been updated to match".
+                                    format(len(rate),
+                                           rate,
+                                           self.name,
+                                           np.array(self.variable).size),
+                                self.variable)
+                    else:
+                        raise FunctionError(
+                            "The length ({}) of the array specified for the rate parameter ({}) of {} "
+                            "must match the length ({}) of the default input ({})".
+                                format(len(rate),
+                                       rate,
+                                       self.name,
+                                       np.array(self.variable).size,
+                                       self.variable))
+                        # OLD:
+                        # self.paramClassDefaults[RATE] = np.zeros_like(np.array(rate))
+
+                        # KAM changed 5/15 b/c paramClassDefaults were being updated and *requiring* future integrator functions
+                        # to have a rate parameter of type ndarray/list
+
+        super()._validate_params(request_set=request_set,
+                                 target_set=target_set,
+                                 context=context)
+
+        if RATE in target_set:
+            if isinstance(target_set[RATE], (list, np.ndarray)):
+                for r in target_set[RATE]:
+                    if r < 0.0 or r > 1.0:
+                        raise FunctionError("The rate parameter ({}) (or all of its elements) of {} must be "
+                                            "between 0.0 and 1.0 when integration_type is set to ADAPTIVE.".
+                                            format(target_set[RATE], self.name))
+            else:
+                if target_set[RATE] < 0.0 or target_set[RATE] > 1.0:
+                    raise FunctionError(
+                        "The rate parameter ({}) (or all of its elements) of {} must be between 0.0 and "
+                        "1.0 when integration_type is set to ADAPTIVE.".format(target_set[RATE], self.name))
+
+        if NOISE in target_set:
+            self._validate_noise(target_set[NOISE])
+        # if INITIALIZER in target_set:
+        #     self._validate_initializer(target_set[INITIALIZER])
+
+
+    def function(self,
+                 variable=None,
+                 params=None,
+                 time_scale=TimeScale.TRIAL,
+                 context=None):
+        """
+        Return: some fraction of `variable <AdaptiveIntegrator.variable>` combined with some fraction of `previous_value
+        <AdaptiveIntegrator.previous_value>`.
+
+        Arguments
+        ---------
+
+        variable : number, list or np.array : default variableClassDefault
+           a single value or array of values to be integrated.
+
+        params : Optional[Dict[param keyword, param value]]
+            a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
+            function.  Values specified for parameters in the dictionary override any assigned to those parameters in
+            arguments of the constructor.
+
+        time_scale :  TimeScale : default TimeScale.TRIAL
+            specifies whether the function is executed on the time_step or trial time scale.
+
+        Returns
+        -------
+
+        updated value of integral : 2d np.array
+
+        """
+        self._check_args(variable=variable, params=params, context=context)
+
+        rate = np.array(self.paramsCurrent[RATE]).astype(float)
+        offset = self.paramsCurrent[OFFSET]
+        # if noise is a function, execute it
+        if self.noise_function:
+            if isinstance(self.noise, (np.ndarray, list)):
+                noise = list(map(lambda x: x(), self.noise))
+            else:
+                noise = self.noise()
+        else:
+            noise = self.noise
+
+        # TBI: execute initializer function if self.initializer_function == True
+
+        # try:
+        #     previous_value = params[INITIALIZER]
+        # except (TypeError, KeyError):
+        previous_value = self.previous_value
+
+        previous_value = np.atleast_2d(previous_value)
+        new_value = self.variable
+
+        value = (1 - rate) * previous_value + rate * new_value + noise
+
+
+        adjusted_value = value + offset
+        # If this NOT an initialization run, update the old value
+        # If it IS an initialization run, leave as is
+        #    (don't want to count it as an execution step)
+        if not context or not INITIALIZING in context:
+            self.previous_value = adjusted_value
+
+        return adjusted_value
+
+class DriftDiffusionIntegrator(
+    Integrator):  # --------------------------------------------------------------------------------
+    """
+    DriftDiffusionIntegrator(           \
+        variable_default=None,          \
+        rate=1.0,                       \
+        noise=0.0,                      \
+        scale: parameter_spec = 1.0,    \
+        offset: parameter_spec = 0.0,   \
+        time_step_size=1.0,             \
+        initializer,                    \
+        params=None,                    \
+        owner=None,                     \
+        prefs=None,                     \
+        )
+
+    .. _DriftDiffusionIntegrator:
+
+    Integrate current value of `variable <DriftDiffusionIntegrator.variable>` with its prior value.
+
+    Arguments
+    ---------
+
+    variable_default : number, list or np.array : default variableClassDefault
+        specifies a template for the value to be integrated;  if it is a list or array, each element is independently
+        integrated.
+
+    rate : float, list or 1d np.array : default 1.0
+        specifies the rate of integration.  If it is a list or array, it must be the same length as
+        `variable <DriftDiffusionIntegrator.variable_default>` (see `rate <DriftDiffusionIntegrator.rate>` for details).
+
+    noise : float, PsyNeuLink Function, list or 1d np.array : default 0.0
+        specifies random value to be added in each call to `function <DriftDiffusionIntegrator.function>`. (see
+        `noise <DriftDiffusionIntegrator.noise>` for details).
+
+    time_step_size : float : default 0.0
+        determines the timing precision of the integration process (see `time_step_size
+        <DriftDiffusionIntegrator.time_step_size>` for details.
+
+    initializer float, list or 1d np.array : default 0.0
+        specifies starting value for integration.  If it is a list or array, it must be the same length as
+        `variable_default <DriftDiffusionIntegrator.variable_default>` (see `initializer <DriftDiffusionIntegrator.initializer>` for details).
+
+    params : Optional[Dict[param keyword, param value]]
+        a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
+        function.  Values specified for parameters in the dictionary override any assigned to those parameters in
+        arguments of the constructor.
+
+    owner : Component
+        `component <Component>` to which to assign the Function.
+
+    prefs : Optional[PreferenceSet or specification dict : Function.classPreferences]
+        the `PreferenceSet` for the Function. If it is not specified, a default is assigned using `classPreferences`
+        defined in __init__.py (see :doc:`PreferenceSet <LINK>` for details).
+
+
+    Attributes
+    ----------
+
+    variable : number or np.array
+        current input value some portion of which (determined by `rate <DriftDiffusionIntegrator.rate>`) that will be
+        added to the prior value;  if it is an array, each element is independently integrated.
+
+    rate : float or 1d np.array
+        determines the rate of integration based on current and prior values.  If integration_type is set to ADAPTIVE,
+        all elements must be between 0 and 1 (0 = no change; 1 = instantaneous change). If it has a single element, it
+        applies to all elements of `variable <DriftDiffusionIntegrator.variable>`;  if it has more than one element, each element
+        applies to the corresponding element of `variable <DriftDiffusionIntegrator.variable>`.
+
+    noise : float, function, list, or 1d np.array
+        scales the random value to be added in each call to `function <DriftDiffusionIntegrator.function>
+
+        Noise must be specified as a float (or list or array of floats) because this
+        value will be used to construct the standard DDM probability distribution.
+
+    time_step_size : float
+        determines the timing precision of the integration process and is used to scale the `noise
+        <DriftDiffusionIntegrator.noise>` parameter appropriately.
+
+    initializer : float, 1d np.array or list
+        determines the starting value for integration (i.e., the value to which
+        `previous_value <DriftDiffusionIntegrator.previous_value>` is set.
+
+        If initializer is a list or array, it must be the same length as `variable <DriftDiffusionIntegrator.variable_default>`.
+
+        TBI:
+
+        Initializer may be a function or list/array of functions.
+
+        If initializer is specified as a single float or function, while `variable <DriftDiffusionIntegrator.variable>` is
+        a list or array, initializer will be applied to each variable element. In the case of an initializer function,
+        this means that the function will be executed separately for each variable element.
+
+    previous_value : 1d np.array : default variableClassDefault
+        stores previous value with which `variable <DriftDiffusionIntegrator.variable>` is integrated.
+
+    owner : Mechanism
+        `component <Component>` to which the Function has been assigned.
+
+    prefs : PreferenceSet or specification dict : Projection.classPreferences
+        the `PreferenceSet` for function. Specified in the **prefs** argument of the constructor for the function;
+        if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
+        (see :doc:`PreferenceSet <LINK>` for details).
+
+    """
+
+    componentName = DRIFT_DIFFUSION_INTEGRATOR_FUNCTION
+
+    variableClassDefault = [[0]]
+
+    multiplicative_param = RATE
+    additive_param = OFFSET
+
+    paramClassDefaults = Function_Base.paramClassDefaults.copy()
+    # paramClassDefaults.update({INITIALIZER: variableClassDefault})
+    paramClassDefaults.update({
+        NOISE: None,
+        RATE: None
+    })
+
+    @tc.typecheck
+    def __init__(self,
+                 variable_default=None,
+                 rate: parameter_spec = 1.0,
+                 noise=0.0,
+                 offset: parameter_spec = 0.0,
+                 time_step_size=1.0,
+                 initializer=variableClassDefault,
+                 params: tc.optional(dict) = None,
+                 owner=None,
+                 prefs: is_pref_set = None,
+                 context="DriftDiffusionIntegrator Init"):
+
+        # Assign args to params and functionParams dicts (kwConstants must == arg names)
+        params = self._assign_args_to_param_dicts(rate=rate,
+                                                  time_step_size=time_step_size,
+                                                  initializer=initializer,
+                                                  noise=noise,
+                                                  offset=offset,
+                                                  params=params)
+
+        # Assign here as default, for use in initialization of function
+        self.previous_value = self.paramClassDefaults[INITIALIZER]
+
+        super().__init__(variable_default=variable_default,
+                         params=params,
+                         owner=owner,
+                         prefs=prefs,
+                         context=context)
+
+        # Reassign to kWInitializer in case default value was overridden
+        self.previous_value = self.initializer
+
+        self.auto_dependent = True
+
+    def _validate_noise(self, noise):
+        self.noise_function = False
+        if not isinstance(noise, float):
+            raise FunctionError(
+                "Invalid noise parameter for {}. DriftDiffusionIntegrator requires noise parameter to be a float. Noise"
+                " parameter is used to construct the standard DDM noise distribution".format(self.name))
+
+    def function(self,
+                 variable=None,
+                 params=None,
+                 time_scale=TimeScale.TRIAL,
+                 context=None):
+        """
+        Return: some fraction of `variable <DriftDiffusionIntegrator.variable>` combined with some fraction of
+        `previous_value <DriftDiffusionIntegrator.previous_value>`.
+
+        Arguments
+        ---------
+
+        variable : number, list or np.array : default variableClassDefault
+           a single value or array of values to be integrated.
+
+        params : Optional[Dict[param keyword, param value]]
+            a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
+            function.  Values specified for parameters in the dictionary override any assigned to those parameters in
+            arguments of the constructor.
+
+        time_scale :  TimeScale : default TimeScale.TRIAL
+            specifies whether the function is executed on the time_step or trial time scale.
+
+        Returns
+        -------
+
+        updated value of integral : 2d np.array
+
+        """
+        self._check_args(variable=variable, params=params, context=context)
+
+        rate = np.array(self.paramsCurrent[RATE]).astype(float)
+        offset = self.paramsCurrent[OFFSET]
+
+        time_step_size = self.paramsCurrent[TIME_STEP_SIZE]
+
+        # if noise is a function, execute it
+        if self.noise_function:
+            if isinstance(self.noise, (np.ndarray, list)):
+                noise = list(map(lambda x: x(), self.noise))
+            else:
+                noise = self.noise()
+        else:
+            noise = self.noise
+
+        # TBI: execute initializer function if self.initializer_function == True
+
+        # try:
+        #     previous_value = params[INITIALIZER]
+        # except (TypeError, KeyError):
+        previous_value = self.previous_value
+
+        previous_value = np.atleast_2d(previous_value)
+        new_value = self.variable
+
+        value = previous_value + rate * new_value * time_step_size + np.sqrt(
+            time_step_size * noise) * np.random.normal()
+
+        adjusted_value = value + offset
+        # If this NOT an initialization run, update the old value
+        # If it IS an initialization run, leave as is
+        #    (don't want to count it as an execution step)
+        if not context or not INITIALIZING in context:
+            self.previous_value = adjusted_value
+
+        return adjusted_value
+
+class OrnsteinUhlenbeckIntegrator(
+    Integrator):  # --------------------------------------------------------------------------------
+    """
+    OrnsteinUhlenbeckIntegrator(                 \
+        variable_default=None,          \
+        rate=1.0,                       \
+        noise=0.0,                      \
+        scale: parameter_spec = 1.0,    \
+        offset: parameter_spec = 0.0,   \
+        time_step_size=1.0,             \
+        initializer,                    \
+        params=None,                    \
+        owner=None,                     \
+        prefs=None,                     \
+        )
+
+    .. _OrnsteinUhlenbeckIntegrator:
+
+    Integrate current value of `variable <OrnsteinUhlenbeckIntegrator.variable>` with its prior value.
+
+    Arguments
+    ---------
+
+    variable_default : number, list or np.array : default variableClassDefault
+        specifies a template for the value to be integrated;  if it is a list or array, each element is independently
+        integrated.
+
+    rate : float, list or 1d np.array : default 1.0
+        specifies the rate of integration.  If it is a list or array, it must be the same length as
+        `variable <OrnsteinUhlenbeckIntegrator.variable_default>` (see `rate <OrnsteinUhlenbeckIntegrator.rate>` for
+        details).
+
+    noise : float, PsyNeuLink Function, list or 1d np.array : default 0.0
+        specifies random value to be added in each call to `function <OrnsteinUhlenbeckIntegrator.function>`. (see
+        `noise <OrnsteinUhlenbeckIntegrator.noise>` for details).
+
+    time_step_size : float : default 0.0
+        determines the timing precision of the integration process (see `time_step_size
+        <OrnsteinUhlenbeckIntegrator.time_step_size>` for details.
+
+    initializer float, list or 1d np.array : default 0.0
+        specifies starting value for integration.  If it is a list or array, it must be the same length as
+        `variable_default <OrnsteinUhlenbeckIntegrator.variable_default>` (see `initializer
+        <OrnsteinUhlenbeckIntegrator.initializer>` for details).
+
+    params : Optional[Dict[param keyword, param value]]
+        a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
+        function.  Values specified for parameters in the dictionary override any assigned to those parameters in
+        arguments of the constructor.
+
+    owner : Component
+        `component <Component>` to which to assign the Function.
+
+    prefs : Optional[PreferenceSet or specification dict : Function.classPreferences]
+        the `PreferenceSet` for the Function. If it is not specified, a default is assigned using `classPreferences`
+        defined in __init__.py (see :doc:`PreferenceSet <LINK>` for details).
+
+
+    Attributes
+    ----------
+
+    variable : number or np.array
+        current input value some portion of which (determined by `rate <OrnsteinUhlenbeckIntegrator.rate>`) that will be
+        added to the prior value;  if it is an array, each element is independently integrated.
+
+    rate : float or 1d np.array
+        determines the rate of integration based on current and prior values.  If integration_type is set to ADAPTIVE,
+        all elements must be between 0 and 1 (0 = no change; 1 = instantaneous change). If it has a single element, it
+        applies to all elements of `variable <OrnsteinUhlenbeckIntegrator.variable>`;  if it has more than one element, each element
+        applies to the corresponding element of `variable <OrnsteinUhlenbeckIntegrator.variable>`.
+
+    noise : float, function, list, or 1d np.array
+        scales the random value to be added in each call to `function <OrnsteinUhlenbeckIntegrator.function>
+
+        Noise must be specified as a float (or list or array of floats) because this
+        value will be used to construct the standard DDM probability distribution.
+
+    time_step_size : float
+        determines the timing precision of the integration process and is used to scale the `noise
+        <OrnsteinUhlenbeckIntegrator.noise>` parameter appropriately.
+
+    initializer : float, 1d np.array or list
+        determines the starting value for integration (i.e., the value to which
+        `previous_value <OrnsteinUhlenbeckIntegrator.previous_value>` is set.
+
+        If initializer is a list or array, it must be the same length as `variable <OrnsteinUhlenbeckIntegrator.variable_default>`.
+
+        TBI:
+
+        Initializer may be a function or list/array of functions.
+
+        If initializer is specified as a single float or function, while `variable <OrnsteinUhlenbeckIntegrator.variable>` is
+        a list or array, initializer will be applied to each variable element. In the case of an initializer function,
+        this means that the function will be executed separately for each variable element.
+
+    previous_value : 1d np.array : default variableClassDefault
+        stores previous value with which `variable <OrnsteinUhlenbeckIntegrator.variable>` is integrated.
+
+    owner : Mechanism
+        `component <Component>` to which the Function has been assigned.
+
+    prefs : PreferenceSet or specification dict : Projection.classPreferences
+        the `PreferenceSet` for function. Specified in the **prefs** argument of the constructor for the function;
+        if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
+        (see :doc:`PreferenceSet <LINK>` for details).
+
+    """
+
+    componentName = ORNSTEIN_UHLENBECK_INTEGRATOR_FUNCTION
+
+    variableClassDefault = [[0]]
+
+    multiplicative_param = RATE
+    additive_param = OFFSET
+
+    paramClassDefaults = Function_Base.paramClassDefaults.copy()
+    # paramClassDefaults.update({INITIALIZER: variableClassDefault})
+    paramClassDefaults.update({
+        NOISE: None,
+        RATE: None
+    })
+
+    @tc.typecheck
+    def __init__(self,
+                 variable_default=None,
+                 rate: parameter_spec = 1.0,
+                 noise=0.0,
+                 offset: parameter_spec = 0.0,
+                 time_step_size=1.0,
+                 decay = 1.0,
+                 initializer=variableClassDefault,
+                 params: tc.optional(dict) = None,
+                 owner=None,
+                 prefs: is_pref_set = None,
+                 context="OrnsteinUhlenbeckIntegrator Init"):
+
+        # Assign args to params and functionParams dicts (kwConstants must == arg names)
+        params = self._assign_args_to_param_dicts(rate=rate,
+                                                  time_step_size=time_step_size,
+                                                  decay = decay,
+                                                  initializer=initializer,
+                                                  noise=noise,
+                                                  offset=offset,
+                                                  params=params)
+
+        # Assign here as default, for use in initialization of function
+        self.previous_value = self.paramClassDefaults[INITIALIZER]
+
+        super().__init__(variable_default=variable_default,
+                         params=params,
+                         owner=owner,
+                         prefs=prefs,
+                         context=context)
+
+        # Reassign to kWInitializer in case default value was overridden
+        self.previous_value = self.initializer
+
+        self.auto_dependent = True
+
+    def _validate_noise(self, noise):
+        self.noise_function = False
+        if not isinstance(noise, float):
+            raise FunctionError(
+                "Invalid noise parameter for {}. OrnsteinUhlenbeckIntegrator requires noise parameter to be a float. "
+                "Noise parameter is used to construct the standard DDM noise distribution".format(self.name))
+
+    def function(self,
+                 variable=None,
+                 params=None,
+                 time_scale=TimeScale.TRIAL,
+                 context=None):
+        """
+        Return: some fraction of `variable <OrnsteinUhenbeckIntegrator.variable>` combined with some fraction of
+        `previous_value <OrnsteinUhenbeckIntegrator.previous_value>`
+
+        Arguments
+        ---------
+
+        variable : number, list or np.array : default variableClassDefault
+           a single value or array of values to be integrated.
+
+        params : Optional[Dict[param keyword, param value]]
+            a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
+            function.  Values specified for parameters in the dictionary override any assigned to those parameters in
+            arguments of the constructor.
+
+        time_scale :  TimeScale : default TimeScale.TRIAL
+            specifies whether the function is executed on the time_step or trial time scale.
+
+        Returns
+        -------
+
+        updated value of integral : 2d np.array
+
+        """
+        self._check_args(variable=variable, params=params, context=context)
+
+        rate = np.array(self.paramsCurrent[RATE]).astype(float)
+        offset = self.paramsCurrent[OFFSET]
+
+        time_step_size = self.paramsCurrent[TIME_STEP_SIZE]
+        decay = self.paramsCurrent[DECAY]
+
+        # if noise is a function, execute it
+        if self.noise_function:
+            if isinstance(self.noise, (np.ndarray, list)):
+                noise = list(map(lambda x: x(), self.noise))
+            else:
+                noise = self.noise()
+        else:
+            noise = self.noise
+
+        # TBI: execute initializer function if self.initializer_function == True
+
+        # try:
+        #     previous_value = params[INITIALIZER]
+        # except (TypeError, KeyError):
+        previous_value = self.previous_value
+
+        previous_value = np.atleast_2d(previous_value)
+        new_value = self.variable
+
+        value = previous_value + decay * rate * new_value * time_step_size + np.sqrt(
+            time_step_size * noise) * np.random.normal()
+
+        # If this NOT an initialization run, update the old value
+        # If it IS an initialization run, leave as is
+        #    (don't want to count it as an execution step)
+        adjusted_value = value + offset
+
+        if not context or not INITIALIZING in context:
+            self.previous_value = adjusted_value
+
+        return adjusted_value
+
+class AccumulatorIntegrator(
+    Integrator):  # --------------------------------------------------------------------------------
+    """
+    ConstantIntegrator(                 \
+        variable_default=None,          \
+        rate=1.0,                       \
+        noise=0.0,                      \
+        scale: parameter_spec = 1.0,    \
+        offset: parameter_spec = 0.0,   \
+        initializer,                    \
+        params=None,                    \
+        owner=None,                     \
+        prefs=None,                     \
+        )
+
+    .. _ConstantIntegrator:
+
+    Integrates prior value by adding `rate <Integrator.rate>` and `noise <Integrator.noise>`. Ignores
+    `variable <Integrator.variable>`).
+
+    `previous_value <Integrator.previous_value>` + `rate <Integrator.rate>` +`noise <Integrator.noise>`
+
+    Arguments
+    ---------
+
+    variable_default : number, list or np.array : default variableClassDefault
+        specifies a template for the value to be integrated;  if it is a list or array, each element is independently
+        integrated.
+
+    rate : float, list or 1d np.array : default 1.0
+        specifies the rate of integration.  If it is a list or array, it must be the same length as
+        `variable <ConstantIntegrator.variable_default>` (see `rate <ConstantIntegrator.rate>` for details).
+
+    noise : float, PsyNeuLink Function, list or 1d np.array : default 0.0
+        specifies random value to be added in each call to `function <ConstantIntegrator.function>`. (see
+        `noise <ConstantIntegrator.noise>` for details).
+
+    initializer float, list or 1d np.array : default 0.0
+        specifies starting value for integration.  If it is a list or array, it must be the same length as
+        `variable_default <ConstantIntegrator.variable_default>` (see `initializer <ConstantIntegrator.initializer>` for details).
+
+    params : Optional[Dict[param keyword, param value]]
+        a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
+        function.  Values specified for parameters in the dictionary override any assigned to those parameters in
+        arguments of the constructor.
+
+    owner : Component
+        `component <Component>` to which to assign the Function.
+
+    prefs : Optional[PreferenceSet or specification dict : Function.classPreferences]
+        the `PreferenceSet` for the Function. If it is not specified, a default is assigned using `classPreferences`
+        defined in __init__.py (see :doc:`PreferenceSet <LINK>` for details).
+
+
+    Attributes
+    ----------
+
+    variable : number or np.array
+        **Ignored** by the ConstantIntegrator function. Refer to SimpleIntegrator or AdaptiveIntegrator for integrator
+         functions that depend on both a prior value and a new value (variable).
+
+    rate : float or 1d np.array
+        determines the rate of integration.
+
+        If it has a single element, that element is added to each element of
+        `previous_value <ConstantIntegrator.previous_value>`.
+
+        If it has more than one element, each element is added to the corresponding element of
+        `previous_value <ConstantIntegrator.previous_value>`.
+
+    noise : float, function, list, or 1d np.array
+        specifies random value to be added in each call to `function <ConstantIntegrator.function>`.
+
+        If noise is a list or array, it must be the same length as `variable <ConstantIntegrator.variable_default>`.
+
+        If noise is specified as a single float or function, while `variable <ConstantIntegrator.variable>` is a list or array,
+        noise will be applied to each variable element. In the case of a noise function, this means that the function
+        will be executed separately for each variable element.
+
+        **Note:**
+        In order to generate random noise, we recommend selecting a probability distribution function
+        (see `Distribution Functions <DistributionFunction>` for details), which will generate a new noise value from
+        its distribution on each execution. If noise is specified as a float or as a function with a fixed output, then
+        the noise will simply be an offset that remains the same across all executions.
+
+    initializer : float, 1d np.array or list
+        determines the starting value for integration (i.e., the value to which
+        `previous_value <ConstantIntegrator.previous_value>` is set.
+
+        If initializer is a list or array, it must be the same length as `variable <ConstantIntegrator.variable_default>`.
+
+        TBI:
+
+        Initializer may be a function or list/array of functions.
+
+        If initializer is specified as a single float or function, while `variable <ConstantIntegrator.variable>` is
+        a list or array, initializer will be applied to each variable element. In the case of an initializer function,
+        this means that the function will be executed separately for each variable element.
+
+    previous_value : 1d np.array : default variableClassDefault
+        stores previous value to which `rate <ConstantIntegrator.rate>` and `noise <ConstantIntegrator.noise>` will be
+        added.
+
+    owner : Mechanism
+        `component <Component>` to which the Function has been assigned.
+
+    prefs : PreferenceSet or specification dict : Projection.classPreferences
+        the `PreferenceSet` for function. Specified in the **prefs** argument of the constructor for the function;
+        if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
+        (see :doc:`PreferenceSet <LINK>` for details).
+
+    """
+
+    componentName = ACCUMULATOR_INTEGRATOR_FUNCTION
+
+    variableClassDefault = [[0]]
+
+    paramClassDefaults = Function_Base.paramClassDefaults.copy()
+    # paramClassDefaults.update({INITIALIZER: variableClassDefault})
+    paramClassDefaults.update({
+        NOISE: None,
+        RATE: None,
+        INCREMENT: None, 
+    })
+
+    # multiplicative param does not make sense in this case
+    multiplicative_param = RATE
+    additive_param = INCREMENT
+
+    @tc.typecheck
+    def __init__(self,
+                 variable_default=None,
+                 # rate: parameter_spec = 1.0,
+                 rate=None,
+                 noise=0.0,
+                 increment = None,
+                 initializer=variableClassDefault,
+                 params: tc.optional(dict) = None,
+                 owner=None,
+                 prefs: is_pref_set = None,
+                 context="AccumulatorIntegrator Init"):
+
+        # Assign args to params and functionParams dicts (kwConstants must == arg names)
+        params = self._assign_args_to_param_dicts(rate=rate,
+                                                  initializer=initializer,
+                                                  noise=noise,
+                                                  increment = increment,
+                                                  params=params)
+
+        super().__init__(
+            # variable_default=variable_default,
+                         params=params,
+                         owner=owner,
+                         prefs=prefs,
+                         context=context)
+
+        self.variable = self.initializer
+
+        self.auto_dependent = True
+
+    def _accumulator_check_args(self, params=None, target_set=None, context=None):
+        """validate params and assign any runtime params.
+
+        Called by AccumulatorIntegrator to validate params
+        Validation can be suppressed by turning parameter_validation attribute off
+        target_set is a params dictionary to which params should be assigned;
+           otherwise, they are assigned to paramsCurrent;
+
+        Does the following:
+        - assign runtime params to paramsCurrent
+        - validate params if PARAM_VALIDATION is set
+
+        :param params: (dict) - params to validate
+        :target_set: (dict) - set to which params should be assigned (default: self.paramsCurrent)
+        :return:
+        """
+
+        # PARAMS ------------------------------------------------------------
+
+        # If target_set is not specified, use paramsCurrent
+        if target_set is None:
+            target_set = self.paramsCurrent
+
+        # # MODIFIED 11/27/16 OLD:
+        # # If parameter_validation is set, the function was called with params,
+        # #   and they have changed, then validate requested values and assign to target_set
+        # if self.prefs.paramValidationPref and params and not params is None and not params is target_set:
+        #     # self._validate_params(params, target_set, context=FUNCTION_CHECK_ARGS)
+        #     self._validate_params(request_set=params, target_set=target_set, context=context)
+
+        # If params have been passed, treat as runtime params and assign to paramsCurrent
+        #   (relabel params as runtime_params for clarity)
+        runtime_params = params
+        if runtime_params and runtime_params is not None:
+            for param_name in self.user_params:
+                # Ignore input_states and output_states -- they should not be modified during run
+                # IMPLEMENTATION NOTE:
+                #    FUNCTION_RUNTIME_PARAM_NOT_SUPPORTED:
+                #        At present, assignment of ``function`` as runtime param is not supported
+                #        (this is because paramInstanceDefaults[FUNCTION] could be a class rather than an bound method;
+                #        i.e., not yet instantiated;  could be rectified by assignment in _instantiate_function)
+                if param_name in {FUNCTION, INPUT_STATES, OUTPUT_STATES}:
+                    continue
+                # If param is specified in runtime_params, then assign it
+                if param_name in runtime_params:
+                    self.paramsCurrent[param_name] = runtime_params[param_name]
+                # Otherwise, (re-)assign to paramInstanceDefaults
+                #    this insures that any params that were assigned as runtime on last execution are reset here
+                #    (unless they have been assigned another runtime value)
+                elif not self.runtimeParamStickyAssignmentPref:
+                    if param_name is FUNCTION_PARAMS:
+                        for function_param in self.function_object.user_params:
+                            self.function_object.paramsCurrent[function_param] = \
+                                self.function_object.paramInstanceDefaults[function_param]
+                        continue
+                    self.paramsCurrent[param_name] = self.paramInstanceDefaults[param_name]
+            self.runtime_params_in_use = True
+
+        # Otherwise, reset paramsCurrent to paramInstanceDefaults
+        elif self.runtime_params_in_use and not self.runtimeParamStickyAssignmentPref:
+            # Can't do the following since function could still be a class ref rather than abound method (see below)
+            # self.paramsCurrent = self.paramInstanceDefaults
+            for param_name in self.user_params:
+                # IMPLEMENTATION NOTE: FUNCTION_RUNTIME_PARAM_NOT_SUPPORTED
+                #    At present, assignment of ``function`` as runtime param is not supported
+                #        (this is because paramInstanceDefaults[FUNCTION] could be a class rather than an bound method;
+                #        i.e., not yet instantiated;  could be rectified by assignment in _instantiate_function)
+                if param_name is FUNCTION:
+                    continue
+                if param_name is FUNCTION_PARAMS:
+                    for function_param in self.function_object.user_params:
+                        self.function_object.paramsCurrent[function_param] = \
+                            self.function_object.paramInstanceDefaults[function_param]
+                    continue
+                self.paramsCurrent[param_name] = self.paramInstanceDefaults[param_name]
+
+            self.runtime_params_in_use = False
+
+        # If parameter_validation is set and they have changed, then validate requested values and assign to target_set
+        if self.prefs.paramValidationPref and params and not params is target_set:
+            self._validate_params(request_set=params, target_set=target_set, context=context)
+
+
+
+    def function(self,
+                 variable=None,
+                 params=None,
+                 time_scale=TimeScale.TRIAL,
+                 context=None):
+        """
+        Return: `previous_value <ConstantIntegrator.previous_value>` combined with `rate <ConstantIntegrator.rate>` and
+        `noise <ConstantIntegrator.noise>`.
+
+        Arguments
+        ---------
+
+        params : Optional[Dict[param keyword, param value]]
+            a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
+            function.  Values specified for parameters in the dictionary override any assigned to those parameters in
+            arguments of the constructor.
+
+        time_scale :  TimeScale : default TimeScale.TRIAL
+            specifies whether the function is executed on the time_step or trial time scale.
+
+        Returns
+        -------
+
+        updated value of integral : 2d np.array
+
+        """
+        self._accumulator_check_args(params=params, context=context)
 
+        # rate = np.array(self.rate).astype(float)
+        # increment = self.increment
+        if self.rate is None:
+            rate = 1.0
+        else:
+            rate = self.rate
+        if self.increment is None:
+            increment = 0.0
+        else:
+            increment = self.increment
         # if noise is a function, execute it
         if self.noise_function:
             if isinstance(self.noise, (np.ndarray, list)):
@@ -2958,188 +4784,23 @@ def function(self,
         else:
             noise = self.noise
 
-        try:
-            previous_value = params[INITIALIZER]
-        except (TypeError, KeyError):
-            previous_value = self.previous_value
+        # TBI: execute initializer function if self.initializer_function == True
 
-        previous_value = np.atleast_2d(previous_value)
-        new_value = self.variable
+        # try:
+        #     previous_value = params[INITIALIZER]
+        # except (TypeError, KeyError):
 
-        # Compute function based on integration_type param
-        if integration_type is CONSTANT:
-            value = previous_value + rate + noise
-        elif integration_type is SIMPLE:
-            value = previous_value + (new_value * rate) + noise
-        elif integration_type is ADAPTIVE:
-            value = (1 - rate) * previous_value + rate * new_value + noise
-        elif integration_type is DIFFUSION:
-            value = previous_value + rate * new_value * time_step_size + np.sqrt(time_step_size * noise) * np.random.normal()
-        else:
-            value = new_value
+        previous_value = np.atleast_2d(self.variable)
+
+        value = previous_value*rate + noise + increment
 
         # If this NOT an initialization run, update the old value
         # If it IS an initialization run, leave as is
         #    (don't want to count it as an execution step)
         if not context or not INITIALIZING in context:
-            self.previous_value = value
-
+            self.variable = value
         return value
 
-# Trying commenting out DDMIntegrator entirely and forcing the integration_type to be DIFFUSION in the DDM *mechanism*
-# # FIX: SHOULD THIS EVEN ALLOW A INTEGRATION_TYPE PARAM IF IT REQUIRES THAT IT BE DIFFUSION??
-# class DDMIntegrator(
-#     Integrator):  # -------------------------------------------------------------------------------------
-#     """
-#     DDMIntegrator(                 \
-#         variable_default=None,  \
-#         rate=1.0,               \
-#         noise=0.5,              \
-#         time_step_size=1.0,     \
-#         params=None,            \
-#         owner=None,             \
-#         prefs:is_pref_set=None, \
-#         )
-#
-#     .. _DDMIntegrator:
-#
-#     Implement drift diffusion integration process.
-#     It is a subclass of the `Integrator` Function that enforces use of the DIFFUSION `integration_type
-#     <Integrator.integration_type>`.
-#
-#     Arguments
-#     ---------
-#
-#     variable_default : number, list or np.array : default variableClassDefault
-#         specifies a template for the value to be integrated;  if it is list or array, each element is independently
-#         integrated.
-#
-#     rate : float, list or 1d np.array : default 1.0
-#         specifies the rate of integration (drift component of a drift diffusion process).  If it is a list or array,
-#         it must be the same length as `variable <DDMIntegrator.variable_default>` and all elements must be floats
-#         between 0 and 1 (see `rate <Integrator.rate>` for details).
-#
-#     noise : float, list or 1d np.array : default 0.0
-#         specifies random value to be added in each call to `function <Integrator.function>` (corresponds to the
-#         diffusion component of the drift diffusion process). If it is a list or array, it must be the same length as
-#         `variable <DDMIntegrator.variable>` and all elements must be floats between 0 and 1
-#         (see `noise <DDMIntegrator.rate>` for details).
-#
-#     time_step_size : float : default 0.0
-#         determines the timing precision of the integration process when
-#         `integration_type <Integrator.integration_type>` is set to
-#         DIFFUSION (see `time_step_size <Integrator.time_step_size>` for details.
-#
-#     initializer float, list or 1d np.array : default 0.0
-#         specifies starting value for integration.  If it is a list or array, it must be the same length as
-#         `variable_default <Integrator.variable_default>` (see `initializer <DDMIntegrator.initializer>` for details).
-#
-#     params : Optional[Dict[param keyword, param value]]
-#         a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
-#         function.  Values specified for parameters in the dictionary override any assigned to those parameters in
-#         arguments of the constructor.
-#
-#     owner : Component
-#         `component <Component>` to which to assign the Function.
-#
-#     prefs : Optional[PreferenceSet or specification dict : Function.classPreferences]
-#         the `PreferenceSet` for the Function. If it is not specified, a default is assigned using `classPreferences`
-#         defined in __init__.py (see :doc:`PreferenceSet <LINK>` for details).
-#
-#
-#     Attributes
-#     ----------
-#
-#     variable : number or np.array
-#         current input value some portion of which (determined by `rate <DDMIntegrator.rate>` that will be added to
-#         prior value.  If it is an array, each element is independently integrated.
-#
-#     rate : 1d np.array
-#         determines the rate of integration based on current and prior values (corresponds to the drift component
-#         of the drift diffusion process).  All elements are between 0 and 1 (0 = no change; 1 = instantaneous change).
-#         If it has a single element, it applies to all elements of `variable <Integrator.variable>`;  if it has more
-#         than one element, each element applies to the corresponding element of `variable <Integrator.variable>`.
-#
-#     noise : float or 1d np.array
-#         determines the random value to be added in each call to `function <Integrator.function>` (corresponds to the
-#         diffusion component of the drift diffusion process).
-#
-#     time_step_size : float
-#         determines the timing precision of the integration process when
-#         `integration_type <Integrator.integration_type>` is set to DIFFUSION
-#         (and used to scale the `noise <Integrator.noise>` parameter appropriately).
-#
-#     initializer : float or 1d np.array
-#         determines the starting value for integration (i.e., the value to which
-#         `previous_value <Integrator.previous_value>`
-#         is set.  If it is assigned as a `runtime_param <LINK>` it resets `previous_value <Integrator.previous_value>`
-#         to the specified value (see `initializer <Integrator.initializer>` for details).
-#
-#     previous_value : 1d np.array : default variableClassDefault
-#         stores previous value with which `variable <Integrator.variable>` is integrated.
-#
-#     owner : Mechanism
-#         `component <Component>` to which the Function has been assigned.
-#
-#     prefs : PreferenceSet or specification dict : Projection.classPreferences
-#         the `PreferenceSet` for function. Specified in the `prefs` argument of the constructor for the function;
-#         if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
-#         (see :doc:`PreferenceSet <LINK>` for details).
-#
-#     """
-#
-#     componentName = DDM_INTEGRATOR_FUNCTION
-#
-#     @tc.typecheck
-#     def __init__(self,
-#                  variable_default=None,
-#                  rate=1.0,
-#                  noise=0.5,
-#                  time_step_size=1.0,
-#                  params: tc.optional(dict) = None,
-#                  owner=None,
-#                  prefs: is_pref_set = None,
-#                  context="DDMIntegrator Init"):
-#
-#         # Assign here as default, for use in initialization of function
-#         self.previous_value = self.paramClassDefaults[INITIALIZER]
-#         integration_type = DIFFUSION
-#
-#         # Assign args to params and functionParams dicts (kwConstants must == arg names)
-#         params = self._assign_args_to_param_dicts(
-#             integration_type=integration_type,
-#             params=params,
-#             noise=noise,
-#             rate=rate,
-#             time_step_size=time_step_size)
-#
-#         super().__init__(variable_default=variable_default,
-#                          params=params,
-#                          owner=owner,
-#                          prefs=prefs,
-#                          context=context)
-#
-#         # Reassign to kWInitializer in case default value was overridden
-#         self.previous_value = [self.paramsCurrent[INITIALIZER]]
-#
-#     def _validate_params(self, request_set, target_set=None, context=None):
-#
-#         super()._validate_params(request_set=request_set,
-#                                  target_set=target_set,
-#                                  context=context)
-#
-#         noise = target_set[NOISE]
-#
-#         if (isinstance(noise, float) == False):
-#             raise FunctionError("noise parameter ({}) for {} must be a float.".
-#                                 format(noise, self.name))
-#
-#         integration_type = target_set[INTEGRATION_TYPE]
-#         if (integration_type != "diffusion"):
-#             raise FunctionError("integration_type parameter ({}) for {} must be diffusion. "
-#                                 "For alternate methods of accumulation, use the Integrator function".
-#                                 format(integration_type, self.name))
-
 
 # Note:  For any of these that correspond to args, value must match the name of the corresponding arg in __init__()
 DRIFT_RATE = 'drift_rate'
@@ -3223,7 +4884,7 @@ class BogaczEtAl(
     ----------
 
     variable : number or 1d np.array
-        holds initial value assigned to :keyword:`variable_default` argument;  
+        holds initial value assigned to :keyword:`variable_default` argument;
         ignored by `function <BogaczEtal.function>`.
 
     drift_rate : float or 1d np.array
@@ -3241,7 +4902,7 @@ class BogaczEtAl(
         Gaussian random process).
 
     t0 : float or 1d np.array
-        determines the assumed non-decision time to determine the response time returned by the solution. 
+        determines the assumed non-decision time to determine the response time returned by the solution.
 
     bias : float or 1d np.array
         normalized starting point:
@@ -3252,7 +4913,7 @@ class BogaczEtAl(
         `component <Component>` to which the Function has been assigned.
 
     prefs : PreferenceSet or specification dict : Projection.classPreferences
-        the `PreferenceSet` for function. Specified in the `prefs` argument of the constructor for the function;
+        the `PreferenceSet` for function. Specified in the **prefs** argument of the constructor for the function;
         if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
         (see :doc:`PreferenceSet <LINK>` for details).
 
@@ -3297,7 +4958,7 @@ def function(self,
                  time_scale=TimeScale.TRIAL,
                  context=None):
         """
-        Return: terminal value of decision variable (equal to threshold), mean accuracy (error rate; ER) and mean 
+        Return: terminal value of decision variable (equal to threshold), mean accuracy (error rate; ER) and mean
         response time (RT)
 
         Arguments
@@ -3322,7 +4983,7 @@ def function(self,
 
         self._check_args(variable=variable, params=params, context=context)
 
-        drift_rate = float(self.drift_rate)
+        drift_rate = float(self.drift_rate) * float(self.variable)
         threshold = float(self.threshold)
         starting_point = float(self.starting_point)
         noise = float(self.noise)
@@ -3389,37 +5050,37 @@ def derivative(self, output=None, input=None):
 
         Calculate the derivative of 1/(reward rate) with respect to the threshold (**output** arg)
         and drift_rate (**input** arg).  Reward rate (RR) is assumed to be:
-          
-            RR = (delay\ :sub:`ITI` + Z/A + ED);
-           
+
+            RR = (delay\\ :sub:`ITI` + Z/A + ED);
+
         the derivative of 1/RR with respect to the `threshold <BogaczEtAl.threshold>` is:
-        
-            1/A - E/A - (2A/c\ :sup:`2`\ )ED;
-          
+
+            1/A - E/A - (2A/c\\ :sup:`2`\\ )ED;
+
         and the derivative of 1/RR with respect to the `drift_rate <BogaczEtAl.drift_rate>` is:
-          
-            -Z/A\ :sup:`2` + (Z/A\ :sup:`2`\ )E - (2Z/c\ :sup:`2`\ )ED
-                                   
+
+            -Z/A\\ :sup:`2` + (Z/A\\ :sup:`2`\\ )E - (2Z/c\\ :sup:`2`\\ )ED
+
         where:
 
             A = `drift_rate <BogaczEtAl.drift_rate>`,
 
-            Z = `threshold <BogaczEtAl.threshold>`,  
+            Z = `threshold <BogaczEtAl.threshold>`,
+
+            c = `noise <BogaczEtAl.noise>`,
+
+            E = exp(-2ZA/\\ c\\ :sup:`2`\\ ), and
 
-            c = `noise <BogaczEtAl.noise>`,  
+            D = delay\\ :sub:`ITI` + delay\\ :sub:`penalty` - Z/A
 
-            E = exp(-2ZA/\ c\ :sup:`2`\ ), and  
+            delay\\ :sub:`ITI` is the intertrial interval and delay\\ :sub:`penalty` is a penalty delay.
 
-            D = delay\ :sub:`ITI` + delay\ :sub:`penalty` - Z/A
-            
-            delay\ :sub:`ITI` is the intertrial interval and delay\ :sub:`penalty` is a penalty delay.
 
-          
         Returns
         -------
 
         derivatives :  List[float, float)
-            of 1/RR with respect to `threshold <BogaczEtAl.threshold>` and `drift_rate <BogaczEtAl.drift_rate>`. 
+            of 1/RR with respect to `threshold <BogaczEtAl.threshold>` and `drift_rate <BogaczEtAl.drift_rate>`.
 
         """
         Z = output or self.threshold
@@ -3550,7 +5211,7 @@ class NavarroAndFuss(
         `component <Component>` to which the Function has been assigned.
 
     prefs : PreferenceSet or specification dict : Projection.classPreferences
-        the `PreferenceSet` for function. Specified in the `prefs` argument of the constructor for the function;
+        the `PreferenceSet` for function. Specified in the **prefs** argument of the constructor for the function;
         if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
         (see :doc:`PreferenceSet <LINK>` for details).
 
@@ -3745,7 +5406,7 @@ def function(self,
         # Validate variable and assign to self.variable, and validate params
         self._check_args(variable=variable, params=params, context=context)
 
-        mean = self.paramsCurrent[MEAN]
+        mean = self.paramsCurrent[DIST_MEAN]
         standard_dev = self.paramsCurrent[STANDARD_DEV]
 
         result = standard_dev * np.random.normal() + mean
@@ -3891,21 +5552,229 @@ class UniformDist(DistributionFunction):
     high : float : default 1.0
         Upper bound of the uniform distribution
 
-    params : Optional[Dict[param keyword, param value]]
-        a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
-        function.  Values specified for parameters in the dictionary override any assigned to those parameters in
-        arguments of the constructor.
+    params : Optional[Dict[param keyword, param value]]
+        a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
+        function.  Values specified for parameters in the dictionary override any assigned to those parameters in
+        arguments of the constructor.
+
+    owner : Component
+        `component <Component>` to which to assign the Function.
+
+    prefs : Optional[PreferenceSet or specification dict : Function.classPreferences]
+        the `PreferenceSet` for the Function. If it is not specified, a default is assigned using `classPreferences`
+        defined in __init__.py (see :doc:`PreferenceSet <LINK>` for details).
+
+
+    """
+    componentName = UNIFORM_DIST_FUNCTION
+
+    variableClassDefault = [0]
+
+    paramClassDefaults = Function_Base.paramClassDefaults.copy()
+
+    @tc.typecheck
+    def __init__(self,
+                 variable_default=variableClassDefault,
+                 low=0.0,
+                 high=1.0,
+                 params=None,
+                 owner=None,
+                 prefs: is_pref_set = None,
+                 context=componentName + INITIALIZING):
+        # Assign args to params and functionParams dicts (kwConstants must == arg names)
+        params = self._assign_args_to_param_dicts(low=low,
+                                                  high=high,
+                                                  params=params)
+
+        super().__init__(variable_default=variable_default,
+                         params=params,
+                         owner=owner,
+                         prefs=prefs,
+                         context=context)
+
+        self.functionOutputType = None
+
+    def function(self,
+                 variable=None,
+                 params=None,
+                 time_scale=TimeScale.TRIAL,
+                 context=None):
+        # Validate variable and assign to self.variable, and validate params
+        self._check_args(variable=variable, params=params, context=context)
+
+        low = self.paramsCurrent[LOW]
+        high = self.paramsCurrent[HIGH]
+
+        result = np.random.uniform(low, high)
+
+        return result
+
+
+class GammaDist(DistributionFunction):
+    """
+    GammaDist(\
+             scale=1.0,\
+             dist_shape=1.0,\
+             params=None,\
+             owner=None,\
+             prefs=None\
+             )
+
+    .. _GammaDist:
+
+    Return a random sample from a gamma distribution using numpy.random.gamma
+
+    Arguments
+    ---------
+
+    scale : float : default 1.0
+        The scale of the gamma distribution. Should be greater than zero.
+
+    dist_shape : float : default 1.0
+        The shape of the gamma distribution. Should be greater than zero.
+
+    params : Optional[Dict[param keyword, param value]]
+        a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
+        function.  Values specified for parameters in the dictionary override any assigned to those parameters in
+        arguments of the constructor.
+
+    owner : Component
+        `component <Component>` to which to assign the Function.
+
+    prefs : Optional[PreferenceSet or specification dict : Function.classPreferences]
+        the `PreferenceSet` for the Function. If it is not specified, a default is assigned using `classPreferences`
+        defined in __init__.py (see :doc:`PreferenceSet <LINK>` for details).
+
+
+    Attributes
+    ----------
+
+    scale : float : default 1.0
+        The dist_shape of the gamma distribution. Should be greater than zero.
+
+    dist_shape : float : default 1.0
+        The scale of the gamma distribution. Should be greater than zero.
+
+    params : Optional[Dict[param keyword, param value]]
+        a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
+        function.  Values specified for parameters in the dictionary override any assigned to those parameters in
+        arguments of the constructor.
+
+    owner : Component
+        `component <Component>` to which to assign the Function.
+
+    prefs : Optional[PreferenceSet or specification dict : Function.classPreferences]
+        the `PreferenceSet` for the Function. If it is not specified, a default is assigned using `classPreferences`
+        defined in __init__.py (see :doc:`PreferenceSet <LINK>` for details).
+
+
+    """
+
+    componentName = GAMMA_DIST_FUNCTION
+
+    variableClassDefault = [0]
+
+    paramClassDefaults = Function_Base.paramClassDefaults.copy()
+
+    @tc.typecheck
+    def __init__(self,
+                 variable_default=variableClassDefault,
+                 scale=1.0,
+                 dist_shape=1.0,
+                 params=None,
+                 owner=None,
+                 prefs: is_pref_set = None,
+                 context=componentName + INITIALIZING):
+        # Assign args to params and functionParams dicts (kwConstants must == arg names)
+        params = self._assign_args_to_param_dicts(scale=scale,
+                                                  dist_shape=dist_shape,
+                                                  params=params)
+
+        super().__init__(variable_default=variable_default,
+                         params=params,
+                         owner=owner,
+                         prefs=prefs,
+                         context=context)
+
+        self.functionOutputType = None
+
+    def function(self,
+                 variable=None,
+                 params=None,
+                 time_scale=TimeScale.TRIAL,
+                 context=None):
+        # Validate variable and assign to self.variable, and validate params
+        self._check_args(variable=variable, params=params, context=context)
+
+        scale = self.paramsCurrent[SCALE]
+        dist_shape = self.paramsCurrent[DIST_SHAPE]
+
+        result = np.random.gamma(dist_shape, scale)
+
+        return result
+
+
+class WaldDist(DistributionFunction):
+    """
+     WaldDist(             \
+              scale=1.0,\
+              mean=1.0,\
+              params=None,\
+              owner=None,\
+              prefs=None\
+              )
+
+     .. _WaldDist:
+
+     Return a random sample from a wald distribution using numpy.random.wald
+
+     Arguments
+     ---------
+
+     scale : float : default 1.0
+         Scale parameter of the wald distribution. Should be greater than zero.
+
+     mean : float : default 1.0
+         Mean of the wald distribution. Should be greater than or equal to zero.
+
+     params : Optional[Dict[param keyword, param value]]
+         a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
+         function.  Values specified for parameters in the dictionary override any assigned to those parameters in
+         arguments of the constructor.
+
+     owner : Component
+         `component <Component>` to which to assign the Function.
+
+     prefs : Optional[PreferenceSet or specification dict : Function.classPreferences]
+         the `PreferenceSet` for the Function. If it is not specified, a default is assigned using `classPreferences`
+         defined in __init__.py (see :doc:`PreferenceSet <LINK>` for details).
+
+
+     Attributes
+     ----------
+
+     scale : float : default 1.0
+         Scale parameter of the wald distribution. Should be greater than zero.
+
+     mean : float : default 1.0
+         Mean of the wald distribution. Should be greater than or equal to zero.
+
+     params : Optional[Dict[param keyword, param value]]
+         a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
+         function.  Values specified for parameters in the dictionary override any assigned to those parameters in
+         arguments of the constructor.
+
+     owner : Component
+         `component <Component>` to which to assign the Function.
 
-    owner : Component
-        `component <Component>` to which to assign the Function.
+     prefs : Optional[PreferenceSet or specification dict : Function.classPreferences]
+         the `PreferenceSet` for the Function. If it is not specified, a default is assigned using `classPreferences`
+         defined in __init__.py (see :doc:`PreferenceSet <LINK>` for details).
 
-    prefs : Optional[PreferenceSet or specification dict : Function.classPreferences]
-        the `PreferenceSet` for the Function. If it is not specified, a default is assigned using `classPreferences`
-        defined in __init__.py (see :doc:`PreferenceSet <LINK>` for details).
 
+     """
 
-    """
-    componentName = UNIFORM_DIST_FUNCTION
+    componentName = WALD_DIST_FUNCTION
 
     variableClassDefault = [0]
 
@@ -3914,15 +5783,15 @@ class UniformDist(DistributionFunction):
     @tc.typecheck
     def __init__(self,
                  variable_default=variableClassDefault,
-                 low=0.0,
-                 high=1.0,
+                 scale=1.0,
+                 mean=1.0,
                  params=None,
                  owner=None,
                  prefs: is_pref_set = None,
                  context=componentName + INITIALIZING):
         # Assign args to params and functionParams dicts (kwConstants must == arg names)
-        params = self._assign_args_to_param_dicts(low=low,
-                                                  high=high,
+        params = self._assign_args_to_param_dicts(scale=scale,
+                                                  mean=mean,
                                                   params=params)
 
         super().__init__(variable_default=variable_default,
@@ -3941,36 +5810,64 @@ def function(self,
         # Validate variable and assign to self.variable, and validate params
         self._check_args(variable=variable, params=params, context=context)
 
-        low = self.paramsCurrent[LOW]
-        high = self.paramsCurrent[HIGH]
+        scale = self.paramsCurrent[SCALE]
+        mean = self.paramsCurrent[DIST_MEAN]
 
-        result = np.random.uniform(low, high)
+        result = np.random.wald(mean, scale)
 
         return result
 
 
-class GammaDist(DistributionFunction):
+# endregion
+
+# region **************************************   OBJECTIVE FUNCTIONS **************************************************
+
+class ObjectiveFunction(Function_Base):
+    """Abstract class of `Function` used for evaluating states.
     """
-    GammaDist(\
-             scale=1.0,\
-             shape=1.0,\
-             params=None,\
-             owner=None,\
-             prefs=None\
-             )
 
-    .. _GammaDist:
+    componentType = OBJECTIVE_FUNCTION_TYPE
 
-    Return a random sample from a gamma distribution using numpy.random.gamma
+
+class Stability(ObjectiveFunction):
+    """
+    Stability(                                  \
+        variable_default=variableCLassDefault,  \
+        matrix=HOLLOW_MATRIX,                   \
+        metric=ENERGY                           \
+        transfer_fct=None                       \
+        normalize=False,                        \
+        params=None,                            \
+        owner=None,                             \
+        prefs=None                              \
+        )
+
+    .. _Stability:
+
+    Return the stability of a vector based an a weight matrix from each element to every other element in the vector.
+    The value of `variable <Stability.variable>` is passed through the `matrix <Stability.matrix>`, transformed
+    using the `transfer_fct <Stability.transfer_fct>` (if specified), and then compared with its initial value
+    using the specified `metric <Stability.metric>`.  If `normalize <Stability.normalize>` is specified, the result
+    is normalized by the number of elements in the `variable <Stability.variable>`.
 
     Arguments
     ---------
 
-    scale : float : default 1.0
-        The shape of the gamma distribution. Should be greater than zero.
+    variable : list of numbers or 1d np.array : Default variableClassDefault
+        the array for which stabilty is calculated.
 
-    shape : float : default 1.0
-        The scale of the gamma distribution. Should be greater than zero.
+    matrix : list, np.ndarray, np.matrix, function keyword, or MappingProjection : default HOLLOW_MATRIX
+        specifies the matrix of recurrent weights;  must be a square matrix with the same width as the
+        length of `variable <Stability.variable>`.
+
+    metric : ENERGY, ENTROPY or keyword in DISTANCE_METRICS : Default ENERGY
+        specifies the metric used to compute stability.
+
+    transfer_fct : function or method : Default None
+        specifies the function used to transform output of weight `matrix <Stability.matrix>`.
+
+    normalize : bool : Default False
+        specifies whether to normalize the stability value by the length of `variable <Stability.variable>`.
 
     params : Optional[Dict[param keyword, param value]]
         a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
@@ -3984,15 +5881,27 @@ class GammaDist(DistributionFunction):
         the `PreferenceSet` for the Function. If it is not specified, a default is assigned using `classPreferences`
         defined in __init__.py (see :doc:`PreferenceSet <LINK>` for details).
 
-
     Attributes
     ----------
 
-    scale : float : default 1.0
-        The shape of the gamma distribution. Should be greater than zero.
+    variable : 1d np.array
+        array for which stability is calculated.
 
-    shape : float : default 1.0
-        The scale of the gamma distribution. Should be greater than zero.
+    matrix : list, np.ndarray, np.matrix, function keyword, or MappingProjection : default HOLLOW_MATRIX
+        weight matrix from each element of `variable <Stability.variablity>` to each other;  if a matrix other
+        than HOLLOW_MATRIX is assigned, it is convolved with HOLLOW_MATRIX to eliminate self-connections from the
+        stability calculation.
+
+    metric : ENERGY, ENTROPY or keyword in DISTANCE_METRICS
+        metric used to compute stability.  If ENTROPY or DISTANCE_METRICS keyword is used, the `Distance` Function
+        is used to compute the stability of `variable <Stability.variable>` with respect to its value after
+        transformation by `matrix <Stability.matrix>` and `transfer_fct <Stability.transfer_fct>`.
+
+    transfer_fct : function or method
+        function used to transform output of weight `matrix <Stability.matrix>` prior to computing stability.
+
+    normalize : bool
+        if `True`, result of stability calculation is normalized by the length of `variable <Stability.variable>`.
 
     params : Optional[Dict[param keyword, param value]]
         a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
@@ -4005,11 +5914,12 @@ class GammaDist(DistributionFunction):
     prefs : Optional[PreferenceSet or specification dict : Function.classPreferences]
         the `PreferenceSet` for the Function. If it is not specified, a default is assigned using `classPreferences`
         defined in __init__.py (see :doc:`PreferenceSet <LINK>` for details).
+     """
 
+    from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
+    from PsyNeuLink.Components.States.ParameterState import ParameterState
 
-    """
-
-    componentName = GAMMA_DIST_FUNCTION
+    componentName = STABILITY_FUNCTION
 
     variableClassDefault = [0]
 
@@ -4018,15 +5928,20 @@ class GammaDist(DistributionFunction):
     @tc.typecheck
     def __init__(self,
                  variable_default=variableClassDefault,
-                 scale=1.0,
-                 shape=1.0,
+                 matrix:tc.any(is_matrix, MappingProjection, ParameterState)=HOLLOW_MATRIX,
+                 # metric:is_distance_metric=ENERGY,
+                 metric:tc.any(tc.enum(ENERGY, ENTROPY), is_distance_metric)=ENERGY,
+                 transfer_fct:tc.optional(tc.any(function_type, method_type))=None,
+                 normalize:bool=False,
                  params=None,
                  owner=None,
                  prefs: is_pref_set = None,
                  context=componentName + INITIALIZING):
         # Assign args to params and functionParams dicts (kwConstants must == arg names)
-        params = self._assign_args_to_param_dicts(scale=scale,
-                                                  shape=shape,
+        params = self._assign_args_to_param_dicts(matrix=matrix,
+                                                  metric=metric,
+                                                  transfer_fct=transfer_fct,
+                                                  normalize=normalize,
                                                   params=params)
 
         super().__init__(variable_default=variable_default,
@@ -4037,100 +5952,228 @@ def __init__(self,
 
         self.functionOutputType = None
 
+    def _validate_params(self, request_set, target_set=None, context=None):
+        """Validate matrix param
+
+        `matrix <Stability.matrix>` argument must be one of the following
+            - 2d list, np.ndarray or np.matrix
+            - ParameterState for one of the above
+            - MappingProjection with a parameterStates[MATRIX] for one of the above
+
+        Parse matrix specification to insure it resolves to a square matrix
+        (but leave in the form in which it was specified so that, if it is a ParameterState or MappingProjection,
+         its current value can be accessed at runtime (i.e., it can be used as a "pointer")
+        """
+
+        super()._validate_params(request_set=request_set, target_set=target_set, context=context)
+
+        # Validate error_matrix specification
+        if MATRIX in target_set:
+
+            from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
+            from PsyNeuLink.Components.States.ParameterState import ParameterState
+
+            matrix = target_set[MATRIX]
+
+            if isinstance(matrix, MappingProjection):
+                try:
+                    matrix = matrix._parameter_states[MATRIX].value
+                    param_type_string = "MappingProjection's ParameterState"
+                except KeyError:
+                    raise FunctionError("The MappingProjection specified for the {} arg of {} ({}) must have a {} "
+                                        "paramaterState that has been assigned a 2d array or matrix".
+                                        format(MATRIX, self.name, matrix.shape, MATRIX))
+
+            elif isinstance(matrix, ParameterState):
+                try:
+                    matrix = matrix.value
+                    param_type_string = "ParameterState"
+                except KeyError:
+                    raise FunctionError("The value of the {} parameterState specified for the {} arg of {} ({}) "
+                                        "must be a 2d array or matrix".
+                                        format(MATRIX, MATRIX, self.name, matrix.shape))
+
+            else:
+                param_type_string = "array or matrix"
+
+            matrix = np.array(matrix)
+            if matrix.ndim != 2:
+                raise FunctionError("The value of the {} specified for the {} arg of {} ({}) "
+                                    "must be a 2d array or matrix".
+                                    format(param_type_string, MATRIX, self.name, matrix))
+            rows = matrix.shape[0]
+            cols = matrix.shape[1]
+            size = len(np.squeeze(self.variable))
+
+            if rows != size:
+                raise FunctionError("The value of the {} specified for the {} arg of {} is the wrong size;"
+                                    "it is {}x{}, but must be square matrix of size {}".
+                                    format(param_type_string, MATRIX, self.name, rows, cols, size))
+
+            if rows != cols:
+                raise FunctionError("The value of the {} specified for the {} arg of {} ({}) "
+                                    "must be a square matrix".
+                                    format(param_type_string, MATRIX, self.name, matrix))
+
+
+    def _instantiate_attributes_before_function(self, context=None):
+        """Instantiate matrix
+
+        Specified matrix specified is convolved with HOLLOW_MATRIX
+            to eliminate the diagonal (self-connections) from the calculation.
+        The `Distance` Function is used for all calculations except ENERGY (which is not really a distance metric).
+        If ENTROPY is specified as the metric, convert to CROSS_ENTROPY for use with the Distance Function.
+
+        """
+
+        size = len(np.squeeze(self.variable))
+
+        from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
+        from PsyNeuLink.Components.States.ParameterState import ParameterState
+        if isinstance(self.matrix,MappingProjection):
+            self._matrix = self.matrix._parameter_states[MATRIX]
+        elif isinstance(self.matrix,ParameterState):
+            pass
+        else:
+            self._matrix = get_matrix(self.matrix, size, size)
+
+        self._hollow_matrix = get_matrix(HOLLOW_MATRIX,size, size)
+
+        if self.metric is ENTROPY:
+            self._metric_fct = Distance(metric=CROSS_ENTROPY)
+
+        elif self.metric in DISTANCE_METRICS:
+            self._metric_fct = Distance(metric=self.metric)
+
+
     def function(self,
                  variable=None,
                  params=None,
                  time_scale=TimeScale.TRIAL,
                  context=None):
+        """Calculate the stability of `variable <Stability.variable>`.
+
+         Compare the value of `variable <Stability.variable>` with its value after transformation by
+         `matrix <Stability.matrix>` and `transfer_fct <Stability.transfer_fct>` (if specified), using the specified
+         `metric <Stability.metric>`.  If `normalize <Stability.normalize>` is `True`, the result is divided
+         by the length of `variable <Stability.variable>`.
+
+        Returns
+        -------
+
+        stability : scalar
+
+        """
         # Validate variable and assign to self.variable, and validate params
         self._check_args(variable=variable, params=params, context=context)
 
-        scale = self.paramsCurrent[SCALE]
-        shape = self.paramsCurrent[SHAPE]
+        from PsyNeuLink.Components.States.ParameterState import ParameterState
+        if isinstance(self.matrix, ParameterState):
+            matrix = self.matrix.value
+        else:
+            matrix = self.matrix
+
+        current = self.variable
+        if self.transfer_fct is not None:
+            transformed = self.transfer_fct(np.dot(matrix * self._hollow_matrix, self.variable))
+        else:
+            transformed = np.dot(matrix * self._hollow_matrix, self.variable)
+
+        if self.metric is ENERGY:
+            result = -np.sum(current * transformed)
+        else:
+            result = self._metric_fct.function(variable=[current,transformed], context=context)
 
-        result = np.random.gamma(shape, scale)
+        if self.normalize:
+            result /= len(self.variable)
 
         return result
 
+# endregion
 
-class WaldDist(DistributionFunction):
+class Distance(ObjectiveFunction):
     """
-     WaldDist(\
-              scale=1.0,\
-              mean=1.0,\
-              params=None,\
-              owner=None,\
-              prefs=None\
-              )
+    Distance(                                  \
+       variable_default=variableCLassDefault,  \
+       metric=EUCLIDEAN                        \
+       normalize=False,                        \
+       params=None,                            \
+       owner=None,                             \
+       prefs=None                              \
+       )
 
-     .. _WaldDist:
+    .. _Distance:
 
-     Return a random sample from a wald distribution using numpy.random.wald
+    Return the distance between two vectors based on a specified metric.
 
-     Arguments
-     ---------
+    Arguments
+    ---------
 
-     scale : float : default 1.0
-         Scale parameter of the wald distribution. Should be greater than zero.
+    variable : 2d np.array with two items : Default variableClassDefault
+        the arrays between which the distance is calculated.
 
-     mean : float : default 1.0
-         Mean of the wald distribution. Should be greater than or equal to zero.
+    metric : keyword in DISTANCE_METRICS : Default EUCLIDEAN
+        specifies the metric used to compute the distance between the two items in `variable <Distance.variable>`.
 
-     params : Optional[Dict[param keyword, param value]]
-         a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
-         function.  Values specified for parameters in the dictionary override any assigned to those parameters in
-         arguments of the constructor.
+    normalize : bool : Default False
+        specifies whether to normalize the distance by the length of `variable <Distance.variable>`.
 
-     owner : Component
-         `component <Component>` to which to assign the Function.
+    params : Optional[Dict[param keyword, param value]]
+        a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
+        function.  Values specified for parameters in the dictionary override any assigned to those parameters in
+        arguments of the constructor.
 
-     prefs : Optional[PreferenceSet or specification dict : Function.classPreferences]
-         the `PreferenceSet` for the Function. If it is not specified, a default is assigned using `classPreferences`
-         defined in __init__.py (see :doc:`PreferenceSet <LINK>` for details).
+    owner : Component
+        `component <Component>` to which to assign the Function.
 
+    prefs : Optional[PreferenceSet or specification dict : Function.classPreferences]
+        the `PreferenceSet` for the Function. If it is not specified, a default is assigned using `classPreferences`
+        defined in __init__.py (see :doc:`PreferenceSet <LINK>` for details).
 
-     Attributes
-     ----------
 
-     scale : float : default 1.0
-         Scale parameter of the wald distribution. Should be greater than zero.
+    Attributes
+    ----------
 
-     mean : float : default 1.0
-         Mean of the wald distribution. Should be greater than or equal to zero.
+    variable : 2d np.array with two items
+        contains the arrays between which the distance is calculated.
 
-     params : Optional[Dict[param keyword, param value]]
-         a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
-         function.  Values specified for parameters in the dictionary override any assigned to those parameters in
-         arguments of the constructor.
+    metric : keyword in DISTANCE_METRICS
+        specifies the metric used to compute the distance between the two items in `variable <Distance.variable>`.
 
-     owner : Component
-         `component <Component>` to which to assign the Function.
+    normalize : bool
+        specifies whether to normalize the distance by the length of `variable <Distance.variable>`.
 
-     prefs : Optional[PreferenceSet or specification dict : Function.classPreferences]
-         the `PreferenceSet` for the Function. If it is not specified, a default is assigned using `classPreferences`
-         defined in __init__.py (see :doc:`PreferenceSet <LINK>` for details).
+    params : Optional[Dict[param keyword, param value]]
+        a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
+        function.  Values specified for parameters in the dictionary override any assigned to those parameters in
+        arguments of the constructor.
 
+    owner : Component
+        `component <Component>` to which to assign the Function.
 
-     """
+    prefs : Optional[PreferenceSet or specification dict : Function.classPreferences]
+        the `PreferenceSet` for the Function. If it is not specified, a default is assigned using `classPreferences`
+        defined in __init__.py (see :doc:`PreferenceSet <LINK>` for details).
+    """
 
-    componentName = GAMMA_DIST_FUNCTION
+    componentName = DISTANCE_FUNCTION
 
-    variableClassDefault = [0]
+    variableClassDefault = [[0],[0]]
 
     paramClassDefaults = Function_Base.paramClassDefaults.copy()
 
     @tc.typecheck
     def __init__(self,
                  variable_default=variableClassDefault,
-                 scale=1.0,
-                 mean=1.0,
+                 metric:tc.enum(EUCLIDEAN, DIFFERENCE, CROSS_ENTROPY, ANGLE)=DIFFERENCE,
+                 normalize:bool=False,
                  params=None,
                  owner=None,
                  prefs: is_pref_set = None,
                  context=componentName + INITIALIZING):
         # Assign args to params and functionParams dicts (kwConstants must == arg names)
-        params = self._assign_args_to_param_dicts(scale=scale,
-                                                  mean=mean,
+        params = self._assign_args_to_param_dicts(metric=metric,
+                                                  normalize=normalize,
                                                   params=params)
 
         super().__init__(variable_default=variable_default,
@@ -4141,26 +6184,80 @@ def __init__(self,
 
         self.functionOutputType = None
 
+    def _validate_params(self, request_set, target_set=None, context=None):
+        """Validate that variable had two items of equal length
+
+        """
+        super()._validate_params(request_set=request_set, target_set=target_set, context=context)
+
+        if len(self.variable) != 2:
+            raise FunctionError("variable for {} ({}) must have two items".format(self.name, self.variable))
+
+        if len(self.variable[0]) != len(self.variable[1]):
+            raise FunctionError("The lengths of the items in the variable for {} ({},{}) must be equal".
+                format(self.name, len(self.variable[0]), len(self.variable[1])))
+
     def function(self,
                  variable=None,
                  params=None,
                  time_scale=TimeScale.TRIAL,
                  context=None):
+        """Calculate the distance between the two arrays in `variable <Stability.variable>`.
+
+        Returns
+        -------
+
+        distance : scalar
+
+        """
         # Validate variable and assign to self.variable, and validate params
         self._check_args(variable=variable, params=params, context=context)
 
-        scale = self.paramsCurrent[SCALE]
-        mean = self.paramsCurrent[MEAN]
+        v1 = self.variable[0]
+        v2 = self.variable[1]
 
-        result = np.random.wald(mean, scale)
+        # Simple Hadamard difference of v1 and v2
+        if self.metric is DIFFERENCE:
+            result = np.sum(np.abs(v1 - v2))
 
-        return result
+        # Euclidean distance between v1 and v2
+        elif self.metric is EUCLIDEAN:
+            result = np.linalg.norm(v2-v1)
+
+        # Cross-entropy of v1 and v2
+        elif self.metric is CROSS_ENTROPY:
+            # FIX: VALIDATE THAT ALL ELEMENTS OF V1 AND V2 ARE 0 TO 1
+            if context is not None and INITIALIZING in context:
+                v1 = np.where(v1==0, EPSILON, v1)
+                v2 = np.where(v2==0, EPSILON, v2)
+            result = -np.sum(v1*np.log(v2))
+
+        # FIX: NEED SCIPY HERE
+        # # Angle (cosyne) of v1 and v2
+        # elif self.metric is ANGLE:
+        #     result = scipy.spatial.distance.cosine(v1,v2)
+
+        # Correlation of v1 and v2
+        elif self.metric is CORRELATION:
+            result = np.correlate(v1, v2)
 
+        # Pearson Correlation of v1 and v2
+        elif self.metric is PEARSON:
+            result = np.corrcoef(v1, v2)
+
+
+        if self.normalize:
+            # if np.sum(denom):
+            # result /= np.sum(x,y)
+            result /= len(self.variable)
+
+        return result
 
 # endregion
 
 # region **************************************   LEARNING FUNCTIONS ***************************************************
 
+ReturnVal = namedtuple('ReturnVal', 'learning_signal, error_signal')
 
 class LearningFunction(Function_Base):
     """Abstract class of `Function` used for learning.
@@ -4181,6 +6278,14 @@ class LearningFunction(Function_Base):
 
     componentType = LEARNING_FUNCTION_TYPE
 
+    # def __init__(self, variable_default, params, owner, prefs, context):
+    #     super().__init__(variable_default=variable_default,
+    #                      params=params,
+    #                      owner=owner,
+    #                      prefs=prefs,
+    #                      context=context)
+    #     self.return_val = return_val(None, None)
+
 
 LEARNING_ACTIVATION_FUNCTION = 'activation_function'
 LEARNING_ACTIVATION_INPUT = 0  # a(j)
@@ -4188,7 +6293,6 @@ class LearningFunction(Function_Base):
 LEARNING_ACTIVATION_OUTPUT = 1  # a(i)
 LEARNING_ERROR_OUTPUT = 2
 
-
 class Reinforcement(
     LearningFunction):  # -------------------------------------------------------------------------------
     """
@@ -4298,7 +6402,7 @@ class Reinforcement(
         `mechanism <Mechanism>` to which the function belongs.
 
     prefs : PreferenceSet or specification dict : Projection.classPreferences
-        the `PreferenceSet` for function. Specified in the `prefs` argument of the constructor for the function;
+        the `PreferenceSet` for function. Specified in the **prefs** argument of the constructor for the function;
         if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
         (see :doc:`PreferenceSet <LINK>` for details).
     """
@@ -4325,6 +6429,8 @@ def __init__(self,
                                                   learning_rate=learning_rate,
                                                   params=params)
 
+        # self.return_val = ReturnVal(None, None)
+
         super().__init__(variable_default=variable_default,
                          params=params,
                          owner=owner,
@@ -4395,13 +6501,12 @@ def function(self,
 
         Returns
         -------
+        error signal : 1d np.array
+            same as value received in `error_signal <Reinforcement.error_signal>` argument.
 
         diagonal weight change matrix : 2d np.array
             has a single non-zero entry in the same row and column as the one in
             `activation_output <Reinforcement.activation_output>` and `error_signal <Reinforcement.error_signal>`.
-
-        error signal : 1d np.array
-            same as value received in `error_signal <Reinforcement.error_signal>` argument.
         """
 
         self._check_args(variable=variable, params=params, context=context)
@@ -4429,8 +6534,12 @@ def function(self,
         # Construct weight change matrix with error term in proper element
         weight_change_matrix = np.diag(error_array)
 
-        # return:
-        # - weight_change_matrix and error_array
+        # self.return_val.error_signal = error_array
+        # self.return_val.learning_signal = weight_change_matrix
+        #
+        # # return:
+        # # - weight_change_matrix and error_array
+        # return list(self.return_val)
         return [weight_change_matrix, error_array]
 
 
@@ -4551,7 +6660,7 @@ class BackPropagation(LearningFunction):
         `mechanism <Mechanism>` to which the function belongs.
 
     prefs : PreferenceSet or specification dict : Projection.classPreferences
-        the `PreferenceSet` for function. Specified in the `prefs` argument of the constructor for the function;
+        the `PreferenceSet` for function. Specified in the **prefs** argument of the constructor for the function;
         if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
         (see :doc:`PreferenceSet <LINK>` for details).
 
@@ -4585,6 +6694,8 @@ def __init__(self,
                                                   learning_rate=learning_rate,
                                                   params=params)
 
+        # self.return_val = ReturnVal(None, None)
+
         super().__init__(variable_default=variable_default,
                          params=params,
                          owner=owner,
@@ -4640,14 +6751,14 @@ def _validate_params(self, request_set, target_set=None, context=None):
             error_matrix = target_set[ERROR_MATRIX]
 
             from PsyNeuLink.Components.States.ParameterState import ParameterState
-            from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
+            from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
             if not isinstance(error_matrix, (list, np.ndarray, np.matrix, ParameterState, MappingProjection)):
                 raise FunctionError("The {} arg for {} must be a list, 2d np.array, ParamaterState or "
                                     "MappingProjection".format(ERROR_MATRIX, self.name))
 
             if isinstance(error_matrix, MappingProjection):
                 try:
-                    error_matrix = error_matrix.parameterStates[MATRIX].value
+                    error_matrix = error_matrix._parameter_states[MATRIX].value
                     param_type_string = "MappingProjection's ParameterState"
                 except KeyError:
                     raise FunctionError("The MappingProjection specified for the {} arg of {} ({}) must have a {} "
@@ -4684,7 +6795,7 @@ def _validate_params(self, request_set, target_set=None, context=None):
             if cols != error_signal_len:
                 raise FunctionError("The width (number of columns, {}) of the \'{}\' arg ({}) specified for {} "
                                     "must match the length of the error signal ({}) it receives".
-                                    format(cols, MATRIX, error_matrix.shape, self.name, cols))
+                                    format(cols, MATRIX, error_matrix.shape, self.name, error_signal_len))
 
             # Validate that rows (number of sender elements) of error_matrix equals length of activity_output,
             if rows != activity_output_len:
@@ -4719,18 +6830,18 @@ def function(self,
         Returns
         -------
 
-        weight change matrix : 2d np.array
-            the modifications to make to the matrix.
-
         weighted error signal : 1d np.array
             `error_signal <BackPropagation.error_signal>`, weighted by the contribution made by each element of
             `activation_output <BackPropagation.activation_output>` as a function of
             `error_matrix <BackPropagation.error_matrix>`.
 
+        weight change matrix : 2d np.array
+            the modifications to make to the matrix.
         """
 
-        from PsyNeuLink.Components.States.ParameterState import ParameterState
         self._check_args(variable=variable, params=params, context=context)
+
+        from PsyNeuLink.Components.States.ParameterState import ParameterState
         if isinstance(self.error_matrix, ParameterState):
             error_matrix = self.error_matrix.value
         else:
@@ -4771,6 +6882,11 @@ def function(self,
         #           "-error_derivative (dE_dW): {}\n".
         #           format(self.owner.name, self.activation_input, dE_dA, dA_dW ,dE_dW))
 
+        # self.return_val.error_signal = dE_dW
+        # self.return_val.learning_signal = weight_change_matrix
+        #
+        # return list(self.return_val)
+
         return [weight_change_matrix, dE_dW]
 
 # region *****************************************   OBJECTIVE FUNCTIONS ***********************************************
@@ -4778,3 +6894,22 @@ def function(self,
 # TBI
 
 # region  *****************************************   REGISTER FUNCTIONS ***********************************************
+
+# region
+
+
+# FIX: IMPLEMENT AS Functions
+def max_vs_next(x):
+    x_part = np.partition(x, -2)
+    max_val = x_part[-1]
+    next = x_part[-2]
+    return max_val - next
+
+
+def max_vs_avg(x):
+    x_part = np.partition(x, -2)
+    max_val = x_part[-1]
+    others = x_part[:-1]
+    return max_val - np.mean(others)
+
+#endregion
diff --git a/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/AdaptiveMechanism.py b/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/AdaptiveMechanism.py
index 8d2b6699813..2932ee1d1bd 100644
--- a/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/AdaptiveMechanism.py
+++ b/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/AdaptiveMechanism.py
@@ -39,8 +39,11 @@
 Structure
 ---------
 
-An AdaptiveMechanism has the same basic structure as a `Mechanism <Mechanisms>`.  See the documentation for
-individual subtypes of AdaptiveMechanism for more specific information about their structure.
+An AdaptiveMechanism has the same basic structure as a `Mechanism <Mechanisms>`.  In addition, every AdaptiveMechanism
+has a `modulation <AdpativeMechanism.modulation>` attribute, that determines the default method by which its
+ModulatorySignals modify the value of objects that they modulate (see the `modulation <ModulatorySignal_Modulation>`
+for a description of how modulation operates, and the documentation for individual subtypes of AdaptiveMechanism for
+more specific information about their structure and modulatory operation).
 
 .. _Comparator_Execution:
 
@@ -65,9 +68,15 @@ class AdaptiveMechanism_Base(Mechanism_Base):
     # IMPLEMENT: consider moving any properties of adaptive mechanisms not used by control mechanisms to here
     """An AdaptiveMechanism is a Type of the `Mechanism <Mechanism>` Category of Component
 
+    Attributes
+    ----------
+
+    modulation : ModulationParam
+        determines how the output of the AdaptiveMechanism's `ModulatorySignal(s) <ModulatorySignal>` are used to
+        modulate the value of the State(s) to which their `ModulatoryProjection(s) <ModulatoryProjection>` project.
    """
 
-    componentType = "AdaptiveMechanism"
+    componentType = ADAPTIVE_MECHANISM
 
     classPreferenceLevel = PreferenceLevel.TYPE
     # Any preferences specified below will override those specified in TypeDefaultPreferences
@@ -77,22 +86,35 @@ class AdaptiveMechanism_Base(Mechanism_Base):
     #     kp<pref>: <setting>...}
 
     # variableClassDefault = defaultControlAllocation
-    # This must be a list, as there may be more than one (e.g., one per controlSignal)
+    # This must be a list, as there may be more than one (e.g., one per control_signal)
     variableClassDefault = defaultControlAllocation
 
     def __init__(self,
-                 variable=None,
-                 params=None,
-                 name=None,
-                 prefs=None,
-                 context=None):
+                 variable,
+                 modulation,
+                 params,
+                 name,
+                 prefs,
+                 context):
         """Abstract class for AdaptiveMechanism
         """
 
-        self.system = None
+        if not hasattr(self, 'system'):
+            self.system = None
+
+        # Assign args to params and functionParams dicts (kwConstants must == arg names)
+        params = self._assign_args_to_param_dicts(params=params,
+                                                  modulation=modulation)
+
 
         super().__init__(variable=variable,
                          params=params,
                          name=name,
                          prefs=prefs,
-                         context=context)
\ No newline at end of file
+                         context=context)
+
+#     def _instantiate_output_states(self, context=None):
+#         super()._instantiate_output_states(context=context)
+#
+#
+# def _instantiate_adaptive_projections()
\ No newline at end of file
diff --git a/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/ControlMechanisms/ControlMechanism.py b/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/ControlMechanisms/ControlMechanism.py
index ef33f500e07..ceb6cb78d91 100644
--- a/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/ControlMechanisms/ControlMechanism.py
+++ b/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/ControlMechanisms/ControlMechanism.py
@@ -15,11 +15,18 @@
 A ControlMechanism is an `AdaptiveMechanism` that modifies the parameter(s) of one or more `ProcessingMechanisms`.
 It's function takes a value (usually the output of an `ObjectiveMechanism`) and uses that to calculate an
 `allocation_policy`:  a list of `allocation` values for each of its ControlSignals that specify the value to assign
-to each parameter of a ProcessingMechanism (or its function) that it controls.  Each of these values is conveyed by
-a `ControlProjection` to the `parameterState <ParameterState>` of the corresopnding ProcessingMechanism.  A
-ControlMechanism can regulate only the parameters of mechanism in the system for which it is the
+to each parameter of a ProcessingMechanism (or its function) that it controls.  Each of these values is assigned as
+the value of a corresponding `ControlSignal` (a subclass of `OutputState` used by ControlMechanisms), and conveyed by
+the associated `ControlProjection` to the `parameterState <ParameterState>` of the relevant ProcessingMechanism.
+A ControlMechanism can regulate only the parameters of mechanisms in the system for which it is the
 `controller <System_Execution_Control>`.  The control components of a system can be displayed using the system's 
-`show_graph` method with its **show_control** argument assigned :keyword:``True`.  The control components of a 
+`show_graph` method with its **show_control** argument assigned :keyword:``True`.  
+COMMENT: TBI
+The control components of a system can be displayed using the system's 
+`show_graph` method with its **show_control** argument assigned as :keyword:``True`.  
+COMMENT
+
+The control components of a 
 system are executed after all ProcessingMechanisms and `learning components <LearningMechanism>` in that system have 
 been executed.
 
@@ -28,36 +35,61 @@
 Creating A ControlMechanism
 ---------------------------
 
-ControlMechanisms can be created by using the standard Python method of calling the constructor for the desired type.
-A ControlMechanism is also created automatically whenever a `system is created <System_Creation>`, and assigned as
-the `controller <System_Execution_Control>` for that system. The `outputStates <OutputState>` to be monitored by a
-ControlMechanism are specified in its `monitored_output_states` argument, which can take  a number of
-`forms <ObjectiveMechanism_Monitored_OutputStates>`.  When the ControlMechanism is created, it automatically creates
-an ObjectiveMechanism that is used to monitor and evaluate the mechanisms and/or outputStates specified in its
-`monitor_for_control <ControlMechanism.monitor_for_control>` attribute.  The result of the evaluation is used to
-specify the value of the ControlMechanism's `ControlProjections <ControlProjection>`. How a ControlMechanism creates its
-ControlProjections and determines their value based on the outcome of its evaluation  depends on the
-`subclass <ControlMechanism>`.
+ControlMechanisms can be created using the standard Python method of calling the constructor for the desired type.
+A ControlMechanism is also created automatically whenever a `system is created <System_Creation>`, and it is assigned as
+the `controller <System_Execution_Control>` for that system. The values to be monitored by the ControlMechanism are  
+specified in the **monitor_for_control** argument of its constructor, and the parameters it controls are specified in
+the **control_signals** argument.  When the ControlMechanism is created, it automatically creates
+an ObjectiveMechanism (used to monitor and evaluate the values specified in **monitor_for_control**) 
+as well as `ControlSignals <ControlSignal>` and `ControlProjections <ControlProjection>` used to control the parameters
+specified in **control_signals**, as described below. The kind of ObjectiveMechanism created by a ControlMechanism,
+and how it evalutes the values it monitors, depends on the `subclass <ControlMechanism>` of ControlMechanism.
+
+.. _ControlMechanism_Control_Signals:
+
+Specifying Parameters to Control
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+ControlMechanisms are used to control the parameter values of mechanisms and/or their functions.  A parameter can be 
+specified for control by assigning it a `ControlProjection` (along with the parameter's value) when creating the 
+mechanism or function to which the parameter belongs (see `Mechanism_Parameters`).  The parameters to be controlled by
+a ControlMechanism can also be specified in the **control_signals**  argument of the constructor for a ControlMechanism.  
+The **control_signals** argument must be a list, each item of which can use any of the forms used for 
+`specifying a ControlSignal <ControlSignal_Specification>`.
+
+A `ControlSignal` is created for each item listed in **control_signals**, and all of the ControlSignals for a 
+ControlMechanism are listed in its `control_signals <ControlMechanism.control_signals>` attribute.  Each ControlSignal
+is assigned a `ControlProjection` to the parameterState of the mechanisms associated with the specified parameter of the
+mechanism or its function, that is used to control the parameter's value. ControlSignals are a type of `OutputState`, 
+and so they are also listed in the ControlMechanism's `output_states <ControlMechanism.outut_states>` attribute.
+
+.. _ControlMechanism_Monitored_Values:
+
+Specifying Values to Monitor for Control
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+When an ControlMechanism is created, it automatically creates an `ObjectiveMechanism` that is used to monitor and 
+evaluate the values specified in the **monitor_for_control** argument of the ControlMechanism's constructor. 
+The **monitor_for_control** argument must be a list, each item of which must refer to a mechanism or the outputState
+of one.  These are assigned to the ObjectiveMechanism's `monitored_values <ObjectiveMechanism>` attribute, and the
+ObjectiveMechanism is referenced by the ControlMechanism's 
+`monitoring_mechanism <ControlMechanism.monitoring_mechanism>` attribute.
 
-.. _ControlMechanism_Specifying_Control:
+ObjectiveMechanism monitors each mechanism and/or outputState listed in the ControlMechanism's
+'monitor_for_control <ControlMechanism.monitor_for_control>` attribute, and evaluates them using the its `function`.
+This information is used to set the value of the ControlMechanism's ControlSignals.
 
-Specifying control for a parameter
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. _ControlMechanism_Examples:
 
-ControlMechanisms are used to control the parameter values of mechanisms and/or their functions.  A parameter can be
-specified for control by assigning a `ControlProjection` as part of its value when creating the mechanism or function
-to which the parameter belongs (see `Mechanism_Parameters`).
+Examples
+~~~~~~~~
 
-.. _ControlMechanism_Monitored_OutputStates:
+EXAMPLES HERE
 
-Monitored OutputStates
-~~~~~~~~~~~~~~~~~~~~~~
+COMMENT:
+EXAMPLES HERE OF THE DIFFERENT FORMS OF SPECIFICATION FOR **monitor_for_control** and **control_signals**
+COMMENT
 
-When an ControlMechanism is constructed automatically, it creates an `ObjectiveMechanism` (specified in its
-`montioring_mechanism` attribute) that is used to monitor and evaluate the system's performance.  The
-ObjectiveMechanism monitors each mechanism and/or outputState listed in the ControlMechanism's
-'monitor_for_control <ControlMechanism.monitor_for_control>` attribute, and evaluates them using the its `function`.
-This information is used to set the value of the ControlMechanism's ControlProjections.
 
 .. _ControlMechanism_Execution:
 
@@ -65,16 +97,18 @@
 ---------
 
 A ControlMechanism that is a system's `controller` is always the last mechanism to be executed (see `System Control
-<System_Execution_Control>`).  Its `function <ControlMechanism.function>` takes as its input the values of the
-outputStates in its `monitored_output_states` attribute, and uses those to determine the value of its
-`ControlProjections <ControlProjection>`. In the subsequent round of execution, each ControlProjection's value is
-used by the `ParameterState` to which it projects to update the parameter being controlled.
+<System_Execution_Control>`).  Its `function <ControlMechanism.function>` takes as its input the value in its 
+*ERROR_SIGNAL* `input_state <ControlMechanism.input_state>`, and use that to determine its 
+`allocation_policy <ControlMechanism.allocation_policy>` that specifies the value assigned to each of its   
+`ControlSignals <ControlSignal>`.  Each of those is used by its associated `ControlProjection` to set the
+value of the ParameterState for the parameter it controls.  In the subsequent round of execution, 
+those parameter values are used by the mechanism when it executes.
 
 .. note::
    A `ParameterState` that receives a `ControlProjection` does not update its value until its owner mechanism
    executes (see `Lazy Evaluation <LINK>` for an explanation of "lazy" updating).  This means that even if a
-   ControlMechanism has executed, a parameter that it controls will not assume its new value until the corresponding
-   receiver mechanism has executed.
+   ControlMechanism has executed, a parameter that it controls will not assume its new value until the mechanism
+   to which it belongs has executed.
 
 .. _ControlMechanism_Class_Reference:
 
@@ -83,31 +117,34 @@
 
 """
 
-# IMPLEMENTATION NOTE: COPIED FROM DefaultProcessingMechanism;
-#                      ADD IN GENERIC CONTROL STUFF FROM DefaultControlMechanism
-
-from collections import OrderedDict
-
-from PsyNeuLink.Components.Mechanisms.Mechanism import Mechanism_Base
+from PsyNeuLink.Components.Functions.Function import ModulationParam, _is_modulation_param
+from PsyNeuLink.Components.Mechanisms.Mechanism import Mechanism_Base, MonitoredOutputStatesOption
+from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.AdaptiveMechanism import AdaptiveMechanism_Base
+from PsyNeuLink.Components.Projections.Projection import _validate_receiver
 from PsyNeuLink.Components.ShellClasses import *
+from PsyNeuLink.Components.States.OutputState import OutputState
+from PsyNeuLink.Components.States.ParameterState import ParameterState
+from PsyNeuLink.Components.States.State import _parse_state_spec
+from PsyNeuLink.Components.States.ModulatorySignals.ModulatorySignal import *
 
 ControlMechanismRegistry = {}
 
-
 class ControlMechanismError(Exception):
     def __init__(self, error_value):
         self.error_value = error_value
 
 
-class ControlMechanism_Base(Mechanism_Base):
+# class ControlMechanism_Base(Mechanism_Base):
+class ControlMechanism_Base(AdaptiveMechanism_Base):
     """
-    ControlMechanism_Base(     \
-    default_input_value=None,  \
-    monitor_for_control=None,  \
-    function=Linear,           \
-    params=None,               \
-    name=None,                 \
-    prefs=None)
+    ControlMechanism_Base(                         \
+        monitor_for_control=None,                  \
+        control_signals=None,                      \
+        modulation=ModulationParam.MULTIPLICATIVE  \
+        function=Linear,                           \
+        params=None,                               \
+        name=None,                                 \
+        prefs=None)
 
     Abstract class for ControlMechanism.
 
@@ -117,17 +154,14 @@ class ControlMechanism_Base(Mechanism_Base):
 
     COMMENT:
         Description:
-            # DOCUMENTATION NEEDED:
-              ._instantiate_control_projection INSTANTIATES OUTPUT STATE FOR EACH CONTROL SIGNAL ASSIGNED TO THE
-             INSTANCE
-            .EXECUTE MUST BE OVERRIDDEN BY SUBCLASS
-            WHETHER AND HOW MONITORING INPUT STATES ARE INSTANTIATED IS UP TO THE SUBCLASS
-
-            Protocol for assigning DefaultController:
-               Initial assignment is to SystemDefaultController (instantiated and assigned in Components.__init__.py)
-               When any other ControlMechanism is instantiated, if its params[MAKE_DEFAULT_CONTROLLER] == True
-                   then its _take_over_as_default_controller method is called in _instantiate_attributes_after_function()
-                   which moves all ControlProjections from DefaultController to itself, and deletes them there
+            Protocol for instantiating unassigned ControlProjections (i.e., w/o a sender specified):
+               If sender is not specified for a ControlProjection (e.g., in a parameter specification tuple) 
+                   it is flagged for deferred_init() in its __init__ method
+               When the next ControlMechanism is instantiated, if its params[MAKE_DEFAULT_CONTROLLER] == True
+                   its _assign_as_controller method is called in _instantiate_attributes_after_function;
+                   it then iterates through all of the parameterStates of all of the mechanisms in its system, 
+                   identifies ones without a sender specified, calls its deferred_init() method,
+                   instantiates a ControlSignal for it, and assigns it as the ControlProjection's sender.
 
             MONITOR_FOR_CONTROL param determines which states will be monitored.
                 specifies the outputStates of the terminal mechanisms in the System to be monitored by ControlMechanism
@@ -148,55 +182,61 @@ class ControlMechanism_Base(Mechanism_Base):
                 + MONITOR_FOR_CONTROL: List[]
     COMMENT
 
-    COMMENT:
-        Arguments
-        ---------
-
-            NOT CURRENTLY IN USE:
-            default_input_value : value, list or np.ndarray : :py:data:`defaultControlAllocation <LINK]>`
-                the default allocation for the ControlMechanism;
-                its length should equal the number of ``controlSignals``.
-
-        monitor_for_control : List[OutputState specification] : default None
-            specifies set of outputStates to monitor (see :ref:`ControlMechanism_Monitored_OutputStates` for
-            specification options).
-
-        function : TransferFunction : default Linear(slope=1, intercept=0)
-            specifies function used to combine values of monitored output states.
-
-        params : Optional[Dict[param keyword, param value]]
-            a `parameter dictionary <ParameterState_Specifying_Parameters>` that can be used to specify the parameters
-            for the mechanism, parameters for its function, and/or a custom function and its parameters. Values
-            specified for parameters in the dictionary override any assigned to those parameters in arguments of the
-            constructor.
-
-        name : str : default ControlMechanism-<index>
-            a string used for the name of the mechanism.
-            If not is specified, a default is assigned by `MechanismRegistry`
-            (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
-
-        prefs : Optional[PreferenceSet or specification dict : Mechanism.classPreferences]
-            the `PreferenceSet` for the mechanism.
-            If it is not specified, a default is assigned using `classPreferences` defined in __init__.py
-            (see :doc:`PreferenceSet <LINK>` for details).
-    COMMENT
-
+    Arguments
+    ---------
+
+    monitor_for_control : List[OutputState specification] : default None
+        specifies set of outputStates to monitor (see :ref:`ControlMechanism_Monitored_OutputStates` for
+        specification options).
+
+    control_signals : List[parameter of Mechanism or its function, \
+                      ParameterState, Mechanism tuple[str, Mechanism] or dict]
+        specifies the parameters to be controlled by the ControlMechanism 
+        (see `control_signals <ControlMechanism.control_signals>` for details).
+
+    modulation : ModulationParam : ModulationParam.MULTIPLICATIVE
+        specifies the default form of modulation to be used by the ControlMechanism's ControlSignals, 
+        unless they are `individually specified <ControlSignal_Specification>`.
+
+    function : TransferFunction : default Linear(slope=1, intercept=0)
+        specifies function used to combine values of monitored output states.
+        
+    params : Optional[Dict[param keyword, param value]]
+        a `parameter dictionary <ParameterState_Specifying_Parameters>` that can be used to specify the parameters
+        for the mechanism, parameters for its function, and/or a custom function and its parameters. Values
+        specified for parameters in the dictionary override any assigned to those parameters in arguments of the
+        constructor.
+
+    name : str : default ControlMechanism-<index>
+        a string used for the name of the mechanism.
+        If not is specified, a default is assigned by `MechanismRegistry`
+        (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
+
+    prefs : Optional[PreferenceSet or specification dict : Mechanism.classPreferences]
+        the `PreferenceSet` for the mechanism.
+        If it is not specified, a default is assigned using `classPreferences` defined in __init__.py
+        (see :doc:`PreferenceSet <LINK>` for details).
 
     Attributes
     ----------
 
-    controlProjections : List[ControlProjection]
-        list of `ControlProjections <ControlProjection>` managed by the ControlMechanism.
-        There is one for each ouputState in the `outputStates` dictionary.
+    monitoring_mechanism : ObjectiveMechanism
+        mechanism that monitors and evaluates the values specified in the ControlMechanism's **monitor_for_control**
+        argument, and transmits the result to the ControlMechanism's *ERROR_SIGNAL* 
+        `input_state <ControlMechanism.input_state>`.    
 
-    controlProjectionCosts : 2d np.array
-        array of costs associated with each of the control signals in the `controlProjections` attribute.
-
-    allocation_policy : 2d np.array
-        array of values assigned to each control signal in the `controlProjections` attribute.
-        This is the same as the ControlMechanism's `value <ControlMechanism.value>` attribute.
+    control_signals : List[ControlSignal]
+        list of `ControlSignals <ControlSignals>` for the ControlMechanism, each of which sends a `ControlProjection`
+        to the `parameterState <ParameterState>` for the parameter it controls (same as ControlMechanism's 
+        `output_states <Mechanism.output_states>` attribute).
 
+    control_projections : List[ControlProjection]
+        list of `ControlProjections <ControlProjection>`, one for each `ControlSignal` in `control_signals`.
 
+    allocation_policy : 2d np.array
+        each item is the value assigned to the corresponding ControlSignal in `control_signals`
+        (same as the ControlMechanism's `value <Mechanism.value>` attribute).
+        
     """
 
     componentType = "ControlMechanism"
@@ -210,8 +250,7 @@ class ControlMechanism_Base(Mechanism_Base):
     #     kwPreferenceSetName: 'ControlMechanismClassPreferences',
     #     kp<pref>: <setting>...}
 
-    # variableClassDefault = defaultControlAllocation
-    # This must be a list, as there may be more than one (e.g., one per controlSignal)
+    # This must be a list, as there may be more than one (e.g., one per control_signal)
     variableClassDefault = defaultControlAllocation
 
     from PsyNeuLink.Components.Functions.Function import Linear
@@ -221,229 +260,510 @@ class ControlMechanism_Base(Mechanism_Base):
     @tc.typecheck
     def __init__(self,
                  default_input_value=None,
+                 system=None,
                  monitor_for_control:tc.optional(list)=None,
                  function = Linear(slope=1, intercept=0),
+                 control_signals:tc.optional(list) = None,
+                 modulation:tc.optional(_is_modulation_param)=ModulationParam.MULTIPLICATIVE,
                  params=None,
                  name=None,
                  prefs:is_pref_set=None,
                  context=None):
 
-        self.system = None
-
         # Assign args to params and functionParams dicts (kwConstants must == arg names)
         params = self._assign_args_to_param_dicts(monitor_for_control=monitor_for_control,
                                                   function=function,
+                                                  control_signals=control_signals,
+                                                  # modulation=modulation,
                                                   params=params)
 
         super(ControlMechanism_Base, self).__init__(variable=default_input_value,
+                                                    modulation=modulation,
                                                     params=params,
                                                     name=name,
                                                     prefs=prefs,
                                                     context=self)
 
     def _validate_params(self, request_set, target_set=None, context=None):
-        """Validate SYSTEM, MONITOR_FOR_CONTROL and FUNCTION_PARAMS
+        """Validate SYSTEM, MONITOR_FOR_CONTROL and CONTROL_SIGNALS
 
-        If SYSTEM is not specified:
-        - OK if controller is DefaultControlMechanism
-        - otherwise, raise an exception
+        If system is specified, validate it
         Check that all items in MONITOR_FOR_CONTROL are Mechanisms or OutputStates for Mechanisms in self.system
-        Check that len(WEIGHTS) = len(MONITOR_FOR_CONTROL)
+        Check that all items in CONTROL_SIGNALS are parameters or ParameterStates for Mechanisms in self.system
         """
 
-        # DefaultController does not require a system specification
-        #    (it simply passes the defaultControlAllocation for default ConrolSignal Projections)
-        from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.DefaultControlMechanism import DefaultControlMechanism
-        if isinstance(self,DefaultControlMechanism):
-            pass
-
-        # For all other ControlMechanisms, validate System specification
-        else:
-            if SYSTEM in request_set:
-                if not isinstance(request_set[SYSTEM], System):
-                    raise KeyError
-                else:
-                    self.paramClassDefaults[SYSTEM] = request_set[SYSTEM]
-
         super(ControlMechanism_Base, self)._validate_params(request_set=request_set,
                                                                  target_set=target_set,
                                                                  context=context)
-
-    def _validate_projection(self, projection, context=None):
-        """Insure that projection is to mechanism within the same system as self
-        """
-
-        receiver_mech = projection.receiver.owner
-        if not receiver_mech in self.system.mechanisms:
-            raise ControlMechanismError("Attempt to assign ControlProjection {} to a mechanism ({}) that is not in {}".
-                                              format(projection.name, receiver_mech.name, self.system.name))
-
-    def _instantiate_attributes_before_function(self, context=None):
-        """Instantiate self.system attribute
-
-        Assign self.system
-        """
-        self.system = self.paramsCurrent[SYSTEM]
-        super()._instantiate_attributes_before_function(context=context)
+        if SYSTEM in target_set:
+            if not isinstance(target_set[SYSTEM], System):
+                raise KeyError
+            else:
+                self.paramClassDefaults[SYSTEM] = request_set[SYSTEM]
+
+        if MONITOR_FOR_CONTROL in target_set:
+            for spec in target_set[MONITOR_FOR_CONTROL]:
+                if isinstance(spec, MonitoredOutputStatesOption):
+                    continue
+                if isinstance(spec, tuple):
+                    spec = spec[0]
+                if isinstance(spec, (OutputState, Mechanism_Base)):
+                    spec = spec.name
+                if not isinstance(spec, str):
+                    raise ControlMechanismError("Invalid specification in {} arg for {} ({})".
+                                                format(MONITOR_FOR_CONTROL, self.name, spec))
+                # If controller has been assigned to a System,
+                #    check that all the items in monitor_for_control are in the same System
+                # IMPLEMENTATION NOTE:  If self.system is None, onus is on doing the validation
+                #                       when the controller is assigned to a system [TBI]
+                if self.system:
+                    if not any((spec is mech.name or spec in mech.output_states.names)
+                               for mech in self.system.mechanisms):
+                        raise ControlMechanismError("Specification in {} arg for {} ({}) must be a "
+                                                    "Mechanism or an OutputState of one in {}".
+                                                    format(MONITOR_FOR_CONTROL, self.name, spec, self.system.name))
+
+        # FIX: REPLACE WITH CALL TO _parse_state_spec WITH APPROPRIATE PARAMETERS
+        if CONTROL_SIGNALS in target_set and target_set[CONTROL_SIGNALS]:
+
+            from PsyNeuLink.Components.States.ModulatorySignals.ControlSignal import ControlSignal
+
+            for spec in target_set[CONTROL_SIGNALS]:
+
+                # Specification is for a ControlSignal
+                if isinstance(spec, ControlSignal):
+                    # If controller has been assigned to a System,
+                    #    check that any ControlProjections the ControlSignal has
+                    #    are to mechanisms in the controller's system
+                    # IMPLEMENTATION NOTE:  If self.system is None, onus is on doing the validation
+                    #                       when the controller is assigned to a system [TBI]
+                    if self.system:
+                        if not all(control_proj.receiver.owner in self.system.mechanisms
+                                   for control_proj in spec.efferents):
+                            raise ControlMechanismError("The {} specified in the {} arg for {} ({}) "
+                                                        "has one or more ControlProjections to a mechanism "
+                                                        "that is not in {}".
+                                                        format(CONTROL_SIGNAL,
+                                                               CONTROL_SIGNALS,
+                                                               self.name,
+                                                               spec.name,
+                                                               self.system.name))
+                    continue
+
+                # Specification is for a ParameterState
+                elif isinstance(spec, ParameterState):
+                    param_name = spec.name
+                    mech = spec.owner
+                    #  Check that owner is in controller's system
+                    if not self.system in mech.systems:
+                        raise ControlMechanismError("The {} specified in the {} arg for {} ({}) "
+                                                    "belongs to a mechanism ({}) that is not in "
+                                                    "the system for which {} is a controller ({})".
+                                                    format(PARAMETER_STATE,
+                                                           CONTROL_SIGNALS,
+                                                           self.name,
+                                                           spec.name,
+                                                           mech.name,
+                                                           self.name,
+                                                           self.system.name))
+
+                # Specification is for a tuple (str, Mechanism):
+                elif isinstance(spec, tuple):
+                    param_name = spec[0]
+                    mech = spec[1]
+                    # Check that 1st item is a str (presumably the name of the mechanism's attribute for the param)
+                    if not isinstance(param_name, str):
+                        raise ControlMechanismError("1st item of tuple in specification of {} for {} ({}) "
+                                                    "must be a string".format(CONTROL_SIGNAL, self.name, param_name))
+                    # Check that 2nd item is a mechanism
+                    if not isinstance(mech, Mechanism):
+                        raise ControlMechanismError("2nd item of tuple in specification of {} for {} ({}) "
+                                                    "must be a Mechanism".format(CONTROL_SIGNAL, self.name, mech))
+
+
+                # ControlSignal specification dictionary, must have the following entries:
+                #    NAME:str - must be the name of an attribute of MECHANISM
+                #    MECHANISM:Mechanism - must have an attribute and corresponding ParameterState with PARAMETER
+                #    PARAMS:dict - entries must be valid ControlSignal parameters (e.g., ALLOCATION_SAMPLES)
+                elif isinstance(spec, dict):
+                    if not NAME in spec:
+                        raise ControlMechanismError("Specification dict for {} of {} must have a NAME entry".
+                                                    format(CONTROL_SIGNAL, self.name))
+                    param_name = spec[NAME]
+                    if not MECHANISM in spec:
+                        raise ControlMechanismError("Specification dict for {} of {} must have a MECHANISM entry".
+                                                    format(CONTROL_SIGNAL, self.name))
+                    mech = spec[MECHANISM]
+                    # Check that all of the other entries in the specification dictionary are valid ControlSignal params
+                    for param in spec:
+                        if param in {NAME, MECHANISM} | modulatory_signal_keywords:
+                            continue
+                        if not hasattr(mech, param):
+                            raise ControlMechanismError("\'{}\' entry in specification dictionary for {} arg of {} "
+                                                       "is not a valid {} specification".
+                                                       format(CONTROL_SIGNAL, param, self.name,
+                                                              ControlSignal.__name__))
+                else:
+                    raise ControlMechanismError("PROGRAM ERROR: unrecognized specification of the {} arg for {} ({})".
+                                                format(CONTROL_SIGNALS, self.name, spec))
+                    # raise ControlMechanismError("Specification of {} for {} ({}) must be a ParameterState, Mechanism, "
+                    #                             "a tuple specifying a parameter and mechanism, "
+                    #                             "a ControlSignal specification dictionary, "
+                    #                             "or an existing ControlSignal".
+                    #                             format(CONTROL_SIGNAL, self.name, spec))
+
+                # Check that param_name is the name of an attribute of the mechanism
+                if not hasattr(mech, param_name) and not hasattr(mech.function_object, param_name):
+                    raise ControlMechanismError("{} (in specification of {} for {}) is not an "
+                                                "attribute of {} or its function"
+                                                .format(param_name, CONTROL_SIGNAL, self.name, mech))
+                # Check that the mechanism has a parameterState for the param
+                if not param_name in mech._parameter_states.names:
+                    raise ControlMechanismError("There is no ParameterState for the parameter ({}) of {} "
+                                                "specified in {} for {}".
+                                                format(param_name, mech.name, CONTROL_SIGNAL, self.name))
+                # If self has been assigned to a System,
+                #    check that the mechanism to which the parameter belongs is in the controller's system
+                # IMPLEMENTATION NOTE:  If self.system is None, onus is on doing the validation
+                #                       when the controller is assigned to a system [TBI]
+                if self.system and not mech in self.system.mechanisms:
+                    raise ControlMechanismError("Specification in {} arg for {} ({} param of {}) "
+                                                "must be for a Mechanism in {}".
+                                                format(CONTROL_SIGNALS,
+                                                       self.name,
+                                                       param_name,
+                                                       mech.name,
+                                                       self.system.name))
 
     def _instantiate_monitored_output_states(self, context=None):
         raise ControlMechanismError("{0} (subclass of {1}) must implement _instantiate_monitored_output_states".
                                           format(self.__class__.__name__,
                                                  self.__class__.__bases__[0].__name__))
 
-    def _instantiate_attributes_after_function(self, context=None):
-        """Take over as default controller (if specified) and implement any specified ControlProjections
-
+    def _instantiate_output_states(self, context=None):
+
+        # Create registry for GatingSignals (to manage names)
+        from PsyNeuLink.Globals.Registry import register_category
+        from PsyNeuLink.Components.States.ModulatorySignals.ControlSignal import ControlSignal
+        from PsyNeuLink.Components.States.State import State_Base
+        register_category(entry=ControlSignal,
+                          base_class=State_Base,
+                          registry=self._stateRegistry,
+                          context=context)
+
+        if self.control_signals:
+            for control_signal in self.control_signals:
+                self._instantiate_control_signal(control_signal=control_signal, context=context)
+
+        # IMPLEMENTATION NOTE:  Don't want to call this because it instantiates undesired default outputState
+        # super()._instantiate_output_states(context=context)
+
+    # ---------------------------------------------------
+    # IMPLEMENTATION NOTE:  IMPLEMENT _instantiate_output_states THAT CALLS THIS FOR EACH ITEM
+    #                       DESIGN PATTERN SHOULD COMPLEMENT THAT FOR _instantiate_input_states of ObjectiveMechanism
+    #                           (with control_signals taking the place of monitored_values)
+    # FIX 5/23/17: PROJECTIONS AND PARAMS SHOULD BE PASSED BY ASSIGNING TO STATE SPECIFICATION DICT
+    # FIX          UPDATE parse_state_spec TO ACCOMODATE (param, ControlSignal) TUPLE
+    # FIX          TRACK DOWN WHERE PARAMS ARE BEING HANDED OFF TO ControlProjection
+    # FIX                   AND MAKE SURE THEY ARE NOW ADDED TO ControlSignal SPECIFICATION DICT
+    #
+    def _instantiate_control_signal(self, control_signal=None, context=None):
+        """Instantiate ControlSignal OutputState and assign (if specified) or instantiate ControlProjection
+
+        # Extends allocation_policy and control_signal_costs attributes to accommodate instantiated projection
+
+        Notes:
+        * control_signal arg can be a:
+            - ControlSignal object;
+            - ControlProjection;
+            - ParameterState;
+            - params dict, from _assign_as_controller(), containing a ControlProjection;
+            - tuple (param_name, Mechanism), from control_signals arg of constructor;
+                    [NOTE: this is a convenience format;
+                           it precludes specification of ControlSignal params (e.g., ALLOCATION_SAMPLES)]
+            - ControlSignal specification dictionary, from control_signals arg of constructor
+                    [NOTE: this must have at least NAME:str (param name) and MECHANISM:Mechanism entries; 
+                           it can also include a PARAMS entry with a params dict containing ControlSignal params] 
+        * State._parse_state_spec() is used to parse control_signal arg
+        * params are expected to be for (i.e., to be passed to) ControlSignal;
+        * wait to instantiate deferred_init() projections until after ControlSignal is instantiated,
+             so that correct outputState can be assigned as its sender;
+        * index of outputState is incremented based on number of ControlSignals already instantiated;
+            this means that the ControlMechanism's function must return as many items as it has ControlSignals,
+            with each item of the function's value used by a corresponding ControlSignal.
+            Note: multiple ControlProjections can be assigned to the same ControlSignal to achieve "divergent control"
+                  (that is, control of many parameters with a single value)
+
+        Returns ControlSignal (OutputState)
         """
+        from PsyNeuLink.Components.States.ModulatorySignals.ControlSignal import ControlSignal
+        from PsyNeuLink.Components.States.ParameterState import _get_parameter_state
+        from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
 
-        try:
-            # If specified as DefaultController, reassign ControlProjections from DefaultController
-            if self.paramsCurrent[MAKE_DEFAULT_CONTROLLER]:
-                self._take_over_as_default_controller(context=context)
-            if not self.system.enable_controller:
-                return
-        except KeyError:
-            pass
+        # EXTEND allocation_policy TO ACCOMMODATE NEW ControlSignal -------------------------------------------------
+        #        also used to determine constraint on ControlSignal value
+
+        if self.allocation_policy is None:
+            self.allocation_policy = np.array(defaultControlAllocation)
+        else:
+            self.allocation_policy = np.append(self.allocation_policy, defaultControlAllocation)
+
+        # PARSE control_signal SPECIFICATION -----------------------------------------------------------------------
+
+        control_projection = None
+        control_signal_params = None
+
+        control_signal_spec = _parse_state_spec(owner=self, state_type=ControlSignal, state_spec=control_signal)
+
+        # Specification is a ParameterState
+        if isinstance(control_signal_spec, ParameterState):
+            mech = control_signal_spec.owner
+            param_name = control_signal_spec.name
+            parameter_state = _get_parameter_state(self, CONTROL_SIGNAL, param_name, mech)
+
+        # Specification was tuple or dict, now parsed into a dict
+        elif isinstance(control_signal_spec, dict):
+            param_name = control_signal_spec[NAME]
+            control_signal_params = control_signal_spec[PARAMS]
+
+            # control_signal was a specification dict, with MECHANISM as an entry (and parameter as NAME)
+            if control_signal_params and MECHANISM in control_signal_params:
+                mech = control_signal_params[MECHANISM]
+                # Delete MECHANISM entry as it is not a parameter of ControlSignal
+                #     (which will balk at it in ControlSignal._validate_params)
+                del control_signal_params[MECHANISM]
+                parameter_state = _get_parameter_state(self, CONTROL_SIGNAL, param_name, mech)
+
+            # Specification was originally a tuple, either in parameter specification or control_signal arg;
+            #    1st item was either assigned to the NAME entry of the control_signal_spec dict
+            #        (if tuple was a (param_name, Mechanism tuple) for control_signal arg;
+            #        or used as param value, if it was a parameter specification tuple
+            #    2nd item was placed CONTROL_SIGNAL_PARAMS entry of params dict in control_signal_spec dict,
+            #        so parse:
+            # IMPLEMENTATION NOTE:
+            #    CONTROL_SIGNAL_SPECS is used by _assign_as_controller,
+            #                         to pass specification from a parameter specification tuple
+            #    STATE_PROJECTIONS is used by _parse_state_spec to place the 2nd item of any tuple in params dict;
+            #                      here, the tuple comes from a (param, mechanism) specification in control_signal arg
+            #    Delete whichever one it was, as neither is a recognized ControlSignal param
+            #        (which will balk at it in ControlSignal._validate_params)
+            elif (control_signal_params and
+                    any(kw in control_signal_spec[PARAMS] for kw in {CONTROL_SIGNAL_SPECS, STATE_PROJECTIONS})):
+                if CONTROL_SIGNAL_SPECS in control_signal_spec[PARAMS]:
+                    spec = control_signal_params[CONTROL_SIGNAL_SPECS]
+                    del control_signal_params[CONTROL_SIGNAL_SPECS]
+                elif STATE_PROJECTIONS in control_signal_spec[PARAMS]:
+                    spec = control_signal_params[STATE_PROJECTIONS]
+                    del control_signal_params[STATE_PROJECTIONS]
+
+                # ControlSignal
+                if isinstance(spec, ControlSignal):
+                    control_signal_spec = spec
 
-        # If ControlProjections were specified, implement them
+                else:
+                    # Mechanism
+                    # IMPLEMENTATION NOTE: Mechanism was placed in list in STATE_PROJECTIONS entry by _parse_state_spec
+                    if isinstance(spec, list) and isinstance(spec[0], Mechanism):
+                        mech = spec[0]
+                        parameter_state = _get_parameter_state(self, CONTROL_SIGNAL, param_name, mech)
+
+                    # Projection (in a list)
+                    elif isinstance(spec, list):
+                        control_projection = spec[0]
+                        if not isinstance(control_projection, ControlProjection):
+                            raise ControlMechanismError("PROGRAM ERROR: list in {} entry of params dict for {} of {} "
+                                                        "must contain a single ControlProjection".
+                                                        format(CONTROL_SIGNAL_SPECS, CONTROL_SIGNAL, self.name))
+                        if len(spec)>1:
+                            raise ControlMechanismError("PROGRAM ERROR: Multiple ControlProjections are not "
+                                                        "currently supported in specification of a ControlSignal")
+                        # Get receiver mech
+                        if control_projection.value is DEFERRED_INITIALIZATION:
+                            parameter_state = control_projection.init_args[RECEIVER]
+                            # ControlProjection was created in response to specification of ControlSignal
+                            #     (in a 2-item tuple where the parameter was specified),
+                            #     so get ControlSignal spec
+                            if SENDER in control_projection.init_args:
+                                control_signal_spec = control_projection.init_args[SENDER]
+                                if control_signal_spec and not isinstance(control_signal_spec, ControlSignal):
+                                    raise ControlMechanismError("PROGRAM ERROR: "
+                                                                "Sender of {} for {} {} of {} is not a {}".
+                                                                format(CONTROL_PROJECTION,
+                                                                       parameter_state.name,
+                                                                       PARAMETER_STATE,
+                                                                       parameter_state.owner.name,
+                                                                       CONTROL_SIGNAL))
+                        else:
+                            parameter_state = control_projection.receiver
+                        param_name = parameter_state.name
+
+                    else:
+                        raise ControlMechanismError("PROGRAM ERROR: failure to parse specification of {} for {}".
+                                                    format(CONTROL_SIGNAL, self.name))
+            else:
+                raise ControlMechanismError("PROGRAM ERROR: No entry found in params dict with specification of "
+                                            "parameter Mechanism or ControlProjection for {} of {}".
+                                            format(CONTROL_SIGNAL, self.name))
+
+
+        default_name = param_name + '_' + ControlSignal.__name__
+
+        # Get constraint for ControlSignal value
+        #    - get ControlMechanism's value
+        self._update_value(context=context)
+        # - get OutputState's index
         try:
-            if self.paramsCurrent[CONTROL_PROJECTIONS]:
-                for key, projection in self.paramsCurrent[CONTROL_PROJECTIONS].items():
-                    self._instantiate_control_projection(projection, context=self.name)
-        except:
-            pass
+            output_state_index = len(self.output_states)
+        except (AttributeError, TypeError):
+            output_state_index = 0
+        # - get constraint for OutputState's value
+        output_state_constraint_value = self.allocation_policy[output_state_index]
+
+        # Specification is a ControlSignal (either passed in directly, or parsed from tuple above)
+        if isinstance(control_signal_spec, ControlSignal):
+            # Deferred Initialization, so assign owner, name, and initialize
+            if control_signal_spec.value is DEFERRED_INITIALIZATION:
+                control_signal_spec.init_args[OWNER] = self
+                control_signal_spec.init_args[NAME] = control_signal_spec.init_args[NAME] or default_name
+                # control_signal_spec.init_args[REFERENCE_VALUE] = output_state_constraint_value
+                control_signal_spec.init_args[REFERENCE_VALUE] = defaultControlAllocation
+                control_signal_spec._deferred_init(context=context)
+                control_signal = control_signal_spec
+            elif not control_signal_spec.owner is self:
+                raise ControlMechanismError("Attempt to assign ControlSignal to {} ({}) that is already owned by {}".
+                                            format(self.name, control_signal_spec.name, control_signal_spec.owner.name))
+            else:
+                control_signal = control_signal_spec
+                control_signal_name = control_signal_spec.name
+                control_projections = control_signal_spec.efferents
+
+                # IMPLEMENTATION NOTE:
+                #    THIS IS TO HANDLE FUTURE POSSIBILITY OF MULTIPLE ControlProjections FROM A SINGLE ControlSignal;
+                #    FOR NOW, HOWEVER, ONLY A SINGLE ONE IS SUPPORTED
+                # parameter_states = [proj.recvr for proj in control_projections]
+                if len(control_projections) > 1:
+                    raise ControlMechanismError("PROGRAM ERROR: list of ControlProjections is not currently supported "
+                                                "as specification in a ControlSignal")
+                else:
+                    control_projection = control_projections[0]
+                    parameter_state = control_projection.receiver
 
-    def _take_over_as_default_controller(self, context=None):
+        # Instantiate OutputState for ControlSignal
+        else:
+            control_signal_name = default_name
+
+            from PsyNeuLink.Components.States.ModulatorySignals.ControlSignal import ControlSignal
+            from PsyNeuLink.Components.States.State import _instantiate_state
+
+            control_signal_params.update({CONTROLLED_PARAM:param_name})
+
+            # FIX 5/23/17: CALL super()_instantiate_output_states ??
+            # FIX:         OR AGGREGATE ALL ControlSignals AND SEND AS LIST (AS FOR input_states IN ObjectiveMechanism)
+            control_signal = _instantiate_state(owner=self,
+                                                state_type=ControlSignal,
+                                                state_name=control_signal_name,
+                                                state_spec=defaultControlAllocation,
+                                                state_params=control_signal_params,
+                                                constraint_value=output_state_constraint_value,
+                                                constraint_value_name='Default control allocation',
+                                                context=context)
+
+        # VALIDATE OR INSTANTIATE ControlProjection(s) TO ControlSignal  -------------------------------------------
+
+        # Validate control_projection (if specified) and get receiver's name
+        if control_projection:
+            _validate_receiver(self, control_projection, Mechanism, CONTROL_SIGNAL, context=context)
+
+            from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
+            if not isinstance(control_projection, ControlProjection):
+                raise ControlMechanismError("PROGRAM ERROR: Attempt to assign {}, "
+                                                  "that is not a ControlProjection, to ControlSignal of {}".
+                                                  format(control_projection, self.name))
+            if control_projection.value is DEFERRED_INITIALIZATION:
+                control_projection.init_args['sender']=control_signal
+                if control_projection.init_args['name'] is None:
+                    # FIX 5/23/17: CLEAN UP NAME STUFF BELOW:
+                    control_projection.init_args['name'] = CONTROL_PROJECTION + \
+                                                   ' for ' + parameter_state.owner.name + ' ' + parameter_state.name
+                control_projection._deferred_init()
+            else:
+                control_projection.sender = control_signal
+
+        # Instantiate ControlProjection
+        else:
+            # IMPLEMENTATION NOTE:  THIS SHOULD BE MOVED TO COMPOSITION ONCE THAT IS IMPLEMENTED
+            from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
+            control_projection = ControlProjection(sender=control_signal,
+                                                   receiver=parameter_state,
+                                                   name=CONTROL_PROJECTION + control_signal_name)
 
-        from PsyNeuLink.Components import DefaultController
+        # Add ControlProjection to list of outputState's outgoing projections
+        # (note: if it was deferred, it just added itself, skip)
+        if not control_projection in control_signal.efferents:
+            control_signal.efferents.append(control_projection)
 
-        # Iterate through old controller's outputStates
-        to_be_deleted_outputStates = []
+        # Add ControlProjection to ControlMechanism's list of ControlProjections
+        try:
+            self.control_projections.append(control_projection)
+        except AttributeError:
+            self.control_projections = [control_projection]
 
+        # Update control_signal_costs to accommodate instantiated projection
         try:
-            DefaultController.outputStates
+            self.control_signal_costs = np.append(self.control_signal_costs, np.empty((1,1)),axis=0)
         except AttributeError:
-            if self.system.verbosePref:
-                warnings.warn("No ControlProjections specified for {};  control will be disabled".
-                              format(self.system.name))
-            self.system.enable_controller = False
-            return
-
-        for outputState in DefaultController.outputStates:
-
-            # Iterate through projections sent for outputState
-            for projection in DefaultController.outputStates[outputState].sendsToProjections:
-
-                # Move ControlProjection to self (by creating new outputState)
-                # IMPLEMENTATION NOTE: Method 1 -- Move old ControlProjection to self
-                #    Easier to implement
-                #    - call _instantiate_control_projection directly here (which takes projection as arg)
-                #        instead of instantiating a new ControlProjection (more efficient, keeps any settings);
-                #    - however, this bypasses call to Projection._instantiate_sender()
-                #        which calls Mechanism.sendsToProjections.append(),
-                #        so need to do that in _instantiate_control_projection
-                #    - this is OK, as it is case of a Mechanism managing its *own* projections list (vs. "outsider")
-                params = projection.control_signal
-                self._instantiate_control_projection(projection, params=params, context=context)
-
-                # # IMPLEMENTATION NOTE: Method 2 - Instantiate new ControlProjection
-                # #    Cleaner, but less efficient and ?? may lose original params/settings for ControlProjection
-                # # TBI: Implement and then use Mechanism.add_project_from_mechanism()
-                # self._add_projection_from_mechanism(projection, new_output_state, context=context)
-
-                # Remove corresponding projection from old controller
-                DefaultController.outputStates[outputState].sendsToProjections.remove(projection)
-
-            # Current controller's outputState has no projections left (after removal(s) above)
-            if not DefaultController.outputStates[outputState].sendsToProjections:
-                # If this is the old controller's primary outputState, set it to None
-                if DefaultController.outputState is DefaultController.outputStates[outputState]:
-                    DefaultController.outputState = None
-                # Delete outputState from old controller's outputState dict
-                to_be_deleted_outputStates.append(DefaultController.outputStates[outputState])
-        for item in to_be_deleted_outputStates:
-            del DefaultController.outputStates[item.name]
-
-
-    def _instantiate_control_projection(self, projection, params=None, context=None):
-        """Add outputState and assign as sender to requesting ControlProjection
-
-        # Updates allocation_policy and controlSignalCosts attributes to accommodate instantiated projection
-
-        Note:  params are expected to be params for controlSignal (outputState of ControlMechanism)
-
-        Assume that:
-            # - self.value is populated (in _update_value) with an array of allocations from self.allocation_policy;
-            - self.allocation_policy has already been extended to include the particular (indexed) allocation
-                to be used for the outputState being created here.
-
-        INCREMENT BASED ON TOTAL NUMBER OF OUTPUTSTATES SO FAR
-
-        Returns state: (OutputState)
-        """
+            self.control_signal_costs = np.empty((1,1))
 
-        self._validate_projection(projection)
+        # UPDATE output_states AND control_projections -------------------------------------------------------------
 
-        from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection
-        if not isinstance(projection, ControlProjection):
-            raise ControlMechanismError("PROGRAM ERROR: Attempt to assign {0}, "
-                                              "that is not a ControlProjection, to outputState of {1}".
-                                              format(projection, self.name))
+        try:
+            self.output_states[control_signal.name] = control_signal
+        except (AttributeError, TypeError):
+            from PsyNeuLink.Components.States.State import State_Base
+            self.output_states = ContentAddressableList(component_type=State_Base,
+                                                        list=[control_signal],
+                                                        name = self.name+'.output_states')
 
+        # Add index assignment to outputState
+        control_signal.index = output_state_index
 
-        #  Update self.value by evaluating function
-        self._update_value(context=context)
+        # (Re-)assign control_signals attribute to output_states
+        self.control_signals = self.output_states
 
-        # Instantiate new outputState and assign as sender of ControlProjection
-        try:
-            output_state_index = len(self.outputStates)
-        except AttributeError:
-            output_state_index = 0
-        from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.ControlSignal import ControlSignal
-        output_state_name = projection.receiver.name + '_' + ControlSignal.__name__
-        output_state_value = self.allocation_policy[output_state_index]
-        from PsyNeuLink.Components.States.State import _instantiate_state
-        from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.ControlSignal import ControlSignal
-        state = _instantiate_state(owner=self,
-                                            state_type=ControlSignal,
-                                            state_name=output_state_name,
-                                            state_spec=defaultControlAllocation,
-                                            state_params=params,
-                                            constraint_value=output_state_value,
-                                            constraint_value_name='Default control allocation',
-                                            # constraint_output_state_index=output_item_output_state_index,
-                                            context=context)
-
-
-        # Assign outputState as ControlProjection's sender
-        projection.sender = state
-
-        # Update self.outputState and self.outputStates
-        try:
-            self.outputStates[state.name] = state
-        except AttributeError:
-            self.outputStates = OrderedDict({output_state_name:state})
-            self.outputState = self.outputStates[output_state_name]
+        return control_signal
 
-        # Add index assignment to outputState
-        state.index = output_state_index
+    def _instantiate_attributes_after_function(self, context=None):
+        """Implment ControlSignals specified in control_signals arg or "locally" in parameter specification(s)
 
-        # Add ControlProjection to list of outputState's outgoing projections
-        state.sendsToProjections.append(projection)
+        Calls super's instantiate_attributes_after_function, which calls _instantiate_output_states;
+            that insures that any ControlSignals specified in control_signals arg are instantiated first
+        Then calls _assign_as_controller to instantiate any ControlProjections/ControlSignals specified
+            along with parameter specification(s) (i.e., as part of a (<param value>, ControlProjection) tuple
+        """
 
-        # Add ControlProjection to ControlMechanism's list of ControlProjections
-        try:
-            self.controlProjections.append(projection)
-        except AttributeError:
-            self.controlProjections = []
+        super()._instantiate_attributes_after_function(context=context)
 
-        # Update controlSignalCosts to accommodate instantiated projection
-        try:
-            self.controlSignalCosts = np.append(self.controlSignalCosts, np.empty((1,1)),axis=0)
-        except AttributeError:
-            self.controlSignalCosts = np.empty((1,1))
+        if MAKE_DEFAULT_CONTROLLER in self.paramsCurrent:
+            if self.paramsCurrent[MAKE_DEFAULT_CONTROLLER]:
+                self._assign_as_controller(context=context)
+            if not self.system.enable_controller:
+                return
 
-        return state
+    def _assign_as_controller(self, context=None):
+
+        # Check the parameterStates of the system's mechanisms for any ControlProjections with deferred_init()
+        # Note: this includes any ControlProjections created where a ControlSignal rather than a ControlProjection
+        #       was used to specify control for a parameter (e.g., in a 2-item tuple specification for the parameter);
+        #       the initialization of the ControlProjection and, if specified, the ControlSignal
+        #       are completed in the call to _instantiate_control_signal() below
+        for mech in self.system.mechanisms:
+            for parameter_state in mech._parameter_states:
+                for projection in parameter_state.mod_afferents:
+                    # If projection was deferred for init, instantiate its ControlSignal and then initialize it
+                    if projection.value is DEFERRED_INITIALIZATION:
+                        control_signal_specs = projection.control_signal_params or {}
+                        control_signal_specs.update({CONTROL_SIGNAL_SPECS: [projection]})
+                        self._instantiate_control_signal(control_signal_specs, context=context)
 
     def _execute(self,
                     variable=None,
@@ -451,7 +771,7 @@ def _execute(self,
                     clock=CentralClock,
                     time_scale=TimeScale.TRIAL,
                     context=None):
-        """Updates ControlProjections based on inputs
+        """Updates ControlSignals based on inputs
 
         Must be overriden by subclass
         """
@@ -463,32 +783,23 @@ def show(self):
 
         print ("\n{0}".format(self.name))
         print("\n\tMonitoring the following mechanism outputStates:")
-        for state_name, state in list(self.monitoring_mechanism.inputStates.items()):
-            for projection in state.receivesFromProjections:
+        for state in self.monitoring_mechanism.input_states:
+            for projection in state.path_afferents:
                 monitored_state = projection.sender
                 monitored_state_mech = projection.sender.owner
                 monitored_state_index = self.monitored_output_states.index(monitored_state)
 
-                # # MODIFIED 1/9/16 OLD:
-                # exponent = \
-                #     np.ndarray.item(self.paramsCurrent[OUTCOME_FUNCTION].__self__.exponents[
-                #     monitored_state_index])
-                # weight = \
-                #     np.ndarray.item(self.paramsCurrent[OUTCOME_FUNCTION].__self__.weights[monitored_state_index])
-
-                # MODIFIED 1/9/16 NEW:
                 weight = self.monitor_for_control_weights_and_exponents[monitored_state_index][0]
                 exponent = self.monitor_for_control_weights_and_exponents[monitored_state_index][1]
-                # MODIFIED 1/9/16 END
 
                 print ("\t\t{0}: {1} (exp: {2}; wt: {3})".
                        format(monitored_state_mech.name, monitored_state.name, weight, exponent))
 
         print ("\n\tControlling the following mechanism parameters:".format(self.name))
         # Sort for consistency of output:
-        state_names_sorted = sorted(self.outputStates.keys())
+        state_names_sorted = sorted(self.output_states.names)
         for state_name in state_names_sorted:
-            for projection in self.outputStates[state_name].sendsToProjections:
+            for projection in self.output_states[state_name].efferents:
                 print ("\t\t{0}: {1}".format(projection.receiver.owner.name, projection.receiver.name))
 
-        print ("\n---------------------------------------------------------")
+        print ("\n---------------------------------------------------------")
\ No newline at end of file
diff --git a/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/ControlMechanisms/DefaultControlMechanism.py b/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/ControlMechanisms/DefaultControlMechanism.py
index 9b3618637ea..9c509e1aa76 100644
--- a/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/ControlMechanisms/DefaultControlMechanism.py
+++ b/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/ControlMechanisms/DefaultControlMechanism.py
@@ -10,15 +10,12 @@
 
 """
 
-The DefaultControlMechanism is created whenever PsyNeuLink is run.  It is assigned as the ControlMechanism for any
-:doc:`System` that is created for which no ControlMechanism is specified.  The DefaultControlMechanism creates
-an inputState for each ControlProjection it is assigned, and uses :py:data:`defaultControlAllocation` as the value for
-the control signal.  By default,  :py:data:`defaultControlAllocation` = 1, so that ControlProjections from the
-DefaultControlMechanism have no effect on their parameters.  Thus, for the most part, the DefaultControlMechanism
-serves as a place marker for ControlProjections (having on effect on the parameters it "controls"), until the system
-is assigned a more useful ControlMechanism (such as the :doc:`EVCMechanism`).  However, it can be used to uniformly
-control the parameters that receive ControlProjections from it, by manually changing the value of
-:any:`defaultControlAllocation`.  See :doc:`ControlMechanism` for additional details of how ControlMechanisms are
+The DefaultControlMechanism is created for a `System` if no other controller type is specified. The 
+DefaultControlMechanism createsan inputState for each ControlProjection it is assigned, and uses 
+`defaultControlAllocation` as the value for the control signal.  By default,  :py:data:`defaultControlAllocation` =  1, 
+so that ControlProjections from the DefaultControlMechanism have no effect on their parameters.  However, it can be 
+used to uniformly control the parameters that receive ControlProjections from it, by manually changing the value of
+`defaultControlAllocation`.  See :doc:`ControlMechanism` for additional details of how ControlMechanisms are
 created, executed and their attributes.
 
 COMMENT:
@@ -78,44 +75,36 @@ class DefaultControlMechanism(ControlMechanism_Base):
 
 
     # variableClassDefault = defaultControlAllocation
-    # This must be a list, as there may be more than one (e.g., one per controlSignal)
+    # This must be a list, as there may be more than one (e.g., one per control_signal)
     variableClassDefault = defaultControlAllocation
 
     from PsyNeuLink.Components.Functions.Function import Linear
     paramClassDefaults = ControlMechanism_Base.paramClassDefaults.copy()
-    paramClassDefaults.update({SYSTEM: None,
-                               # MAKE_DEFAULT_CONTROLLER:True  <- No need, it is the default by default
+    paramClassDefaults.update({# MAKE_DEFAULT_CONTROLLER:True  <- No need, it is the default by default
                                FUNCTION:Linear,
                                FUNCTION_PARAMS:{SLOPE:1, INTERCEPT:0},
-                               MONITOR_FOR_CONTROL:None
+                               MONITOR_FOR_CONTROL:None,
+                               MODULATION:None,
                                })
 
     from PsyNeuLink.Components.Functions.Function import Linear
     @tc.typecheck
     def __init__(self,
-                 default_input_value=None,
+                 # default_input_value=None,
+                 system=None,
+                 monitor_for_control:tc.optional(list)=None,
+                 control_signals:tc.optional(list)=None,
                  params=None,
                  name=None,
                  prefs:is_pref_set=None):
 
-
-        super(DefaultControlMechanism, self).__init__(default_input_value =default_input_value,
-                                                         params=params,
-                                                         name=name,
-                                                         prefs=prefs,
-                                                         context=self)
-
-    def _validate_projection(self, projection, context=None):
-        """Override super method
-
-        DefaultControlMechanism may not have a system assigned, in which case super's method will crash,
-        since it checks whether projection is to a mechanism with the same system as self
-
-        IMPLEMENTATION NOTE:
-        This can be reomved if/when ControlMechanism uses deferred initialization for ControlProjection
-
-        """
-        pass
+        super(DefaultControlMechanism, self).__init__(#default_input_value =default_input_value,
+                                                    monitor_for_control=monitor_for_control,
+                                                    control_signals=control_signals,
+                                                    params=params,
+                                                    name=name,
+                                                    prefs=prefs,
+                                                    context=self)
 
     def _execute(self,
                     variable=None,
@@ -124,51 +113,56 @@ def _execute(self,
                     time_scale=TimeScale.TRIAL,
                     context=None):
 
-        return self.inputValue or [defaultControlAllocation]
+        return self.input_values or [defaultControlAllocation]
 
     def _instantiate_input_states(self, context=None):
-        """Instantiate inputValue attribute
+        """Instantiate input_value attribute
 
-        Instantiate inputValue, inputState and inputStates attributes (in case they are referenced).
-        Otherwise, no need to do anything, as DefaultControllerMechanism only adds inputStates
+        Instantiate input_value, inputState and input_states attributes (in case they are referenced).
+        Otherwise, no need to do anything, as DefaultControllerMechanism only adds input_states
         when a ControlProjection is instantiated, and uses _instantiate_control_mechanism_input_state to do so.
 
         """
 
-        try:
-            self.inputStates
-        except AttributeError:
-            self.inputValue = None
-            self.inputStates = None
-        else:
-            pass
+        if not hasattr(self, INPUT_STATES):
+            self._input_states = None
+        # if self.input_states is None:
+        #     self.input_value = None
 
-    def _instantiate_control_projection(self, projection, params=None, context=None):
+    def _instantiate_control_signal(self, control_signal, context=None):
         """Instantiate requested controlProjection and associated inputState
         """
 
-        # Instantiate inputStates and allocation_policy attribute for controlSignal allocations
-        input_name = 'DefaultControlAllocation for ' + projection.receiver.name + '_ControlSignal'
+        if isinstance(control_signal, dict):
+            if CONTROL in control_signal:
+                projection = control_signal[CONTROL][0]
+                input_name = 'DefaultControlAllocation for ' + projection.receiver.name + '_ControlSignal'
+            elif NAME in control_signal:
+                input_name = 'DefaultControlAllocation for ' + control_signal[NAME] + '_ControlSignal'
+
+        elif isinstance(control_signal, tuple):
+            input_name = 'DefaultControlAllocation for ' + control_signal[0] + '_ControlSignal'
+
+        # Instantiate input_states and allocation_policy attribute for control_signal allocations
         self._instantiate_default_input_state(input_name, defaultControlAllocation, context=context)
-        self.allocation_policy = self.inputValue
+        self.allocation_policy = self.input_values
 
-        # Call super to instantiate outputStates
-        # Note: params carries any specified with ControlProjection for the control_signal
-        super()._instantiate_control_projection(projection=projection,
-                                                params=params,
-                                                context=context)
+        # Call super to instantiate ControlSignal
+        # Note: any params specified with ControlProjection for the control_signal
+        #           should be in PARAMS entry of dict passed in control_signal arg
+        super()._instantiate_control_signal(control_signal=control_signal, context=context)
 
     def _instantiate_default_input_state(self, input_state_name, input_state_value, context=None):
         """Instantiate inputState for ControlMechanism
 
         NOTE: This parallels ObjectMechanism._instantiate_input_state_for_monitored_state()
               It is implemented here to spare having to instantiate a "dummy" (and superfluous) ObjectiveMechanism
-              for the sole purpose of creating inputStates for each value of defaultControlAllocation to assign
+              for the sole purpose of creating input_states for each value of defaultControlAllocation to assign
               to the ControlProjections.
 
         Extend self.variable by one item to accommodate new inputState
         Instantiate the inputState using input_state_name and input_state_value
-        Update self.inputState and self.inputStates
+        Update self.input_state and self.input_states
 
         Args:
             input_state_name (str):
@@ -188,22 +182,22 @@ def _instantiate_default_input_state(self, input_state_name, input_state_value,
 
         # If there is a single item in self.variable, it could be the one assigned on initialization
         #     (in order to validate ``function`` and get its return value as a template for self.value);
-        #     in that case, there should be no inputStates yet, so pass
+        #     in that case, there should be no input_states yet, so pass
         #     (i.e., don't bother to extend self.variable): it will be used for the new inputState
         elif len(self.variable) == 1:
-            if self.inputStates:
+            if self.input_states:
                 self.variable = np.append(self.variable, np.atleast_2d(input_state_value), 0)
             else:
-                # If there are no inputStates, this is the usual initialization condition;
+                # If there are no input_states, this is the usual initialization condition;
                 # Pass to create a new inputState that will be assigned to existing the first item of self.variable
                 pass
         # Other than on initialization (handled above), it is a PROGRAM ERROR if
-        #    the number of inputStates is not equal to the number of items in self.variable
-        elif len(self.variable) != len(self.inputStates):
-            raise ControlMechanismError("PROGRAM ERROR:  The number of inputStates ({}) does not match "
+        #    the number of input_states is not equal to the number of items in self.variable
+        elif len(self.variable) != len(self.input_states):
+            raise ControlMechanismError("PROGRAM ERROR:  The number of input_states ({}) does not match "
                                         "the number of items found for the variable attribute ({}) of {}"
                                         "when creating {}".
-                                        format(len(self.inputStates),
+                                        format(len(self.input_states),
                                                len(self.variable),
                                                self.name,input_state_name))
 
@@ -225,13 +219,15 @@ def _instantiate_default_input_state(self, input_state_name, input_state_value,
                                          constraint_value_name='Default control allocation',
                                          context=context)
 
-        #  Update inputState and inputStates
-        if self.inputStates:
-            self.inputStates[input_state.name] = input_state
+        #  Update inputState and input_states
+        if self.input_states:
+            self._input_states[input_state.name] = input_state
         else:
-            self.inputStates = OrderedDict({input_state_name:input_state})
-            self.inputState = list(self.inputStates.values())[0]
+            from PsyNeuLink.Components.States.State import State_Base
+            self._input_states = ContentAddressableList(component_type=State_Base,
+                                                        list=[input_state],
+                                                        name=self.name+'.input_states')
 
-        self.inputValue = list(state.value for state in self.inputStates.values())
+        # self.input_value = [state.value for state in self.input_states]
 
         return input_state
diff --git a/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/ControlMechanisms/EVCAuxiliary.py b/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/ControlMechanisms/EVCAuxiliary.py
index e887f1250d6..9ca8f272d6f 100644
--- a/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/ControlMechanisms/EVCAuxiliary.py
+++ b/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/ControlMechanisms/EVCAuxiliary.py
@@ -25,7 +25,7 @@
 kwEVCAuxFunction = "EVC AUXILIARY FUNCTION"
 kwEVCAuxFunctionType = "EVC AUXILIARY FUNCTION TYPE"
 kwValueFunction = "EVC VALUE FUNCTION"
-kwControlSignalGridSearchFunction = "EVC CONTROL SIGNAL GRID SEARCH FUNCTION"
+CONTROL_SIGNAL_GRID_SEARCH_FUNCTION = "EVC CONTROL SIGNAL GRID SEARCH FUNCTION"
 CONTROLLER = 'controller'
 OUTCOME = 'outcome'
 COSTS = 'costs'
@@ -47,7 +47,7 @@ class EVCAuxiliaryFunction(Function_Base):
     variableClassDefault = None
     paramClassDefaults = Function_Base.paramClassDefaults.copy()
     paramClassDefaults.update({
-                               kwFunctionOutputTypeConversion: False,
+                               FUNCTION_OUTPUT_TYPE_CONVERSION: False,
                                PARAMETER_STATE_PARAMS: None})
 
     # MODIFIED 11/29/16 NEW:
@@ -123,7 +123,7 @@ def function(self, **kwargs):
 
 class ControlSignalGridSearch(EVCAuxiliaryFunction):
 
-    componentName = kwControlSignalGridSearchFunction
+    componentName = CONTROL_SIGNAL_GRID_SEARCH_FUNCTION
 
     def __init__(self,
                  variable_default=None,
@@ -137,18 +137,18 @@ def __init__(self,
                          context=self.componentName+INITIALIZING)
 
     def function(self, **kwargs):
-        """Grid search combinations of controlSignals in specified allocation ranges to find one that maximizes EVC
+        """Grid search combinations of control_signals in specified allocation ranges to find one that maximizes EVC
 
         Description
         -----------
             * Called by ControlSignalGridSearch.
-            * Call system.execute for each `allocation_policy` in `controlSignalSearchSpace`.
-            * Store an array of values for outputStates in `monitored_output_states` (i.e., the inputStates in `inputStates`)
+            * Call system.execute for each `allocation_policy` in `control_signal_search_space`.
+            * Store an array of values for outputStates in `monitored_output_states` (i.e., the input_states in `input_states`)
                 for each `allocation_policy`.
             * Call `_compute_EVC` for each allocation_policy to calculate the EVC, identify the  maximum,
                 and assign to `EVC_max`.
             * Set `EVC_max_policy` to the `allocation_policy` (outputState.values) corresponding to EVC_max.
-            * Set value for each controlSignal (outputState.value) to the values in `EVC_max_policy`.
+            * Set value for each control_signal (outputState.value) to the values in `EVC_max_policy`.
             * Return an allocation_policy.
 
             Note:
@@ -156,7 +156,7 @@ def function(self, **kwargs):
               it is NOT used for system.execute -- that uses the runtime_params provided for the Mechanisms in each
                 Process.configuration
 
-            Return (2D np.array): value of outputState for each monitored state (in self.inputStates) for EVC_max
+            Return (2D np.array): value of outputState for each monitored state (in self.input_states) for EVC_max
 
         """
 
@@ -203,17 +203,17 @@ def function(self, **kwargs):
         # Print progress bar
         if controller.prefs.reportOutputPref:
             progress_bar_rate_str = ""
-            search_space_size = len(controller.controlSignalSearchSpace)
+            search_space_size = len(controller.control_signal_search_space)
             progress_bar_rate = int(10 ** (np.log10(search_space_size)-2))
             if progress_bar_rate > 1:
                 progress_bar_rate_str = str(progress_bar_rate) + " "
             print("\n{0} evaluating EVC for {1} (one dot for each {2}of {3} samples): ".
                   format(controller.name, controller.system.name, progress_bar_rate_str, search_space_size))
 
-        # Evaluate all combinations of controlSignals (policies)
+        # Evaluate all combinations of control_signals (policies)
         sample = 0
         controller.EVC_max_state_values = controller.variable.copy()
-        controller.EVC_max_policy = controller.controlSignalSearchSpace[0] * 0.0
+        controller.EVC_max_policy = controller.control_signal_search_space[0] * 0.0
 
         # Parallelize using multiprocessing.Pool
         # NOTE:  currently fails on attempt to pickle lambda functions
@@ -221,7 +221,7 @@ def function(self, **kwargs):
         if PY_MULTIPROCESSING:
             EVC_pool = Pool()
             results = EVC_pool.map(_compute_EVC, [(controller, arg, runtime_params, time_scale, context)
-                                                 for arg in controller.controlSignalSearchSpace])
+                                                 for arg in controller.control_signal_search_space])
 
         else:
 
@@ -231,24 +231,24 @@ def function(self, **kwargs):
                 rank = Comm.Get_rank()
                 size = Comm.Get_size()
 
-                chunk_size = (len(controller.controlSignalSearchSpace) + (size-1)) // size
+                chunk_size = (len(controller.control_signal_search_space) + (size-1)) // size
                 print("Rank: {}\nChunk size: {}".format(rank, chunk_size))
                 start = chunk_size * rank
                 end = chunk_size * (rank+1)
-                if start > len(controller.controlSignalSearchSpace):
-                    start = len(controller.controlSignalSearchSpace)
-                if end > len(controller.controlSignalSearchSpace):
-                    end = len(controller.controlSignalSearchSpace)
+                if start > len(controller.control_signal_search_space):
+                    start = len(controller.control_signal_search_space)
+                if end > len(controller.control_signal_search_space):
+                    end = len(controller.control_signal_search_space)
             else:
                 start = 0
-                end = len(controller.controlSignalSearchSpace)
+                end = len(controller.control_signal_search_space)
 
             if MPI_IMPLEMENTATION:
                 print("START: {0}\nEND: {1}".format(start,end))
 
             #region EVALUATE EVC
 
-            # Compute EVC for each allocation policy in controlSignalSearchSpace
+            # Compute EVC for each allocation policy in control_signal_search_space
             # Notes on MPI:
             # * breaks up search into chunks of size chunk_size for each process (rank)
             # * each process computes max for its chunk and returns
@@ -256,8 +256,8 @@ def function(self, **kwargs):
 
             result = None
             EVC_max = float('-Infinity')
-            EVC_max_policy = np.empty_like(controller.controlSignalSearchSpace[0])
-            EVC_max_state_values = np.empty_like(controller.inputValue)
+            EVC_max_policy = np.empty_like(controller.control_signal_search_space[0])
+            EVC_max_state_values = np.empty_like(controller.input_values)
             max_value_state_policy_tuple = (EVC_max, EVC_max_state_values, EVC_max_policy)
             # FIX:  INITIALIZE TO FULL LENGTH AND ASSIGN DEFAULT VALUES (MORE EFFICIENT):
             EVC_values = np.array([])
@@ -266,9 +266,9 @@ def function(self, **kwargs):
             # # TEST PRINT:
             # print("\nEVC SIMULATION\n")
 
-            for allocation_vector in controller.controlSignalSearchSpace[start:end,:]:
-            # for iter in range(rank, len(controller.controlSignalSearchSpace), size):
-            #     allocation_vector = controller.controlSignalSearchSpace[iter,:]:
+            for allocation_vector in controller.control_signal_search_space[start:end,:]:
+            # for iter in range(rank, len(controller.control_signal_search_space), size):
+            #     allocation_vector = controller.control_signal_search_space[iter,:]:
 
                 if controller.prefs.reportOutputPref:
                     increment_progress_bar = (progress_bar_rate < 1) or not (sample % progress_bar_rate)
@@ -291,7 +291,7 @@ def function(self, **kwargs):
                     # FIX:  ASSIGN BY INDEX (MORE EFFICIENT)
                     EVC_values = np.append(EVC_values, np.atleast_1d(EVC), axis=0)
                     # Save policy associated with EVC for each process, as order of chunks
-                    #     might not correspond to order of policies in controlSignalSearchSpace
+                    #     might not correspond to order of policies in control_signal_search_space
                     if len(EVC_policies[0])==0:
                         EVC_policies = np.atleast_2d(allocation_vector)
                     else:
@@ -299,14 +299,14 @@ def function(self, **kwargs):
 
                 # If EVC is greater than the previous value:
                 # - store the current set of monitored state value in EVC_max_state_values
-                # - store the current set of controlSignals in EVC_max_policy
+                # - store the current set of control_signals in EVC_max_policy
                 # if EVC_max > EVC:
                 # FIX: PUT ERROR HERE IF EVC AND/OR EVC_MAX ARE EMPTY (E.G., WHEN EXECUTION_ID IS WRONG)
                 if EVC == EVC_max:
                     # Keep track of state values and allocation policy associated with EVC max
-                    # EVC_max_state_values = controller.inputValue.copy()
+                    # EVC_max_state_values = controller.input_value.copy()
                     # EVC_max_policy = allocation_vector.copy()
-                    EVC_max_state_values = controller.inputValue
+                    EVC_max_state_values = controller.input_values
                     EVC_max_policy = allocation_vector
                     max_value_state_policy_tuple = (EVC_max, EVC_max_state_values, EVC_max_policy)
 
@@ -355,32 +355,32 @@ def function(self, **kwargs):
 
         #region ASSIGN CONTROL SIGNAL VALUES
 
-        # Assign allocations to controlSignals for optimal allocation policy:
+        # Assign allocations to control_signals for optimal allocation policy:
         EVC_maxStateValue = iter(controller.EVC_max_state_values)
 
-        # Assign max values for optimal allocation policy to controller.inputStates (for reference only)
-        for i in range(len(controller.inputStates)):
-            controller.inputStates[list(controller.inputStates.keys())[i]].value = np.atleast_1d(next(EVC_maxStateValue))
+        # Assign max values for optimal allocation policy to controller.input_states (for reference only)
+        for i in range(len(controller.input_states)):
+            controller.input_states[controller.input_states.names[i]].value = np.atleast_1d(next(EVC_maxStateValue))
 
 
         # Report EVC max info
         if controller.prefs.reportOutputPref:
             print ("\nMaximum EVC for {0}: {1}".format(controller.system.name, float(controller.EVC_max)))
             print ("ControlProjection allocation(s) for maximum EVC:")
-            for i in range(len(controller.controlSignals)):
-                print("\t{0}: {1}".format(controller.controlSignals[i].name,
+            for i in range(len(controller.control_signals)):
+                print("\t{0}: {1}".format(controller.control_signals[i].name,
                                         controller.EVC_max_policy[i]))
             print()
 
-        #endregion
+        #endregionj
 
         # # TEST PRINT:
         # print ("\nEND OF TRIAL 1 EVC outputState: {0}\n".format(controller.outputState.value))
 
         #region ASSIGN AND RETURN allocation_policy
-        # Convert EVC_max_policy into 2d array with one controlSignal allocation per item,
+        # Convert EVC_max_policy into 2d array with one control_signal allocation per item,
         #     assign to controller.allocation_policy, and return (where it will be assigned to controller.value).
-        #     (note:  the conversion is to be consistent with use of controller.value for assignments to controlSignals.value)
+        #     (note:  the conversion is to be consistent with use of controller.value for assignments to control_signals.value)
         controller.allocation_policy = np.array(controller.EVC_max_policy).reshape(len(controller.EVC_max_policy), -1)
         return controller.allocation_policy
         #endregion
@@ -416,8 +416,12 @@ def _compute_EVC(args):
                         context=context)
 
     EVC_current = ctlr.paramsCurrent[VALUE_FUNCTION].function(controller=ctlr,
-                                                              outcome=ctlr.inputValue,
-                                                              costs=ctlr.controlSignalCosts,
+                                                              # MODIFIED 5/7/17 OLD:
+                                                              # outcome=ctlr.input_values,
+                                                              # MODIFIED 5/7/17 NEW:
+                                                              outcome=ctlr.variable,
+                                                              # MODIFIED 5/7/17 END
+                                                              costs=ctlr.control_signal_costs,
                                                               context=context)
 
 
diff --git a/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/ControlMechanisms/EVCMechanism.py b/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/ControlMechanisms/EVCMechanism.py
index 66278bace9d..6f96ba91821 100644
--- a/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/ControlMechanisms/EVCMechanism.py
+++ b/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/ControlMechanisms/EVCMechanism.py
@@ -58,7 +58,7 @@
 `parameterStates <ParameterState>` for the parameters of the mechanisms and/or functions controlled by that
 ControlSignal.  In addition, a set of prediction mechanisms  are created that are used to keep a running average of
 inputs to the system over the course of multiple executions.   These averages are used to generate input to the
-system when the EVCMechanism simulates its execution. Each of these specialized components is described in the 
+system when the EVCMechanism simulates its execution. Each of these specialized components is described in the
 sections that follow.
 
 .. _EVCMechanism_Structure:
@@ -90,7 +90,7 @@
 The `function <EVCMechanism.function>` of an EVCMechanism returns an `allocation_policy` -- that is, the `intensity` of
 each of its `ControlSignals <ControlSignal>` -- that will be used in the next round of the system's execution.  Any
 function can be used that returns an appropriate value (i.e., that specifies an `allocation_policy` for the exact
-number of ControlSignals in the EVCMechanism's `controlSignals` attribute, using the correct format for the `allocation`
+number of ControlSignals in the EVCMechanism's `control_signals` attribute, using the correct format for the `allocation`
 value of each ControlSignal). The default function is `ControlSignalGridSearch`, which evaluates the performance of the
 system under a range of specified allocationPolicies, and returns the `allocation_policy` that generates the best
 performance (the greatest EVC). This evaluation and selection procedure, including the four evaluation functions that it
@@ -105,9 +105,9 @@
 grid search over every possible `allocation_policy`.  The set of allocationPolicies sampled is determined by the
 `allocation_samples` attribute of each `ControlSignal`. Each policy is constructed by drawing one value from the
 `allocation_samples` attribute of each of the EVCMechanism's ControlSignals.  An `allocation_policy` is constructed
-for every possible combination of values, and stored in the EVCMechanism's `controlSignalSearchSpace` attribute.  The
+for every possible combination of values, and stored in the EVCMechanism's `control_signal_search_space` attribute.  The
 EVCMechanism's `run_simulation` method is then used to simulate the system under each `allocation_policy` in
-`controlSignalSearchSpace`, calculate the EVC for each of those policies, and return the policy with the greatest EVC.
+`control_signal_search_space`, calculate the EVC for each of those policies, and return the policy with the greatest EVC.
 By default, only the maximum EVC is saved and returned.  However, by setting the `save_all_values_and_policies`
 attribute to `True`, each policy and its EVC can be saved for each simulation run (in `EVC_policies` and `EVC_values`,
 respectively). The EVC is calculated for each policy using the following four functions, each of which can be
@@ -116,11 +116,11 @@
 below):
 
 COMMENT:
-  [TBI:]  The ``controlSignalSearchSpace`` described above is constructed by default.  However, this can be customized
-          by assigning either a 2d array or a function that returns a 2d array to the ``controlSignalSearchSpace``
+  [TBI:]  The ``control_signal_search_space`` described above is constructed by default.  However, this can be customized
+          by assigning either a 2d array or a function that returns a 2d array to the ``control_signal_search_space``
           attribute.  The first dimension (or axis 0) of the 2d array must be an array of control allocation
           policies (of any length), each of which contains a value for each ControlProjection in the
-          EVCMechanism, assigned in the same order they are listed in its ``controlProjections`` attribute.
+          EVCMechanism, assigned in the same order they are listed in its ``control_projections`` attribute.
 COMMENT
 
 .. _EVC_Auxiliary_Functions:
@@ -177,7 +177,7 @@
 each of its ControlSignals, and the value of that outputState is the ControlSignal's `intensity`.  When an EVCMechanism
 is `created automatically <EVCMechanism_Creation>`, it creates a ControlSignal for each parameter that has been
 specified for control in the system (a parameter is specified  for control by assigning it a ControlProjection;
-see `Mechanism_Parameters`).  The ControlSignals of an EVCMechanism are listed in it `controlSignals`
+see `Mechanism_Parameters`).  The ControlSignals of an EVCMechanism are listed in it `control_signals`
 attribute. Each ControlSignal is associated with a `ControlProjection` that projects to the
 `parameterState <ParameterState>` for the parameter controlled by that ControlSignal. The EVCMechanism's
 `function <EVCMechanism.function>` assigns an `allocation` value to each of its ControlSignals. The
@@ -200,8 +200,8 @@
 mechanism in the system, based on a running average of inputs to that mechanism in previous rounds of execution.
 The EVCMechanism uses these estimates to provide input to the system each time it simulates it to evaluate its
 performance.  When an EVCMechanism is `created automatically <EVCMechanism_Creation>`, a prediction mechanism is
-created for each `ORIGIN` mechanism in the system. For each projection received by the `ORIGIN` mechanism,   
-a `MappingProjection` from the same source is created that projects to the prediction mechanism.  The type of 
+created for each `ORIGIN` mechanism in the system. For each projection received by the `ORIGIN` mechanism,
+a `MappingProjection` from the same source is created that projects to the prediction mechanism.  The type of
 mechanism used for the prediction mechanisms can be specified using the EVCMechanism's
 `prediction_mechanism_type` attribute, and their parameters can be specified using the EVCMechanism's
 `prediction_mechanism_params` attribute.  The default type is an 'IntegratorMechanism`, that calculates an
@@ -222,8 +222,8 @@
 .. _EVCMechanism_Default_Function:
 
 The default `function <EVCMechanism.function>` for an EVCMechanism selects an `allocation_policy` by assessing
-the performance of the system under each of the policies in its `controlSignalSearchSpace`, and selecting the
-one that yields the maximum EVC. The `controlSignalSearchSpace` is constructed by creating a set of
+the performance of the system under each of the policies in its `control_signal_search_space`, and selecting the
+one that yields the maximum EVC. The `control_signal_search_space` is constructed by creating a set of
 allocationPolicies that represent all permutations of the `allocation` values to be sampled for each ControlSignal.
 Each `allocation_policy` in the set is constructed by drawing one value from the `allocation_samples` of each
 ControlSignal, and the set contains all combinations of these values.  For each `allocation_policy`, the default
@@ -288,40 +288,23 @@
 ---------------
 
 """
+from PsyNeuLink.Components.Functions.Function import ModulationParam, _is_modulation_param
+from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.ControlMechanism import *
 from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.EVCAuxiliary import \
     ControlSignalGridSearch, ValueFunction
-
-from PsyNeuLink.Components.Process import Process_Base
-from PsyNeuLink.Components.Mechanisms.Mechanism import MechanismList, MechanismTuple
-from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.ControlMechanism import *
+from PsyNeuLink.Components.Mechanisms.Mechanism import MechanismList
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.IntegratorMechanism import IntegratorMechanism
-from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ObjectiveMechanism import ObjectiveMechanism
-from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
-
-OBJECT = 0
-WEIGHT = 1
-EXPONENT = 2
-
-# # Default control allocation mode values:
-# class DefaultControlAllocationMode(Enum):
-#     GUMBY_MODE = 0.0
-#     BADGER_MODE = 1.0
-#     TEST_MODE = 240
-# defaultControlAllocation = DefaultControlAllocationMode.BADGER_MODE.value
-DEFAULT_ALLOCATION_SAMPLES = np.arange(0.1, 1.01, 0.3)
-
-class ControlSignalCostOptions(IntEnum):
-    NONE               = 0
-    INTENSITY_COST     = 1 << 1
-    ADJUSTMENT_COST    = 1 << 2
-    DURATION_COST      = 1 << 3
-    ALL                = INTENSITY_COST | ADJUSTMENT_COST | DURATION_COST
-    DEFAULTS           = INTENSITY_COST
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ObjectiveMechanisms.ObjectiveMechanism import ObjectiveMechanism
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
+
+
+OBJECT_INDEX = 0
+WEIGHT_INDEX = 1
+EXPONENT_INDEX = 2
 
 # -------------------------------------------    KEY WORDS  -------------------------------------------------------
 
 ALLOCATION_POLICY = 'allocation_policy'
-CONTROL_SIGNAL_COST_OPTIONS = 'controlSignalCostOptions'
 
 # ControlProjection Function Names
 INTENSITY_COST_FUNCTION = 'intensity_cost_function'
@@ -357,6 +340,7 @@ class EVCMechanism(ControlMechanism_Base):
     prediction_mechanism_type=IntegratorMechanism,                     \
     prediction_mechanism_params=None,                                  \
     monitor_for_control=None,                                          \
+    control_signals=None,                                              \
     function=ControlSignalGridSearch                                   \
     value_function=ValueFunction,                                      \
     outcome_function=LinearCombination(operation=PRODUCT),             \
@@ -385,12 +369,12 @@ class EVCMechanism(ControlMechanism_Base):
 
         # - specification of system:  required param: SYSTEM
         # - kwDefaultController:  True =>
-        #         takes over all projections from default Controller;
+        #         takes over all unassigned ControlProjections (i.e., without a sender) in its system;
         #         does not take monitored states (those are created de-novo)
         # TBI: - CONTROL_PROJECTIONS:
         #         list of projections to add (and for which outputStates should be added)
 
-        # - inputStates: one for each performance/environment variable monitiored
+        # - input_states: one for each performance/environment variable monitiored
 
         ControlProjection Specification:
         #    - wherever a ControlProjection is specified, using kwEVC instead of CONTROL_PROJECTION
@@ -398,7 +382,7 @@ class EVCMechanism(ControlMechanism_Base):
         #    ? expclitly, in call to "EVC.monitor(input_state, parameter_state=NotImplemented) method
 
         # - specification of function: default is default allocation policy (BADGER/GUMBY)
-        #   constraint:  if specified, number of items in variable must match number of inputStates in INPUT_STATES
+        #   constraint:  if specified, number of items in variable must match number of input_states in INPUT_STATES
         #                  and names in list in kwMonitor must match those in INPUT_STATES
 
        **********************************************************************************************
@@ -431,23 +415,27 @@ class EVCMechanism(ControlMechanism_Base):
         specifies set of outputState values to monitor (see `ControlMechanism_Monitored_OutputStates` for
         specification options).
 
-    function : function : ControlSignalGridSearch
+    control_signals : List[Attribute of Mechanism or its function, ParameterState, or tuple[str, Mechanism]
+        specifies the parameters to be controlled by the EVCMechanism 
+        (see `control_signals <EVCMechanism.control_signals>` for details).
+
+    function : function or method : ControlSignalGridSearch
         specifies the function used to determine the `allocation_policy` for the next execution of the system
         (see `function <EVCMechanism.function>` attribute for a description of the default function).
 
-    value_function : function : value_function
+    value_function : function or method : value_function
         specifies the function used to calculate the value of the current `allocation_policy`
         (see `value_function` attribute for additional details).
 
-    outcome_function : function : LinearCombination(operation=PRODUCT)
+    outcome_function : function or method : LinearCombination(operation=PRODUCT)
         specifies the function used to calculate the outcome associated with the current `allocation_policy`
         (see `outcome_function` attribute for additional details).
 
-    cost_function : function : LinearCombination(operation=SUM)
+    cost_function : function or method : LinearCombination(operation=SUM)
         specifies the function used to calculate the cost associated with the current `allocation_policy`
         (see `cost_function` attribute for additional details).
 
-    combine_outcome_and_cost_function : function : LinearCombination(operation=SUM)
+    combine_outcome_and_cost_function : function or method : LinearCombination(operation=SUM)
         specifies the function used to combine the outcome and cost associated with the current `allocation_policy`,
         to determine its value (see `combine_outcome_and_cost_function` attribute for additional details).
 
@@ -474,17 +462,18 @@ class EVCMechanism(ControlMechanism_Base):
     ----------
 
     make_default_controller : bool : default True
-        if `True`, assigns EVCMechanism when instantiated as the DefaultController
+        if `True`, calls deferred_init() for each ControlProjection in its system without a sender,
+        creates a ControlSignal for it, and assigns itself as its sender.
 
     system : System
         the `system <System>` for which EVCMechanism is the `controller`.
 
-    controlSignals : OrderedDict[str, ControlSignal]
+    control_signals : ContentAddressableList[ControlSignal]
         list of `outputStates <OutputState>` for the EVCMechanism, each of which corresponds to one of its
         ControlSignals.
 
     predictionMechanisms : MechanismList
-        a list of `prediction mechanisms <EVCMechanism_Prediction_Mechanisms>` added to the system, along with any 
+        a list of `prediction mechanisms <EVCMechanism_Prediction_Mechanisms>` added to the system, along with any
         `runtime_params <Mechanism.runtime_params>` and the `phase <Mechanism.phase>` in which they execute.
 
     origin_prediction_mechanisms : Dict[ProcessingMechanism, ProcessingMechanism]
@@ -539,7 +528,7 @@ class EVCMechanism(ControlMechanism_Base):
     [TBI]
         monitored_values : 3D np.array
             an array of values of the outputStates in `monitored_output_states` (equivalent to the values of
-            the EVCMechanism's `inputStates <EVCMechanism.inputStates>`).
+            the EVCMechanism's `input_states <EVCMechanism.input_states>`).
     COMMENT
 
     monitor_for_control_weights_and_exponents: List[Tuple[scalar, scalar]]
@@ -550,10 +539,10 @@ class EVCMechanism(ControlMechanism_Base):
         determines the `allocation_policy <EVCMechanism.allocation_policy>` to use for the next round of the system's
         execution. The default function, `ControlSignalGridSearch`, conducts an exhaustive (*grid*) search of all
         combinations of the `allocation_samples` of its ControlSignals (and contained in its
-        `controlSignalSearchSpace` attribute), by executing the system (using `run_simulation`) for each
+        `control_signal_search_space` attribute), by executing the system (using `run_simulation`) for each
         combination, evaluating the result using `value_function`, and returning the allocation_policy that generated
         the highest value.  If a custom function is specified, it must accommodate a :keyword:`controller` argument that
-        specifies an EVCMechanism (and provides access to its attributes, including `controlSignalSearchSpace`),
+        specifies an EVCMechanism (and provides access to its attributes, including `control_signal_search_space`),
         and must return an array with the same format (number and type of elements) as the EVCMechanism's
         `allocation_policy` attribute.
 
@@ -569,14 +558,14 @@ class EVCMechanism(ControlMechanism_Base):
             controller._assign_simulation_inputs assigns value of prediction_mechanisms to inputs of ORIGIN mechanisms
             controller.run will execute a specified number of trials with the simulation inputs
             controller.monitored_states is a list of the mechanism outputStates being monitored for outcome
-            controller.inputValue is a list of current outcome values (values for monitored_states)
+            controller.input_value is a list of current outcome values (values for monitored_states)
             controller.monitor_for_control_weights_and_exponents is a list of parameterizations for outputStates
-            controller.controlSignals is a list of controlSignal objects
-            controller.controlSignalSearchSpace is a list of all allocationPolicies specifed by allocation_samples
-            controlSignal.allocation_samples is the set of samples specified for that controlSignal
-            [TBI:] controlSignal.allocation_range is the range that the controlSignal value can take
+            controller.control_signals is a list of control_signal objects
+            controller.control_signal_search_space is a list of all allocationPolicies specifed by allocation_samples
+            control_signal.allocation_samples is the set of samples specified for that control_signal
+            [TBI:] control_signal.allocation_range is the range that the control_signal value can take
             controller.allocation_policy - holds current allocation_policy
-            controller.outputValue is a list of current controlSignal values
+            controller.output_values is a list of current control_signal values
             controller.value_function - calls the three following functions (done explicitly, so each can be specified)
             controller.outcome_function - aggregates monitored outcomes (using specified weights and exponentiation)
             controller.cost_function - aggregate costs of control signals
@@ -587,7 +576,7 @@ class EVCMechanism(ControlMechanism_Base):
         determines the value assigned as the `variable <ControlSignal.variable>` for each `ControlSignal` and its
         associated `ControlProjection`.  Each item of the array must be a 1d array (usually containing a scalar)
         that specifies an `allocation` for the corresponding ControlSignal, and the number of items must equal the
-        number of ControlSignals in the EVCMechanism's `controlSignals` attribute.
+        number of ControlSignals in the EVCMechanism's `control_signals` attribute.
 
     value_function : function : default value_function()
         calculates the value for a given `allocation_policy`.  The default uses `outcome_function` to determine the
@@ -602,7 +591,7 @@ class EVCMechanism(ControlMechanism_Base):
         the outcome of the function of the ObjectiveMechanism (based on the value of the outputStates being monitored
         (and specified in the EVCMechanism's `monitored_output_states` attribute;
         and a :keyword:`costs` argument that is a 2d array of costs, each item of which is the `cost` of a
-        ControlSignal in the EVCMechanism's `controlSignals` attribute.  A custom function assigned to
+        ControlSignal in the EVCMechanism's `control_signals` attribute.  A custom function assigned to
         `value_function` can also call any of the other EVCMechanism functions described below (however,
         see `note <EVCMechanism_Calling_and_Assigning_Functions>` above).
 
@@ -629,19 +618,19 @@ class EVCMechanism(ControlMechanism_Base):
 
     cost_function : function : default LinearCombination(operation=SUM)
         calculates the cost for a given `allocation_policy`.  The default combines the `cost` of each ControlSignals in
-        `controlSignals` by summing them using the `LinearCombination` function. If the default `cost_function` is
+        `control_signals` by summing them using the `LinearCombination` function. If the default `cost_function` is
         called by a custom `value_function`, the weights and/or exponents parameters of the function can be used,
         respectively, to scale and/or exponentiate the contribution of each ControlSignal's cost to the aggregated
         value.  These must be specified as 1d arrays in a `WEIGHTS` and/or `EXPONENTS` entry of a
         `parameter dictionary <ParameterState_Specifying_Parameters>` specified for the `params` argument of the
         `LinearCombination` function; the length of each array must equal the number of (and the values listed in the
-        same order as) the ControlSignals in the EVCMechanism's `controlSignals` attribute, and be in the same order.
+        same order as) the ControlSignals in the EVCMechanism's `control_signals` attribute, and be in the same order.
         The default function can also be replaced with any
         `custom function <EVCMechanism_Calling_and_Assigning_Functions>` that returns a scalar value.  If used with
         the EVCMechanism's default `value_function`, a custom cost_function must accommodate two arguments (passed by
         name): a :keyword:`controller` argument that is the EVCMechanism itself;  and a
         :keyword:`costs` argument, that is 1d array of scalar values specifying the `cost` for each ControlSignal listed
-        in the `controlSignals` attribute of the :keyword:`controller` argument.
+        in the `control_signals` attribute of the :keyword:`controller` argument.
 
     combine_outcome_and_cost_function : function : default LinearCombination(operation=SUM)
         combines the outcome and cost for given `allocation_policy` to determine its value.  The default uses the
@@ -649,8 +638,8 @@ class EVCMechanism(ControlMechanism_Base):
         default `combine_outcome_and_cost_function` is called by a custom `value_function`, the weights and/or
         exponents parameters of the `LinearCombination` function can be used, respectively, to scale and/or exponentiate
         the contribution of the outcome and/or cost to the result.  These must be specified as 1d arrays in a `WEIGHTS`
-        and/or EXPONENTS entry of a  `parameter specifiction dictionary <ParameterState_Specifying_Parameters>` 
-        assigned to the function's `params` argument; each array must have two elements, the first for the outcome 
+        and/or EXPONENTS entry of a  `parameter specifiction dictionary <ParameterState_Specifying_Parameters>`
+        assigned to the function's `params` argument; each array must have two elements, the first for the outcome
         and second for the cost. The default function can also be replaced with any
         `custom function <EVCMechanism_Calling_and_Assigning_Functions>` that returns a scalar value.  If used with
         the EVCMechanism's default `value_function`, a custom combine_outcome_and_cost_function must accomoudate three
@@ -658,13 +647,13 @@ class EVCMechanism(ControlMechanism_Base):
         :keyword:`outcome` argument that is a 1d array with the outcome of the current `allocation_policy`; and a
         :keyword:`cost` argument that is 1d array with the cost of the current `allocation_policy`.
 
-    controlSignalSearchSpace : 2d np.array
+    control_signal_search_space : 2d np.array
         an array that contains arrays of allocation policies.  Each allocation policy contains one value for each of
         the mechanism's ControlSignals.  By default, it is assigned a set of all possible allocation policies
         (using np.meshgrid to construct all permutations of ControlSignal values).
 
     EVC_max : 1d np.array with single value
-        the maximum EVC value over all allocation policies in `controlSignalSearchSpace`.
+        the maximum EVC value over all allocation policies in `control_signal_search_space`.
 
     EVC_max_state_values : 2d np.array
         an array of the values for the outputStates in `monitored_output_states` using the allocation policy that
@@ -675,11 +664,11 @@ class EVCMechanism(ControlMechanism_Base):
 
     save_all_values_and_policies : bool : default False
         specifies whether or not to save all allocation policies and associated EVC values (in addition to the max).
-        If it is specified, each policy tested in the `controlSignalSearchSpace` is saved in `EVC_policies` and their
+        If it is specified, each policy tested in the `control_signal_search_space` is saved in `EVC_policies` and their
         values are saved in `EVC_values`.
 
     EVC_policies : 2d np.array
-        array of allocation policies tested in `controlSignalSearchSpace`.  The values of each are stored in
+        array of allocation policies tested in `control_signal_search_space`.  The values of each are stored in
         `EVC_values`.
 
     EVC_values :  1d np.array
@@ -699,7 +688,7 @@ class EVCMechanism(ControlMechanism_Base):
     #     kwPreferenceSetName: 'DefaultControlMechanismCustomClassPreferences',
     #     kp<pref>: <setting>...}
 
-    # This must be a list, as there may be more than one (e.g., one per controlSignal)
+    # This must be a list, as there may be more than one (e.g., one per control_signal)
     variableClassDefault = defaultControlAllocation
 
     from PsyNeuLink.Components.Functions.Function import LinearCombination
@@ -711,11 +700,13 @@ class EVCMechanism(ControlMechanism_Base):
 
     @tc.typecheck
     def __init__(self,
-                 # system:System,
+                 system=None,
                  # default_input_value=None,
                  prediction_mechanism_type=IntegratorMechanism,
                  prediction_mechanism_params:tc.optional(dict)=None,
                  monitor_for_control:tc.optional(list)=None,
+                 control_signals:tc.optional(list) = None,
+                 modulation:tc.optional(_is_modulation_param)=ModulationParam.MULTIPLICATIVE,
                  function=ControlSignalGridSearch,
                  value_function=ValueFunction,
                  outcome_function=LinearCombination(operation=PRODUCT),
@@ -733,26 +724,28 @@ def __init__(self,
         prediction_mechanism_params = prediction_mechanism_params or {MONITOR_FOR_CONTROL:None}
 
         # Assign args to params and functionParams dicts (kwConstants must == arg names)
-        params = self._assign_args_to_param_dicts(# system=system,
-                                              prediction_mechanism_type=prediction_mechanism_type,
-                                              prediction_mechanism_params=prediction_mechanism_params,
-                                              monitor_for_control=monitor_for_control,
-                                              function=function,
-                                              value_function=value_function,
-                                              outcome_function=outcome_function,
-                                              cost_function=cost_function,
-                                              combine_outcome_and_cost_function=combine_outcome_and_cost_function,
-                                              save_all_values_and_policies=save_all_values_and_policies,
-                                              params=params)
+        params = self._assign_args_to_param_dicts(system=system,
+                                                  prediction_mechanism_type=prediction_mechanism_type,
+                                                  prediction_mechanism_params=prediction_mechanism_params,
+                                                  monitor_for_control=monitor_for_control,
+                                                  control_signals=control_signals,
+                                                  modulation=modulation,
+                                                  function=function,
+                                                  value_function=value_function,
+                                                  outcome_function=outcome_function,
+                                                  cost_function=cost_function,
+                                                  combine_outcome_and_cost_function=combine_outcome_and_cost_function,
+                                                  save_all_values_and_policies=save_all_values_and_policies,
+                                                  params=params)
 
         super(EVCMechanism, self).__init__(# default_input_value=default_input_value,
                                            monitor_for_control=monitor_for_control,
+                                           control_signals=control_signals,
                                            function=function,
                                            params=params,
                                            name=name,
                                            prefs=prefs,
                                            context=self)
-        TEST = True
 
     def _validate_params(self, request_set, target_set=None, context=None):
 
@@ -801,7 +794,7 @@ def _instantiate_prediction_mechanisms(self, context=None):
         # self.predictionProcesses = []
 
         # List of prediction mechanism tuples (used by system to execute them)
-        self.prediction_mech_tuples = []
+        self.prediction_mechs = []
 
         # Get any params specified for predictionMechanism(s) by EVCMechanism
         try:
@@ -816,14 +809,14 @@ def _instantiate_prediction_mechanisms(self, context=None):
             # #           (AS THEY CAN CURRENTLY FOR ObjectiveMechanisms)
             # state_names = []
             # variables = []
-            # for state_name in origin_mech.inputStates.keys():
+            # for state_name in origin_mech.input_states.keys():
             #     state_names.append(state_name)
             #     variables.append(origin_mech_intputStates[state_name].variable)
 
             # Instantiate predictionMechanism
             prediction_mechanism = self.paramsCurrent[PREDICTION_MECHANISM_TYPE](
                                                             name=origin_mech.name + " " + PREDICTION_MECHANISM,
-                                                            default_input_value = origin_mech.inputState.variable,
+                                                            default_input_value = origin_mech.input_state.variable,
                                                             # default_input_value=variables,
                                                             # INPUT_STATES=state_names,
                                                             params = prediction_mechanism_params,
@@ -833,11 +826,11 @@ def _instantiate_prediction_mechanisms(self, context=None):
 
             # Assign projections to prediction_mechanism that duplicate those received by origin_mech
             #    (this includes those from ProcessInputState, SystemInputState and/or recurrent ones
-            for orig_state_name, prediction_state_name in zip(origin_mech.inputStates.keys(),
-                                                                prediction_mechanism.inputStates.keys()):
-                for projection in origin_mech.inputStates[orig_state_name].receivesFromProjections:
+            for orig_input_state, prediction_input_state in zip(origin_mech.input_states,
+                                                            prediction_mechanism.input_states):
+                for projection in orig_input_state.path_afferents:
                     MappingProjection(sender=projection.sender,
-                                      receiver=prediction_mechanism.inputStates[prediction_state_name],
+                                      receiver=prediction_input_state,
                                       matrix=projection.matrix)
 
             # Assign list of processes for which prediction_mechanism will provide input during the simulation
@@ -849,23 +842,25 @@ def _instantiate_prediction_mechanisms(self, context=None):
             # # FIX: REPLACE REFERENCE TO THIS ELSEWHERE WITH REFERENCE TO MECH_TUPLES BELOW
             self.origin_prediction_mechanisms[origin_mech] = prediction_mechanism
 
-            # Add to list of EVCMechanism's prediction_mech_tuples
-            prediction_mech_tuple = MechanismTuple(prediction_mechanism, None, origin_mech.phaseSpec)
-            self.prediction_mech_tuples.append(prediction_mech_tuple)
+            # Add to list of EVCMechanism's prediction_object_items
+            # prediction_object_item = prediction_mechanism
+            self.prediction_mechs.append(prediction_mechanism)
 
             # Add to system executionGraph and executionList
-            self.system.executionGraph[prediction_mech_tuple] = set()
-            self.system.executionList.append(prediction_mech_tuple)
+            self.system.executionGraph[prediction_mechanism] = set()
+            self.system.executionList.append(prediction_mechanism)
 
-        self.predictionMechanisms = MechanismList(self, self.prediction_mech_tuples)
+        self.predictionMechanisms = MechanismList(self, self.prediction_mechs)
 
         # Assign list of destinations for predicted_inputs:
         #    the variable of the ORIGIN mechanism for each process in the system
         self.predictedInput = {}
         for i, origin_mech in zip(range(len(self.system.originMechanisms)), self.system.originMechanisms):
-            # self.predictedInput[origin_mech] = self.system.processes[i].originMechanisms[0].inputValue
+            # self.predictedInput[origin_mech] = self.system.processes[i].originMechanisms[0].input_value
             self.predictedInput[origin_mech] = self.system.processes[i].originMechanisms[0].variable
 
+    # IMPLEMENTATION NOTE:  THIS SHOULD BE MOVED TO COMPOSITION ONCE THAT IS IMPLEMENTED
+    # FIX: MOVE THIS TO ControlMechanism??
     def _instantiate_monitoring_mechanism(self, context=None):
         """
         Assign inputState to controller for each state to be monitored;
@@ -880,17 +875,23 @@ def _instantiate_monitoring_mechanism(self, context=None):
         Notes:
         * self.monitored_output_states is a list, each item of which is a Mechanism.outputState from which a projection
             will be instantiated to a corresponding inputState of the ControlMechanism
-        * self.inputStates is the usual ordered dict of states,
+        * self.input_states is the usual ordered dict of states,
             each of which receives a projection from a corresponding outputState in self.monitored_output_states
         """
 
         self._get_monitored_states(context=context)
 
-        for state in self.monitored_output_states:
+        monitoring_input_states = []
+        for i, state in enumerate(self.monitored_output_states):
             self._validate_monitored_state_in_system(state)
+            monitoring_input_states.append({NAME: state.name,
+                                            WEIGHT: float(self.monitor_for_control_weights_and_exponents[i][0]),
+                                            EXPONENT: float(self.monitor_for_control_weights_and_exponents[i][1])
+                                            })
 
         # Note: weights and exponents are assigned as parameters of outcome_function in _get_monitored_states
         self.monitoring_mechanism = ObjectiveMechanism(monitored_values=self.monitored_output_states,
+                                                       # input_states=monitoring_input_states,
                                                        function=self.outcome_function,
                                                        name=self.name + ' Monitoring Mechanism')
 
@@ -907,6 +908,9 @@ def _instantiate_monitoring_mechanism(self, context=None):
                           name = self.system.name + ' outcome signal'
                           )
 
+        self.system.executionList.append(self.monitoring_mechanism)
+        self.system.executionGraph[self.monitoring_mechanism] = set(self.system.executionList[:-1])
+
     def _get_monitored_states(self, context=None):
         """
         Parse paramsCurent[MONITOR_FOR_CONTROL] for system, controller, mechanisms and/or their outputStates:
@@ -928,25 +932,24 @@ def _get_monitored_states(self, context=None):
             - precedence is given to MonitoredOutputStatesOptions specification in mechanism > controller > system
         * self.monitored_output_states is a list, each item of which is a Mechanism.outputState from which a projection
             will be instantiated to a corresponding inputState of the ControlMechanism
-        * self.inputStates is the usual ordered dict of states,
+        * self.input_states is the usual ordered dict of states,
             each of which receives a projection from a corresponding outputState in self.monitored_output_states
 
         """
 
-        from PsyNeuLink.Components.States.OutputState import OutputState
         from PsyNeuLink.Components.Mechanisms.Mechanism import MonitoredOutputStatesOption
-        from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ObjectiveMechanism import validate_monitored_value
+        from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ObjectiveMechanisms.ObjectiveMechanism import _validate_monitored_value
 
         # PARSE SPECS
 
         # Get controller's MONITOR_FOR_CONTROL specifications (optional, so need to try)
         try:
-            controller_specs = self.paramsCurrent[MONITOR_FOR_CONTROL] or []
+            controller_specs = self.paramsCurrent[MONITOR_FOR_CONTROL].copy() or []
         except KeyError:
             controller_specs = []
 
         # Get system's MONITOR_FOR_CONTROL specifications (specified in paramClassDefaults, so must be there)
-        system_specs = self.system.paramsCurrent[MONITOR_FOR_CONTROL]
+        system_specs = self.system.monitor_for_control.copy()
 
         # If the controller has a MonitoredOutputStatesOption specification, remove any such spec from system specs
         if controller_specs:
@@ -954,6 +957,9 @@ def _get_monitored_states(self, context=None):
                 option_item = next((item for item in system_specs if isinstance(item, MonitoredOutputStatesOption)),None)
                 if option_item is not None:
                     del system_specs[option_item]
+            for item in controller_specs:
+                if item in system_specs:
+                    del system_specs[system_specs.index(item)]
 
         # Combine controller and system specs
         # If there are none, assign PRIMARY_OUTPUT_STATES as default
@@ -983,10 +989,10 @@ def _get_monitored_states(self, context=None):
                 item = item[0]
             # MODIFIED 2/22/17 END
             # Validate by ObjectiveMechanism:
-            validate_monitored_value(self, item, context=context)
+            _validate_monitored_value(self, item, context=context)
             # Extract references from specification tuples
             if isinstance(item, tuple):
-                all_specs_extracted_from_tuples.append(item[OBJECT])
+                all_specs_extracted_from_tuples.append(item[OBJECT_INDEX])
             # Otherwise, add item as specified:
             else:
                 all_specs_extracted_from_tuples.append(item)
@@ -1056,7 +1062,7 @@ def _get_monitored_states(self, context=None):
                 # Extract refs from tuples and add to local_specs
                 for item in mech_specs:
                     if isinstance(item, tuple):
-                        local_specs.append(item[OBJECT])
+                        local_specs.append(item[OBJECT_INDEX])
                         continue
                     local_specs.append(item)
 
@@ -1074,7 +1080,7 @@ def _get_monitored_states(self, context=None):
             # PARSE OUTPUT STATE'S SPECS
 
             # for output_state_name, output_state in list(mech.outputStates.items()):
-            for output_state_name, output_state in mech.outputStates.items():
+            for output_state in mech.output_states:
 
                 # Get MONITOR_FOR_CONTROL specification from outputState
                 try:
@@ -1106,19 +1112,19 @@ def _get_monitored_states(self, context=None):
                     # Extract refs from tuples and add to local_specs
                     for item in output_state_specs:
                         if isinstance(item, tuple):
-                            local_specs.append(item[OBJECT])
+                            local_specs.append(item[OBJECT_INDEX])
                             continue
                         local_specs.append(item)
 
             # Ignore MonitoredOutputStatesOption if any outputStates are explicitly specified for the mechanism
-            for output_state_name, output_state in list(mech.outputStates.items()):
+            for output_state in mech.output_states:
                 if (output_state in local_specs or output_state.name in local_specs):
                     option_spec = None
 
 
             # ASSIGN SPECIFIED OUTPUT STATES FOR MECHANISM TO self.monitored_output_states
 
-            for output_state_name, output_state in list(mech.outputStates.items()):
+            for output_state in mech.output_states:
 
                 # If outputState is named or referenced anywhere, include it
                 if (output_state in local_specs or output_state.name in local_specs):
@@ -1142,14 +1148,14 @@ def _get_monitored_states(self, context=None):
 
                     # If MonitoredOutputStatesOption is PRIMARY_OUTPUT_STATES and outputState is primary, include it
                     if option_spec is MonitoredOutputStatesOption.PRIMARY_OUTPUT_STATES:
-                        if output_state is mech.outputState:
+                        if output_state is mech.output_state:
                             self.monitored_output_states.append(output_state)
                             continue
                     # If MonitoredOutputStatesOption is ALL_OUTPUT_STATES, include it
                     elif option_spec is MonitoredOutputStatesOption.ALL_OUTPUT_STATES:
                         self.monitored_output_states.append(output_state)
                     elif mech.name in local_specs or mech in local_specs:
-                        if output_state is mech.outputState:
+                        if output_state is mech.output_state:
                             self.monitored_output_states.append(output_state)
                             continue
                     elif option_spec is None:
@@ -1157,7 +1163,7 @@ def _get_monitored_states(self, context=None):
                     else:
                         raise EVCError("PROGRAM ERROR: unrecognized specification of MONITOR_FOR_CONTROL for "
                                        "{0} of {1}".
-                                       format(output_state_name, mech.name))
+                                       format(output_state.name, mech.name))
 
 
         # ASSIGN WEIGHTS AND EXPONENTS TO OUTCOME_FUNCTION
@@ -1172,21 +1178,20 @@ def _get_monitored_states(self, context=None):
         # Get and assign specification of weights and exponents for mechanisms or outputStates specified in tuples
         for spec in all_specs:
             if isinstance(spec, tuple):
-                object_spec = spec[OBJECT]
+                object_spec = spec[OBJECT_INDEX]
                 # For each outputState in monitored_output_states
                 for item in self.monitored_output_states:
                     # If either that outputState or its owner is the object specified in the tuple
                     if item is object_spec or item.name is object_spec or item.owner is object_spec:
                         # Assign the weight and exponent specified in the tuple to that outputState
                         i = self.monitored_output_states.index(item)
-                        weights[i] = spec[WEIGHT]
-                        exponents[i] = spec[EXPONENT]
+                        weights[i] = spec[WEIGHT_INDEX]
+                        exponents[i] = spec[EXPONENT_INDEX]
 
         # Assign weights and exponents to corresponding attributes of default OUTCOME_FUNCTION
         # Note: done here (rather than in call to outcome_function in value_function) for efficiency
-        self.paramsCurrent[OUTCOME_FUNCTION]._assign_params(request_set={WEIGHTS:weights,
-                                                                         EXPONENTS:exponents},
-                                                            context=context)
+        self.outcome_function.weights = weights
+        self.outcome_function.exponents = exponents
 
         # Assign weights and exponents to monitor_for_control_weights_and_exponents attribute
         #    (so that it is accessible to custom functions)
@@ -1215,31 +1220,6 @@ def _validate_monitored_state_in_system(self, state_spec, context=None):
                 print("Request for controller in {0} to monitor the outputState(s) of a mechanism ({1}) that is not"
                       " a terminal mechanism in {2}".format(self.system.name, state_spec.name, self.system.name))
 
-    def _instantiate_control_projection(self, projection, params=None, context=None):
-        """
-        """
-
-        if self.allocation_policy is None:
-            self.allocation_policy = np.array(defaultControlAllocation)
-        else:
-            self.allocation_policy = np.append(self.allocation_policy, defaultControlAllocation)
-
-        # Call super to instantiate ControlSignal outputStates
-        super()._instantiate_control_projection(projection=projection,
-                                                params=params,
-                                                context=context)
-
-        # Assign controlSignals in the order they are stored of OutputStates
-        self.controlSignals = [self.outputStates[state_name] for state_name in self.outputStates.keys()]
-
-        # # TEST PRINT
-        # print("\n{}.controlSignals: ".format(self.name))
-        # for control_signal in self.controlSignals:
-        #     print("{}".format(control_signal.name))
-
-    def _instantiate_function(self, context=None):
-        super()._instantiate_function(context=context)
-
     def _instantiate_attributes_after_function(self, context=None):
 
         super()._instantiate_attributes_after_function(context=context)
@@ -1276,7 +1256,7 @@ def _instantiate_attributes_after_function(self, context=None):
         if isinstance(cost_Function, Function):
             # Insure that length of the weights and/or exponents arguments for the cost_function
             #    matches the number of control signals
-            num_control_projections = len(self.controlProjections)
+            num_control_projections = len(self.control_projections)
             if cost_Function.weights is not None:
                 num_cost_weights = len(cost_Function.weights)
                 if  num_cost_weights != num_control_projections:
@@ -1305,9 +1285,9 @@ def _execute(self,
         """Determine allocation_policy for next run of system
 
         Update prediction mechanisms
-        Construct controlSignalSearchSpace (from allocation_samples of each item in controlSignals):
-            * get `allocation_samples` for each ControlSignal in `controlSignals`
-            * construct `controlSignalSearchSpace`: a 2D np.array of control allocation policies, each policy of which
+        Construct control_signal_search_space (from allocation_samples of each item in control_signals):
+            * get `allocation_samples` for each ControlSignal in `control_signals`
+            * construct `control_signal_search_space`: a 2D np.array of control allocation policies, each policy of which
               is a different combination of values, one from the `allocation_samples` of each ControlSignal.
         Call self.function -- default is ControlSignalGridSearch
         Return an allocation_policy
@@ -1319,19 +1299,19 @@ def _execute(self,
         # CONSTRUCT SEARCH SPACE
 
         control_signal_sample_lists = []
-        control_signals = self.controlSignals
+        control_signals = self.control_signals
 
         # Get allocation_samples for all ControlSignals
         num_control_signals = len(control_signals)
 
-        for control_signal in self.controlSignals:
+        for control_signal in self.control_signals:
             control_signal_sample_lists.append(control_signal.allocation_samples)
 
-        # Construct controlSignalSearchSpace:  set of all permutations of ControlProjection allocations
+        # Construct control_signal_search_space:  set of all permutations of ControlProjection allocations
         #                                     (one sample from the allocationSample of each ControlProjection)
         # Reference for implementation below:
         # http://stackoverflow.com/questions/1208118/using-numpy-to-build-an-array-of-all-combinations-of-two-arrays
-        self.controlSignalSearchSpace = \
+        self.control_signal_search_space = \
             np.array(np.meshgrid(*control_signal_sample_lists)).T.reshape(-1,num_control_signals)
 
         # EXECUTE SEARCH
@@ -1408,7 +1388,7 @@ def run_simulation(self,
 
         allocation_vector : (1D np.array)
             the allocation policy to use in running the simulation, with one allocation value for each of the
-            EVCMechanism's ControlSignals (listed in `controlSignals`).
+            EVCMechanism's ControlSignals (listed in `control_signals`).
 
         runtime_params : Optional[Dict[str, Dict[str, Dict[str, value]]]]
             a dictionary that can include any of the parameters used as arguments to instantiate the mechanisms,
@@ -1426,8 +1406,8 @@ def run_simulation(self,
 
         # Implement the current allocation_policy over ControlSignals (outputStates),
         #    by assigning allocation values to EVCMechanism.value, and then calling _update_output_states
-        for i in range(len(self.controlSignals)):
-            # self.controlSignals[list(self.controlSignals.values())[i]].value = np.atleast_1d(allocation_vector[i])
+        for i in range(len(self.control_signals)):
+            # self.control_signals[list(self.control_signals.values())[i]].value = np.atleast_1d(allocation_vector[i])
             self.value[i] = np.atleast_1d(allocation_vector[i])
         self._update_output_states(runtime_params=runtime_params, time_scale=time_scale,context=context)
 
@@ -1437,13 +1417,13 @@ def run_simulation(self,
         self.system.run(inputs=inputs, clock=sim_clock, time_scale=time_scale, context=context)
 
         # Get outcomes for current allocation_policy
-        #    = the values of the monitored output states (self.inputStates)
-        #    stored in self.inputValue = list(self.variable)
-        self.monitoring_mechanism.execute(context=EVC_SIMULATION)
+        #    = the values of the monitored output states (self.input_states)
+        #    stored in self.input_value = list(self.variable)
+        # self.monitoring_mechanism.execute(context=EVC_SIMULATION)
         self._update_input_states(runtime_params=runtime_params, time_scale=time_scale,context=context)
 
-        for i in range(len(self.controlSignals)):
-            self.controlSignalCosts[i] = self.controlSignals[i].cost
+        for i in range(len(self.control_signals)):
+            self.control_signal_costs[i] = self.control_signals[i].cost
 
 
     # The following implementation of function attributes as properties insures that even if user sets the value of a
diff --git a/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/GatingMechanisms/GatingMechanism.py b/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/GatingMechanisms/GatingMechanism.py
new file mode 100644
index 00000000000..7f18d58a777
--- /dev/null
+++ b/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/GatingMechanisms/GatingMechanism.py
@@ -0,0 +1,562 @@
+# Princeton University licenses this file to You under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.  You may obtain a copy of the License at:
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed
+# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and limitations under the License.
+
+
+# **************************************  GatingMechanism ************************************************
+
+"""
+Overview
+--------
+
+A GatingMechanism is an `AdaptiveMechanism` that modulates the value of the inputState(s) and/or outputState(s) of 
+one or more `ProcessingMechanisms`.   It's function takes a value 
+COMMENT:
+    ??FROM WHERE?
+COMMENT
+and uses that to calculate a `gating_policy`:  a list of `gating <LINK>` values, one for each of states that it 
+gates.  Each of these values is assigned as the value of a `GatingSignal` (a subclass of `OutputState`) in the
+GatingMechanism, and used by an associated `GatingProjection` to modulate the value of the state to which it projects.  
+A GatingMechanism can regulate only the parameters of mechanisms in the `System` to which it belongs. 
+COMMENT: TBI
+The gating components of a system can be displayed using the system's 
+`show_graph` method with its **show_gating** argument assigned as :keyword:``True`.  
+COMMENT
+The gating components of a system are executed after all `Proces singMechanisms <ProcessingMechanism>`, 
+`LearningMechanisms <LearningMechanism>`, and  `ControlMechanisms <ControlMechanism>` in that system have been executed.
+
+
+.. _GatingMechanism_Creation:
+
+Creating A GatingMechanism
+---------------------------
+
+GatingMechanisms can be created using the standard Python method of calling the constructor for the desired type.
+A GatingMechanism is also created automatically if `gating is specified <GatingMechanism_Specifying_Gating>` for an 
+inputState or outputState, in which case a `GatingProjection` is also automatically created that projects 
+from the GatingMechanism to the specified state. How a GatingMechanism creates its `GatingProjections 
+<GatingProjection>` and determines their value depends on the `subclass <GatingMechanism>`.
+
+.. _GatingMechanism_Specifying_Gating:
+
+Specifying gating
+~~~~~~~~~~~~~~~~~
+
+GatingMechanisms are used to modulate the value of an `inputState <InputState>` or `outputState <OutputState>`.  
+An inputState or outputState can be specified for gating by assigning it a `GatingProjection` in the 
+**input_states** or **output_states** arguments of the constructor for the mechanism to which it belongs 
+(see `Mechanism_States <LINK>`).  The inputStates and outputStates to be gated by a GatingMechanism can also be 
+specified in the  **gating_signals**  argument of the constructor for a GatingMechanism.  The **gating_signals** 
+argument must be a list, each item of which must refer to one or more states to be gated by that GatingSignal.  
+The specification for each item in the list can use any of the forms used to 
+`specify a GatingSignal <GatingSignal_Specification>`.
+
+.. _GatingMechanism_GatingSignals:
+
+GatingSignals
+^^^^^^^^^^^^^
+
+A `GatingSignal` is created for each item listed in **gating_signals**, and all of the GatingSignals for a  
+GatingMechanism are listed in its `gating_signals <GatingMechanism.gating_signals>` attribute.  Each GatingSignal is 
+assigned one or more `GatingProjections <GatingProjection>` to the inputState(s) and/or outputState(s) it gates. 
+GatingSignals are a type of `OutputState`, and so they are also listed in the GatingMechanism's 
+`output_states <GatingMechanism.outut_states>` attribute.  
+
+.. _GatingMechanism_Modulation:
+
+Modulation
+^^^^^^^^^^
+
+Each GatingMechanism has a `modulation <GatingSignal.modulation>` attribute, that provides a default for the way
+in which its GatingSignals modulate the value of the states they gate 
+(see `modulation <ModulatoryProjection.modulation>` for an explanation of how this attribute is specified and used to 
+modulate the value of a state).  Each GatingSignal uses this value, unless its value is 
+`individually specified <GatingSignal_Modulation>`. 
+
+.. _GatingMechanism_Execution:
+
+Execution
+---------
+
+A GatingMechanism executes in the same way as a ProcessingMechanism, based on its place in the system's 
+`graph <System.graph>`.  Because GatingProjections are likely to introduce cycles (loops) in the graph,
+the effects of a GatingMechanism and its projections will generally not be applied in the first
+`round of execution <LINK>` (see `initialization <LINK>` for a description of how to configure the initialization
+of feedback loops in a System).  When executd, a GatingMechanism uses its input to determine the value of its
+`GatingSignals <GatingSignal>` and their corresponding `GatingProjections <GatingProjection>`.  In the subsequent 
+round of execution , each GatingProjection's value is used by the state to which it projects to modulate the 
+`value <State.value>` of that state.
+
+When a GatingMechanism executes, the value of each item in its `gating_policy` is assigned as the value of each of
+the corresponding GatingSignals in its `gating_signals` attribute.  These, in turn, are used by their associated
+`GatingProjections` to modulate the value of the state to which they project.  This is done by assigning the
+GatingSignal's value to a parameter of the state's function, as specified by the GatingSignal's `modulation` 
+parameter (see `GatingSignal_Execution` for details). 
+
+.. note::
+   A state that receives a `GatingProjection` does not update its value until its owner mechanism executes 
+   (see `Lazy Evaluation <LINK>` for an explanation of "lazy" updating).  This means that even if a GatingMechanism 
+   has executed, a state that it gates will not assume its new value until the state's owner has executed.
+
+.. _GatingMechanism_Class_Reference:
+
+Class Reference
+---------------
+
+"""
+
+# IMPLEMENTATION NOTE: COPIED FROM DefaultProcessingMechanism;
+#                      ADD IN GENERIC CONTROL STUFF FROM DefaultGatingMechanism
+
+from PsyNeuLink.Components.Functions.Function import ModulationParam, _is_modulation_param
+from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.AdaptiveMechanism import AdaptiveMechanism_Base
+from PsyNeuLink.Components.Mechanisms.Mechanism import Mechanism_Base
+from PsyNeuLink.Components.Projections.Projection import _validate_receiver
+from PsyNeuLink.Components.ShellClasses import *
+from PsyNeuLink.Components.States.ModulatorySignals.GatingSignal import GatingSignal, _parse_gating_signal_spec
+from PsyNeuLink.Components.States.State import State_Base, _instantiate_state
+
+GatingMechanismRegistry = {}
+
+
+class GatingMechanismError(Exception):
+    def __init__(self, error_value):
+        self.error_value = error_value
+
+
+class GatingMechanism(AdaptiveMechanism_Base):
+    """
+    GatingMechanism_Base(                           \
+        default_input_value=None,                   \
+        gating_signals=None                         \
+        modulation=ModulationParam.MULTIPLICATIVE   \
+        function=Linear,                            \
+        params=None,                                \
+        name=None,                                  \
+        prefs=None)
+
+    Abstract class for GatingMechanism.
+
+    .. note::
+       GatingMechanisms should NEVER be instantiated by a direct call to the base class.
+       They should be instantiated using the constructor for a :doc:`subclass <GatingMechanism>`.
+
+    COMMENT:
+        Description:
+            # VERIFY:
+            Protocol for instantiating unassigned GatingProjections (i.e., w/o a sender specified):
+               If sender is not specified for a GatingProjection (e.g., in an inputState or OutputState tuple spec) 
+                   it is flagged for deferred_init() in its __init__ method
+               When the next GatingMechanism is instantiated, if its params[MAKE_DEFAULT_GATING_MECHANISM] == True, its
+                   _take_over_as_default_gating_mechanism method is called in _instantiate_attributes_after_function;
+                   it then iterates through all of the inputStates and outputStates of all of the mechanisms in its 
+                   system, identifies ones without a sender specified, calls its deferred_init() method,
+                   instantiates a GatingSignal for it, and assigns it as the GatingProjection's sender.
+
+        Class attributes:
+            + componentType (str): System Default Mechanism
+            + paramClassDefaults (dict):
+                + FUNCTION: Linear
+                + FUNCTION_PARAMS:{SLOPE:1, INTERCEPT:0}
+    COMMENT
+
+    Arguments
+    ---------
+
+    default_gating_policy : value, list or np.ndarray : :py:data:`defaultGatingPolicy <LINK]>`
+        the default value for each of the GatingMechanism's GatingSignals;
+        its length must equal the number of items specified in the **gating_signals** arg.
+
+    gating_signals : List[InputState or OutputState, Mechanism, tuple[str, Mechanism], or dict]
+        specifies the inputStates and/or outputStates to be gated by the GatingMechanism;
+        the number of items must equal the length of the **default_gating_policy** arg;
+        if a Mechanism is specified, its `primary inputState <Mechanism_InputStates>` will be used
+        (see `gating_signals <GatingMechanism.gating_signals>` for details).
+        
+    modulation : ModulationParam : ModulationParam.MULTIPLICATIVE
+        specifies the default form of modulation to be used by the GatingMechanism's GatingSignals, unless
+        they are `individually specified <GatingSignal_Specification>`.
+
+    function : TransferFunction : default Linear(slope=1, intercept=0)
+        specifies function used to transform input to `gating_policy` to value;  the default is an identity function
+        that simply assigns the GatingMechanism's input to its `value`.
+        
+    params : Optional[Dict[param keyword, param value]]
+        a `parameter dictionary <ParameterState_Specifying_Parameters>` that can be used to specify the parameters
+        for the mechanism, parameters for its function, and/or a custom function and its parameters. Values
+        specified for parameters in the dictionary override any assigned to those parameters in arguments of the
+        constructor.
+
+    name : str : default ControlMechanism-<index>
+        a string used for the name of the mechanism.
+        If not is specified, a default is assigned by `MechanismRegistry`
+        (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
+
+    prefs : Optional[PreferenceSet or specification dict : Mechanism.classPreferences]
+        the `PreferenceSet` for the mechanism.
+        If it is not specified, a default is assigned using `classPreferences` defined in __init__.py
+        (see :doc:`PreferenceSet <LINK>` for details).
+
+
+    Attributes
+    ----------
+
+    gating_signals : List[GatingSignal]
+        list of `GatingSignals <ControlSignals>` for the GatingMechanism, each of which sends a `GatingProjection`
+        to the `inputState <InputState>` or `outputState <OutputState>` that it gates (same as GatingMechanism's 
+        `output_states <Mechanism.output_states>` attribute).
+
+    gating_projections : List[GatingProjection]
+        list of `GatingProjections <GatingProjection>`, one for each `GatingSignal` in `gating_signals`.
+
+    gating_policy : 2d np.array
+        each item is the value assigned to the corresponding GatingSignal listed in `gating_signals`;
+        (same as the GatingMechanism's `value <Mechanism.value>` attribute).
+        Default is a single item used by all of the `gating_signals`. 
+
+    modulation : ModulationParam
+        the default form of modulation used by the GatingMechanism's GatingSignals, unless they are 
+        `individually specified <GatingSignal_Specification>`.
+        
+    """
+
+    componentType = "GatingMechanism"
+
+    initMethod = INIT__EXECUTE__METHOD_ONLY
+
+    classPreferenceLevel = PreferenceLevel.TYPE
+    # Any preferences specified below will override those specified in TypeDefaultPreferences
+    # Note: only need to specify setting;  level will be assigned to TYPE automatically
+    # classPreferences = {
+    #     kwPreferenceSetName: 'GatingMechanismClassPreferences',
+    #     kp<pref>: <setting>...}
+
+    # This must be a list, as there may be more than one (e.g., one per GATING_SIGNAL)
+    variableClassDefault = defaultGatingPolicy
+
+    from PsyNeuLink.Components.Functions.Function import Linear
+    paramClassDefaults = Mechanism_Base.paramClassDefaults.copy()
+    paramClassDefaults.update({GATING_PROJECTIONS: None})
+
+    @tc.typecheck
+    def __init__(self,
+                 default_gating_policy=None,
+                 function = Linear(slope=1, intercept=0),
+                 gating_signals:tc.optional(list) = None,
+                 modulation:tc.optional(_is_modulation_param)=ModulationParam.MULTIPLICATIVE,
+                 params=None,
+                 name=None,
+                 prefs:is_pref_set=None,
+                 context=None):
+
+        # self.system = None
+
+        # Assign args to params and functionParams dicts (kwConstants must == arg names)
+        params = self._assign_args_to_param_dicts(gating_signals=gating_signals,
+                                                  function=function,
+                                                  params=params)
+
+        super().__init__(variable=default_gating_policy,
+                         modulation=modulation,
+                         params=params,
+                         name=name,
+                         prefs=prefs,
+                         context=self)
+
+    def _validate_params(self, request_set, target_set=None, context=None):
+        """Validate items in the GATING_SIGNALS param (**gating_signals** argument of constructor)
+
+        Check that GATING_SIGNALS is a list, and that every item is valid state_spec
+        """
+
+        super(GatingMechanism, self)._validate_params(request_set=request_set,
+                                                      target_set=target_set,
+                                                      context=context)
+
+        if GATING_SIGNALS in target_set and target_set[GATING_SIGNALS]:
+            
+            if not isinstance(target_set[GATING_SIGNALS], list):
+                raise GatingMechanismError("{} arg of {} must be list".
+                                           format(GATING_SIGNAL, self.name))
+
+            for spec in target_set[GATING_SIGNALS]:
+                _parse_gating_signal_spec(self, spec)
+
+    def _instantiate_output_states(self, context=None):
+
+        # Create registry for GatingSignals (to manage names)
+        from PsyNeuLink.Globals.Registry import register_category
+        register_category(entry=GatingSignal,
+                          base_class=State_Base,
+                          registry=self._stateRegistry,
+                          context=context)
+
+        self.gating_signal_specs = self.gating_signals
+
+        if self.gating_signal_specs:
+            for gating_signal in self.gating_signal_specs:
+                self._instantiate_gating_signal(gating_signal=gating_signal, context=context)
+                TEST = True
+
+        # IMPLEMENTATION NOTE:  Don't want to call this because it instantiates undesired default outputState
+        # super()._instantiate_output_states(context=context)
+
+    def _instantiate_gating_signal(self, gating_signal=None, context=None):
+        """Instantiate GatingSignal OutputState and assign (if specified) or instantiate GatingProjection
+
+        # Extends gating_policy and to accommodate instantiated projection
+
+        Notes:
+        * gating_signal arg can be a:
+            - GatingSignal object;
+            - GatingProjection;
+            - InputState or OutputState;
+            - params dict, from _take_over_as_default_gating_mechanism(), containing a GatingProjection;
+            - tuple (state_name, Mechanism), from gating_signals arg of constructor;
+                    [NOTE: this is a convenience format;
+                           it precludes specification of GatingSignal params (e.g., MODULATION_OPERARATION)]
+            - GatingSignal specification dictionary, from gating_signals arg of constructor
+                    [NOTE: this must have at least NAME:str (state name) and MECHANISM:Mechanism entries; 
+                           it can also include a PARAMS entry with a params dict containing GatingSignal params] 
+        * State._parse_state_spec() is used to parse gating_signal arg
+        * params are expected to be for (i.e., to be passed to) GatingSignal;
+        * wait to instantiate deferred_init() projections until after GatingSignal is instantiated,
+            so that correct outputState can be assigned as its sender;
+        * index of outputState is incremented based on number of GatingSignals already instantiated;
+            this means that the GatingMechanism's function must return as many items as it has GatingSignals,
+            with each item of the function's value used by a corresponding GatingSignal.
+            Note: multiple GatingProjections can be assigned to the same GatingSignal to achieve "divergent gating"
+                  (that is, gating of many states with a single value -- e.g., LC)
+
+        Returns GatingSignal (OutputState)
+        """
+
+        from PsyNeuLink.Components.Projections.ModulatoryProjections.GatingProjection import GatingProjection
+
+        # EXTEND gating_policy TO ACCOMMODATE NEW GatingSignal -------------------------------------------------
+        #        also used to determine constraint on GatingSignal value
+
+        if not hasattr(self, GATING_POLICY) or self.gating_policy is None:
+            self.gating_policy = np.array(defaultGatingPolicy)
+        else:
+            self.gating_policy = np.append(self.gating_policy, defaultGatingPolicy)
+
+        # GET index FOR GatingSignal OutputState
+        try:
+            # If there are as many outputStates as items in self.value, assume that each
+            if len(self.gating_signals) == len(self.value):
+                output_state_index = len(self.gating_signals) - 1
+            else:
+                output_state_index = 0
+        except (AttributeError, TypeError):
+            output_state_index = 0
+
+
+        # PARSE gating_signal SPECIFICATION -----------------------------------------------------------------------
+
+        gating_projections = []
+        gating_signal_params = {}
+
+        gating_signal_spec = _parse_gating_signal_spec(owner=self, state_spec=gating_signal)
+
+        # Specification is a GatingSignal (either passed in directly, or parsed from tuple above)
+        if isinstance(gating_signal_spec, GatingSignal):
+            gating_signal = gating_signal_spec[GATING_SIGNAL]
+            # Deferred Initialization, so assign owner, name, and initialize
+            if gating_signal.value is DEFERRED_INITIALIZATION:
+                # FIX 5/23/17:  IMPLEMENT DEFERRED_INITIALIZATION FOR GatingSignal
+                # CALL DEFERRED INIT WITH SELF AS OWNER ??AND NAME FROM gating_signal_dict?? (OR WAS IT SPECIFIED)
+                # OR ASSIGN NAME IF IT IS DEFAULT, USING GATING_SIGNAL_DICT??
+                pass
+            elif not gating_signal_spec.owner is self:
+                raise GatingMechanismError("Attempt to assign GatingSignal to {} ({}) that is already owned by {}".
+                                            format(self.name, gating_signal_spec.name, gating_signal_spec.owner.name))
+            gating_signal_name = gating_signal.name
+            gating_projections = gating_signal.efferents
+
+        # Instantiate OutputState for GatingSignal
+        else:
+
+            gating_signal_name = gating_signal_spec[NAME]
+            # FIX: ??CALL REGISTRY FOR NAME HERE (AS FOR OUTPUTSTATE IN MECHANISM?? -
+            # FIX:  OR IS THIS DONE AUTOMATICALLY IN _instantiate_state??)
+            if gating_signal_name in [gs.name for gs in self.gating_signals if isinstance(gs, GatingSignal)]:
+                gating_signal_name = gating_signal_name + '-' + repr(len(self.gating_signals))
+
+            # from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.GatingMechanisms.GatingSignal import GatingSignal
+            # from PsyNeuLink.Components.States.State import _instantiate_state
+
+            # Get constraint for OutputState's value
+            #    - get GatingMechanism's value
+            self._update_value(context=context)
+            # - get constraint for OutputState's value
+            output_state_constraint_value = self.gating_policy[output_state_index]
+
+            # gating_signal_params.update({GATED_STATE:state_name})
+            gating_signal_params.update(gating_signal_spec[PARAMS])
+
+            # FIX 5/23/17: CALL super()_instantiate_output_states ??
+            # FIX:         OR AGGREGATE ALL GatingSignals AND SEND AS LIST (AS FOR input_states IN ObjectiveMechanism)
+            gating_signal = _instantiate_state(owner=self,
+                                               state_type=GatingSignal,
+                                               state_name=gating_signal_name,
+                                               state_spec=defaultGatingPolicy,
+                                               state_params=gating_signal_params,
+                                               constraint_value=output_state_constraint_value,
+                                               constraint_value_name='Default control allocation',
+                                               context=context)
+
+        # VALIDATE OR INSTANTIATE GatingProjection(s) TO GatingSignal  -------------------------------------------
+
+        # Validate gating_projections (if specified)
+        if gating_projections:
+            for gating_projection in gating_projections:
+                if not isinstance(gating_projection, GatingProjection):
+                    raise GatingMechanismError("PROGRAM ERROR: Attempt to assign {}, "
+                                                      "that is not a GatingProjection, to GatingSignal of {}".
+                                                      format(gating_projection, self.name))
+                _validate_receiver(self, gating_projection, Mechanism, GATING_SIGNAL, context=context)
+                state = gating_projection.receiver
+                if gating_projection.value is DEFERRED_INITIALIZATION:
+                    gating_projection.init_args['sender']=gating_signal
+                    if gating_projection.init_args['name'] is None:
+                        gating_projection.init_args['name'] = GATING_PROJECTION + \
+                                                              ' for ' + state.name + ' of ' + state.owner.name
+                    gating_projection._deferred_init()
+                else:
+                    gating_projection.sender = gating_signal
+
+        # Instantiate GatingProjection
+        else:
+            for state in gating_signal_spec[STATES]:
+                # IMPLEMENTATION NOTE:  THIS SHOULD BE MOVED TO COMPOSITION ONCE THAT IS IMPLEMENTED
+                gating_projection = GatingProjection(sender=gating_signal,
+                                                     receiver=state,
+                                                     name=GATING_PROJECTION + gating_signal_name)
+                # Add gating_projection to GatingSignal.efferents
+                # gating_signal.efferents.append(gating_projection)
+                gating_projections.append(gating_projection)
+
+        # Add GatingProjections to GatingMechanism's list of GatingProjections
+        for gating_projection in gating_projections:
+            try:
+                self.gating_projections.append(gating_projection)
+            except AttributeError:
+                self.gating_projections = [gating_projection]
+
+        # FIX: CONSIDER OVERRIDING output_states PROPERTY WITH ASSIGNMENT TO gating_signals
+        # UPDATE output_states
+        try:
+            self.output_states[gating_signal.name] = gating_signal
+        except (AttributeError, TypeError):
+            self.output_states = ContentAddressableList(component_type=State_Base,
+                                                        list=[gating_signal],
+                                                        name=self.name + '.output_states')
+        # Add index assignment to outputState
+        gating_signal.index = output_state_index
+        # (Re-)assign control_signals attribute to output_states
+        self._gating_signals = self.output_states
+
+        return gating_signal
+
+    def _instantiate_attributes_after_function(self, context=None):
+        """Take over as default GatingMechanism (if specified) and implement any specified GatingProjections
+        """
+
+        super()._instantiate_attributes_after_function(context=context)
+
+        if MAKE_DEFAULT_GATING_MECHANISM in self.paramsCurrent:
+            if self.paramsCurrent[MAKE_DEFAULT_GATING_MECHANISM]:
+                self._take_over_as_default_gating_mechanism(context=context)
+
+        # FIX: 5/23/17 CONSOLIDATE/SIMPLIFY THIS RE: gating_signal ARG??  USE OF STATE_PROJECTIONS, ETC.
+        # FIX:         ?? WHERE WOULD GATING_PROJECTIONS HAVE BEEN SPECIFIED IN paramsCURRENT??
+        # FIX:         DOCUMENT THAT VALUE OF GATING ENTRY CAN BE A PROJECTION
+        # FIX:         RE-WRITE parse_state_spec TO TAKE TUPLE THAT SPECIFIES (PARAM VALUE, GATING SIGNAL)
+        #                       RATHER THAN (PARAM VALUE, GATING PROJECTION)
+        # FIX: NOT CLEAR THIS IS GETTING USED AT ALL; ALSO, ??REDUNDANT WITH CALL IN _instantiate_output_states
+        # If GatingProjections were specified, implement them
+        if GATING_PROJECTIONS in self.paramsCurrent:
+            if self.paramsCurrent[GATING_PROJECTIONS]:
+                for key, projection in self.paramsCurrent[GATING_PROJECTIONS].items():
+                    self._instantiate_gating_projection(projection, context=self.name)
+
+    def _take_over_as_default_gating_mechanism(self, context=None):
+
+        # FIX 5/23/17: INTEGRATE THIS WITH ASSIGNMENT OF gating_signals
+        # FIX:         (E.G., CHECK IF SPECIFIED GatingSignal ALREADY EXISTS)
+        # Check the input_states and output_states of the system's mechanisms
+        #    for any GatingProjections with deferred_init()
+        for mech in self.system.mechanisms:
+            for state in mech._input_states + mech._output_states:
+                for projection in state.mod_afferents:
+                    # If projection was deferred for init, initialize it now and instantiate for self
+                    if projection.value is DEFERRED_INITIALIZATION and projection.init_args['sender'] is None:
+                        # FIX 5/23/17: MODIFY THIS WHEN (param, GatingProjection) tuple
+                        # FIX:         IS REPLACED WITH (param, GatingSignal) tuple
+                        # Add projection itself to any params specified in the GatingProjection for the GatingSignal
+                        #    (cached in the GatingProjection's gating_signal attrib)
+                        gating_signal_specs = projection.gating_signal or {}
+                        gating_signal_specs.update({GATING_SIGNAL_SPECS: [projection]})
+                        self._instantiate_gating_signal(gating_signal_specs, context=context)
+
+    # IMPLEMENTATION NOTE: This is needed if GatingMechanism is added to a system but does not have any afferents
+    #                      (including from ProcessInputState or SystemInputState)
+    #                      and therefore variable = None
+    def _execute(self,
+                    variable=None,
+                    runtime_params=None,
+                    clock=CentralClock,
+                    time_scale=TimeScale.TRIAL,
+                    context=None):
+        """Updates GatingSignals based on inputs
+        """
+
+        if variable is None or variable[0] is None:
+            variable = self.variableClassDefault
+
+        return super()._execute(variable=variable,
+                                runtime_params=runtime_params,
+                                clock=clock,
+                                time_scale=time_scale,
+                                context=context)
+        # gating_policy = self.function(variable=variable,
+        #                               function_params=function_params,
+        #                               time_scale=time_scale,
+        #                               context=context)
+        # return gating_policy
+
+
+    def show(self):
+
+        print ("\n---------------------------------------------------------")
+
+        print ("\n{0}".format(self.name))
+        print ("\n\tGating the following Mechanism InputStates and/or OutputStates:".format(self.name))
+        # Sort for consistency of output:
+        state_names_sorted = sorted(self.output_states.keys())
+        for state_name in state_names_sorted:
+            for projection in self.output_states[state_name].efferents:
+                print ("\t\t{0}: {1}".format(projection.receiver.owner.name, projection.receiver.name))
+        print ("\n---------------------------------------------------------")
+
+
+# IMPLEMENTATION NOTE:  THIS SHOULD BE MOVED TO COMPOSITION ONCE THAT IS IMPLEMENTED
+def _add_gating_mechanism_to_system(owner:GatingMechanism):
+
+    if owner.gating_signals:
+        for gating_signal in owner.gating_signals:
+            for mech in [proj.receiver.owner for proj in gating_signal.efferents]:
+                for system in mech.systems:
+                    if owner not in system.executionList:
+                        system.executionList.append(owner)
+                        system.executionGraph[owner] = set()
+                        # FIX: NEED TO ALSO ADD SystemInputState (AND ??ProcessInputState) PROJECTIONS
+                        # # Add self to system's list of OriginMechanisms if it doesn't have any afferents
+                        # if not any(state.path_afferents for state in owner.input_states):
+                        #     system.originMechanisms.mechs.append(owner)
+
+
diff --git a/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/LearningMechanisms/LearningAuxilliary.py b/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/LearningMechanisms/LearningAuxilliary.py
index dea2f684da1..838b8abd3b8 100644
--- a/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/LearningMechanisms/LearningAuxilliary.py
+++ b/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/LearningMechanisms/LearningAuxilliary.py
@@ -48,8 +48,8 @@
                       use error_mech.outputState.valuee to initialize variable[ACTIVITY]
                       use outputState.value of error_mech's objective_mechanism to initialize variable[ERROR]
                 Assign mapping projections:
-                      nextLevel.outputState.value -> inputStates[ACTIVITY] of ObjectiveMechanism
-                      nextLevel.objective_mechanism.outputState.value  -> inputStates[ERROR] of ObjectiveMechanism
+                      nextLevel.outputState.value -> input_states[ACTIVITY] of ObjectiveMechanism
+                      nextLevel.objective_mechanism.outputState.value  -> input_states[ERROR] of ObjectiveMechanism
                 NOTE: For TERMINAL mechanism:
                           error_mech is Process or System InputState (function=Linear, so derivative =1), so that
                              error_mech.outputState.value is the target, and
@@ -64,9 +64,9 @@
                 Assign error_derivative using function of error_mech
                 Assign error_matrix as runtime_param using projection to error_mech [ALT: ADD TO VARIABLE]
                 Assign mapping projections:
-                      mapping_projection.sender -> inputStates[ACTIVATION_INPUT_INDEX] of LearningMechanism
-                      activation_mech_output.outputState -> inputStates[ACTIVATION_OUTPUT_INDEX] of LearningMechanism
-                      error_mech.objective_mechanism.OutputState.value -> inputStates[ERROR_SIGNAL_INDEX]
+                      mapping_projection.sender -> input_states[ACTIVATION_INPUT_INDEX] of LearningMechanism
+                      activation_mech_output.outputState -> input_states[ACTIVATION_OUTPUT_INDEX] of LearningMechanism
+                      error_mech.objective_mechanism.OutputState.value -> input_states[ERROR_SIGNAL_INDEX]
 
             For TARGET MECHANISM:  Matrix is IDENTITY MATRIX??
             For TARGET MECHANISM:  derivative for ObjectiveMechanism IDENTITY FUNCTION
@@ -114,9 +114,9 @@
                 Assign:
                     NOTE:  should do these in their own Learning module function, called by LearningMechanaism directly
                         as is done for ObjectiveMechanism
-                    MappingProjection: activation_mech_projection.sender -> LearningMechanism.inputState[ACTIVATION_INPUT]
-                    MappingProjection: activation_mech.outputState -> LearningMechanism.inputState[ACTIVATION_OUTPUT]
-                    MappingProjection: ObjectiveMechanism -> LearningMechanism.inputState[ERROR_SIGNAL]
+                    MappingProjection: activation_mech_projection.sender -> LearningMechanism.input_state[ACTIVATION_INPUT]
+                    MappingProjection: activation_mech.outputState -> LearningMechanism.input_state[ACTIVATION_OUTPUT]
+                    MappingProjection: ObjectiveMechanism -> LearningMechanism.input_state[ERROR_SIGNAL]
 
 
 COMMENT
@@ -133,29 +133,21 @@
 
 from PsyNeuLink.Components.Functions.Function import Function, function_type, method_type
 from PsyNeuLink.Components.Functions.Function import Linear, LinearCombination, Reinforcement, BackPropagation
-from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.LearningMechanisms.LearningMechanism import ACTIVATION_INPUT,\
-    ACTIVATION_OUTPUT, ERROR_SIGNAL, LEARNING_SIGNAL
-from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.AdaptiveMechanism import AdaptiveMechanism_Base
+from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.LearningMechanisms.LearningMechanism import ACTIVATION_INPUT, \
+    ACTIVATION_OUTPUT, ERROR_SIGNAL
 from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.LearningMechanisms.LearningMechanism import LearningMechanism
-from PsyNeuLink.Components.Mechanisms.Mechanism import Mechanism
-from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ObjectiveMechanism import ObjectiveMechanism, \
-    _objective_mechanism_role
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ObjectiveMechanisms.ObjectiveMechanism import \
+    ObjectiveMechanism
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ObjectiveMechanisms.ComparatorMechanism \
+    import ComparatorMechanism, MSE
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ProcessingMechanism import ProcessingMechanism_Base
-from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection
-from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
-from PsyNeuLink.Components.Projections.Projection import _is_projection_spec, _add_projection_from, _add_projection_to
-from PsyNeuLink.Components.States.InputState import InputState
+from PsyNeuLink.Components.Projections.ModulatoryProjections.LearningProjection import LearningProjection
+from PsyNeuLink.Components.Projections.Projection import _is_projection_spec
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
 from PsyNeuLink.Components.States.OutputState import OutputState
 from PsyNeuLink.Components.States.ParameterState import ParameterState
 from PsyNeuLink.Globals.Keywords import *
 
-TARGET_ERROR = "TARGET_ERROR"
-TARGET_ERROR_MEAN = "TARGET_ERROR_MEAN"
-TARGET_ERROR_SUM = "TARGET_ERROR_SUM"
-TARGET_SSE = "TARGET_SSE"
-TARGET_MSE = "TARGET_MSE"
-
-
 class LearningAuxilliaryError(Exception):
     def __init__(self, error_value):
         self.error_value = error_value
@@ -241,7 +233,7 @@ def _instantiate_learning_components(learning_projection, context=None):
     # IMPLEMENTATION NOTE:  this may be overly restrictive in the context of a system --
     #                       will need to be dealt with in Composition implementation (by examining its learning graph??)
     if any((isinstance(projection, LearningProjection) and not projection is learning_projection) for projection in
-           learning_projection.receiver.receivesFromProjections):
+           learning_projection.receiver.mod_afferents):
         raise LearningAuxilliaryError("{} can't be assigned as LearningProjection to {} since that already has one.".
                                       format(learning_projection.name, learning_projection.receiver.owner.name))
 
@@ -273,7 +265,7 @@ def _instantiate_learning_components(learning_projection, context=None):
     objective_mechanism = None
 
     # if activation_mech has outgoing projections
-    if (lc.activation_mech_output.sendsToProjections and
+    if (lc.activation_mech_output.efferents and
             # if the ProcessingMechanisms to which activation_mech projects do not belong to any of the same processes
             # to which the mechanisms that project to activation_mech belong, then it should be treated as a TERMINAL
             # and is_target should be set to True
@@ -293,12 +285,12 @@ def _instantiate_learning_components(learning_projection, context=None):
                                     process in projection.receiver.owner.processes
                                                    # processes of mechanism that project to activation_mech
                                     for process in lc.activation_mech_input.owner.processes)
-                        for projection in lc.activation_mech_output.sendsToProjections)):
+                        for projection in lc.activation_mech_output.efferents)):
         is_target = True
 
     else:
 
-        for projection in lc.activation_mech_output.sendsToProjections:
+        for projection in lc.activation_mech_output.efferents:
 
             receiver_mech = projection.receiver.owner
             receiver_state = projection.receiver
@@ -325,7 +317,7 @@ def _instantiate_learning_components(learning_projection, context=None):
                 #         so issue warning, assign it as the sender, and return
                 if (receiver_state.name is ACTIVATION_OUTPUT and
                         any(projection.sender.owner is lc.activation_mech_input.owner
-                            for projection in receiver_mech.inputStates[ACTIVATION_INPUT].receivesFromProjections)):
+                            for projection in receiver_mech.input_states[ACTIVATION_INPUT].path_afferents)):
                         warnings.warn("An existing LearningMechanism ({}) was found for and is being assigned to {}".
                                       format(receiver_mech.name, learning_projection.name))
                         learning_projection.sender = receiver_mech
@@ -350,8 +342,8 @@ def _instantiate_learning_components(learning_projection, context=None):
                 # If the ObjectiveMechanism projects to a LearningMechanism that is the sender for the
                 #     learning_projection, raise exception as this function should not have been called
                 elif (isinstance(learning_projection.sender, LearningMechanism) and
-                          any(learning_projection.sender.owner is project.receiver.owner
-                              for projection in receiver_mech.outputState.sendsToProjections)):
+                          any(learning_projection.sender.owner is projection.receiver.owner
+                              for projection in receiver_mech.output_state.efferents)):
                     raise LearningAuxilliaryError("PROGRAM ERROR:  {} already has an "
                                                   "ObjectiveMechanism ({}) and a "
                                                   "LearningMechanism ({}) assigned to it".
@@ -433,7 +425,7 @@ def _instantiate_learning_components(learning_projection, context=None):
 
         else:
             error_output = np.zeros_like(lc.error_mech_output.value)
-            error_signal = np.zeros_like(lc.error_signal_mech.outputStates[ERROR_SIGNAL].value)
+            error_signal = np.zeros_like(lc.error_signal_mech.output_states[ERROR_SIGNAL].value)
             error_matrix = lc.error_matrix
             try:
                 error_derivative = lc.error_derivative
@@ -449,13 +441,10 @@ def _instantiate_learning_components(learning_projection, context=None):
                                                               # error_output,
                                                               error_signal],
                                             activation_derivative_fct=activation_derivative,
-                                            # # MODIFIED 3/5/17 OLD:
-                                            # error_derivative_fct=error_derivative,
-                                            # MODIFIED 3/5/17 NEW:
                                             error_derivative_fct=activation_derivative,
-                                            # MODIFIED 3/5/17 END
                                             error_matrix=error_matrix,
-                                            learning_rate=learning_rate)
+                                            learning_rate=learning_rate,
+                                            context=context)
 
     else:
         raise LearningAuxilliaryError("PROGRAM ERROR: unrecognized learning function ({}) for {}".
@@ -470,7 +459,7 @@ def _instantiate_learning_components(learning_projection, context=None):
         if objective_mechanism is None:
             # Instantiate ObjectiveMechanism
             # Notes:
-            # * MappingProjections for ObjectiveMechanism's inputStates will be assigned in its own call to Composition
+            # * MappingProjections for ObjectiveMechanism's input_states will be assigned in its own call to Composition
             # * Need to specify both default_input_value and monitored_values since they may not be the same
             #    sizes (e.g., for RL the monitored_value for the sample may be a vector, but its input_value must be scalar)
             # SAMPLE inputState for ObjectiveMechanism should come from activation_mech_output
@@ -478,34 +467,47 @@ def _instantiate_learning_components(learning_projection, context=None):
             #     so that it is left free to later be assigned a projection from ProcessInputState and/or SystemInputState
             # Assign derivative of Linear to lc.error_derivative (as default, until TARGET projection is assigned);
             #    this will induce a simple subtraction of target-sample (i.e., implement a comparator)
-
             sample_input = target_input = error_output
-            # Assign outputStates for TARGET ObjectiveMechanism (used for reporting)
-            object_mech_params = {OUTPUT_STATES:
-                                      [{NAME:TARGET_ERROR},
-                                       {NAME:TARGET_ERROR_MEAN,
-                                        CALCULATE:lambda x: np.mean(x)},
-                                       {NAME:TARGET_ERROR_SUM,
-                                        CALCULATE:lambda x: np.sum(x)},
-                                       {NAME:TARGET_SSE,
-                                        CALCULATE:lambda x: np.sum(x*x)},
-                                       {NAME:TARGET_MSE,
-                                        CALCULATE:lambda x: np.sum(x*x)/len(x)}]}
-
-            objective_mechanism = ObjectiveMechanism(default_input_value=[sample_input,
-                                                                          target_input],
-                                                     monitored_values=[lc.activation_mech_output,
-                                                                       TARGET],
-                                                     names=['SAMPLE','TARGET'],
-                                                     function=LinearCombination(weights=[[-1], [1]]),
-                                                     params=object_mech_params,
-                                                     name=lc.activation_mech.name + " " + OBJECTIVE_MECHANISM)
+
+            objective_mechanism = ComparatorMechanism(sample=lc.activation_mech_output,
+                                                      target=TARGET,
+                                                      # input_states=[sample_input, target_input],
+                                                      # FOR TESTING: ALTERNATIVE specifications of input_states arg:
+                                                      # input_states=[(sample_input, FULL_CONNECTIVITY_MATRIX),
+                                                      #               target_input],
+                                                      # input_states=[(sample_input, RANDOM_CONNECTIVITY_MATRIX),
+                                                      #               target_input],
+                                                      input_states=[{NAME:SAMPLE,
+                                                                     VARIABLE:sample_input,
+                                                                     WEIGHT:-1
+                                                                     },
+                                                                    {NAME:TARGET,
+                                                                     VARIABLE:target_input,
+                                                                     # WEIGHT:1
+                                                                     }],
+                                                      name="{} {}".format(lc.activation_mech.name,
+                                                                          COMPARATOR_MECHANISM),
+                                                      context=context)
+
+            # # FOR TESTING: ALTERNATIVE to Direct call to ObjectiveMechanism
+            # #              (should produce identical result to use of ComparatorMechanism above)
+            # objective_mechanism = ObjectiveMechanism(monitored_values=[lc.activation_mech_output,
+            #                                                            TARGET],
+            #                                          input_states=[{SAMPLE:sample_input},
+            #                                                        {TARGET:target_input}],
+            #                                          function=LinearCombination(weights=[[-1], [1]]),
+            #                                          output_states=[ERROR_SIGNAL,
+            #                                                         {NAME:MSE,
+            #                                                          CALCULATE:lambda x: np.sum(x*x)/len(x)}],
+            #                                          name="\'{}\' {}".format(lc.activation_mech.name,
+            #                                                                  COMPARATOR_MECHANISM),
+            #                                          context=context)
 
             objective_mechanism._role = LEARNING
             objective_mechanism._learning_role = TARGET
 
         try:
-            lc.error_projection = objective_mechanism.inputState.receivesFromProjections[0]
+            lc.error_projection = objective_mechanism.input_state.path_afferents[0]
             # FIX: THIS IS TO FORCE ASSIGNMENT (SINCE IT DOESN'T SEEM TO BE ASSIGNED BY TEST BELOW)
         except AttributeError:
             raise LearningAuxilliaryError("PROGRAM ERROR: problem finding projection to TARGET ObjectiveMechanism "
@@ -539,7 +541,10 @@ def _instantiate_learning_components(learning_projection, context=None):
                                                      error_signal],
                                            error_source=error_source,
                                            function=learning_function,
-                                           name = lc.activation_mech_projection.name + " " +LEARNING_MECHANISM)
+                                           # learning_signals=[lc.activation_mech_projection],
+                                           learning_signals=[learning_projection],
+                                           name = lc.activation_mech_projection.name + " " +LEARNING_MECHANISM,
+                                           context=context)
 
     # IMPLEMENTATION NOTE:
     # ADD ARGUMENTS TO LearningMechanism FOR activation_input AND activation_output, AND THEN INSTANTIATE THE
@@ -548,22 +553,25 @@ def _instantiate_learning_components(learning_projection, context=None):
 
     # Assign MappingProjection from activation_mech_input to LearningMechanism's ACTIVATION_INPUT inputState
     MappingProjection(sender=lc.activation_mech_input,
-                      receiver=learning_mechanism.inputStates[ACTIVATION_INPUT],
+                      receiver=learning_mechanism.input_states[ACTIVATION_INPUT],
                       matrix=IDENTITY_MATRIX,
-                      name = lc.activation_mech_input.owner.name + ' to ' + ACTIVATION_INPUT)
+                      name = lc.activation_mech_input.owner.name + ' to ' + ACTIVATION_INPUT,
+                      context=context)
 
     # Assign MappingProjection from activation_mech_output to LearningMechanism's ACTIVATION_OUTPUT inputState
     MappingProjection(sender=lc.activation_mech_output,
-                      receiver=learning_mechanism.inputStates[ACTIVATION_OUTPUT],
+                      receiver=learning_mechanism.input_states[ACTIVATION_OUTPUT],
                       matrix=IDENTITY_MATRIX,
-                      name = lc.activation_mech_output.owner.name + ' to ' + ACTIVATION_OUTPUT)
+                      name = lc.activation_mech_output.owner.name + ' to ' + ACTIVATION_OUTPUT,
+                      context=context)
 
-    # Assign learning_mechanism as sender of learning_projection and return
-    # Note: learning_projection still has to be assigned to the learning_mechanism's outputState;
-    #       however, this requires that it's variable be assigned (which occurs in the rest of its
-    #       _instantiate_sender method, from which this was called) and that its value be assigned
-    #       (which occurs in its _instantiate_function method).
-    learning_projection.sender = learning_mechanism.outputState
+    # IMPLEMENTATION NOTE: [6/4/17] THIS IS NOW DONE IN LearningMechanism._instantiate_learning_signal
+    # # Assign learning_mechanism as sender of learning_projection and return
+    # # Note: learning_projection still has to be assigned to the learning_mechanism's outputState;
+    # #       however, this requires that it's variable be assigned (which occurs in the rest of its
+    # #       _instantiate_sender method, from which this was called) and that its value be assigned
+    # #       (which occurs in its _instantiate_function method).
+    # learning_projection.sender = learning_mechanism.output_state
 
 
 class LearningComponents(object):
@@ -774,7 +782,7 @@ def _get_act_sample():
             # If MONITOR_FOR_LEARNING specifies an outputState, use that
             try:
                 sample_state_name = self.activation_mech.paramsCurrent[MONITOR_FOR_LEARNING]
-                self.activation_mech_output = self.activation_mech.outputStates[sample_state_name]
+                self.activation_mech_output = self.activation_mech.output_states[sample_state_name]
                 if not isinstance(self.activation_mech_output, OutputState):
                     raise LearningAuxilliaryError("The specification of the MONITOR_FOR_LEARNING parameter ({}) "
                                                   "for {} is not an outputState".
@@ -782,12 +790,12 @@ def _get_act_sample():
             except KeyError:
                 # No outputState specified so use primary outputState
                 try:
-                    self.activation_mech_output = self.activation_mech.outputState
+                    self.activation_mech_output = self.activation_mech.output_state
                 except AttributeError:
                     raise LearningAuxilliaryError("activation_mech_output not identified: activation_mech ({})"
                                                   "not appear to have been assigned a primary outputState.".
                                                   format(self.learning_projection))
-            return self.activation_mech.outputState
+            return self.activation_mech.output_state
 
         return self._activation_mech_output or _get_act_sample()
 
@@ -811,8 +819,8 @@ def _get_err_matrix():
             if not self.error_projection:
                 return None
             try:
-                self.error_matrix = self.error_projection.parameterStates[MATRIX]
-                return self.error_projection.parameterStates[MATRIX]
+                self.error_matrix = self.error_projection._parameter_states[MATRIX]
+                return self.error_projection._parameter_states[MATRIX]
             except AttributeError:
                 raise LearningAuxilliaryError("error_matrix not identified: error_projection ({})"
                                               "not not have a {} parameterState".
@@ -836,7 +844,7 @@ def error_projection(self):
         def _get_error_proj():
             if not self.activation_mech_output:
                 return None
-            projections = self.activation_mech_output.sendsToProjections
+            projections = self.activation_mech_output.efferents
             # MODIFIED 3/11/17 OLD:
             # error_proj must be a MappingProjection that has a LearningProjection to it
             error_proj = next((projection for projection in projections if
@@ -934,7 +942,7 @@ def _get_err_mech_out():
             if not self.error_mech:
                 return None
             try:
-                self.error_mech_output = self.error_mech.outputState
+                self.error_mech_output = self.error_mech.output_state
             except AttributeError:
                 raise LearningAuxilliaryError("error_mech_output not identified: error_mech ({})"
                                               "does not appear to have an outputState".
@@ -943,7 +951,7 @@ def _get_err_mech_out():
                 raise LearningAuxilliaryError("error_mech_output found ({}) but it does not "
                                               "appear to be an OutputState".
                                               format(self.error_mech_output.name))
-            return self.error_mech.outputState
+            return self.error_mech.output_state
         return self._error_mech_output or _get_err_mech_out()
 
     @error_mech_output.setter
@@ -963,7 +971,7 @@ def _get_error_signal_mech():
             if not self.error_matrix:
                 return None
             # search the projections to the error_matrix parameter state for a LearningProjection
-            learning_proj = next((proj for proj in self.error_matrix.receivesFromProjections
+            learning_proj = next((proj for proj in self.error_matrix.mod_afferents
                                  if isinstance(proj, LearningProjection)), None)
             # if there are none, the error_matrix might be for an error_projection to an ObjectiveMechanism
             #   (i.e., the TARGET mechanism)
@@ -994,7 +1002,7 @@ def _get_error_signal_mech():
             if learning_mech.processes and not any(process in self.activation_mech_input.owner.processes
                                                    for process in learning_mech.processes):
                 new_learning_proj = next((projection for projection in
-                                          self.activation_mech.outputState.sendsToProjections
+                                          self.activation_mech.output_state.efferents
                                           if isinstance(projection.receiver.owner, ObjectiveMechanism)), None)
                 if new_learning_proj is None:
                     return None
@@ -1029,7 +1037,7 @@ def _get_err_sig_mech_out():
                 return None
             if isinstance(self.error_signal_mech, ObjectiveMechanism):
                 try:
-                    self.error_signal_mech_output = self.error_signal_mech.outputState
+                    self.error_signal_mech_output = self.error_signal_mech.output_state
                 except AttributeError:
                     raise LearningAuxilliaryError("error_signal_mech_output not identified: error_signal_mech ({})"
                                                   "does not appear to have an outputState".
@@ -1041,7 +1049,7 @@ def _get_err_sig_mech_out():
                                                          self.error_signal_mech_output.name))
             elif isinstance(self.error_signal_mech, LearningMechanism):
                 try:
-                    self.error_signal_mech_output = self.error_signal_mech.outputStates[ERROR_SIGNAL]
+                    self.error_signal_mech_output = self.error_signal_mech.output_states[ERROR_SIGNAL]
                 except AttributeError:
                     raise LearningAuxilliaryError("error_signal_mech_output not identified: error_signal_mech ({})"
                                                   "does not appear to have an ERROR_SIGNAL outputState".
@@ -1051,7 +1059,7 @@ def _get_err_sig_mech_out():
                                                   "appear to be an OutputState".
                                                   format(self.error_signal_mech.name,
                                                          self.error_signal_mech_output.name))
-            return self.error_signal_mech.outputState
+            return self.error_signal_mech.output_state
         return self._error_signal_mech_output or _get_err_sig_mech_out()
 
     @error_signal_mech_output.setter
@@ -1070,7 +1078,7 @@ def error_signal_mech_output(self, assignment):
     #     def _get_obj_mech():
     #         if not self.error_matrix:
     #             return None
-    #         learning_proj = next((proj for proj in self.error_matrix.receivesFromProjections
+    #         learning_proj = next((proj for proj in self.error_matrix.path_afferents
     #                              if isinstance(proj, LearningProjection)),None)
     #         if not learning_proj:
     #             # error_matrix is for a MappingProjection that projects to the TARGET ObjectiveMechanism, so return that
@@ -1091,7 +1099,7 @@ def error_signal_mech_output(self, assignment):
     #                                           "LearningMechanism")
     #         try:
     #             error_obj_mech = next((proj.sender.owner
-    #                                    for proj in learning_mech.inputStates[ERROR_SIGNAL].receivesFromProjections
+    #                                    for proj in learning_mech.input_states[ERROR_SIGNAL].path_afferents
     #                                    if isinstance(proj.sender.owner, ObjectiveMechanism)),None)
     #         except AttributeError:
     #             # return None
diff --git a/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/LearningMechanisms/LearningMechanism.py b/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/LearningMechanisms/LearningMechanism.py
index 03f0bb95398..eb212dbccf1 100644
--- a/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/LearningMechanisms/LearningMechanism.py
+++ b/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/LearningMechanisms/LearningMechanism.py
@@ -8,13 +8,6 @@
 
 # *******************************************  LearningMechanism *******************************************************
 
-# DOCUMENT:
-#    IF objective_mechanism IS  None, IT IS LEFT UNSPECIFIED (FOR FURTHER IMPLEMENTATION BY COMPOSITION)
-#    THESE ARE HANDLED BY A MODULE METHOD _instantiate_objective_mechanism (AS PER OBJECTIVE MECHANISM):
-#        IF objective_mechanism IS SPECIFIED AS ObjectiveMechanism, AN OBJECTIVE MECHANISM IS CREATED FOR IT
-#        IF objective_mechanism IS SPECIFIED AS A MECHANISM OR OUTPUTSTATE,
-#               a MappingProjection WITH AN IDENTITY MATRIX IS IMPLEMENTED FROM IT TO THE LearningMechanism
-
 """
 .. _LearningMechanism_Overview:
 
@@ -23,20 +16,16 @@
 
 A LearningMechanism is an `AdaptiveMechanism <AdaptiveMechanism>` that modifies a parameter (usually the `matrix
 <MappingProjection.matrix>`) of a `MappingProjection`.  It's function takes an `error_signal` (usually the output of
-an `ObjectiveMechanism` or another `LearningMechanism`) and generates a `learning_signal` that is used to modify
-the MappingProjection by way of a `LearningProjection`.  A LearningMechanism can modify only MappingProjections that
-link ProcessingMechanisms in the same `system <System>` or `process <Process>` to which it belongs.  The learning  
-components of a system can be displayed using the system's `show_graph` method with its **show_learning** argument 
-assigned :keyword:``True`.  LearningMechanisms are execute after all of the ProcessingMechanisms in that system or 
-process have been executed.  If they belong to a system, they are executed before the  
-`control components <ControlMechanism>` for that system have been executed.
+an `ObjectiveMechanism` or another `LearningMechanism`) and generates a `learning_signal` that is used to modify the
+MappingProjection by way of a `LearningProjection`.  The MappingProjection(s) modified by a LearningMechanism must
+link `ProcessingMechanisms <ProcessingMechanism>` that belong to the same `system <System>` or `process <Process>`.
+The learning components of a system can be displayed using the system's `show_graph` method with its
+**show_learning** argument assigned :keyword:`True`.  LearningMechanisms are executed after all of the
+ProcessingMechanisms in that system or process have been executed, and before any
+`control components <ControlMechanism>` of the system have been executed.
 
 COMMENT:
-  @@@ SEARCH FOR LearningProjection_Automatic_Creation AND REPLACE WITH REFERENCE TO THIS LABEL:
-COMMENT
-
-COMMENT:
-  AT PRESENT, LearningMechanisms SUPPORTS MODIFICATION OF ONLY A SINGLE MappingProjection;  FUTURE VERSIONS MAY
+  AT PRESENT, LearningMechanisms SUPPORT MODIFICATION OF ONLY A SINGLE MappingProjection;  FUTURE VERSIONS MAY
   ALLOW MODIFICATION OF MULTIPLE MappingProjections (USING MULTIPLE CORRESPONDING error_signals).
 COMMENT
 
@@ -45,13 +34,13 @@
 Creating a LearningMechanism
 ----------------------------
 
-LearningMechanisms can be created in any of the ways that can be used to `create mechanisms <Mechanism_Creation>`.
+LearningMechanisms can be created in any of the ways used to `create mechanisms <Mechanism_Creation>`.
 More commonly, however, they are created automatically when:
 
-* the learning attribute is specified for a :ref:`system <LINK>` or :ref:`process <LINK>`;
+* the learning attribute is specified for a `system <System_Execution_Learning>` or `process <Process_Learning>`;
 ..
 * a `LearningProjection` (or the keyword `LEARNING`) is specified in a
-  `tuple specification <MappingProjection_Tuple_Specification>`  for a MappingProjection in the `pathway` of a process;
+  `tuple specification <MappingProjection_Tuple_Specification>` for a `MappingProjection` in the `pathway` of a process;
 ..
 * a `LearningProjection` is created without specifying its `sender <LearningProjection.sender>` attribute.
 
@@ -59,135 +48,174 @@
 required to implement learning that do not already exist are also instantiated.  This is described below, under
 `Learning Configurations <LearningMechanism_Learning_Configurations>`.
 
+
+COMMENT:
+    XXXXX
+    ADD MORE ABOUT SPECIFYING LearningMechanism HERE
+    DESCRIPTION OF LearningSignals AND THEIR SPECIFICATION HERE
+    INCLUDING SPECIFICATION, DEFAULTS AND INDEXING (VIS A VIS ERROR_SIGNAL) OutputState
+   can have more than one LearningSignal outputState, but they all use the same learning_signal value
+   (multiple LearningSignals can be used to implemented different modulation params, learning rates, etc.)
+COMMENT
+
+
 .. _LearningMechanism_Structure:
 
 Structure
 ---------
 
-A LearningMechanism has three `inputStates <InputState>`, a learning `function <LearningMechanism.function>`,
-and two `outputStates <OutputState>` that are used to receive, compute, and transmit the information needed to modify
-the MappingProjection for which it is responsible.
+A LearningMechanism has three `InputStates <InputState>`, a learning `function <LearningMechanism.function>`,
+and two types of `OutputStates <OutputState>` that are used, respectively, to receive, compute, and transmit the
+information needed to modify the MappingProjection(s) for which it is responsible.  In addition, it has several
+attributes that govern its operation.  These are all described below.
 
 .. _LearningMechanism_InputStates:
 
 InputStates
 ~~~~~~~~~~~
 
-These receive the information required by the LearningMechanism's `function <LearningMechanism_Function>`:
+These receive the information required by the LearningMechanism's `function <LearningMechanism_Function>`.  They are
+listed in the LearningMechanism's `input_states <LearningMechanism.input_states>` attribute, and have the following
+names and roles (shown in the `figure` below <LearningMechanism_Single_Layer_Learning_Figure>`):
 
 .. _LearningMechanism_Activation_Input:
 
-* `ACTIVATION_INPUT`
-   This receives the value of the input to the MappingProjection being learned (that is, the
-   value of its `sender <MappingProjection.sender>`).  It's value is assigned as the first item of the
-   LearningMechanism's `variable <LearningMechanism.variable>` attribute.
+* *ACTIVATION_INPUT* - receives the value of the input to the MappingProjection being learned (that is, the
+  `value <MappingProjection.value> of the MappingProjection's `sender <MappingProjection.sender>`).
+  The value is assigned as the first item of the LearningMechanism's `variable <LearningMechanism.variable>` attribute.
 
 .. _LearningMechanism_Activation_Output:
 
-* `ACTIVATION_OUTPUT`
-   This receives the value of the LearningMechanism's `error_source <LearningMechanism_Additional_Attributes>`
-   (that is, the output of the ProcessingMechanism to which the MappingProjection being learned projects).  By
-   default, this uses the `primary outputState <OutputState_Primary>` of the `error_source`, but a different
-   outputState can be designated in the `parameter dictionary <ParameterState_Specifying_Parameters>` for the
-   params argument of the `error_source`, by including an entry with `MONITOR_FOR_LEARNING` as its key and a list
-   containing the desired outputState(s) as its value. The value of the `ACTIVATION_OUTPUT` inputState is assigned
-   as the second item of the LearningMechanism's `variable <LearningMechanism.variable>` attribute.
+* *ACTIVATION_OUTPUT* - receives the value of the LearningMechanism's
+  `error_source <LearningMechanism_Additional_Attributes>` (that is, the output of the *ProcessingMechanism* to which
+  the MappingProjection being learned projects).  By default, this uses the `primary outputState <OutputState_Primary>`
+  of the `error_source`.  However, a different outputState can be designated in the constructor for the `error_source`,
+  by assigning a `parameter specification dictionary <ParameterState_Specifying_Parameters>` to its **params**
+  argument with an entry that uses *MONITOR_FOR_LEARNING* as its key and a list containing the desired
+  outputState(s) as its value.  The :keyword:`value` of the *ACTIVATION_OUTPUT* inputState is assigned as the
+  second item of the LearningMechanism's `variable <LearningMechanism.variable>` attribute.
 
 .. _LearningMechanism_Input_Error_Signal:
 
-* `ERROR_SIGNAL`
-   This receives an error_signal from either an `ObjectiveMechanism` or another LearningMechanism.
-   If the MappingProjection being learned projects to the `TERMINAL` mechanism of the process or system being learned,
-   or is not part of a `multilayer learning sequence <LearningMechanism_Multi_Layer>`, then the error_signal comes
-   from an ObjectiveMechanism. If the MappingProjection is part of a multilayer learning sequence, then the
-   LearningMechanism receives the error_signal from the next LearningMechanism in the sequence (i.e., the layer "above"
-   it).  Its value is assigned as the third item of the LearningMechanism's
-   `variable <LearningMechanism.variable>` attribute.
+* *ERROR_SIGNAL* - receives the value of an `error_signal <LearningMechanism.error_signal>` from either an
+  `ObjectiveMechanism` or another LearningMechanism. If the MappingProjection being learned projects to the `TERMINAL`
+  mechanism of the process or system being learned, or is not part of a
+  `multilayer learning sequence <LearningMechanism_Multi_Layer>`, then the error_signal comes from an
+  ObjectiveMechanism. If the MappingProjection being learned is part of a multilayer learning sequence,
+  then the error_signal comes from the next LearningMechanism in the sequence (i.e., the layer "above"
+  the current one).  The value of the *ERROR_SIGNAL* inputState is assigned as the third item of the LearningMechanism's
+  `variable <LearningMechanism.variable>` attribute.
 
    .. note::
-      The value of a LearningMechanism's `ERROR_SIGNAL <LearningMechanism_Input_Error_Signal>` inputState is distinct
-      from its `error_signal` attribute. The former is received from an ObjectiveMechanism or another
-      LearningMechanism.  The latter is generated as a result of the LearningMechanism's
-      `function <LearningMechanism.function>` and potentially passed on to other LearningMechanisms.
+      The value of a LearningMechanism's *ERROR_SIGNAL* inputState is distinct from its
+      `error_signal <LearningMechanism.error_signal>` attribute. The former is received from an ObjectiveMechanism or
+      another LearningMechanism, whereas the latter is generated as a result of the LearningMechanism's
+      `function <LearningMechanism.function>` (and potentially passed on to other LearningMechanisms).
 
 .. _LearningMechanism_Function:
 
 Learning Function
 ~~~~~~~~~~~~~~~~~
 
-This uses the three values received by the LearningMechanism's `inputStates <LearningMechanism_InputStates>` to
-calculate a `learning_signal` and its own `error_signal`.  The `learning_signal` is the set of changes to the
-`matrix <MappingProjection.matrix>` parameter of the MappingProjection required to reduce the value received by the
-LearningMechanism's `ERROR_SIGNAL <LearningMechanism_Input_Error_Signal>` inputState .  In
-`multilayer learning <LearningMechanism_Multi_Layer>`, the `error_signal` it calculates reflects the contribution --
-to the error_signal received -- made by the input to the MappingProjection being learned and the current value of
-its `matrix <MappingProjection.matrix>` parameter (i.e., before it has been modified). The default
-`function <LearningMechanism.function>` is BackPropagation` (also known as the *Generalized Delta Rule*; see
-`Rumelhart et al., 1986 <http://www.nature.com/nature/journal/v323/n6088/abs/323533a0.html>`_).  However, it can be any
-other PsyNeuLink `LearningFunction`
-COMMENT:
-, or any other python function that takes as its input a value with three 1d arrays or
-lists, and returns two 1d arrays or lists.  The two return values
-COMMENT
-It returns two values that
-are assigned to the LearningMechanism's
-`learning_signal` and `error_signal` attributes, respectively, as well as to its two outputStates, as described below.
+This uses the three values received by the LearningMechanism's inputStates
+(described `above <LearningMechanism_InputStates>` to calculate the value of its `learning_signal` and
+`error_signal <LearningMechanism.error_signal>` attributes.  The `learning_signal` is the set
+of changes to the `matrix <MappingProjection.matrix>` parameter of the MappingProjection being learned
+calculated to reduce the value of the LearningMechanism's *ERROR_SIGNAL* inputState .  In `multilayer
+learning <LearningMechanism_Multi_Layer>`, the :keyword:`error_signal`
+reflects the contribution made to vaue of the *ERROR_SIGNAL* inputstate by the input to the
+MappingProjection being learned, weighted by the current value of its `matrix <MappingProjection.matrix>` parameter
+(i.e., before it has been modified). The default `function <LearningMechanism.function>` of a LearningMechanism is
+BackPropagation` (also known as the *Generalized Delta Rule*; see
+`Rumelhart et al., 1986 <http://www.nature.com/nature/journal/v323/n6088/abs/323533a0.html>`_).  However, it can be
+assigned to be any other PsyNeuLink `LearningFunction`, or any other python function that takes as its input a value
+with three 1d arrays or lists, and returns two 1d arrays or lists.  The two return values it returns are assigned to
+the LearningMechanism's `learning_signal` and `error_signal <LearningSignal.error_signal>` attributes, respectively,
+as well as to its two outputStates, as described below.
 
 .. _LearningMechanism_OutputStates:
 
 OutputStates
 ~~~~~~~~~~~~
 
-These receive the output of the LearningMechanism's `function <LearningMechanism.function>`:
+By default, a LearningMechanism has two outputStates that receive, respectively, the
+`error_signal <LearningMechanism.error_signal>` and  `learning_signal` values returned by the LearningMechanism's
+`function <LearningMechanism.function>`.
 
-.. _LearningMechanism_Learning_Signal:
+.. _LearningMechanism_Output_Error_Signal:
 
-* `LEARNING_SIGNAL`
-   This is assigned the value used to modify the `matrix <MappingProjection.matrix>` parameter
-   of the MappingProjection being learned.  It is assigned as the `sender <LearningProjection.sender>` for the
-   LearningProjection that projects to the MappingProjection.  It's value is accessible as the LearningMechanism's
-   `learning_signal` attribute, and as the first item of the LearningMechanism's
-   `outputValue <LearningMechanism.outputValue>` attribute.
+* *ERROR_SIGNAL* - receives the value of the error signal used to calculate the `learning_signal`, which may have been
+  weighted by the contribution that the MappingProjection being learned and the `error_source` made to the value of
+  the error signal received by the LearningProjection (i.e., the value of its *ERROR SIGNAL* inputState (see
+  `above <LearningMechanism_Input_Error_Signal>`.  This is always a LearningMechamism's first (i.e.,
+  `primary <OutputState_Primary>` outputState, and is always named *ERROR_SIGNAL*.  It's value is assigned as the
+  value of the LearningMechanism's `learning_signal` attribute, and as the first item of the LearningMechanism's
+  `output_values <LearningMechanism.output_values>` attribute.  If the LearningMechanism is part of a
+  `multilayer learning sequence <LearningMechanism_Multi_Layer>`, the *ERROR_SIGNAL* outputState is assigned a
+  a projection to the LearningMechanism for the previous MappingProjection being learned in the sequence -
+  see `figure <LearningMechanism_Multilayer_Learning_Figure>` below).
 
-.. _LearningMechanism_Output_Error_Signal:
+.. _LearningMechanism_Learning_Signal:
 
-* `ERROR_SIGNAL`
-   This receives the error_signal used to calculate the learning_signal, which may have been
-   weighted by the contribution that the MappingProjection and the mechanism to which it projects made to the
-   `error_signal` received by the LearningProjection.  If the LearningMechanism is in a
-   `multilayer learning sequence <LearningMechanism_Multi_Layer>`, it serves as the `sender <MappingProjection.sender>`
-   for a MappingProjection to the LearningMechanism for the MappingProjection before it in the sequence (i.e.,
-   the layer "below" it).  It's value is accessible as the LearningMechanism's `learning_signal` attribute,
-   and as the first item of the LearningMechanism's `outputValue <LearningMechanism.outputValue>` attribute.
+* `LearningSignals` - these are a special class of outputState, that receive the matrix of weight changes calculated by
+  a LearningMechanism's function, and use this to modify the `matrix <MappingProjection.matrix>` of a
+  `MappingProjection` being learned.  By default a LearningMechanism has just one LearningSignal.  It is assigned as
+  the second item in the list of the LearningMechanism's outputStates (i.e., in its
+  `output_states <LearningMechanism.output_states>` attribute).  Its `value <LearningSignal.value>` is assigned as the
+  value of the LearningMechanism's `learning_signal` attribute, and as the second item of the LearningMechanism's
+  `output_values <LearningMechanism.output_values>` attribute.  It is also assigned as the
+  `sender <LearningProjection.sender>` of the *LearningProjection* that projects to the MappingProjection being
+  learned.  Though not common, it is possible to assign more than one LearningSignal to a LearningMechanism (e.g.,
+  in order to "yoke" the learning of multiple MappingProjections to a single LearningMechanism).  This can be done by
+  specifying multiple LearningSignals in a list assigned to the **learning_signals** argument of the LearningMechanism's
+  constructor.  All of the LearningSignals are assigned the same value (i.e., weight change matrix), however each can
+  be assigned a LearningProjection to a different MappingProjection, although all of them must have matrix parameters
+  with the same shape.  The LearningSignals can use different values for their
+  `learning_rate <LearningSignal.learning_rate>` and `modulation <LearningSignal.modulation>` attributes.
+  All of the LearningSignals of a LearningMechanism are listed in its `learning_signals` attribute (in addition to its
+  `output_states <LearningMechanism.output_states>` attribute), and all of their LearningProjections are listed in the
+  LearningMechanism's `learning_projections` attribute.
 
 .. _LearningMechanism_Additional_Attributes:
 
 Additional Attributes
 ~~~~~~~~~~~~~~~~~~~~~
 
-In addition to these constituent components, a LearningMechanism is assigned attributes that refer to the
-components being learned:
+In addition to its states and function, a LearningMechanism has the following attributes that refer to the
+components being learned and/or its operation:
 
-* `learned_projection`
-   the MappingProjection for which the LearningMechanism is responsible;  that is, the one with the
-   `matrix <MappingProjection.matrix>` parameter that the LearningMechanism modifies.
+* `learned_projection` - the MappingProjection for which the LearningMechanism is responsible;  that is, the one with
+  the `matrix <MappingProjection.matrix>` parameter that the LearningMechanism modifies;
 ..
-* `error_source`
-   the mechanism that receives the `learned_projection`;  that is, the one that generates the output
-   used to calculate the error_signal that the LearningMechanism attempts to reduce.
+* `error_source` - the mechanism that receives the `learned_projection`;  that is, the one that generates the output
+  used to calculate the error signal that the LearningMechanism attempts to reduce.
+..
+* `learning_signals` - the list of all of the LearningMechanism's LearningSignals that have been assigned to the
+  LearningMechanism in the **learning_signals** argument of its constructor.
 
+* `learning_projections` - the list of LearningProjections for all of the LearningMechanism's LearningSignals;
+..
+* `modulation` - this specifies the way in which the `learning_signal <LearningMechanism.learning_signal>` is used
+  to modify the `matrix <MappingProjection.matrix>` parameter of the `learned_projection`.  By default its value is
+  Modulation.ADD, which causes the weight changes in the `learning_signal` to be added to the current value of the
+  `matrix <MappingProjection.matrix>` parameter (see `LearningMechanism_Execution` for a description of how the
+  modifications are executed).
+..
 .. _LearningMechanism_Learning_Rate:
 
-* `learning_rate <LearningMechanism.learning_rate>`
-   the learning rate for the LearningMechanism (used to specify the :keyword:`learning_rate` parameter for its
-   `function <LearningMechanism.function>`.  Specifiying this (or the learning_rate parameter of the
-   `function <LearningMechanism.function>` directly) supercedes any specification of a learning_rate for
-   any `process <Process.Process_Base.learning_rate>` and/or `system <System.System_Base.learning_rate>` to which
-   the LearningMechanism belongs.  The default is `None`, in which case the LearingMechanism (and its
-   `function <LearningMechanism.function>`) inherit the specification of the `learning_rate
-   <Process.Process_Base.learning_rate>` for the process in which the LearningMechanism is being executed.
-   If that is `None`, then it inherits it from the system in which it is being executed.  If that is also `None`,
-   then it uses the default value assigned by its `function <LearningMechanism.function>`.
+* `learning_rate <LearningMechanism.learning_rate>` - the learning rate for the LearningMechanism.  This is used to
+  specify the :keyword:`learning_rate` parameter for its `function <LearningMechanism.function>`.  In general, the
+  `learning_rate <LearningMechanism.learning_rate>` multiplies the weights changes provided by the LearningMechanism to
+  its function before conveying these to the `LearningProjection` used to modify the MappingProjection's matrix
+  parameter. Specifiying the `learning_rate <LearningMechanism.learning_rate>` for LearningMechanism (or the
+  :keyword:`learning_rate` parameter of its `function <LearningMechanism.function>` directly) supercedes any
+  specification of the :keyword:`learning_rate` for any `process <Process.Process_Base.learning_rate>` and/or
+  `system <System.System_Base.learning_rate>` to which the LearningMechanism belongs.  The default value for a
+  LearningMechanism's `learning_rate <LearningMechanism>` attribute is `None`, in which case the LearningMechanism
+  (and its `function <LearningMechanism.function>`) inherit the specification of the
+  `learning_rate <Process.Process_Base.learning_rate>` for the process in which the LearningMechanism is executed.
+  If that is `None`, then it inherits it from the system in which it is executed.  If that is also `None`,
+  then it uses the default value assigned by its `function <LearningMechanism.function>`.
 
 COMMENT:
 @@@ THE FOLLOWING SECTION SHOULD BE MOVED TO THE "USER'S MANUAL" WHEN THAT IS WRITTEN
@@ -242,7 +270,7 @@
        :scale: 50%
 
        ObjectiveMechanism, LearningMechanism and associated projections created for a single learned_projection
-       and error_source.  Each mechanism is labeled by its type (uppler line, in bold) and its designated
+       and error_source.  Each mechanism is labeled by its type (upper line, in bold) and its designated
        status in the process and/or system to which it belongs (lower line, caps).  Italicized labels beside a
        component indicates the attribute of the LearningMechanism with which it is associated.
 
@@ -332,17 +360,26 @@
 ---------
 
 LearningMechanisms are executed after all of the ProcessingMechanisms in the process or system to which it belongs have
-been executed, including the ObjectiveMechanism(s) that provide the `error_signal` to each LearningMechanism.  When the
+been executed, including the ObjectiveMechanism(s) that provide an error signal to the LearningMechanism(s).  When a
 LearningMechanism is executed, it uses the value of its `ERROR_SIGNAL <LearningMechanism_Input_Error_Signal>`
-inputState to calculate changes to the `matrix <MappingProjection.MappingProjection.matrix>` of its
-`MappingProjection`.  The changes are assigned as the value of its `learning_signal` attribute (as well as the 1st item
-of its `outputValue <LearningMechanism.outputValue>` attribute) and used as the `value <LearningProjection.value>` of
-the `LearningProjection` from the LearningMechanism to the `MATRIX` parameterState of its `learned_projection`.
-However, these but are not applied to the `matrix <MappingProjection.MappingProjection.matrix>` itself until the next
+inputState to calculate changes to the `matrix <MappingProjection.MappingProjection.matrix>` of the MappingProjections
+being learned.  That value is assigned to its `learning_signal` attribute, as the value of each of the LearningSignals
+in its `learning_signal` attribute, and as the value of each of their LearningProjections.  That value is used,
+in turn, to modify the value of the `MATRIX` ParameterState of each of the MappingProjections being learned
+(listed in the LearningMechanism's `learned_projections` attribute).  Each ParameterState uses the value it receives
+from the `LearningProjection` to modify the parameter of its function in a manner specified by the
+`modulation <LearningSignal.modulation>` attribute of the `LearningSignal` from which it receives the
+LearningProjection (see `modulation <ModulatorySignals_Modulation` for a description of state value modulation).
+By default, the `modulation <LearningSignal.modulation>` attribute of a LearningSignal is Modulation.ADD,
+the `function <ParameterState.function>` of a `MATRIX` ParameterState for a MappingProjection is
+`Accumulator`, and the parameter it uses for `additive modulation` is its `increment <Accumulator.increment>`.  These
+assignments cause the value of a LearningProjection to be added to the previous value of the `MATRIX` ParameterState,
+thus incrementing the weights by the `learning_signal` specified by the LearningMechanism.  Note, however, that these
+changes are not applied to the `matrix <MappingProjection.MappingProjection.matrix>` itself until the next
 time the `learned_projection` is executed (see :ref:`Lazy Evaluation <LINK>` for an explanation of "lazy" updating).
-In addition, the LearningMechanism assigns the `error_signal` signal computed by its
-`function <LearningMechanism.function>` to its `error_signal` attribute (as well as the 2nd item of its
-`outputValue <LearningMechanism.outputValue>` attribute).
+In addition to computing and conveying its `learning_signal`, the LearningMechanism's
+`function <LearningMechanism.function>` also computes an error signal that is assigned to its
+`error_signal <LearningMechanism.error_signal>` attribute and as the value of its *ERROR_SIGNAL* outputState.
 
 .. _LearningMechanism_Class_Reference:
 
@@ -351,22 +388,19 @@
 
 """
 
-
-from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ObjectiveMechanism import ObjectiveMechanism
-from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ObjectiveMechanism import _objective_mechanism_role
-from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ProcessingMechanism import ProcessingMechanism_Base
+from PsyNeuLink.Components.Functions.Function import BackPropagation
+from PsyNeuLink.Components.Functions.Function import _is_modulation_param
 from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.AdaptiveMechanism import AdaptiveMechanism_Base
-from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ObjectiveMechanisms.ObjectiveMechanism \
+    import ObjectiveMechanism, ERROR_SIGNAL
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
 from PsyNeuLink.Components.Projections.Projection import *
-from PsyNeuLink.Components.Projections.Projection import _is_projection_spec
-from PsyNeuLink.Components.States.OutputState import OutputState
-from PsyNeuLink.Components.States.ParameterState import ParameterState
-from PsyNeuLink.Components.Functions.Function import BackPropagation, Logistic
+from PsyNeuLink.Components.Projections.Projection import _is_projection_spec, _validate_receiver
 
 # Params:
 
-parameter_keywords.update({LEARNING_PROJECTION})
-projection_keywords.update({LEARNING_PROJECTION})
+parameter_keywords.update({LEARNING_PROJECTION, LEARNING})
+projection_keywords.update({LEARNING_PROJECTION, LEARNING})
 
 def _is_learning_spec(spec):
     """Evaluate whether spec is a valid learning specification
@@ -387,14 +421,12 @@ def _is_learning_spec(spec):
 ERROR_OUTPUT_INDEX = 2
 ERROR_SIGNAL_INDEX = 3
 
-# Used to name inputStates:
+# Used to name input_states:
 ACTIVATION_INPUT = 'activation_input'     # inputState
 ACTIVATION_OUTPUT = 'activation_output'   # inputState
-ERROR_SIGNAL = 'error_signal'             # inputState and outputState
-LEARNING_SIGNAL = 'learning_signal'       #                outputState
 
 input_state_names =  [ACTIVATION_INPUT, ACTIVATION_OUTPUT, ERROR_SIGNAL]
-output_state_names = [LEARNING_SIGNAL, ERROR_SIGNAL]
+output_state_names = [ERROR_SIGNAL, LEARNING_SIGNAL]
 
 ERROR_SOURCE = 'error_source'
 
@@ -410,13 +442,16 @@ def __str__(self):
 
 class LearningMechanism(AdaptiveMechanism_Base):
     """
-    LearningMechanism(                       \
-                 variable,                   \
-                 error_source                \
-                 function=BackPropagation    \
-                 params=None,                \
-                 name=None,                  \
-                 prefs=None)
+    LearningMechanism(                             \
+        variable,                                  \
+        error_source                               \
+        function=BackPropagation                   \
+        learning_rate=None                         \
+        learning_signals=LEARNING_SIGNAL,          \
+        modulation=ModulationParam.MULTIPLICATIVE  \
+        params=None,                               \
+        name=None,                                 \
+        prefs=None)
 
     Implements a mechanism that modifies the `matrix <MappingProjection.matrix>` parameter of a `MappingProjection`.
 
@@ -428,6 +463,13 @@ class LearningMechanism(AdaptiveMechanism_Base):
             learning_signal (2d arry of parameter changes) to be used by the recipient of a LearningProjection
             that projects from the LearningMechanism to a MappingProjection.
 
+        # DOCUMENT: ??NOT SURE WHETHER THIS IS STILL RELEVANT
+        #    IF objective_mechanism IS None, IT IS LEFT UNSPECIFIED (FOR FURTHER IMPLEMENTATION BY COMPOSITION)
+        #    THESE ARE HANDLED BY A MODULE METHOD _instantiate_objective_mechanism (AS PER OBJECTIVE MECHANISM):
+        #        IF objective_mechanism IS SPECIFIED AS ObjectiveMechanism, AN OBJECTIVE MECHANISM IS CREATED FOR IT
+        #        IF objective_mechanism IS SPECIFIED AS A MECHANISM OR OUTPUTSTATE,
+        #               a MappingProjection WITH AN IDENTITY MATRIX IS IMPLEMENTED FROM IT TO THE LearningMechanism
+
         Learning function:
             Generalized delta rule:
             dE/dW  =          learning_rate   *    dE/dA          *       dA/dW             *    I
@@ -473,6 +515,14 @@ class LearningMechanism(AdaptiveMechanism_Base):
         specifies the mechanism that generates the output on which the error_signal received by the LearningMechanism
         (in its `ERROR_SIGNAL <LearningMechanism_Input_Error_Signal>` inputState) is based.
 
+    learning_signals : List[parameter of Projection, ParameterState, Projection, tuple[str, Projection] or dict]
+        specifies the parameter(s) to be trained by the LearningMechanism
+        (see `learning_signals <LearningMechanism.learning_signals>` for details).
+
+    modulation : ModulationParam : ModulationParam.ADDITIVE
+        specifies the default form of modulation to be used by the LearningMechanism's LearningSignals, 
+        unless they are `individually specified <LearningSignal_Specification>`.
+
     function : LearningFunction or function
         specifies the function used to compute the `learning_signal` used by a LearningProjection, and the
         and `error_signal` passed to the next LearningMechanism in a
@@ -480,8 +530,8 @@ class LearningMechanism(AdaptiveMechanism_Base):
         (see `function <LearningMechanism.function>` for details).
 
     learning_rate : float
-        specifies the learning rate for this LearningMechanism (see `learning_rate <LearningMechanism.learning_rate>`
-        for details).
+        specifies the learning rate for this LearningMechanism 
+        (see `learning_rate <LearningMechanism.learning_rate>` for details).
 
     params : Optional[Dict[param keyword, param value]]
         a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
@@ -510,9 +560,6 @@ class LearningMechanism(AdaptiveMechanism_Base):
         the error signal received from either an ObjectiveMechanism or the next LearningMechanism in a
         `learning sequence <LearningMechanism_Learning_Configurations>`.
 
-    learned_projection : MappingProjection
-        the projection, the `matrix <MappingProjection.matrix>` of which is  modified by the LearningMechanism.
-
     COMMENT:
       error_output : 1d np.array
             the output of the next mechanism in the pathway (the one to which the `error_signal` pertains, and projected
@@ -523,7 +570,7 @@ class LearningMechanism(AdaptiveMechanism_Base):
 
         error_source : ObjectiveMechanism or LearningMechanism
             the mechanism from which the LearningMechanism gets its `error_signal`.  The LearningMechanism receives a
-            projection from the `error_source` to its `ERROR_SIGNAL <LearningMechanism.inputStates>` inputState.
+            projection from the `error_source` to its `ERROR_SIGNAL <LearningMechanism.input_states>` inputState.
             If the `error_source` is an ObjectiveMechanism, the projection is from its
             `primary outputState <OutputState_Primary>`.  If the `error_source` is another LearningMechanism,
             the projection is from its `ERROR_SIGNAL <LearningMechanism.outputStates>` outputState.  In either case,
@@ -531,10 +578,18 @@ class LearningMechanism(AdaptiveMechanism_Base):
             `error_source` must be equal in length to the value of the LearningMechanism's `ERROR_SIGNAL` inputstate.
     COMMENT
 
+    input_states : ContentAddressableList[OutputState]
+        list containing the LearningMechanism's three `InputStates <LearningMechanism_InputStates>`:
+        *ACTIVATION_INPUT*,  *ACTIVATION_OUTPUT*, and *ERROR_SIGNAL*.
+
     error_source : ProcessingMechanism
         the mechanism that generates the output upon which the error signal received by LearningMechanism
         (in its `ERROR_SIGNAL <LearningMechanism_Input_Error_SIgnal>` inputState) is based.
 
+    learned_projections : List[Projections]
+        the projections, the parameters of which are modified by the LearningMechanism.  Usually this is a single
+        `matrix <MappingProjection.matrix>` parameter of a `MappingProjection`.  
+
     function : LearningFunction or function : default BackPropagation
         specifies function used to compute the `learning_signal`.  Must take the following arguments:
         `input` (list or 1d array), `output` (list or 1d array), `derivative` (function) and `error` (list or 1d array).
@@ -551,22 +606,35 @@ class LearningMechanism(AdaptiveMechanism_Base):
         learning <LearningMechanism_Multi_Layer>`, it is a modified version of the value received, that takes
         account of the contribution to the error signal received, made by the learned_projection and its input.
 
-    learning_signal : 2d np.array
-        matrix of changes to be used by recipient of `LearningProjection` to adjust its parameters (e.g.,
-        matrix weights, in rows correspond to sender, columns to receiver); same as `value <LearningMechanism.value>`.
-
-    outputValue : 2d np.array
-        the 1st item is the same as `learning_signal`;  the 2nd item is the same as `error_signal`.
+    learning_signals : List[LearningSignal]
+        list of `LearningSignals <LearningSignals>` for the LearningMechanism, each of which sends a 
+        `LearningProjection` to the `parameterState <ParameterState>` for the parameter it controls.
+        The value of each LearningSignal generally contains a 2d np.array or matrix of changes to be used by recipient 
+        of a `LearningProjection` from the LearningMechanism, to adjust its parameters (e.g., matrix weights, in which 
+        rows correspond to sender, and columns to receiver).  Since LearningSignals are OutputStates, these are also 
+        listed in the ControlMechanism's `output_states <Mechanism.output_states>` attribute, along with its 
+        *ERROR_SIGNAL* OutputState, and any others it may have.                
+        
+    output_states : ContentAddressableList[OutputState]
+        contains list of OutputStates for the LearningMechanism, including: its LearningSignal(s) which appear(s) at
+        the begining of the list; its *ERROR_SIGNAL* OutputState, which appears after the LearningSignal(s); any
+        additional (e.g., user-specified) OutputStates, which appear at the end of the list.
+
+    #  FIX: THIS MAY NEED TO BE A 3d array (TO ACCOMDOATE 2d array (MATRICES) AS ENTRIES)
+    output_values : 2d np.array
+        the initial item(s) is/are the value(s) of the LearningMechanism's LearningSignal(s);  the next is 
+        the value of its *ERROR_SIGNAL* OutputState (same as `error_signal`);  subsequent items are the value of
+        the corresponding OutputStates in the `output_states <ControlMechanism.outputStates>` attribute.
 
     name : str : default LearningProjection-<index>
         the name of the LearningMechanism.
-        Specified in the `name` argument of the constructor for the projection;
+        Specified in the **name** argument of the constructor for the projection;
         if not is specified, a default is assigned by ProjectionRegistry
         (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
 
     prefs : PreferenceSet or specification dict : Projection.classPreferences
         the `PreferenceSet` for projection.
-        Specified in the `prefs` argument of the constructor for the projection;
+        Specified in the **prefs** argument of the constructor for the projection;
         if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
         (see :doc:`PreferenceSet <LINK>` for details).
 
@@ -582,18 +650,21 @@ class LearningMechanism(AdaptiveMechanism_Base):
 
     paramClassDefaults = Projection_Base.paramClassDefaults.copy()
     paramClassDefaults.update({
+        CONTROL_PROJECTIONS: None,
         INPUT_STATES:input_state_names,
-        OUTPUT_STATES:[{NAME:LEARNING_SIGNAL,
-                        INDEX:0},
-                       {NAME:ERROR_SIGNAL,
-                        INDEX:1}]
-    })
+        OUTPUT_STATES:[{NAME:ERROR_SIGNAL,
+                        INDEX:1},
+                       {NAME:LEARNING_SIGNAL,  # NOTE: This is the default, but is overridden by any LearningSignal arg
+                        INDEX:0}
+                       ]})
 
     @tc.typecheck
     def __init__(self,
                  variable:tc.any(list, np.ndarray),
                  error_source:tc.optional(Mechanism)=None,
                  function:is_function_type=BackPropagation,
+                 learning_signals:tc.optional(list) = None,
+                 modulation:tc.optional(_is_modulation_param)=ModulationParam.ADDITIVE,
                  learning_rate:tc.optional(parameter_spec)=None,
                  params=None,
                  name=None,
@@ -603,6 +674,7 @@ def __init__(self,
         # Assign args to params and functionParams dicts (kwConstants must == arg names)
         params = self._assign_args_to_param_dicts(error_source=error_source,
                                                   function=function,
+                                                  learning_signals=learning_signals,
                                                   params=params)
 
         # # USE FOR IMPLEMENTATION OF deferred_init()
@@ -610,7 +682,7 @@ def __init__(self,
         # self.init_args = locals().copy()
         # self.init_args['context'] = self
         # self.init_args['name'] = name
-        # delete self.init_args[ERROR_MATRIX]
+        # delete self.init_args[ERROR_SOURCE]
 
         # # Flag for deferred initialization
         # self.value = DEFERRED_INITIALIZATION
@@ -618,6 +690,7 @@ def __init__(self,
         self._learning_rate = learning_rate
 
         super().__init__(variable=variable,
+                         modulation=modulation,
                          params=params,
                          name=name,
                          prefs=prefs,
@@ -653,13 +726,132 @@ def _validate_params(self, request_set, target_set=None, context=None):
 
         super()._validate_params(request_set=request_set, target_set=target_set,context=context)
 
-        try:
+        if ERROR_SOURCE in target_set:
             if not isinstance(target_set[ERROR_SOURCE], (ObjectiveMechanism, LearningMechanism)):
                 raise LearningMechanismError("{} arg for {} must be an ObjectiveMechanism or another LearningMechanism".
                                              format(ERROR_SOURCE, self.name))
 
-        except KeyError:
-            pass
+
+        # FIX: REPLACE WITH CALL TO _parse_state_spec WITH APPROPRIATE PARAMETERS
+        if LEARNING_SIGNALS in target_set and target_set[LEARNING_SIGNALS]:
+
+            from PsyNeuLink.Components.States.ModulatorySignals.LearningSignal \
+                import LearningSignal
+            from PsyNeuLink.Components.States.ParameterState import ParameterState
+            from PsyNeuLink.Components.Projections.ModulatoryProjections.LearningProjection import LearningProjection
+
+            for spec in target_set[LEARNING_SIGNALS]:
+
+                learning_proj = None  # Projection from LearningSignal to MappingProjection
+                mapping_proj = None   # MappingProjection that receives projection from LearningSignal
+
+                # Specification is for a LearningSignal
+                if isinstance(spec, LearningSignal):
+                    #  Check that any LearningProjections it has
+                    #    are to MappingProjections to mechanisms in the same system
+                    for learning_proj in spec.efferents:
+                        _validate_receiver(self,learning_proj, MappingProjection, LEARNING_SIGNAL, context)
+                    continue
+
+                # Specification is for a ParameterState
+                elif isinstance(spec, ParameterState):
+                    param_name = spec.name
+                    mapping_proj = spec.owner
+
+                # Specification is for a Projection
+                elif isinstance(spec, Projection):
+                    if isinstance(spec, LearningProjection):
+                        param_name = spec.receiver.name
+                        learning_proj = spec
+                        mapping_proj = learning_proj.receiver.owner
+                    elif isinstance(spec, MappingProjection):
+                        param_name = MATRIX
+                        mapping_proj = spec
+                    else:
+                        raise LearningMechanismError("The {} specified in the {} arg for {} ({}) must be a {}".
+                                                     format(PROJECTION,
+                                                            LEARNING_SIGNALS,
+                                                            self.name,
+                                                            spec.name,
+                                                            MAPPING_PROJECTION))
+
+                # Specification is for a tuple (str, MappingProjection):
+                elif isinstance(spec, tuple):
+                    param_name = spec[0]
+                    mapping_proj = spec[1]
+                    # Check that 1st item is a str (presumably the name of the learned projection's attribute
+                    #    for the param to be learned; e.g., 'MATRIX' for MappingProjection)
+                    if not isinstance(param_name, str):
+                        raise LearningMechanismError("1st item of tuple in specification of {} for {} ({}) "
+                                                     "must be a string".format(LEARNING_SIGNAL, self.name, param_name))
+                    # Check that 2nd item is a MappingProjection
+                    if not isinstance(mapping_proj, MappingProjection):
+                        raise LearningMechanismError("2nd item of tuple in specification of {} for {} ({}) "
+                                                     "must be a {}".
+                                                     format(LEARNING_SIGNAL,
+                                                            self.name,
+                                                            mapping_proj,
+                                                            MAPPING_PROJECTION))
+
+                # ControlSignal specification dictionary, must have the following entries:
+                #    NAME:str - must be the name of an attribute of PROJECTION
+                #    PROJECTION:Projection - must be a MappingProjection
+                #                            and have an attribute and corresponding ParameterState named NAME
+                #    PARAMS:dict - entries must be valid LearningSignal parameters (e.g., LEARNING_RATE)
+                elif isinstance(spec, dict):
+                    if not NAME in spec:
+                        raise LearningMechanismError("Specification dict for {} of {} must have a {} entry".
+                                                    format(LEARNING_SIGNAL, self.name, NAME))
+                    param_name = spec[NAME]
+                    if not PROJECTION in spec:
+                        raise LearningMechanismError("Specification dict for {} of {} must have a {} entry".
+                                                    format(LEARNING_SIGNAL, self.name, PROJECTION))
+                    mapping_proj = spec[PROJECTION]
+                    if not isinstance(mapping_proj, MappingProjection):
+                        raise LearningMechanismError("{} entry of specification dict for {} of {} must be a {}".
+                                                    format(PROJECTION, LEARNING_SIGNAL, self.name, MAPPING_PROJECTION))
+                    # Check that all of the other entries in the specification dictionary
+                    #    are valid LearningSignal params
+                    for param in spec:
+                        if param in {NAME, PROJECTION}:
+                            continue
+                        if not hasattr(LearningSignal, param):
+                            raise LearningMechanismError("Entry in specification dictionary for {} arg of {} ({}) "
+                                                       "is not a valid {} parameter".
+                                                       format(LEARNING_SIGNAL, self.name, param,
+                                                              LearningSignal.__class__.__name__))
+                else:
+                    raise LearningMechanismError("PROGRAM ERROR: unrecognized specification for {} arg of {} ({})".
+                                                format(LEARNING_SIGNALS, self.name, spec))
+                    # raise LearningMechanismError("Specification of {} for {} ({}) must be a "
+                    #                             "ParameterState, Projection, a tuple specifying a parameter and "
+                    #                              "projection, a LearningSignal specification dictionary, "
+                    #                              "or an existing LearningSignal".
+                    #                             format(CONTROL_SIGNAL, self.name, spec))
+
+                # Validate that the receiver of the LearningProjection (if specified)
+                #     is a MappingProjection and in the same system as self (if specified)
+                if learning_proj:
+                    _validate_receiver(sender_mech=self,
+                                       projection=learning_proj,
+                                       expected_owner_type=MappingProjection,
+                                       spec_type=LEARNING_SIGNAL,
+                                       context=context)
+
+                # IMPLEMENTATION NOTE: the tests below allow for the possibility that the MappingProjection
+                #                      may not yet be fully implemented (e.g., this can occur if the
+                #                      LearningMechanism being implemented here is as part of a LearningProjection
+                #                      specification for the MappingProjection's matrix param)
+                # Check that param_name is the name of a parameter of the MappingProjection to be learned
+                if not param_name in (set(mapping_proj.user_params) | set(mapping_proj.user_params[FUNCTION_PARAMS])):
+                    raise LearningMechanismError("{} (in specification of {} for {}) is not an "
+                                                "attribute of {} or its function"
+                                                .format(param_name, LEARNING_SIGNAL, self.name, mapping_proj))
+                # Check that the MappingProjection to be learned has a parameterState for the param
+                if mapping_proj._parameter_states and not param_name in mapping_proj._parameter_states.names:
+                    raise LearningMechanismError("There is no ParameterState for the parameter ({}) of {} "
+                                                "specified in {} for {}".
+                                                format(param_name, mapping_proj.name, LEARNING_SIGNAL, self.name))
 
     def _instantiate_attributes_before_function(self, context=None):
         """Instantiates MappingProjection from error_source (if specified) to the LearningMechanism
@@ -669,23 +861,292 @@ def _instantiate_attributes_before_function(self, context=None):
 
         super()._instantiate_attributes_before_function(context=context)
 
-
         if self.error_source:
             _instantiate_error_signal_projection(sender=self.error_source, receiver=self)
 
-    def _instantiate_function(self, context=None):
-        super()._instantiate_function(context=context)
+    def _instantiate_output_states(self, context=None):
+
+        # Create registry for LearningSignals (to manage names)
+        from PsyNeuLink.Globals.Registry import register_category
+        from PsyNeuLink.Components.States.ModulatorySignals.LearningSignal import LearningSignal
+        from PsyNeuLink.Components.States.State import State_Base
+        register_category(entry=LearningSignal,
+                          base_class=State_Base,
+                          registry=self._stateRegistry,
+                          context=context)
+
+        # Instantiate LearningSignals if they are specified, and assign to self._output_states
+        # Note: if any LearningSignals are specified they will replace the default LEARNING_SIGNAL OutputState
+        #          in the OUTPUT_STATES entry of paramClassDefaults;
+        #       the LearningSignals will be inserted into _output_states at the beginning of the list
+        #       leaving ERROR_SIGNAL as the last entry
+        if self.learning_signals:
+            # Delete default LEARNING_SIGNAL item in output_states
+            del self._output_states[1]
+            for i, learning_signal in enumerate(self.learning_signals):
+                # Instantiate LearningSignal
+                ls = self._instantiate_learning_signal(learning_signal=learning_signal, context=context)
+                # Add LearningSignal to ouput_states list
+                self._output_states.append(ls)
+                # Replace spec in learning_signals list with actual LearningSignal
+                self.learning_signals[i] = ls
+
+        super()._instantiate_output_states(context=context)
+
+    def _instantiate_learning_signal(self, learning_signal=None, context=None):
+        """Instantiate LearningSignal OutputState and assign (if specified) or instantiate LearningProjection
+
+        Notes:
+        * learning_signal arg can be a:
+            - LearningSignal object;
+            - LearningProjection;
+            - ParameterState;
+            - Projection (in which case, MATRIX parameter is used as receiver of LearningProjection)
+            - params dict containing a LearningProjection;
+            - tuple (param_name, PROJECTION), from learning_signals arg of constructor;
+                    [NOTE: this is a convenience format;
+                           it precludes specification of LearningSignal params (??e.g., LEARNING_RATE)]
+            - LearningSignal specification dictionary, from learning_signals arg of constructor
+                    [NOTE: this must have at least NAME:str (param name) and MECHANISM:Mechanism entries; 
+                           it can also include a PARAMS entry with a params dict containing LearningSignal params]
+            * NOTE: parameterState must be for a projection, and generally for MATRIX parameter of a MappingProjection; 
+                    however, LearningSignal is implemented to be applicable for any ParameterState of any Projection.
+        * State._parse_state_spec() is used to parse learning_signal arg
+        * params are expected to be for (i.e., to be passed to) LearningSignal;
+        * wait to instantiate deferred_init() projections until after LearningSignal is instantiated,
+             so that correct outputState can be assigned as its sender;
+        * index of outputState is incremented based on number of LearningSignals already instantiated;
+            this means that a LearningMechanism's function must return as many items as it has LearningSignals,
+            with each item of the function's value used by a corresponding LearningSignal.
+            NOTE: multiple LearningProjections can be assigned to the same LearningSignal to implement "ganged" learning
+                  (that is, learning of many projections with a single value)
+
+        Returns LearningSignal (OutputState)
+        """
 
+# FIX: THESE NEEDS TO BE DEALT WITH
+# FIX: learning_projection -> learning_projections
+# FIX: trained_projection -> learned_projection
+# FIX: error_signal ??OR?? error_signals??
+# FIX: LearningMechanism: learned_projection attribute -> learned_projections list
+#                         learning_signal -> learning_signals (WITH SINGULAR ONE INDEXING INTO learning_signals.values)
+#  FIX: THIS MAY NEED TO BE A 3d array (TO ACCOMDOATE 2d array (MATRICES) AS ENTRIES)
+
+        from PsyNeuLink.Components.States.ModulatorySignals.LearningSignal import LearningSignal
+        from PsyNeuLink.Components.States.State import _parse_state_spec
+        from PsyNeuLink.Components.States.ParameterState import ParameterState, _get_parameter_state
+        from PsyNeuLink.Components.Projections.ModulatoryProjections.LearningProjection import LearningProjection
+
+        # FIX: NEED TO CHARACTERIZE error_signal FOR BELOW
+        # # EXTEND error_signals TO ACCOMMODATE NEW LearningSignal -------------------------------------------------
+        # #        also used to determine constraint on LearningSignal output value
+        #
+        # if not hasattr(self, ERROR_SIGNALS) or self.error_signals is None:
+        #     self.error_signals = np.array(defaultErrorSignal)
+        # else:
+        #     self.error_signals = np.append(self.error_signals, defaultErrorSignal)
+
+        # GET index FOR LearningSignal OutputState
+        try:
+            output_state_index = len(self.output_states)
+        except (AttributeError, TypeError):
+            output_state_index = 0
+
+
+        # PARSE learning_signal SPECIFICATION -----------------------------------------------------------------------
+
+        learning_projection = None
+        mapping_projection = None
+        learning_signal_params = None
+
+        learning_signal_spec = _parse_state_spec(owner=self, state_type=LearningSignal, state_spec=learning_signal)
+
+        # Specification is a ParameterState
+        if isinstance(learning_signal_spec, ParameterState):
+            mapping_projection = learning_signal_spec.owner
+            if not isinstance(mapping_projection, MappingProjection):
+                raise LearningMechanismError("{} specified for {} of {} ({}) must be a {}".
+                                             format(PARAMETER_STATE, 
+                                                    LEARNING_SIGNAL, 
+                                                    self.name, 
+                                                    mapping_projection,
+                                                    PROJECTION))
+            param_name = learning_signal_spec.name
+            parameter_state = _get_parameter_state(self, LEARNING_SIGNAL, param_name, mapping_projection)
+
+        # Specification was tuple or dict, and parsed into a dict
+        elif isinstance(learning_signal_spec, dict):
+            param_name = learning_signal_spec[NAME]
+            learning_signal_params = learning_signal_spec[PARAMS]
+
+            # learning_signal was a specification dict, with PROJECTION as an entry (and parameter as NAME)
+            if learning_signal_params and PROJECTION in learning_signal_params:
+                mapping_projection = learning_signal_params[PROJECTION]
+                # Delete PROJECTION entry as it is not a parameter of LearningSignal
+                #     (which will balk at it in LearningSignal._validate_params)
+                del learning_signal_params[PROJECTION]
+                parameter_state = _get_parameter_state(self, LEARNING_SIGNAL, param_name, mapping_projection)
+
+            # Specification was originally a tuple, either in parameter specification or learning_signal arg;
+            #    1st item was either assigned to the NAME entry of the learning_signal_spec dict
+            #        (if tuple was a (param_name, Projection tuple) for learning_signal arg;
+            #        or used as param value, if it was a parameter specification tuple
+            #    2nd item was placed in learning_signal_params entry of params dict in learning_signal_spec dict,
+            #        so parse:
+            # FIX 5/23/17: NEED TO GET THE KEYWORDS STRAIGHT FOR PASSING LearningSignal SPECIFICATIONS
+            # IMPLEMENTATION NOTE:
+            #    STATE_PROJECTIONS is used by _parse_state_spec to place the 2nd item of any tuple in params dict;
+            #                      here, the tuple comes from a (param, projection) specification in learning_signal arg
+            #    Delete whichever one it was, as neither is a recognized LearningSignal param
+            #        (which will balk at it in LearningSignal._validate_params)
+            elif (learning_signal_params and
+                    any(kw in learning_signal_spec[PARAMS] for kw in {LEARNING_SIGNAL_SPECS, STATE_PROJECTIONS})):
+                if LEARNING_SIGNAL_SPECS in learning_signal_spec[PARAMS]:
+                    spec = learning_signal_params[LEARNING_SIGNAL_SPECS]
+                    del learning_signal_params[LEARNING_SIGNAL_SPECS]
+                elif STATE_PROJECTIONS in learning_signal_spec[PARAMS]:
+                    spec = learning_signal_params[STATE_PROJECTIONS]
+                    del learning_signal_params[STATE_PROJECTIONS]
+
+                # LearningSignal
+                if isinstance(spec, LearningSignal):
+                    learning_signal_spec = spec
+
+                else:
+                    # Projection
+                    # IMPLEMENTATION NOTE: Projection was placed in list in STATE_PROJECTIONS entry by _parse_state_spec
+                    if isinstance(spec, list) and isinstance(spec[0], Projection):
+                        if isinstance(spec[0], MappingProjection):
+                            mapping_projection = spec[0]
+                            param_name = MATRIX
+                            parameter_state = _get_parameter_state(self,
+                                                                   LEARNING_SIGNAL,
+                                                                   param_name,
+                                                                   mapping_projection)
+                        elif isinstance(spec[0], LearningProjection):
+                            learning_projection = spec[0]
+                            if learning_projection.value is DEFERRED_INITIALIZATION:
+                                parameter_state = learning_projection.init_args['receiver']
+                            else:
+                                parameter_state = learning_projection.receiver
+                            param_name = parameter_state.name
+                        else:
+                            raise LearningMechanismError("PROGRAM ERROR: list in {} entry of params dict for {} of {} "
+                                                        "must contain a single MappingProjection or LearningProjection".
+                                                        format(LEARNING_SIGNAL_SPECS, learning_signal, self.name))
+
+                        if len(spec)>1:
+                            raise LearningMechanismError("PROGRAM ERROR: Multiple LearningProjections is not "
+                                                        "currently supported in specification of a LearningSignal")
+                    else:
+                        raise LearningMechanismError("PROGRAM ERROR: failure to parse specification of {} for {}".
+                                                    format(learning_signal, self.name))
+            else:
+                raise LearningMechanismError("PROGRAM ERROR: No entry found in params dict with specification of "
+                                            "parameter Projection or LearningProjection for {} of {}".
+                                            format(learning_signal, self.name))
+
+
+        # INSTANTIATE LearningSignal -----------------------------------------------------------------------------------
+
+        # Specification is a LearningSignal (either passed in directly, or parsed from tuple above)
+        if isinstance(learning_signal_spec, LearningSignal):
+            # Deferred Initialization, so assign owner, name, and initialize
+            if learning_signal_spec.value is DEFERRED_INITIALIZATION:
+                # FIX 5/23/17:  IMPLEMENT DEFERRED_INITIALIZATION FOR LearningSignal
+                # CALL DEFERRED INIT WITH SELF AS OWNER ??AND NAME FROM learning_signal_dict?? (OR WAS IT SPECIFIED)
+                # OR ASSIGN NAME IF IT IS DEFAULT, USING learning_signal_DICT??
+                pass
+            elif not learning_signal_spec.owner is self:
+                raise LearningMechanismError("Attempt to assign LearningSignal to {} ({}) that is already owned by {}".
+                                            format(self.name, learning_signal_spec.name, learning_signal_spec.owner.name))
+            learning_signal = learning_signal_spec
+            learning_signal_name = learning_signal_spec.name
+            learning_projections = learning_signal_spec.efferents
+
+            # IMPLEMENTATION NOTE:
+            #    THIS IS TO HANDLE FUTURE POSSIBILITY OF MULTIPLE ControlProjections FROM A SINGLE LearningSignal;
+            #    FOR NOW, HOWEVER, ONLY A SINGLE ONE IS SUPPORTED
+            # parameter_states = [proj.recvr for proj in learning_projections]
+            if len(learning_projections) > 1:
+                raise LearningMechanismError("PROGRAM ERROR: list of ControlProjections is not currently supported "
+                                            "as specification in a LearningSignal")
+            else:
+                learning_projection = learning_projections[0]
+                parameter_state = learning_projection.receiver
+
+        # Specification is not a LearningSignal, so create OutputState for it
+        else:
+            learning_signal_name = param_name + '_' + LearningSignal.__name__
+
+            from PsyNeuLink.Components.States.ModulatorySignals.LearningSignal \
+                import LearningSignal
+            from PsyNeuLink.Components.States.State import _instantiate_state
+
+            # Get constraint for OutputState's value
+            # - assume that LearningMechanism.value has only two items (learning_signal and error_signal)
+            # - use learning_signal (stored in self.learning_signal) as value for all LearningSignals
+            self._update_value(context=context)
+            constraint_value = self.learning_signal
+            learning_signal_params.update({LEARNED_PARAM:param_name})
+
+            learning_signal = _instantiate_state(owner=self,
+                                                state_type=LearningSignal,
+                                                state_name=learning_signal_name,
+                                                state_spec=constraint_value,
+                                                state_params=learning_signal_params,
+                                                constraint_value=constraint_value,
+                                                constraint_value_name='Default control allocation',
+                                                context=context)
+
+        # VALIDATE OR INSTANTIATE LearningProjection(s) TO LearningSignal  -------------------------------------------
+
+        # Validate learning_projection (if specified) and get receiver's name
+        if learning_projection:
+            _validate_receiver(self, learning_projection, MappingProjection, LEARNING_SIGNAL,context=context)
+
+            from PsyNeuLink.Components.Projections.ModulatoryProjections.LearningProjection import LearningProjection
+            if not isinstance(learning_projection, LearningProjection):
+                raise LearningMechanismError("PROGRAM ERROR: Attempt to assign {}, "
+                                                  "that is not a LearningProjection, to LearningSignal of {}".
+                                                  format(learning_projection, self.name))
+            if learning_projection.value is DEFERRED_INITIALIZATION:
+                learning_projection.init_args['sender']=learning_signal
+                if learning_projection.init_args['name'] is None:
+                    # FIX 5/23/17: CLEAN UP NAME STUFF BELOW:
+                    learning_projection.init_args['name'] = LEARNING_PROJECTION + \
+                                                   ' for ' + parameter_state.owner.name + ' ' + parameter_state.name
+                learning_projection._deferred_init()
+            else:
+                learning_projection.sender = learning_signal
+
+        # # Instantiate LearningProjection
+        # else:
+        #     # IMPLEMENTATION NOTE:  THIS SHOULD BE MOVED TO COMPOSITION ONCE THAT IS IMPLEMENTED
+        #     from PsyNeuLink.Components.Projections.ModulatoryProjections.LearningProjection import LearningProjection
+        #     learning_projection = LearningProjection(sender=learning_signal,
+        #                                              receiver=parameter_state,
+        #                                              name=LEARNING_PROJECTION + learning_signal_name)
+
+            # Add LearningProjection to list of outputState's outgoing projections
+            # (note: if it was deferred, it just added itself, skip)
+            if not learning_projection in learning_signal.efferents:
+                learning_signal.efferents.append(learning_projection)
+
+            # Add LearningProjection to LearningMechanism's list of LearningProjections
+            try:
+                self.learning_projections.append(learning_projection)
+            except AttributeError:
+                self.learning_projections = [learning_projection]
+
+        return learning_signal
 
-    # MODIFIED 3/22/17 NEW:
     def _instantiate_attributes_after_function(self, context=None):
 
         super()._instantiate_attributes_after_function(context=context)
 
         if self._learning_rate is not None:
             self.learning_rate = self._learning_rate
-    # MODIFIED 3/22/17 END
-
 
     def _execute(self,
                 variable=None,
@@ -712,15 +1173,15 @@ def _execute(self,
         self.value = [self.learning_signal, self.error_signal]
         return self.value
 
-    # IMPLEMENTATION NOTE: Assumes that the LearningMechanism projects to and modifies only a single MappingProjection
-    @property
-    def learned_projection(self):
-        learning_projections = self.outputStates[LEARNING_SIGNAL].sendsToProjections
-        if learning_projections:
-            return learning_projections[0].receiver.owner
-        else:
-            return None
-
+    # # IMPLEMENTATION NOTE: Assumes that the LearningMechanism projects to and modifies only a single MappingProjection
+    # @property
+    # def learned_projection(self):
+    #     learning_projections = self.output_states[LEARNING_SIGNAL].efferents
+    #     if learning_projections:
+    #         return learning_projections[0].receiver.owner
+    #     else:
+    #         return None
+    #
     @property
     def learning_rate(self):
         return self.function_object.learning_rate
@@ -729,6 +1190,12 @@ def learning_rate(self):
     def learning_rate(self, assignment):
         self.function_object.learning_rate = assignment
 
+    @property
+    def learned_projections(self):
+        # learned_projections = []
+        # return [learned_projections.append(learning_signal.efferents) for learning_signal in self.learning_signals]
+        return [lp.receiver.owner for lp in self.learning_projections]
+
 
 # IMPLEMENTATION NOTE:  THIS SHOULD BE MOVED TO COMPOSITION ONCE THAT IS IMPLEMENTED
 def _instantiate_error_signal_projection(sender, receiver):
@@ -737,22 +1204,22 @@ def _instantiate_error_signal_projection(sender, receiver):
     Can take as the sender an `ObjectiveMechanism` or a `LearningMechanism`.
     If the sender is an ObjectiveMechanism, uses its `primary outputState <OutputState_Primary>`.
     If the sender is a LearningMechanism, uses its `ERROR_SIGNAL <LearningMechanism.outputStates>` outputState.
-    The receiver must be a LearningMechanism; its `ERROR_SIGNAL <LearningMechanism.inputStates>` inputState is used.
+    The receiver must be a LearningMechanism; its `ERROR_SIGNAL <LearningMechanism.input_states>` inputState is used.
     Uses and IDENTITY_MATRIX for the MappingProjection, so requires that the sender be the same length as the receiver.
 
     """
 
     if isinstance(sender, ObjectiveMechanism):
-        sender = sender.outputState
+        sender = sender.output_states[ERROR_SIGNAL]
     elif isinstance(sender, LearningMechanism):
-        sender = sender.outputStates[ERROR_SIGNAL]
+        sender = sender.output_states[ERROR_SIGNAL]
     else:
         raise LearningMechanismError("Sender of the error signal projection {} must be either "
                                      "an ObjectiveMechanism or a LearningMechanism".
                                      format(sender))
 
     if isinstance(receiver, LearningMechanism):
-        receiver = receiver.inputStates[ERROR_SIGNAL]
+        receiver = receiver.input_states[ERROR_SIGNAL]
     else:
         raise LearningMechanismError("Receiver of the error signal projection {} must be a LearningMechanism".
                                      format(receiver))
diff --git a/PsyNeuLink/Components/Mechanisms/Mechanism.py b/PsyNeuLink/Components/Mechanisms/Mechanism.py
index 1980df0bd6c..5a16f686a87 100644
--- a/PsyNeuLink/Components/Mechanisms/Mechanism.py
+++ b/PsyNeuLink/Components/Mechanisms/Mechanism.py
@@ -30,22 +30,33 @@
 Overview
 --------
 
-A mechanism takes an input, transforms it in some way, and makes the result available as its output.  There are three
+A mechanism takes an input, transforms it in some way, and makes the result available as its output.  There are two
 types of mechanisms in PsyNeuLink:
 
-    * A `ProcessingMechanisms` aggregrates the input it receives from other mechanisms, and/or the input to the
-      the `process <Process>` or `system <System>` to which it belongs, transforms it in some way, and provides the
-      result as input to other mechanisms in the process or system, or as the output for the process or system
-      itself.
+    * `ProcessingMechanisms <ProcessingMechanism>` aggregrate the input they receive from other mechanisms, and/or the 
+      input to the the `process <Process>` or `system <System>` to which they belong, transform it in some way, and 
+      provide the result as input to other mechanisms in the process or system, or as the output for a process or 
+      system itself.  There are a variety of different types of ProcessingMechanism, that accept various forms of
+      input and transform them in different ways (see `ProcessingMechanisms <ProcessingMechanism>` for a list).
     ..
-    * A `MonitoringMechanism` monitors the output of one or more other mechanisms, receives training (target) values,
-      and compares these to generate error signals used for `learning <Process_Learning>`).
-    ..
-    * A `ControlMechanism` evaluates the output of one or more other mechanisms in the system to which it belongs,
-      and uses this to modify the parameters of those or other mechanisms in the system.
+    * `AdpativeMechanisms <AdpativeMechanism>` monitor the output of one or more other mechanisms, and use this  
+      to modulate the parameters of other mechanisms or projections.  There are three basic AdaptiveMechanisms:
+      
+      * `LearningMechanisms <LearningMechanism>` - these receive training (target) values, and compare them with the 
+        output of a mechanism to generate learning signals that are used to modify 
+        `MappingProjection <MappingProjections>` (see `learning <Process_Learning>`).
+      
+      * `ControlMechanisms <ControlMechanism>` - these evaluate the output of a specified set of mechanisms, and 
+        generate control signals used to modify the parameters of those or other mechanisms.  
+      
+      * `GatingMechanisms <GatingMechanism>` - these receive input, and use this to determine whether and how to 
+        modify the value of the inputState(s) and/or outputState(s) of other mechanisms.
+      
+      Each type of AdaptiveMechanism is associated with a corresponding type of Projection (`LearningProjection`,
+      `ControlProjection` and `GatingProjection`, respectively).
 
 A mechanism is made up of four fundamental components: the function it uses to transform its input; and the states it
-uses to represent its input, processing parameters, and output.
+uses to represent its input(s), parameter(s), and output(s).
 
 .. _Mechanism_Creation:
 
@@ -53,7 +64,7 @@
 --------------------
 
 Mechanisms can be created in several ways.  The simplest is to use the standard Python method of calling the
-constructor for the desired type of mechanism.  Alternatively, the :py:func:`mechanism` function can be used to
+constructor for the desired type of mechanism.  Alternatively, the `mechanism() <mechanism>` function can be used to
 instantiate a specified type of mechanism or a default mechanism. Mechanisms can also be specified "in context,"
 for example in the `pathway` attribute of a process; the mechanism can be specified in either of the ways mentioned
 above, or using one of the following:
@@ -90,15 +101,16 @@
 and to execute its `function <Mechanism_Function>`).  When a mechanism is created, it automatically creates the
 parameterStates it needs to represent its parameters, including those of its `function <Mechanism_Base.function>`.
 It also creates any inputStates and outputStates required for the projections it has been assigned. InputStates and
-outputStates, and corresponding projections, can also be specified in the mechanism's params dictionary, using entries
-with the keys INPUT_STATES and OUTPUT_STATES, respectively. The value of each entry can be the name of the state's
-class (to create a default), an existing state, a specification dictionary for one, a value (used as the state's
-``variable``) or a list containing of any of these to create multiple states (see `InputStates <InputState_Creation>`
-and `OutputStates <OutputStates_Creation>` for details).
-
-COMMENT:
-    PUT EXAMPLE HERE
-COMMENT
+outputStates, and corresponding projections, can also be specified in **input_states** and **output_state** arguments
+of the mechanism's constructor, or in its params dictionary using entries with the keys INPUT_STATES and OUTPUT_STATES, 
+respectively. The value of each entry can be the name of an existing state, a specification dictionary for one, a value 
+(used as the state's ``variable``), a string (used to create a default state with that name), or a list containing of 
+any of these to create multiple states (see `InputStates <InputState_Creation>` and 
+`OutputStates <OutputStates_Creation>` for details).  The following is an example that creates an instance of a
+TransferMechanism with a default inputState named "MY_INPUT" and three 
+`pre-specified types of outputStates <OutputState_Specification>`::
+ 
+     my_mech = TransferMechanism(input_states:['MY_INPUT'], output_states:[RESULT, MEAN, VARIANCE]) 
 
 .. _Mechanism_Structure:
 
@@ -152,11 +164,11 @@ class (to create a default), an existing state, a specification dictionary for o
 
 The input to a mechanism's `function <Mechanism_Base.function>` is provided by the mechanism's
 `variable <Mechanism_Base.variable>` attribute.  This is a 2d array with one item for each of the mechanism's
-`inputStates <Mechanism_InputStates>.  The result of the :keyword:`function` is placed in the mechanism's
+`input_states <Mechanism_InputStates>.  The result of the :keyword:`function` is placed in the mechanism's
 `value <Mechanism_Base.value>` attribute, which is also a 2d array with one or more items.  The
 mechanism's :keyword:`value` is used by its `outputStates <Mechanism_OutputStates>` to generate their :keyword:`value`
 attributes, each of which is assigned as an item of the list in the mechanism's
-`outputValue <Mechanism_Base.outputValue>` attribute.
+`output_values <Mechanism_Base.output_values>` attribute.
 
 .. note::
    The input to a mechanism is not necessarily the same as the input to its `function <Mechanism_Base.function>`.
@@ -164,7 +176,7 @@ class (to create a default), an existing state, a specification dictionary for o
    `variable <Mechanism_Base>` attribute, which is used as the input to its `function <Mechanism_Base.function>`.
    Similarly, the result of a mechanism's function is not necessarily the same as the mechanism's output.  The result
    of the `function <Mechanism_Base.function>` is assigned to the mechanism's  `value <Mechanism_Base.value>` attribute,
-   which is then used by its outputStates to assign items to its `outputValue <Mechanism_Base.outputValue>` attribute.
+   which is then used by its outputStates to assign items to its `output_values <Mechanism_Base.output_values>` attribute.
 
 .. _Mechanism_States:
 
@@ -188,10 +200,11 @@ class (to create a default), an existing state, a specification dictionary for o
 ^^^^^^^^^^^
 
 These receive and represent the input to a mechanism. A mechanism usually has only one (**primary**) `inputState
-<InputState>, identified in its `inputState, <Mechanism_Base.inputState>` attribute.  However some mechanisms have
+<InputState>, identified by its `input_state, <Mechanism_Base.input_state>` attribute.  However some mechanisms have
 more  than one inputState. For example, a `ComparatorMechanism` has one inputState for its `sample` and another for its
-`target` input. If a mechanism has more than one inputState, they are identified in an OrderedDict in the mechanism's
-`inputStates <Mechanism_Base.inputStates>` attribute (note the plural).
+`target` input. If a mechanism has more than one inputState, they are identified in a ContentAddressableList in the 
+mechanism's `input_states <Mechanism_Base.input_states>` attribute (note the plural).  A specific inputState in the
+list can be accessed by using its name as the index for the list (e.g., my_mechanism['input state name']. 
 
 COMMENT:
 [TBI:]
@@ -200,7 +213,7 @@ class (to create a default), an existing state, a specification dictionary for o
 COMMENT
 
 Each inputState of a mechanism can receive one or more projections from other mechanisms.  If the mechanism is an
-`ORIGIN` mechanism of a process, it also receives a projection from the `ProcessInputState <Process_Input_And_Ouput>`
+`ORIGIN` mechanism of a process, it also receives a projection from the `ProcessInputState <Process_Input_And_Output>`
 for that process. Each inputState's :keyword:`function <InputState.InputState.function>` aggregates the values received
 from its projections (usually by summing them), and assigns the result to the inputState's :keyword:`value` attribute.
 
@@ -208,13 +221,13 @@ class (to create a default), an existing state, a specification dictionary for o
 
 The value of each inputState for the mechanism is assigned as the value of an item of the mechanism's
 `variable <Mechanism_Base.variable>` attribute (a 2d np.array), as well as in a corresponding item of its
-`inputValue <Mechanism_Base.inputValue>` attribute (a list).  The :keyword:`variable` provides the input to the
-mechanism's `function <Mechanism_Base.function>`, while its :kewyord:`inputValue` provides a more convenient way
+`input_value <Mechanism_Base.input_value>` attribute (a list).  The :keyword:`variable` provides the input to the
+mechanism's `function <Mechanism_Base.function>`, while its :kewyord:`input_value` provides a more convenient way
 of accessing its individual items.
 
 COMMENT:
-Therefore, the number of inputStates for the mechanism must match the number of tems specified for the mechanism's
-``variable`` (that is, its size along its first dimension, axis 0).  An exception is if the mechanism's ``variable``
+The number of input_states for the mechanism must match the number of tems specified for the mechanism's
+``variable`` (that is, its size along its first dimension, axis 0).  An exception is if the mechanism's `variable``
 has more than one item, but only a single inputState;  in that case, the ``value`` of that inputState must have the
 same number of items as the mechanisms's ``variable``.
 COMMENT
@@ -224,15 +237,17 @@ class (to create a default), an existing state, a specification dictionary for o
 ParameterStates
 ^^^^^^^^^^^^^^^
 
-These represent the parameters that control the operation of a mechanism, including the parameters of its
+These represent the parameters that determine the operation of a mechanism, including the parameters of its
 :keyword:`function`.  One `parameterState <ParameterState>` is assigned to each of the parameters of the mechanism
 and/or its :keyword:`function` (these correspond to the arguments in their constructors).  Like other states,
 parameterStates can receive projections. Typically these are from the `ControlProjections <ControlProjection>`
 of a `ControlMechanism` that is used to modify parameter values in response to the outcome(s) of
 processing.  A parameter value (and the value of its associated parameterState) can be specified when a mechanism or
 its function is first created  using the corresponding argument in the object's constructor.  Parameter values can
-also be assigned later, using the mechanism's `assign_param` method (the safest means) or by direct assignment to the
-corresponding attribute (see `ParameterState_Specifying_Parameters`).
+also be assigned later, by direct assignment of a value to the corresponding attribute, or by using the mechanism's 
+`assign_param` method (the safest means;  see `ParameterState_Specifying_Parameters`).  All of the mechanism's
+parameters are list in a dict in its `user_params` attribute; the dict contains a `function_params` entry which
+in turn contains a dict of the parameters for the mechanism's `function <Mechanism.function>`.
 
 .. _Mechanism_OutputStates:
 
@@ -240,36 +255,38 @@ class (to create a default), an existing state, a specification dictionary for o
 ^^^^^^^^^^^^
 These represent the output(s) of a mechanism. A mechanism can have several `outputStates <OutputState>`, and each can
 send projections that transmit its value to other mechanisms and/or the output of the process or system to which the
-mechanism belongs. Similar to inputStates, the ** *primary* (first or only) outputState is assigned to the mechanism's
-`outputState <Mechanism_Base.outputState>` attribute, while all of its outputStates (including the primary one) are
-identified in an OrderedDict in its `outputStates <Mechanism_Base.outputStates>` attribute (note the plural).  The
-key for each entry in the :keyword:`outputStates` dict is the name of an outputState, and the value is the outputState
-itself.  Every mechanism has at least one ("primary") outputState, the :keyword:`value` of which is assigned the
-first item of the mechanism's `value <Mechanism_Base.value>` attribute (usually the direct output of the mechanism's
-`function <Mechanism_Base.function>`).  Other outputStates may be used for other purposes.  For example,
-some `ProcessingMechanisms <ProcessingMechanism>` (such as `TransferMechanism`) use outputStates to represent
-values derived from the value of their `primary outputState <OutputState_Primary>` (e.g., the mean and variance).
-`ControlMechanisms <ControlMechanism>` assign one outputState for each of their `ControlProjections
-<ControlProjection>`.  An outputState can be assigned to a particular item of the mechanism's 
-`value <Mechanism_Base.value>` attribute using the outputState's `index` parameter, and its `calculate` parameter can 
-be used to modify that item before assigning it as the outputState's :keyword:`value` (see `OutputStates_Creation`). 
-The :keyword:`value` attributes of all of a mechanism's outputStates  are assigned to the mechanism's 
-`outputValue <Mechanism_Base.outputValue>` attribute (a list), in the same order in which they appear in mechanism's
-`outputStates <Mechanism_Base.outputStates>`  attribute.  Note that this is distinct from the mechanism's `value
-<Mechanism_Base.value>` attribute, which contains the full and unmodified results of its
-`function <Mechanism_Base.function>`.
+mechanism belongs.  Every mechanism has at least one outputState, referred to as its 
+`primary outputState <OutputState_Primary>`.  If outputStates are not explicitly specified for a mechanism, a primary 
+outputState is automatically created and assigned to its `outputState <Mechanism.Mechanism_Base.outputState>` 
+attribute (note the singular), and also to the first entry of the mechanism's `outputStates 
+<Mechanism.Mechanism_Base.outputStates>` attribute (note the plural).  The `value <OutputState.value>` of the primary 
+outputState is assigned as the first (and often only) item of the mechanism's 
+`output_value <Mechanism.Mechanism_Base.output_value>`, which is the result of the mechanism`s 
+`function <Mechanism.Mechanism_Base.function>`.  Additional outputStates can be assigned to represent values derived 
+from the result of the mechanism's `function <Mechanism.function>`.  Standard outputStates are available for each 
+type of mechanism, and custom ones can also be configured (see `OutputState Specification <OutputState_Specification>`.
+These can be assigned in the **output_states** argument of the mechanism's constructor.  All of the outputStates of a 
+mechanism (including the primary one) are represented in its `output_states <Mechanism_Base.outputStates>` attribute 
+(note the plural), that contains a ContentAddressableList of the outputStates.  A specific outputState in the list can 
+be accessed by using its name as the index for the list (e.g., my_mechanism['output state name'].  This can also be
+used to assign additional outputStates to the mechanism after it has been created.
 
 .. _Mechanism_Parameters:
 
 Mechanism Parameters
 ~~~~~~~~~~~~~~~~~~~~
 
+COMMENT:
+   ADD: SIZE XXXXX
+   REFORMAT TO DESCRIBE SPECIFICATION AS ARGS IN CONSTRUCTOR RATHER THAN AS ENTRIES IN PARAMS DICT
+COMMENT
+
 Most mechanisms implement a standard set of parameters, that can be specified by direct reference to the corresponding
 attribute of the mechanisms (e.g., myMechanism.attribute), in a 
 `parameter dictionary <ParameterState_Specifying_Parameters>` assigned to `params` argument in the mechanism's
 constructor, or with the mechanism's `assign_params` method, using the following keywords:
 
-    * INPUT_STATES - specifies specialized inputStates required by a mechanism subclass
+    * INPUT_STATES - specifies specialized input_states required by a mechanism subclass
       (see :ref:`inputState specification <InputState_Creation>` for details of specification).
     ..
     * FUNCTION - specifies the `function <Mechanism_Base.function>` for the mechanism;  can be one of several
@@ -286,9 +303,8 @@ class (to create a default), an existing state, a specification dictionary for o
       * a ControlProjection or LearningProjection specification (see `Projection_Creation`),
         that assigns the parameter its default value, and a projection to it's ParameterState of the specified type;
       |
-      * a tuple with exactly two items: the parameter value and a projection type specifying either a
-        `ControlProjection` or a `LearningProjection` (a `ParamValueProjection` namedtuple can be used for
-        clarity).
+      * a tuple with exactly two items: the parameter value and a projection type specifying a
+        `LearningProjection`, `ControlProjection` or `GatingProjection`.
       |
       .. note::
          Some Mechanism subclasses include the function parameters as arguments in mechanism's constructor.
@@ -341,11 +357,11 @@ class (to create a default), an existing state, a specification dictionary for o
 Mechanisms that are part of one or more processes are assigned designations that indicate the
 `role <Process_Mechanisms>` they play in those processes, and similarly for `role <System_Mechanisms>` they play in
 any systems to which they belong. These designations are listed in the mechanism's `processes` and `systems`
-attributes, respectively.  Any mechanism designated as `ORIGIN` receives a projection to its primary inputState from
-the process(es) to which it belongs.  Accordingly, when the process (or system of which the process is a part) is
-executed, those mechainsms receive the input provided to the process (or system).  The `outputValue
-<Mechanism_Base.outputValue>` of any mechanism designated as the `TERMINAL` mechanism for a process is assigned as
-the `output` of that process, and similarly for systems to which it belongs.
+attributes, respectively.  Any mechanism designated as `ORIGIN` receives a projection to its 
+`primary inputState <Mechanism_InputStates>` from the process(es) to which it belongs.  Accordingly, when the process 
+(or system of which the process is a part) is executed, those mechainsms receive the input provided to the process 
+(or system).  The `output_values <Mechanism_Base.output_values>` of any mechanism designated as the `TERMINAL` 
+mechanism for a process is assigned as the `output` of that process, and similarly for systems to which it belongs.
 
 .. note:: A mechanism can be the `ORIGIN` or `TERMINAL` of a process but not of a system to which that
           process belongs;  see :ref:`Chain Example <LINK>` for further explanation.
@@ -390,8 +406,9 @@ class (to create a default), an existing state, a specification dictionary for o
 <ParameterState_Specifying_Parameters>`.  Parameters for all of the state's projections can be specified in an entry 
 with the key PROJECTION_PARAMS, and a subdictionary that contains the parameter specifications;  parameters for 
 projections of a particular type can be placed in an entry with a key specifying the type (MAPPING_PROJECTION_PARAMS, 
-CONTROL_PROJECTION_PARAMS, or LEARNING_PROJECTION_PARAMS; and parameters can for a specific projection can be placed 
-in an entry with a key specifying the name of the project and a dictionary with the specifications.
+LEARNING_PROJECTION_PARAMS, CONTROL_PROJECTION_PARAMS, or GATING_PROJECTION_PARAMS; and parameters can for a specific
+projection can be placed in an entry with a key specifying the name of the project and a dictionary with the
+specifications.
 
 COMMENT:
 ?? DO PROJECTION DICTIONARIES PERTAIN TO INCOMING OR OUTGOING PROJECTIONS OR BOTH??
@@ -417,9 +434,15 @@ class (to create a default), an existing state, a specification dictionary for o
             + MAPPING_PROJECTION_PARAMS:<dict>:
                  entry will be passed to all of the State's MappingProjections,
                  along with any in a PROJECTION_PARAMS dict, and override paramInstanceDefaults
+            + LEARNING_PROJECTION_PARAMS:<dict>:
+                 entry will be passed to all of the State's LearningProjections,
+                 along with any in a PROJECTION_PARAMS dict, and override paramInstanceDefaults
             + CONTROL_PROJECTION_PARAMS:<dict>:
                  entry will be passed to all of the State's ControlProjections,
                  along with any in a PROJECTION_PARAMS dict, and override paramInstanceDefaults
+            + GATING_PROJECTION_PARAMS:<dict>:
+                 entry will be passed to all of the State's GatingProjections,
+                 along with any in a PROJECTION_PARAMS dict, and override paramInstanceDefaults
             + <projectionName>:<dict>:
                  entry will be passed to the State's projection with the key's name,
                  along with any in the PROJECTION_PARAMS and MappingProjection or ControlProjection dicts
@@ -432,12 +455,15 @@ class (to create a default), an existing state, a specification dictionary for o
 
 """
 
+import logging
+
 from collections import OrderedDict
 from inspect import isclass
 
 from PsyNeuLink.Components.ShellClasses import *
 from PsyNeuLink.Globals.Registry import register_category
 
+logger = logging.getLogger(__name__)
 MechanismRegistry = {}
 
 class MonitoredOutputStatesOption(AutoNumber):
@@ -550,17 +576,17 @@ class Mechanism_Base(Mechanism):
         -----------
             Mechanism is a Category of the Component class.
             A mechanism is associated with a name and:
-            - one or more inputStates:
-                two ways to get multiple inputStates, if supported by mechanism subclass being instantiated:
+            - one or more input_states:
+                two ways to get multiple input_states, if supported by mechanism subclass being instantiated:
                     • specify 2d variable for mechanism (i.e., without explicit inputState specifications)
                         once the variable of the mechanism has been converted to a 2d array, an inputState is assigned
                         for each item of axis 0, and the corresponding item is assigned as the inputState's variable
-                    • explicitly specify inputStates in params[INPUT_STATES] (each with its own variable specification);
+                    • explicitly specify input_states in params[INPUT_STATES] (each with its own variable specification);
                         those variables will be concantenated into a 2d array to create the mechanism's variable
                 if both methods are used, they must generate the same sized variable for the mechanims
                 ?? WHERE IS THIS CHECKED?  WHICH TAKES PRECEDENCE: inputState SPECIFICATION (IN _instantiate_state)??
             - an execute method:
-                coordinates updating of inputStates, parameterStates (and params), execution of the function method
+                coordinates updating of input_states, parameterStates (and params), execution of the function method
                 implemented by the subclass, (by calling its _execute method), and updating of the outputStates
             - one or more parameters, each of which must be (or resolve to) a reference to a ParameterState
                 these determine the operation of the function of the mechanism subclass being instantiated
@@ -576,7 +602,7 @@ class Mechanism_Base(Mechanism):
 
         Constraints
         -----------
-            - the number of inputStates must correspond to the length of the variable of the mechanism's execute method
+            - the number of input_states must correspond to the length of the variable of the mechanism's execute method
             - the value of each inputState must be compatible with the corresponding item in the
                 variable of the mechanism's execute method
             - the value of each parameterState must be compatible with the corresponding parameter of  the mechanism's
@@ -598,7 +624,7 @@ class Mechanism_Base(Mechanism):
             + classPreferenceLevel (PreferenceLevel): PreferenceLevel.CATEGORY
             + variableClassDefault (list)
             + paramClassDefaults (dict):
-                + kwMechanismTimeScale (TimeScale): TimeScale.TRIAL (timeScale at which mechanism executes)
+                + MECHANISM_TIME_SCALE (TimeScale): TimeScale.TRIAL (timeScale at which mechanism executes)
                 + [TBI: kwMechanismExecutionSequenceTemplate (list of States):
                     specifies order in which types of States are executed;  used by self.execute]
             + paramNames (dict)
@@ -632,21 +658,21 @@ class Mechanism_Base(Mechanism):
 
         .. _receivesProcessInput (bool): flags if Mechanism (as first in Pathway) receives Process input projection
 
-    inputState : InputState : default default InputState
-        primary `inputState <Mechanism_InputStates>` for the mechanism;  same as first entry of its `inputStates
-        <Mechanism_Base.inputStates>` attribute.
+    input_state : InputState : default default InputState
+        primary `inputState <Mechanism_InputStates>` for the mechanism;  same as first entry of its `input_states
+        <Mechanism_Base.input_states>` attribute.
 
-    inputStates : OrderedDict[str, InputState]
-        a dictionary of the mechanism's `inputStates <Mechanism_InputStates>`.
-        The key of each entry is the name of an inputState, and its value is the inputState.
-        There is always at least one entry, which identifies the mechanism's primary inputState
-        (i.e., the one in the its `inputState <Mechanism_Base.inputState>` attribute).
+    input_states : OrderedDict[str, InputState]
+        a dictionary of the mechanism's `input_states <Mechanism_InputStates>`.
+        The key of each entry is the name of an inputState, and its value is the inputState.  There is always
+        at least one entry, which identifies the mechanism's `primary inputState <Mechanism_InputStates>`
+        (i.e., the one in the its `inputState <Mechanism_Base.input_state>` attribute).
 
-    inputValue : List[List or 1d np.array] : default variableInstanceDefault
+    input_value : List[List or 1d np.array] : default variableInstanceDefault
         a list of values, one for each `inputState <Mechanism_InputStates>` in the mechanism's
-        `inputStates <Mechanism_Base.inputStates>` attribute.  The value of each item is the same as the corresponding
+        `input_states <Mechanism_Base.input_states>` attribute.  The value of each item is the same as the corresponding
         item in the mechanism's `variable <Mechanism_Base.variable>` attribute.  The latter is a 2d np.array;
-        the :keyword:`inputValue attribute provides this information in a simpler list format.
+        the :keyword:`input_value attribute provides this information in a simpler list format.
 
     parameterStates : OrderedDict[str, ParameterState]
         a dictionary of parameterStates, one for each of the specifiable parameters of the mechanism and its function
@@ -673,7 +699,7 @@ class Mechanism_Base(Mechanism):
 
     value : 2d np.array : default None
         output of the mechanism's `function <Mechanism_Base.function>`.
-        Note: this is not necessarily the same as the mechanism's `outputValue <Mechanism_Base.outputValue>` attribute,
+        Note: this is not necessarily the same as the mechanism's `output_values <Mechanism_Base.output_values>` attribute,
         which lists the values of its `outputStates <Mechanism_Base.outputStates>`.
         The keyword:`value` is `None` until the mechanism has been executed at least once.
 
@@ -681,32 +707,32 @@ class Mechanism_Base(Mechanism):
                set equal to the value attribute when the mechanism is first initialized;
                maintains its value even when value is reset to None when (re-)initialized prior to execution.
 
-    outputState : OutputState : default default OutputState
+    output_state : OutputState : default default OutputState
         primary `outputState <Mechanism_OutputStates>` for the mechanism;  same as first entry of its
         `outputStates <Mechanism_Base.outputStates>` attribute.
 
-    outputStates : OrderedDict[str, InputState]
+    output_states : OrderedDict[str, OutputState]
         a dictionary of the mechanism's `outputStates <Mechanism_OutputStates>`.
         The key of each entry is the name of an outputState, and its value is the outputState.  There is always
         at least one entry, which identifies the mechanism's `primary outputState <OutputState_Primary>`.
 
-    outputValue : List[value] : default mechanism.function(variableInstanceDefault)
+    output_values : List[value] : default mechanism.function(variableInstanceDefault)
         a list of values, one for each `outputState <Mechanism_OutputStates>` in the mechanism's
         :keyword:`outputStates` attribute.
 
-        .. note:: The :keyword:`outputValue` of a mechanism is not necessarily the same as its
+        .. note:: The :keyword:`output_values` of a mechanism is not necessarily the same as its
                   `value <Mechanism_Base.value>` attribute, since the outputState's
                   `function <OutputState.OutputState.function>` and/or its `calculate <Mechanism_Base.calculate>`
                   attribute may use the mechanism's `value <Mechanism_Base.value>` to generate a derived quantity for
                   the `value <OutputState.OutputState.value>` of the outputState and its corresponding item in the
-                  the mechanism's :keyword:`outputValue` attribute.
+                  the mechanism's :keyword:`output_values` attribute.
 
         COMMENT:
             EXAMPLE HERE
         COMMENT
 
         .. _outputStateValueMapping : Dict[str, int]:
-               contains the mappings of outputStates to their indices in the outputValue list
+               contains the mappings of outputStates to their indices in the output_values list
                The key of each entry is the name of an outputState, and the value is its position in the
                     :py:data:`outputStates <Mechanism_Base.outputStates>` OrderedDict.
                Used in ``_update_output_states`` to assign the value of each outputState to the correct item of
@@ -740,13 +766,13 @@ class Mechanism_Base(Mechanism):
 
     name : str : default <Mechanism subclass>-<index>
         the name of the mechanism.
-        Specified in the `name` argument of the constructor for the mechanism;  if not is specified,
+        Specified in the **name** argument of the constructor for the mechanism;  if not is specified,
         a default is assigned by `MechanismRegistry` based on the mechanism's subclass
         (see `Registry <LINK>` for conventions used in naming, including for default and duplicate names).
 
     prefs : PreferenceSet or specification dict : Mechanism.classPreferences
         the `PreferenceSet` for the mechanism.
-        Specified in the `prefs` argument of the constructor for the mechanism;
+        Specified in the **prefs** argument of the constructor for the mechanism;
         if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
         (see :doc:`PreferenceSet <LINK>` for details).
 
@@ -794,24 +820,29 @@ class Mechanism_Base(Mechanism):
     # Category specific defaults:
     paramClassDefaults = Component.paramClassDefaults.copy()
     paramClassDefaults.update({
-        kwMechanismTimeScale: TimeScale.TRIAL,
+        INPUT_STATES:None,
+        OUTPUT_STATES:None,
         MONITOR_FOR_CONTROL: NotImplemented,  # This has to be here to "register" it as a valid param for the class
                                               # but is set to NotImplemented so that it is ignored if it is not
                                               # assigned;  setting it to None actively disallows assignment
                                               # (see EVCMechanism_instantiate_input_states for more details)
-        MONITOR_FOR_LEARNING: None
+        MONITOR_FOR_LEARNING: None,
         # TBI - kwMechanismExecutionSequenceTemplate: [
         #     Components.States.InputState.InputState,
         #     Components.States.ParameterState.ParameterState,
         #     Components.States.OutputState.OutputState]
+        MECHANISM_TIME_SCALE: TimeScale.TRIAL
         })
 
     # def __new__(cls, *args, **kwargs):
     # def __new__(cls, name=NotImplemented, params=NotImplemented, context=None):
     #endregion
 
+    @tc.typecheck
     def __init__(self,
                  variable=None,
+                 input_states=None,
+                 output_states=None,
                  params=None,
                  name=None,
                  prefs=None,
@@ -838,9 +869,37 @@ def __init__(self,
             raise MechanismError("Direct call to abstract class Mechanism() is not allowed; "
                                  "use mechanism() or a subclass")
 
-# IMPLEMENT **args (PER State)
+        # IMPLEMENT **kwargs (PER State)
+
+
+        # Ensure that all input_states and output_states, whether from paramClassDefaults or constructor arg,
+        #    have been included in user_params and implemented as properties
+        #    (in case the subclass did not include one and/or the other as an argument in its constructor)
+
+        kwargs = {}
+
+        # input_states = []
+        # if INPUT_STATES in self.paramClassDefaults and self.paramClassDefaults[INPUT_STATES]:
+        #     input_states.extend(self.paramClassDefaults[INPUT_STATES])
+        # if INPUT_STATES in self.user_params and self.user_params[INPUT_STATES]:
+        #     input_states.extend(self.user_params[INPUT_STATES])
+        # if input_states:
+        #     kwargs[INPUT_STATES] = input_states
+        #
+        # output_states = []
+        # if OUTPUT_STATES in self.paramClassDefaults and self.paramClassDefaults[OUTPUT_STATES]:
+        #     output_states.extend(self.paramClassDefaults[OUTPUT_STATES])
+        # if OUTPUT_STATES in self.user_params and self.user_params[OUTPUT_STATES]:
+        #     output_states.extend(self.user_params[OUTPUT_STATES])
+        # if output_states:
+        #     kwargs[OUTPUT_STATES] = output_states
+        #
+        # kwargs[PARAMS] = params
+        #
+        # params = self._assign_args_to_param_dicts(**kwargs)
 
         self._execution_id = None
+        self.is_finished = False
 
         # Register with MechanismRegistry or create one
         if not context is VALIDATE:
@@ -853,6 +912,7 @@ def __init__(self,
         # Create mechanism's _stateRegistry and state type entries
         from PsyNeuLink.Components.States.State import State_Base
         self._stateRegistry = {}
+
         # InputState
         from PsyNeuLink.Components.States.InputState import InputState
         register_category(entry=InputState,
@@ -911,43 +971,6 @@ def __init__(self,
         self.processes = {}
         self.systems = {}
 
-    def plot(self,x_range = None):
-        """
-        Generate a plot of the mechanism's function using the specified parameter values. See (see
-        `DDM.plot <DDM.plot>` for details of the animated DDM plot).
-
-        Arguments
-        ---------
-
-        x_range: List
-             specify the range over which the function should be plotted. x_range must be provides as a list containing
-             two floats: lowest value of x and highest value of x.  Default values depend on the mechanism's function.
-
-            - Logistic Function: default x_range = [-5.0, 5.0]
-            - Exponential Function: default x_range = [0.1, 5.0]
-            - All Other Functions: default x_range = [-10.0, 10.0]
-
-
-
-        Returns
-        -------
-        mechanism's function plot : Matplotlib window
-            Matplotlib window of the mechanism's function plotted with specified parameters over the specified x_range
-
-        """
-
-        import matplotlib.pyplot as plt
-
-        if not x_range:
-            if "Logistic" in str(self.function):
-                x_range= [-5.0, 5.0]
-            elif "Exponential" in str(self.function):
-                x_range = [0.1, 5.0]
-            else:
-                x_range = [-10.0, 10.0]
-        x_space = np.linspace(x_range[0],x_range[1])
-        plt.plot(x_space, self.function(x_space), lw=3.0, c='r')
-        plt.show()
 
     def _validate_variable(self, variable, context=None):
         """Convert variableClassDefault and self.variable to 2D np.array: one 1D value for each input state
@@ -976,67 +999,111 @@ def _filter_params(self, params):
         rather than override those in paramClassDefaults (the default behavior)
         """
 
+        import copy
+
         # INPUT_STATES:
-        try:
-            input_states_spec = params[INPUT_STATES]
-        except KeyError:
-            pass
-        else:
-            # Convert input_states_spec to list if it is not one
-            if not isinstance(input_states_spec, list):
-                input_states_spec = [input_states_spec]
-            # Get inputStates specified in paramClassDefaults
-            default_input_states = self.paramClassDefaults[INPUT_STATES].copy()
-            # Convert inputStates from paramClassDeafults to a list if it is not one
-            if not isinstance(default_input_states, list):
-                default_input_states = [default_input_states]
-            # Add inputState specified in params to those in paramClassDefaults
-            #    Note: order is important here;  new ones should be last, as paramClassDefaults defines the
-            #          the primary inputState which must remain first for the inputStates OrderedDictionary
-            default_input_states.extend(input_states_spec)
-            # Assign full set back to params_arg
-            params[INPUT_STATES] = default_input_states
+
+        # Check if input_states is in params (i.e., was specified in arg of constructor)
+        if not INPUT_STATES in params or params[INPUT_STATES] is None:
+            # If it wasn't, assign from paramClassDefaults (even if it is None) to force creation of input_states attrib
+            if self.paramClassDefaults[INPUT_STATES] is not None:
+                params[INPUT_STATES] = copy.deepcopy(self.paramClassDefaults[INPUT_STATES])
+            else:
+                params[INPUT_STATES] = None
+        # Convert input_states_spec to list if it is not one
+        if params[INPUT_STATES] is not None and not isinstance(params[INPUT_STATES], (list, dict)):
+            params[INPUT_STATES] = [params[INPUT_STATES]]
+        self.user_params.__additem__(INPUT_STATES, params[INPUT_STATES])
 
         # OUTPUT_STATES:
-        try:
-            output_states_spec = params[OUTPUT_STATES]
-        except KeyError:
-            pass
-        else:
-            # Convert output_states_spec to list if it is not one
-            if not isinstance(output_states_spec, list):
-                output_states_spec = [output_states_spec]
-            # Get outputStates specified in paramClassDefaults
-            default_output_states = self.paramClassDefaults[OUTPUT_STATES].copy()
-            # Convert outputStates from paramClassDeafults to a list if it is not one
-            if not isinstance(default_output_states, list):
-                default_output_states = [default_output_states]
-            # Add outputState specified in params to those in paramClassDefaults
-            #    Note: order is important here;  new ones should be last, as paramClassDefaults defines the
-            #          the primary outputState which must remain first for the outputStates OrderedDictionary
-            default_output_states.extend(output_states_spec)
-            # Assign full set back to params_arg
-            params[OUTPUT_STATES] = default_output_states
+
+        # Check if OUTPUT_STATES is in params (i.e., was specified in arg of contructor)
+        if not OUTPUT_STATES in params or params[OUTPUT_STATES] is None:
+            if self.paramClassDefaults[OUTPUT_STATES] is not None:
+                params[OUTPUT_STATES] = copy.deepcopy(self.paramClassDefaults[OUTPUT_STATES])
+            else:
+                params[OUTPUT_STATES] = None
+        # Convert OUTPUT_STATES_spec to list if it is not one
+        if params[OUTPUT_STATES] is not None and not isinstance(params[OUTPUT_STATES], (list, dict)):
+            params[OUTPUT_STATES] = [params[OUTPUT_STATES]]
+        self.user_params.__additem__(OUTPUT_STATES, params[OUTPUT_STATES])
+
+        # try:
+        #     input_states_spec = params[INPUT_STATES]
+        # except KeyError:
+        #     pass
+        # else:
+        #     # Convert input_states_spec to list if it is not one
+        #     if not isinstance(input_states_spec, list):
+        #         input_states_spec = [input_states_spec]
+        #     # # Get input_states specified in paramClassDefaults
+        #     # if self.paramClassDefaults[INPUT_STATES] is not None:
+        #     #     default_input_states = self.paramClassDefaults[INPUT_STATES].copy()
+        #     # else:
+        #     #     default_input_states = None
+        #     # # Convert input_states from paramClassDefaults to a list if it is not one
+        #     # if default_input_states is not None and not isinstance(default_input_states, list):
+        #     #     default_input_states = [default_input_states]
+        #     # # Add inputState specified in params to those in paramClassDefaults
+        #     # #    Note: order is important here;  new ones should be last, as paramClassDefaults defines the
+        #     # #          the primary inputState which must remain first for the input_states ContentAddressableList
+        #     # default_input_states.extend(input_states_spec)
+        #     # # Assign full set back to params_arg
+        #     # params[INPUT_STATES] = default_input_states
+        #
+        #     # Get inputStates specified in paramClassDefaults
+        #     if self.paramClassDefaults[INPUT_STATES] is not None:
+        #         default_input_states = self.paramClassDefaults[INPUT_STATES].copy()
+        #         # Convert inputStates from paramClassDefaults to a list if it is not one
+        #         if not isinstance(default_input_states, list):
+        #             default_input_states = [default_input_states]
+        #         # Add input_states specified in params to those in paramClassDefaults
+        #         #    Note: order is important here;  new ones should be last, as paramClassDefaults defines the
+        #         #          the primary inputState which must remain first for the input_states ContentAddressableList
+        #         default_input_states.extend(input_states_spec)
+        #         # Assign full set back to params_arg
+        #         params[INPUT_STATES] = default_input_states
+
+        # # OUTPUT_STATES:
+        # try:
+        #     output_states_spec = params[OUTPUT_STATES]
+        # except KeyError:
+        #     pass
+        # else:
+        #     # Convert output_states_spec to list if it is not one
+        #     if not isinstance(output_states_spec, list):
+        #         output_states_spec = [output_states_spec]
+        #     # Get outputStates specified in paramClassDefaults
+        #     default_output_states = self.paramClassDefaults[OUTPUT_STATES].copy()
+        #     # Convert outputStates from paramClassDefaults to a list if it is not one
+        #     if not isinstance(default_output_states, list):
+        #         default_output_states = [default_output_states]
+        #     # Add output_states specified in params to those in paramClassDefaults
+        #     #    Note: order is important here;  new ones should be last, as paramClassDefaults defines the
+        #     #          the primary outputState which must remain first for the output_states ContentAddressableList
+        #     default_output_states.extend(output_states_spec)
+        #     # Assign full set back to params_arg
+        #     params[OUTPUT_STATES] = default_output_states
 
     def _validate_params(self, request_set, target_set=None, context=None):
         """validate TimeScale, INPUT_STATES, FUNCTION_PARAMS, OUTPUT_STATES and MONITOR_FOR_CONTROL
-
+        
         Go through target_set params (populated by Component._validate_params) and validate values for:
             + TIME_SCALE:  <TimeScale>
             + INPUT_STATES:
                 <MechanismsInputState or Projection object or class,
-                specification dict for one, ParamValueProjection tuple, or numeric value(s)>;
+                specification dict for one, 2-item tuple, or numeric value(s)>;
                 if it is missing or not one of the above types, it is set to self.variable
             + FUNCTION_PARAMS:  <dict>, every entry of which must be one of the following:
-                ParameterState or Projection object or class, specification dict for one,
-                ParamValueProjection tuple, or numeric value(s);
+                ParameterState or Projection object or class, specification dict for one, 2-item tuple, or numeric 
+                value(s);
                 if invalid, default (from paramInstanceDefaults or paramClassDefaults) is assigned
             + OUTPUT_STATES:
                 <MechanismsOutputState object or class, specification dict, or numeric value(s);
                 if it is missing or not one of the above types, it is set to None here;
-                    and then to default value of self.value (output of execute method) in instantiate_outputState
+                    and then to default value of self.value (output of execute method) in instantiate_output_state
                     (since execute method must be instantiated before self.value is known)
-                if OUTPUT_STATES is a list or OrderedDict, it is passed along (to instantiate_outputStates)
+                if OUTPUT_STATES is a list or OrderedDict, it is passed along (to instantiate_output_states)
                 if it is a OutputState class ref, object or specification dict, it is placed in a list
             + MONITORED_STATES:
                 ** DOCUMENT
@@ -1046,11 +1113,6 @@ def _validate_params(self, request_set, target_set=None, context=None):
         TBI - Generalize to go through all params, reading from each its type (from a registry),
                                    and calling on corresponding subclass to get default values (if param not found)
                                    (as PROJECTION_TYPE and PROJECTION_SENDER are currently handled)
-
-        :param request_set: (dict)
-        :param target_set: (dict)
-        :param context: (str)
-        :return:
         """
 
         # Perform first-pass validation in Function.__init__():
@@ -1059,11 +1121,11 @@ def _validate_params(self, request_set, target_set=None, context=None):
 
         params = target_set
 
-        #region VALIDATE TIME SCALE
+        #VALIDATE TIME SCALE
         try:
             param_value = params[TIME_SCALE]
         except KeyError:
-            if any(context_string in context for context_string in {COMMAND_LINE, 'ATTRIBUTE_SETTER'}):
+            if any(context_string in context for context_string in {COMMAND_LINE, SET_ATTRIBUTE}):
                 pass
             else:
                 self.timeScale = timeScaleSystemDefault
@@ -1074,35 +1136,21 @@ def _validate_params(self, request_set, target_set=None, context=None):
                 if self.prefs.verbosePref:
                     print("Value for {0} ({1}) param of {2} must be of type {3};  default will be used: {4}".
                           format(TIME_SCALE, param_value, self.name, type(TimeScale), timeScaleSystemDefault))
-        #endregion
 
-        #region VALIDATE INPUT STATE(S)
+        # VALIDATE INPUT STATE(S)
 
-        # MODIFIED 6/10/16
-        # FIX: SHOULD CHECK LENGTH OF INPUT_STATES PARAM (LIST OF NAMES OR SPECIFICATION DICT) AGAINST LENGTH OF
-        # FIX: self.variable 2D ARRAY AND COMPARE variable SPECS, IF PROVIDED, WITH CORRESPONDING ELEMENTS OF
-        # FIX: self.variable 2D ARRAY
-        try:
-            param_value = params[INPUT_STATES]
+        # INPUT_STATES is specified, so validate:
+        if INPUT_STATES in params and params[INPUT_STATES] is not None:
 
-        except KeyError:
-            if any(context_string in context for context_string in {COMMAND_LINE, 'ATTRIBUTE_SETTER'}):
-                pass
-            else:
-                # INPUT_STATES not specified:
-                # - set to None, so that it is set to default (self.variable) in instantiate_inputState
-                # - if in VERBOSE mode, warn in instantiate_inputState, where default value is known
-                params[INPUT_STATES] = None
+            param_value = params[INPUT_STATES]
 
-        else:
-            # INPUT_STATES is specified, so validate:
             # If it is a single item or a non-OrderedDict, place in a list (for use here and in instantiate_inputState)
-            if not isinstance(param_value, (list, OrderedDict)):
+            if not isinstance(param_value, (list, OrderedDict, ContentAddressableList)):
                 param_value = [param_value]
             # Validate each item in the list or OrderedDict
             # Note:
-            # * number of inputStates is validated against length of the owner mechanism's execute method variable (EMV)
-            #     in instantiate_inputState, where an inputState is assigned to each item (value) of the EMV
+            # * number of input_states is validated against length of the owner mechanism's variable
+            #     in instantiate_inputState, where an input_state is assigned to each item of variable
             i = 0
             for key, item in param_value if isinstance(param_value, dict) else enumerate(param_value):
                 from PsyNeuLink.Components.States.InputState import InputState
@@ -1110,9 +1158,8 @@ def _validate_params(self, request_set, target_set=None, context=None):
                 if not ((isclass(item) and (issubclass(item, InputState) or # InputState class ref
                                                 issubclass(item, Projection))) or    # Project class ref
                             isinstance(item, InputState) or      # InputState object
-                            isinstance(item, dict) or                     # InputState specification dict
-                            isinstance(item, ParamValueProjection) or     # ParamValueProjection tuple
-                            isinstance(item, str) or                      # Name (to be used as key in inputStates dict)
+                            isinstance(item, dict) or            # InputState specification dict
+                            isinstance(item, str) or             # Name (to be used as key in input_states dict)
                             iscompatible(item, **{kwCompatibilityNumeric: True})):   # value
                     # set to None, so it is set to default (self.variable) in instantiate_inputState
                     param_value[key] = None
@@ -1129,13 +1176,23 @@ def _validate_params(self, request_set, target_set=None, context=None):
                                      self.execute.__self__.name))
                 i += 1
             params[INPUT_STATES] = param_value
-        #endregion
 
-        #region VALIDATE EXECUTE METHOD PARAMS
+        # INPUT_STATES is not specified
+        else:
+            # pass if call is from assign_params (i.e., not from an init method)
+            if any(context_string in context for context_string in {COMMAND_LINE, SET_ATTRIBUTE}):
+                pass
+            else:
+                # INPUT_STATES not specified:
+                # - set to None, so that it is set to default (self.variable) in instantiate_inputState
+                # - if in VERBOSE mode, warn in instantiate_inputState, where default value is known
+                params[INPUT_STATES] = None
+
+        # VALIDATE FUNCTION_PARAMS
         try:
             function_param_specs = params[FUNCTION_PARAMS]
         except KeyError:
-            if any(context_string in context for context_string in {COMMAND_LINE, 'ATTRIBUTE_SETTER'}):
+            if any(context_string in context for context_string in {COMMAND_LINE, SET_ATTRIBUTE}):
                 pass
             elif self.prefs.verbosePref:
                 print("No params specified for {0}".format(self.__class__.__name__))
@@ -1157,42 +1214,26 @@ def _validate_params(self, request_set, target_set=None, context=None):
                         isinstance(param_value, ParameterState) or
                         isinstance(param_value, Projection) or
                         isinstance(param_value, dict) or
-                        isinstance(param_value, ParamValueProjection) or
                         iscompatible(param_value, default_value)):
                     params[FUNCTION_PARAMS][param_name] = default_value
                     if self.prefs.verbosePref:
                         print("{0} param ({1}) for execute method {2} of {3} is not a ParameterState, "
-                              "projection, ParamValueProjection, or value; default value ({4}) will be used".
+                              "projection, tuple, or value; default value ({4}) will be used".
                               format(param_name,
                                      param_value,
                                      self.execute.__self__.componentName,
                                      self.__class__.__name__,
                                      default_value))
-        #endregion
-        # FIX: MAKE SURE OUTPUT OF EXECUTE FUNCTION / SELF.VALUE  IS 2D ARRAY, WITH LENGTH == NUM OUTPUT STATES
 
-        #region VALIDATE OUTPUT STATE(S)
+        # VALIDATE OUTPUT STATE(S)
 
-        # FIX: MAKE SURE # OF OUTPUTS == LENGTH OF OUTPUT OF EXECUTE FUNCTION / SELF.VALUE
-        try:
-            param_value = params[OUTPUT_STATES]
+        # OUTPUT_STATES is specified, so validate:
+        if OUTPUT_STATES in params and params[OUTPUT_STATES] is not None:
 
-        except KeyError:
-            if any(context_string in context for context_string in {COMMAND_LINE, 'ATTRIBUTE_SETTER'}):
-                pass
-            else:
-                # OUTPUT_STATES not specified:
-                # - set to None, so that it is set to default (self.value) in instantiate_outputState
-                # Notes:
-                # * if in VERBOSE mode, warning will be issued in instantiate_outputState, where default value is known
-                # * number of outputStates is validated against length of owner mechanism's execute method output (EMO)
-                #     in instantiate_outputState, where an outputState is assigned to each item (value) of the EMO
-                params[OUTPUT_STATES] = None
+            param_value = params[OUTPUT_STATES]
 
-        else:
-            # OUTPUT_STATES is specified, so validate:
-            # If it is a single item or a non-OrderedDict, place in a list (for use here and in instantiate_outputState)
-            if not isinstance(param_value, (list, OrderedDict)):
+            # If it is a single item or a non-OrderedDict, place in list (for use here and in instantiate_output_state)
+            if not isinstance(param_value, (ContentAddressableList, list, OrderedDict)):
                 param_value = [param_value]
             # Validate each item in the list or OrderedDict
             i = 0
@@ -1204,7 +1245,7 @@ def _validate_params(self, request_set, target_set=None, context=None):
                             isinstance(item, dict) or                   # OutputState specification dict
                             isinstance(item, str) or                    # Name (to be used as key in outputStates dict)
                             iscompatible(item, **{kwCompatibilityNumeric: True})):  # value
-                    # set to None, so it is set to default (self.value) in instantiate_outputState
+                    # set to None, so it is set to default (self.value) in instantiate_output_state
                     param_value[key] = None
                     if self.prefs.verbosePref:
                         print("Item {0} of {1} param ({2}) in {3} is not a"
@@ -1220,6 +1261,20 @@ def _validate_params(self, request_set, target_set=None, context=None):
                 i += 1
             params[OUTPUT_STATES] = param_value
 
+        # OUTPUT_STATES is not specified
+        else:
+            # pass if call is from assign_params (i.e., not from an init method)
+            if any(context_string in context for context_string in {COMMAND_LINE, SET_ATTRIBUTE}):
+                pass
+            else:
+                # OUTPUT_STATES not specified:
+                # - set to None, so that it is set to default (self.value) in instantiate_output_state
+                # Notes:
+                # * if in VERBOSE mode, warning will be issued in instantiate_output_state, where default value is known
+                # * number of outputStates is validated against length of owner mechanism's execute method output (EMO)
+                #     in instantiate_output_state, where an outputState is assigned to each item (value) of the EMO
+                params[OUTPUT_STATES] = None
+
     def _validate_inputs(self, inputs=None):
         # Only ProcessingMechanism supports run() method of Function;  ControlMechanism and MonitoringMechanism do not
         raise MechanismError("{} does not support run() method".format(self.__class__.__name__))
@@ -1230,6 +1285,44 @@ def _instantiate_attributes_before_function(self, context=None):
         self._instantiate_parameter_states(context=context)
         super()._instantiate_attributes_before_function(context=context)
 
+    def _instantiate_function(self, context=None):
+        """Assign weights and exponents if specified in input_states
+        """
+
+        super()._instantiate_function(context=context)
+
+        if self.input_states and any(input_state.weight is not None for input_state in self.input_states):
+
+            # Construct defaults:
+            #    from function_object.weights if specified else 1's
+            try:
+                default_weights = self.function_object.weights
+            except AttributeError:
+                default_weights = None
+            if default_weights is None:
+                default_weights = default_weights or [1.0] * len(self.input_states)
+
+            # Assign any weights specified in input_state spec
+            weights = [[input_state.weight if input_state.weight is not None else default_weight]
+                       for input_state, default_weight in zip(self.input_states, default_weights)]
+            self.function_object._weights = weights
+
+        if self.input_states and any(input_state.exponent is not None for input_state in self.input_states):
+
+            # Construct defaults:
+            #    from function_object.weights if specified else 1's
+            try:
+                default_exponents = self.function_object.exponents
+            except AttributeError:
+                default_exponents = None
+            if default_exponents is None:
+                default_exponents = default_exponents or [1.0] * len(self.input_states)
+
+            # Assign any exponents specified in input_state spec
+            exponents = [[input_state.exponent if input_state.exponent is not None else default_exponent]
+                       for input_state, default_exponent in zip(self.input_states, default_exponents)]
+            self.function_object._exponents = exponents
+
     def _instantiate_attributes_after_function(self, context=None):
 
         self._instantiate_output_states(context=context)
@@ -1276,6 +1369,7 @@ def execute(self,
                 runtime_params=None,
                 clock=CentralClock,
                 time_scale=TimeScale.TRIAL,
+                ignore_execution_id = False,
                 context=None):
         """Carry out a single execution of the mechanism.
 
@@ -1284,24 +1378,22 @@ def execute(self,
             Update inputState(s) and parameter(s), call subclass _execute, update outputState(s), and assign self.value
 
             Execution sequence:
-            - Call self.inputState.execute() for each entry in self.inputStates:
-                + execute every self.inputState.receivesFromProjections.[<Projection>.execute()...]
-                + aggregate results using self.inputState.params[FUNCTION]()
-                + store the result in self.inputState.value
+            - Call self.input_state.execute() for each entry in self.input_states:
+                + execute every self.input_state.path_afferents.[<Projection>.execute()...]
+                + aggregate results and/or gate state using self.input_state.function()
+                + assign the result in self.input_state.value
             - Call every self.params[<ParameterState>].execute(); for each:
-                + execute self.params[<ParameterState>].receivesFromProjections.[<Projection>.execute()...]
+                + execute self.params[<ParameterState>].mod_afferents.[<Projection>.execute()...]
                     (usually this is just a single ControlProjection)
-                + aggregate results (if > one) using self.params[<ParameterState>].params[FUNCTION]()
-                + apply the result to self.params[<ParameterState>].value
+                + aggregate results for each ModulationParam or assign value from an OVERRIDE specification
+                + assign the result to self.params[<ParameterState>].value
             - Call subclass' self.execute(params):
-                - use self.inputState.value as its variable,
-                - use params[kw<*>] or self.params[<ParameterState>].value for each param of subclass self.execute,
-                - apply the output to self.outputState.value
+                - use self.input_state.value as its variable,
+                - use self.params[<ParameterState>].value for each param of subclass' self.function
+                - call self._update_output_states() to assign the output to each self.output_states[<OutputState>].value
                 Note:
-                * if execution is occuring as part of initialization, outputState(s) are reset to 0
-                * otherwise, they are left in the current state until the next update
-
-            - [TBI: Call self.outputState.execute() (output gating) to update self.outputState.value]
+                * if execution is occurring as part of initialization, each output_state is reset to 0
+                * otherwise, their values are left as is until the next update
         COMMENT
 
         Arguments
@@ -1311,7 +1403,7 @@ def execute(self,
             input to use for execution of the mechanism.
             This must be consistent with the format of the mechanism's inputState(s):
             the number of items in the  outermost level of the list, or axis 0 of the ndarray, must equal the number
-            of the mechanism's `inputStates  <Mechanism_Base.inputStates>`, and each item must be compatible with the
+            of the mechanism's `input_states  <Mechanism_Base.input_states>`, and each item must be compatible with the
             format (number and type of elements) of the corresponding inputState's
             `variable <InputState.InputState.variable>` (see `Run Inputs <Run_Inputs>` for details of input
             specification formats).
@@ -1330,12 +1422,12 @@ def execute(self,
         Returns
         -------
 
-        mechanism's outputValue : List[value]
+        mechanism's output_values : List[value]
             list of the :keyword:`value` of each of the mechanism's `outputStates <Mechanism_OutputStates>` after
             either one time_step or a trial.
 
         """
-
+        self.ignore_execution_id = ignore_execution_id
         context = context or NO_CONTEXT
 
         # IMPLEMENTATION NOTE: Re-write by calling execute methods according to their order in functionDict:
@@ -1347,7 +1439,7 @@ def execute(self,
             if PROCESS_INIT in context or SYSTEM_INIT in context:
                 # Run full execute method for init of Process and System
                 pass
-            # Only call mechanism's _execute method for init
+            # Only call subclass' _execute method and then return (do not complete the rest of this method)
             elif self.initMethod is INIT__EXECUTE__METHOD_ONLY:
                 return_value =  self._execute(variable=self.variable,
                                                  runtime_params=runtime_params,
@@ -1382,7 +1474,7 @@ def execute(self,
                     return converted_to_2d
                 # MODIFIED 3/3/17 END
 
-            # Only call mechanism's function method for init
+            # Call only subclass' function during initialization (not its full _execute method nor rest of this method)
             elif self.initMethod is INIT_FUNCTION_METHOD_ONLY:
                 return_value = self.function(variable=self.variable,
                                              params=runtime_params,
@@ -1419,24 +1511,25 @@ def execute(self,
 
         # FIX: ??MAKE CONDITIONAL ON self.prefs.paramValidationPref??
         #region VALIDATE INPUT STATE(S) AND RUNTIME PARAMS
-        self._check_args(variable=self.inputValue,
+        self._check_args(variable=self.variable,
                         params=runtime_params,
                         target_set=runtime_params)
         #endregion
 
         #region UPDATE INPUT STATE(S)
-        # Executing or simulating process or system, get input by updating inputStates
-        if input is None and (EXECUTING in context or EVC_SIMULATION in context):
+
+        # Executing or simulating process or system, get input by updating input_states
+
+        if input is None and (EXECUTING in context or EVC_SIMULATION in context) and (self.input_state.path_afferents != []):
             self._update_input_states(runtime_params=runtime_params, time_scale=time_scale, context=context)
 
-        # Direct call to execute mechanism with specified input, so assign input to mechanism's inputStates
+        # Direct call to execute mechanism with specified input, so assign input to mechanism's input_states
         else:
             if context is NO_CONTEXT:
                 context = EXECUTING + ' ' + append_type_to_name(self)
             if input is None:
                 input = self.variableClassDefault
             self._assign_input(input)
-        # MODIFIED 11/27/16 END
 
         #endregion
 
@@ -1446,22 +1539,12 @@ def execute(self,
 
         #region CALL SUBCLASS _execute method AND ASSIGN RESULT TO self.value
 
-        # # MODIFIED 2/23/17 OLD:
-        # self.value = self._execute(variable=self.inputValue,
-        # MODIFIED 2/23/17 NEW:
         self.value = self._execute(variable=self.variable,
-        # MODIFIED 2/23/17 END
                                       runtime_params=runtime_params,
                                       clock=clock,
                                       time_scale=time_scale,
                                       context=context)
 
-        # MODIFIED 1/28/17 NEW:
-        # # context = context + ' ' + ASSIGN_VALUE
-        # context = EXECUTING + ' ' + self.name + ASSIGN_VALUE
-        # MODIFIED 1/28/17 END
-
-
         # # MODIFIED 3/3/17 OLD:
         # self.value = np.atleast_2d(self.value)
         # # MODIFIED 3/3/17 NEW:
@@ -1507,22 +1590,21 @@ def execute(self,
 
         #region REPORT EXECUTION
         if self.prefs.reportOutputPref and context and EXECUTING in context:
-            self._report_mechanism_execution(self.inputValue, self.user_params, self.outputState.value)
+            self._report_mechanism_execution(self.input_values, self.user_params, self.output_state.value)
         #endregion
 
         #region RE-SET STATE_VALUES AFTER INITIALIZATION
         # If this is (the end of) an initialization run, restore state values to initial condition
         if '_init_' in context:
-            for state in self.inputStates:
-                self.inputStates[state].value = self.inputStates[state].variable
-            for state in self.parameterStates:
-                self.parameterStates[state].value =  self.parameterStates[state].baseValue
-            for state in self.outputStates:
+            for state in self.input_states:
+                self.input_states[state].value = self.input_states[state].variable
+            for state in self._parameter_states:
+                self._parameter_states[state].value =  getattr(self, '_'+state)
+            for state in self.output_states:
                 # Zero outputStates in case of recurrence:
                 #    don't want any non-zero values as a residuum of initialization runs to be
                 #    transmittted back via recurrent projections as initial inputs
-# FIX: IMPLEMENT zero_all_values METHOD
-                self.outputStates[state].value = self.outputStates[state].value * 0.0
+                self.output_states[state].value = self.output_states[state].value * 0.0
         #endregion
 
         #endregion
@@ -1560,7 +1642,7 @@ def run(self,
         Returns
         -------
 
-        mechanism's outputValue : List[value]
+        mechanism's output_values : List[value]
             list of the :keyword:`value` of each of the mechanism's `outputStates <Mechanism_OutputStates>` for
             each execution of the mechanism.
 
@@ -1577,7 +1659,7 @@ def _assign_input(self, input):
 
         input = np.atleast_2d(input)
         num_inputs = np.size(input,0)
-        num_input_states = len(self.inputStates)
+        num_input_states = len(self.input_states)
         if num_inputs != num_input_states:
             # Check if inputs are of different lengths (indicated by dtype == np.dtype('O'))
             num_inputs = np.size(input)
@@ -1585,12 +1667,14 @@ def _assign_input(self, input):
                 pass
             else:
                 raise SystemError("Number of inputs ({0}) to {1} does not match "
-                                  "its number of inputStates ({2})".
+                                  "its number of input_states ({2})".
                                   format(num_inputs, self.name,  num_input_states ))
         for i in range(num_input_states):
-            input_state = list(self.inputStates.values())[i]
+            # input_state = list(self.input_states.values())[i]
+            input_state = self.input_states[i]
             # input_item = np.ndarray(input[i])
             input_item = input[i]
+
             if len(input_state.variable) == len(input_item):
                 input_state.value = input_item
             else:
@@ -1601,73 +1685,75 @@ def _assign_input(self, input):
                                             len(input_state.variable),
                                             input_state.name,
                                             append_type_to_name(self)))
+        self.variable = np.array(self.input_values)
 
     def _update_input_states(self, runtime_params=None, time_scale=None, context=None):
-        """ Update value for each inputState in self.inputStates:
+        """ Update value for each inputState in self.input_states:
 
-        Call execute method for all (MappingProjection) projections in inputState.receivesFromProjections
+        Call execute method for all (MappingProjection) projections in inputState.path_afferents
         Aggregate results (using inputState execute method)
         Update inputState.value
         """
-        for i in range(len(self.inputStates)):
-            state_name, state = list(self.inputStates.items())[i]
+        for i in range(len(self.input_states)):
+            state = self.input_states[i]
             state.update(params=runtime_params, time_scale=time_scale, context=context)
-            self.inputValue[i] = state.value
-        self.variable = np.array(self.inputValue)
+            # self.input_value[i] = state.value
+        self.variable = np.array(self.input_values)
 
     def _update_parameter_states(self, runtime_params=None, time_scale=None, context=None):
 
-        for state_name, state in self.parameterStates.items():
+        for state in self._parameter_states:
 
-            state.update(params=runtime_params, time_scale=time_scale, context=context)
+            # state_name = state.name
 
-            # If runtime_params is specified has a spec for the current param
-            #    assign parameter value there as parameterState's value
-            if runtime_params and PARAMETER_STATE_PARAMS in runtime_params and state_name in runtime_params[
-                PARAMETER_STATE_PARAMS]:
-                params = runtime_params
-            # Otherwise use paramsCurrent
-            else:
-                params = self.paramsCurrent
+            state.update(params=runtime_params, time_scale=time_scale, context=context)
 
-            # Determine whether template (param to type-match) is at top level or in a function_params dictionary
-            try:
-                params[state_name]
-            except KeyError:
-                params = self.function_object.paramsCurrent
-
-            # param_spec is the existing specification for the parameter in paramsCurrent or runtime_params
-            param_spec = params[state_name]
-
-            # If param_spec is a projection (i.e., ControlProjection or LearningProjection)
-            #    then its value will be provided by the execution of the parameterState's function
-            #    (which gets and aggregates the values of its projections), so execute function
-            #    to get a sample of its output as the param_spec
-            if isclass(param_spec) and issubclass(param_spec, Projection):
-                param_spec = state.function()
-
-            # Get type of param_spec:
-            param_type = type(param_spec)
-            # If param is a tuple, get type of parameter itself (= 1st item;  2nd is projection or ModulationOperation)
-            if param_type is tuple:
-                param_type = type(param_spec[0])
-
-            # Assign version of parameterState.value matched to type of template
-            #    to runtime param or paramsCurrent (per above)
-            params[state_name] = type_match(state.value, param_type)
+            # # MODIFIED 6/1/17 OLD:
+            # #   [OBVIATED BY UPDATING OF ParameterState.value AND ITS ASSOCIATED PARAMETER DIRECTLY BY PROPERTIES
+            # #    AND MORATORIUM ON runtime_params
+            # # If runtime_params is specified has a spec for the current param
+            # #    assign parameter value there as parameterState's value
+            # if (runtime_params and
+            #             PARAMETER_STATE_PARAMS in runtime_params and
+            #             state_name in runtime_params[PARAMETER_STATE_PARAMS]):
+            #     params = runtime_params
+            # # Otherwise use paramsCurrent
+            # else:
+            #     params = self.paramsCurrent
+            #
+            # # Determine whether template (param to type-match) is at top level or in a function_params dictionary
+            # try:
+            #     params[state_name]
+            # except KeyError:
+            #     params = self.function_object.paramsCurrent
+            #
+            # # param_spec is the existing specification for the parameter in paramsCurrent or runtime_params
+            # param_spec = params[state_name]
+            #
+            # # If param_spec is a projection (i.e., ControlProjection or LearningProjection)
+            # #    then its value will be provided by the execution of the parameterState's function
+            # #    (which gets and aggregates the values of its projections), so execute function
+            # #    to get a sample of its output as the param_spec
+            # if isclass(param_spec) and issubclass(param_spec, Projection):
+            #     param_spec = state.function()
+            #
+            # # Get type of param_spec:
+            # param_type = type(param_spec)
+            # # If param is a tuple, get type of parameter itself (= 1st item;  2nd is projection or Modulation)
+            # if param_type is tuple:
+            #     param_type = type(param_spec[0])
+            #
+            # # Assign version of parameterState.value matched to type of template
+            # #    to runtime param or paramsCurrent (per above)
+            # params[state_name] = type_match(state.value, param_type)
+            # MODIFIED 6/1/17 END
 
     def _update_output_states(self, runtime_params=None, time_scale=None, context=None):
-        """Execute function for each outputState and assign result of each to corresponding item of self.outputValue
+        """Execute function for each outputState and assign result of each to corresponding item of self.output_values
 
         """
-        for i in range(len(self.outputStates)):
-            state = list(self.outputStates.values())[i]
+        for state in self.output_states:
             state.update(params=runtime_params, time_scale=time_scale, context=context)
-            # self.outputValue[i] = state.value
-
-        # Assign value of each outputState to corresponding item in self.outputValue
-        self.outputValue = list(state.value for state in list(self.outputStates.values()))
-
 
     def initialize(self, value):
         """Assign an initial value to the mechanism's `value <Mechanism_Base.value>` attribute and update its
@@ -1702,9 +1788,9 @@ def _execute(self,
     def _report_mechanism_execution(self, input_val=None, params=None, output=None):
 
         if input_val is None:
-            input_val = self.inputValue
+            input_val = self.input_values
         if output is None:
-            output = self.outputState.value
+            output = self.output_state.value
         params = params or self.user_params
 
         import re
@@ -1713,14 +1799,12 @@ def _report_mechanism_execution(self, input_val=None, params=None, output=None):
         else:
             mechanism_string = ' mechanism'
 
-        # # MODIFIED 2/20/17 NEW:
-        input_string = [float("{:0.3}".format(float(i))) for i in input_val].__str__().strip("[]")
-        # # MODIFIED 4/21/17 NEWER: [NEW CRASHES IF input_val IS AN ARRAY]
-        # if isinstance(input_val, np.ndarray) and input_val.ndim > 1 and input_val.shape[1] > 1:
-        #     input_string = input_val
-        # else:
-        #     input_string = [float("{:0.3}".format(float(i))) for i in input_val].__str__().strip("[]")
-        # MODIFIED 2/20/17 END
+        # kmantel: previous version would fail on anything but iterables of things that can be cast to floats
+        #   if you want more specific output, you can add conditional tests here
+        try:
+            input_string = [float("{:0.3}".format(float(i))) for i in input_val].__str__().strip("[]")
+        except TypeError:
+            input_string = input_val
 
         print ("\n\'{}\'{} executed:\n- input:  {}".
                format(self.name,
@@ -1732,8 +1816,10 @@ def _report_mechanism_execution(self, input_val=None, params=None, output=None):
             # Sort for consistency of output
             params_keys_sorted = sorted(params.keys())
             for param_name in params_keys_sorted:
-                # No need to report these here, as they will be reported for the function itself below
-                if param_name is FUNCTION_PARAMS:
+                # No need to report:
+                #    function_params here, as they will be reported for the function itself below;
+                #    input_states or output_states, as these are not really params
+                if param_name in {FUNCTION_PARAMS, INPUT_STATES, OUTPUT_STATES}:
                     continue
                 param_is_function = False
                 param_value = params[param_name]
@@ -1757,17 +1843,55 @@ def _report_mechanism_execution(self, input_val=None, params=None, output=None):
                                format(fct_param_name,
                                       str(self.function_object.user_params[fct_param_name]).__str__().strip("[]")))
 
-        # # MODIFIED 2/20/17 OLD:
-        # if not isinstance(output, Iterable):
-        #     output_string = re.sub('[\[,\],\n]','',str([float("{:0.3}".format(float(i))) for i in output]))
-        # else:
-        #     output_string = output
-        # MODIFIED 2/20/17 NEW:
-        output_string = re.sub('[\[,\],\n]','',str([float("{:0.3}".format(float(i))) for i in output]))
-        # MODIFIED 2/20/17 END
+        # kmantel: previous version would fail on anything but iterables of things that can be cast to floats
+        #   if you want more specific output, you can add conditional tests here
+        try:
+            output_string = re.sub(r'[\[,\],\n]', '', str([float("{:0.3}".format(float(i))) for i in output]))
+        except TypeError:
+            output_string = output
 
         print("- output: {}".format(output_string))
 
+
+    def plot(self,x_range = None):
+        """
+        Generate a plot of the mechanism's function using the specified parameter values. See (see
+        `DDM.plot <DDM.plot>` for details of the animated DDM plot).
+
+        Arguments
+        ---------
+
+        x_range: List
+             specify the range over which the function should be plotted. x_range must be provides as a list containing
+             two floats: lowest value of x and highest value of x.  Default values depend on the mechanism's function.
+
+            - Logistic Function: default x_range = [-5.0, 5.0]
+            - Exponential Function: default x_range = [0.1, 5.0]
+            - All Other Functions: default x_range = [-10.0, 10.0]
+
+
+
+        Returns
+        -------
+        mechanism's function plot : Matplotlib window
+            Matplotlib window of the mechanism's function plotted with specified parameters over the specified x_range
+
+        """
+
+        import matplotlib.pyplot as plt
+
+        if not x_range:
+            if "Logistic" in str(self.function):
+                x_range= [-5.0, 5.0]
+            elif "Exponential" in str(self.function):
+                x_range = [0.1, 5.0]
+            else:
+                x_range = [-10.0, 10.0]
+        x_space = np.linspace(x_range[0],x_range[1])
+        plt.plot(x_space, self.function(x_space), lw=3.0, c='r')
+        plt.show()
+
+
     # def adjust_function(self, params, context=None):
     #     """Modify control_signal_allocations while process is executing
     #
@@ -1780,7 +1904,7 @@ def _report_mechanism_execution(self, input_val=None, params=None, output=None):
     #     :rtype CurrentStateTuple(state, confidence, duration, controlModulatedParamValues)
     #     """
     #
-    #     self._instantiate_defaults(self.inputState, params)
+    #     self._instantiate_defaults(self.input_state, params)
     # # IMPLEMENTATION NOTE: *** SHOULD THIS UPDATE AFFECTED PARAM(S) BY CALLING self._update_parameter_states??
     #     return self.outputState.value
 
@@ -1803,6 +1927,18 @@ def _get_mechanism_param_values(self):
         return dict((param, value.value) for param, value in self.paramsCurrent.items()
                     if isinstance(value, ParameterState) )
 
+    def _get_primary_state(self, state_type):
+        from PsyNeuLink.Components.States.InputState import InputState
+        from PsyNeuLink.Components.States.ParameterState import ParameterState
+        from PsyNeuLink.Components.States.OutputState import OutputState
+        if issubclass(state_type, InputState):
+            return self.input_state
+        if issubclass(state_type, OutputState):
+            return self.output_state
+        if issubclass(state_type, ParameterState):
+            raise Mechanism("PROGRAM ERROR:  illegal call to {} for a primary {}".format(self.name, PARAMETER_STATE))
+
+
     @property
     def value(self):
         return self._value
@@ -1832,6 +1968,25 @@ def value(self, assignment):
         #     self.log.entries[self.name] = LogEntry(CurrentTime(), context, assignment)
         # # MODIFIED 1/28/17 END
 
+    @property
+    def input_state(self):
+        return self.input_states[0]
+
+    @property
+    def input_values(self):
+        try:
+            return self.input_states.values
+        except (TypeError, AttributeError):
+            return None
+
+    @property
+    def output_state(self):
+        return self.output_states[0]
+
+    @property
+    def output_values(self):
+        return self.output_states.values
+
     @property
     def status(self):
         return self._status
@@ -1850,22 +2005,6 @@ def status(self, current_value):
             self._status = CHANGED
 
 
-    @property
-    def inputState(self):
-        return self._inputState
-
-    @inputState.setter
-    def inputState(self, assignment):
-        self._inputState = assignment
-
-    @property
-    def outputState(self):
-        return self._outputState
-
-    @outputState.setter
-    def outputState(self, assignment):
-        self._outputState = assignment
-
 def _is_mechanism_spec(spec):
     """Evaluate whether spec is a valid Mechanism specification
 
@@ -1883,11 +2022,11 @@ def _is_mechanism_spec(spec):
     return False
 
 # MechanismTuple indices
-OBJECT_ITEM = 0
-PARAMS_ITEM = 1
-PHASE_ITEM = 2
-
-MechanismTuple = namedtuple('MechanismTuple', 'mechanism, params, phase')
+# OBJECT_ITEM = 0
+# # PARAMS_ITEM = 1
+# # PHASE_ITEM = 2
+#
+# MechanismTuple = namedtuple('MechanismTuple', 'mechanism')
 
 from collections import UserList, Iterable
 class MechanismList(UserList):
@@ -1912,41 +2051,42 @@ class MechanismList(UserList):
         each item is an outputState.value
     """
 
-    def __init__(self, owner, tuples_list:list):
+    def __init__(self, owner, components_list:list):
         super().__init__()
-        self.mech_tuples = tuples_list
+        self.mechs = components_list
         self.owner = owner
-        for item in tuples_list:
-            if not isinstance(item, MechanismTuple):
-                raise MechanismError("The following item in the tuples_list arg of MechanismList()"
-                                     " is not a MechanismTuple: {}".format(item))
-        self.process_tuples = tuples_list
+        # for item in components_list:
+        #     if not isinstance(item, MechanismTuple):
+        #         raise MechanismError("The following item in the components_list arg of MechanismList()"
+        #                              " is not a MechanismTuple: {}".format(item))
+
+        self.process_tuples = components_list
 
     def __getitem__(self, item):
         """Return specified mechanism in MechanismList
         """
-        # return list(self.mech_tuples[item])[MECHANISM]
-        return self.mech_tuples[item].mechanism
+        # return list(self.mechs[item])[MECHANISM]
+        return self.mechs[item]
 
     def __setitem__(self, key, value):
-        raise ("MyList is read only ")
+        raise ("MechanismList is read only ")
 
     def __len__(self):
-        return (len(self.mech_tuples))
+        return (len(self.mechs))
 
-    def _get_tuple_for_mech(self, mech):
-        """Return first mechanism tuple containing specified mechanism from the list of mech_tuples
-        """
-        if list(item.mechanism for item in self.mech_tuples).count(mech):
-            if self.owner.verbosePref:
-                print("PROGRAM ERROR:  {} found in more than one mech_tuple in {} in {}".
-                      format(append_type_to_name(mech), self.__class__.__name__, self.owner.name))
-        return next((mech_tuple for mech_tuple in self.mech_tuples if mech_tuple.mechanism is mech), None)
+    # def _get_tuple_for_mech(self, mech):
+    #     """Return first mechanism tuple containing specified mechanism from the list of mechs
+    #     """
+    #     if list(item for item in self.mechs).count(mech):
+    #         if self.owner.verbosePref:
+    #             print("PROGRAM ERROR:  {} found in more than one object_item in {} in {}".
+    #                   format(append_type_to_name(mech), self.__class__.__name__, self.owner.name))
+    #     return next((object_item for object_item in self.mechs if object_item is mech), None)
 
     @property
-    def mech_tuples_sorted(self):
-        """Return list of mech_tuples sorted by mechanism name"""
-        return sorted(self.mech_tuples, key=lambda mech_tuple: mech_tuple[0].name)
+    def mechs_sorted(self):
+        """Return list of mechs sorted by mechanism name"""
+        return sorted(self.mechs, key=lambda object_item: object_item.name)
 
     @property
     def mechanisms(self):
@@ -1968,8 +2108,8 @@ def outputStateNames(self):
         """Return names of all outputStates for all mechanisms in MechanismList"""
         names = []
         for item in self.mechanisms:
-            for output_state in item.outputStates:
-                names.append(output_state)
+            for output_state in item.output_states:
+                names.append(output_state.name)
         return names
 
     @property
@@ -1977,6 +2117,6 @@ def outputStateValues(self):
         """Return values of outputStates for all mechanisms in MechanismList"""
         values = []
         for item in self.mechanisms:
-            for output_state_name, output_state in list(item.outputStates.items()):
+            for output_state in item.output_states:
                 values.append(output_state.value)
         return values
diff --git a/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/DDM.py b/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/DDM.py
index db582bde3c1..462a771129e 100644
--- a/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/DDM.py
+++ b/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/DDM.py
@@ -7,7 +7,7 @@
 
 
 # ***************************************************  DDM *************************************************************
-#
+
 """
 ..
     Sections:
@@ -31,7 +31,7 @@
 Creating a DDM Mechanism
 -----------------------------
 A DDM Mechanism can be instantiated directly by calling its constructor, or by using the `mechanism` function
-and specifying DDM as its `mech_spec` argument.  The analytic solution used in `TRIAL` mode is selected
+and specifying DDM as its **mech_spec** argument.  The analytic solution used in `TRIAL` mode is selected
 using the `function <DDM.function>` argument, which can be simply the name of a DDM function (first example below),
 or a call to the function with arguments specifying its parameters (see `DDM_Execution` below for a description of
 DDM function parameters) and, optionally, a `ControlProjection` (second example)::
@@ -86,13 +86,14 @@
 below).  The number of `outputStates <OutputState>` is determined by the `function <DDM.function>` in use (see
 :ref:`list of output values <DDM_Results>` below).
 COMMENT
+
 COMMENT:
 [TBI - average_output_states ARGUMENT/OPTION AFTER IMPLEMENTING MULTIPROCESS DDM]
 OUTPUT MEASURE?? OUTCOME MEASURE?? RESULT?? TYPE OF RESULT??
 If only a single decision process was run, then the value of each outputState is the corresponding output of
 the decision process.  If there is more than one decision process (i.e., the input has more than one item), then
 the content of the outputStates is determined by the ``average_output_states`` argument.  If it is :keyword:`True`,
-then each outputState (and item of ``outputValue``) contains a single value, which is the average of the output
+then each outputState (and item of ``output_values``) contains a single value, which is the average of the output
 values of that type over all of the processes run.  If ``average_output_states`` is :keyword:`False` (the default),
 then the value of each ouputState is a 1d array, each element of which is the outcome of that type for the
 corresponding decision process.
@@ -252,10 +253,10 @@
 After each execution of the mechanism:
 
     * the value of the **decision variable** is assigned to the mechanism's `value <DDM.value>` attribute, the value of
-      the 1st item of its `outputValue <DDM.outputState>` attribute, and as the value of its `DDM_DECISION_VARIABLE`
+      the 1st item of its `output_values <DDM.outputState>` attribute, and as the value of its `DDM_DECISION_VARIABLE`
       outputState.
     ..
-    * **response time** is assigned as the value of the 2nd item of the mechanism's `outputValue <DDM.outputValue>`
+    * **response time** is assigned as the value of the 2nd item of the mechanism's `output_values <DDM.output_values>`
       attribute and as the value of its `RESPONSE_TIME` outputState.  If `time_scale <DDM.time_scale>` is
       `TimeScale.TRIAL`, the value is the mean response time (in seconds) estimated by the analytic solution used in
       `function <DDM.function>`.
@@ -273,7 +274,7 @@
     :py:data:`TimeScale.TIME_STEP <TimeScale.TimeScale.TIME_STEP>`;  otherwise the value of the corresponding
     attributes is `None`.
     * **probability of reaching the upper threshold** is assigned to the 3rd item of the mechanism's
-      `outputValue <DDM.outputValue>` attribute, and as the value of its `PROBABILITY_UPPER_THRESHOLD` outputState.
+      `output_values <DDM.output_values>` attribute, and as the value of its `PROBABILITY_UPPER_THRESHOLD` outputState.
       If `time_scale <DDM.time_scale>` is `TimeScale.TRIAL`, the value is the probability (calculated by the analytic
       solution used in `function <DDM.function>`) that the value of the decision variable reached the upper (
       positive) threshold. Often, by convention, the upper threshold is associated with the ccorrect response,
@@ -288,7 +289,7 @@
 
     ..
     * **probability of reaching the lower threshold** is assigned to the 4th item of the mechanism's
-      `outputValue <DDM.outputValue>` attribute and as the value of its `PROBABILITY_LOWER_THRESHOLD` outputState.
+      `output_values <DDM.output_values>` attribute and as the value of its `PROBABILITY_LOWER_THRESHOLD` outputState.
       If `time_scale <DDM.time_scale>` is `TimeScale.TRIAL`, the value is the probability (calculated by the analytic
       solution used in `function <DDM.function>`) that the value of the decision variable reached the lower (negative)
       threshold.  Often, by convention, the lower threshold is associated with the incorrect response, in which case
@@ -303,14 +304,14 @@
 
     ..
     The following assignments are made only assigned if the `NavarroAndFuss` function is used, and
-    `time_scale <DDM.time_scale>` is `TimeScale.TRIAL`.  Otherwise, neither the `outputValue <DDM.outputValue>`
+    `time_scale <DDM.time_scale>` is `TimeScale.TRIAL`.  Otherwise, neither the `output_values <DDM.output_values>`
     nor the `outputState` attribute have a 6th item (if another function is assigned) or they are asssigned
     `None` if `time_scale <DDM.time_scale>` is `TimeScale.TIME_STEP`.
     * **mean correct response time** (in seconds) is assigned to the 5th item of the mechanism's
-    `outputValue <DDM.outputValue>` attribute and as  the value of its `RT_CORRECT_MEAN` outputState.
+    `output_values <DDM.output_values>` attribute and as  the value of its `RT_CORRECT_MEAN` outputState.
     ..
     * **variance of correct response time** is assigned to the 6th item of the mechanism's
-      `outputValue <DDM.outputValue>` attribute and as the value of its `RT_CORRECT_VARIANCE` outputState.
+      `output_values <DDM.output_values>` attribute and as the value of its `RT_CORRECT_VARIANCE` outputState.
 
         In time_step mode, compute and report variance of the path
         (e.g., as confirmation of /deviation from noise param??)??
@@ -322,29 +323,41 @@
 Class Reference
 ---------------
 """
+import logging
 
 # from numpy import sqrt, random, abs, tanh, exp
+import random
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ProcessingMechanism import *
 from PsyNeuLink.Components.Functions.Function import *
-
-# DDM outputs (used to create and name outputStates):
-DDM_DECISION_VARIABLE = "DDM_decision_variable"
-DDM_RESPONSE_TIME = "DDM_response_time"
-DDM_PROBABILITY_UPPER_THRESHOLD = "DDM_probability_upperBound"
-DDM_PROBABILITY_LOWER_THRESHOLD = "DDM_probability_lowerBound"
-DDM_RT_CORRECT_MEAN = "DDM_RT_Correct_Mean"
-DDM_RT_CORRECT_VARIANCE = "DDM_RT_correct_variance"
-
-
-# Indices for results used in return value tuple; auto-numbered to insure sequentiality
-class DDM_Output(AutoNumber):
-    DECISION_VARIABLE = ()
-    RESPONSE_TIME = ()
-    P_UPPER_MEAN = ()
-    P_LOWER_MEAN = ()
-    RT_CORRECT_MEAN = ()
-    RT_CORRECT_VARIANCE = ()
-    NUM_OUTPUT_VALUES = ()
+from PsyNeuLink.Components.States.OutputState import PRIMARY_OUTPUT_STATE, SEQUENTIAL
+
+logger = logging.getLogger(__name__)
+
+DECISION_VARIABLE='DECISION_VARIABLE'
+RESPONSE_TIME = 'RESPONSE_TIME'
+PROBABILITY_UPPER_THRESHOLD = 'PROBABILITY_UPPER_THRESHOLD'  # Probability of hitting upper bound
+PROBABILITY_LOWER_THRESHOLD = 'PROBABILITY_LOWER_THRESHOLD'  # Probability of hitting lower bound
+RT_CORRECT_MEAN = 'RT_CORRECT_MEAN'  # NavarroAnd Fuss only
+RT_CORRECT_VARIANCE = 'RT_CORRECT_VARIANCE'  # NavarroAnd Fuss only
+
+DDM_standard_output_states = [{NAME: DECISION_VARIABLE,},
+                              {NAME: RESPONSE_TIME},
+                              {NAME: PROBABILITY_UPPER_THRESHOLD},  # Probability of hitting upper bound
+                              {NAME: PROBABILITY_LOWER_THRESHOLD},  # Probability of hitting lower bound
+                              {NAME: RT_CORRECT_MEAN},  # NavarroAnd Fuss only
+                              {NAME: RT_CORRECT_VARIANCE}]  # NavarroAnd Fuss only
+
+# This is a convenience class that provides list of standard_output_state names in IDE
+class DDM_OUTPUT():
+    DECISION_VARIABLE=DECISION_VARIABLE
+    RESPONSE_TIME=RESPONSE_TIME
+    PROBABILITY_UPPER_THRESHOLD=PROBABILITY_UPPER_THRESHOLD
+    PROBABILITY_LOWER_THRESHOLD=PROBABILITY_LOWER_THRESHOLD
+    RT_CORRECT_MEAN=RT_CORRECT_MEAN
+    RT_CORRECT_VARIANCE=RT_CORRECT_VARIANCE
+# THIS WOULD HAVE BEEN NICE, BUT IDE DOESN'T EXECUTE IT, SO NAMES DON'T SHOW UP
+# for item in [item[NAME] for item in DDM_standard_output_states]:
+#     setattr(DDM_OUTPUT.__class__, item, item)
 
 
 class DDMError(Exception):
@@ -458,15 +471,15 @@ class DDM(ProcessingMechanism_Base):
         contains one entry for each parameter of the mechanism's function.
         The key of each entry is the name of (keyword for) a function parameter, and its value is the parameter's value.
     value : 2d np.array[array(float64),array(float64),array(float64),array(float64)]
-        result of executing DDM `function <DDM.function>`; same items as `outputValue <DDM.outputValue>`.
+        result of executing DDM `function <DDM.function>`; same items as `output_values <DDM.output_values>`.
 
     COMMENT:
         CORRECTED:
         value : 1d np.array
             the output of `function <DDM.function>`;  also assigned to `value <DDM.value>` of the
-            `DDM_DECISION_VARIABLE` outputState and the first item of `outputValue <DDM.outputValue>`.
+            `DDM_DECISION_VARIABLE` outputState and the first item of `output_values <DDM.output_values>`.
     COMMENT
-    outputValue : List[array(float64),array(float64),array(float64),array(float64)]
+    output_values : List[array(float64),array(float64),array(float64),array(float64)]
         a list with the following items:
         * **decision variable** (value of `DDM_DECISION_VARIABLE` outputState);
         * **response time** (value of `DDM_RESPONSE_TIME` outputState);
@@ -532,29 +545,7 @@ class DDM(ProcessingMechanism_Base):
     paramClassDefaults = Mechanism_Base.paramClassDefaults.copy()
     paramClassDefaults.update({
         TIME_SCALE: TimeScale.TRIAL,
-        # Assign internal params here (not accessible to user)
-        # User accessible params are assigned in _instantiate_defaults_to_paramClassDefaults (in __init__)
-        # # MONITOR_FOR_CONTROL:[PROBABILITY_LOWER_THRESHOLD,(RESPONSE_TIME, -1, 1)]
-        OUTPUT_STATES: [  # Full set specified to include Navarro and Fuss outputs
-            {NAME: DDM_DECISION_VARIABLE,
-             INDEX: DDM_Output.DECISION_VARIABLE.value},
-
-            {NAME: DDM_RESPONSE_TIME,
-             INDEX: DDM_Output.RESPONSE_TIME.value},
-
-            {NAME: DDM_PROBABILITY_UPPER_THRESHOLD,  # Probability of hitting upper bound
-             INDEX: DDM_Output.P_UPPER_MEAN.value},
-
-            {NAME: DDM_PROBABILITY_LOWER_THRESHOLD,  # Probability of hitting lower bound
-             INDEX: DDM_Output.P_LOWER_MEAN.value},
-
-            {NAME: DDM_RT_CORRECT_MEAN,  # NavarroAnd Fuss only
-             INDEX: DDM_Output.RT_CORRECT_MEAN.value},
-
-            {NAME: DDM_RT_CORRECT_VARIANCE,  # NavarroAnd Fuss only
-             INDEX: DDM_Output.RT_CORRECT_VARIANCE.value}
-        ]
-    })
+        OUTPUT_STATES: None})
 
     # Set default input_value to default bias for DDM
     paramNames = paramClassDefaults.keys()
@@ -562,25 +553,28 @@ class DDM(ProcessingMechanism_Base):
     @tc.typecheck
     def __init__(self,
                  default_input_value=None,
+                 size:tc.optional(int)=None,
                  # function:tc.enum(type(BogaczEtAl), type(NavarroAndFuss))=BogaczEtAl(drift_rate=1.0,
                  function=BogaczEtAl(drift_rate=1.0,
                                      starting_point=0.0,
                                      threshold=1.0,
                                      noise=0.5,
                                      t0=.200),
+                 output_states:tc.optional(tc.any(list, dict))=[DECISION_VARIABLE, RESPONSE_TIME],
                  params=None,
                  time_scale=TimeScale.TRIAL,
                  name=None,
                  # prefs:tc.optional(ComponentPreferenceSet)=None,
                  prefs: is_pref_set = None,
-                 # context=None):
-                 context=componentType + INITIALIZING):
+                 thresh=0,
+                 context=componentType + INITIALIZING
+    ):
 
         # Assign args to params and functionParams dicts (kwConstants must == arg names)
         params = self._assign_args_to_param_dicts(function=function,
+                                                  output_states=output_states,
                                                   time_scale=time_scale,
-                                                  params=params,
-                                                  )
+                                                  params=params)
 
         self.variableClassDefault = self.paramClassDefaults[FUNCTION_PARAMS][STARTING_POINT]
 
@@ -588,10 +582,17 @@ def __init__(self,
             try:
                 default_input_value = params[FUNCTION_PARAMS][STARTING_POINT]
             except:
-
                 default_input_value = 0.0
 
+        # # Conflict with above
+        # self.size = size
+        self.threshold = thresh
+
+        from PsyNeuLink.Components.States.OutputState import StandardOutputStates
+        self.standard_output_states = StandardOutputStates(self, DDM_standard_output_states, SEQUENTIAL)
+
         super(DDM, self).__init__(variable=default_input_value,
+                                  output_states=output_states,
                                   params=params,
                                   name=name,
                                   prefs=prefs,
@@ -603,6 +604,7 @@ def __init__(self,
         # for param in self.user_params.keys():
         #     print("\t{}: {}".format(param, self.user_params[param]))
 
+
     def plot(self, stimulus=1.0, threshold=10.0):
         """
         Generate a dynamic plot of the DDM integrating over time towards a threshold.
@@ -715,7 +717,7 @@ def _validate_variable(self, variable, context=None):
     def _validate_params(self, request_set, target_set=None, context=None):
 
         super()._validate_params(request_set=request_set, target_set=target_set, context=context)
-        functions = {BogaczEtAl, NavarroAndFuss, Integrator}
+        functions = {BogaczEtAl, NavarroAndFuss, DriftDiffusionIntegrator}
 
         if FUNCTION in target_set:
             # If target_set[FUNCTION] is a method of a Function (e.g., being assigned in _instantiate_function),
@@ -735,20 +737,26 @@ def _validate_params(self, request_set, target_set=None, context=None):
                                    "solution for the function param: BogaczEtAl or NavarroAndFuss.".
                                    format(FUNCTION, self.name))
             else:
-                if function != Integrator:
+                if function != DriftDiffusionIntegrator:
                     raise DDMError("In TIME_STEP mode, the {} param of {} "
-                                   "must be Integrator with DIFFUSION integration.".
+                                   "must be DriftDiffusionIntegrator.".
                                    format(FUNCTION, self.name))
                 else:
-                    self.get_axes_function = Integrator(integration_type=DIFFUSION, rate=self.function_params['rate'],
+                    self.get_axes_function = DriftDiffusionIntegrator(rate=self.function_params['rate'],
                                                         noise=self.function_params['noise'], context='plot').function
-                    self.plot_function = Integrator(integration_type=DIFFUSION, rate=self.function_params['rate'],
+                    self.plot_function = DriftDiffusionIntegrator(rate=self.function_params['rate'],
                                                     noise=self.function_params['noise'], context='plot').function
 
             if not isinstance(function, NavarroAndFuss) and OUTPUT_STATES in target_set:
                 # OUTPUT_STATES is a list, so need to delete the first, so that the index doesn't go out of range
-                del target_set[OUTPUT_STATES][DDM_Output.RT_CORRECT_VARIANCE.value]
-                del target_set[OUTPUT_STATES][DDM_Output.RT_CORRECT_MEAN.value]
+                # if DDM_OUTPUT_INDEX.RT_CORRECT_VARIANCE.value in target_set[OUTPUT_STATES]:
+                #     del target_set[OUTPUT_STATES][DDM_OUTPUT_INDEX.RT_CORRECT_VARIANCE.value]
+                # if DDM_OUTPUT_INDEX.RT_CORRECT_VARIANCE.value in target_set[OUTPUT_STATES]:
+                #     del target_set[OUTPUT_STATES][DDM_OUTPUT_INDEX.RT_CORRECT_MEAN.value]
+                if self.RT_CORRECT_MEAN in target_set[OUTPUT_STATES]:
+                    del target_set[OUTPUT_STATES][self.RT_CORRECT_MEAN_INDEX]
+                if self.RT_CORRECT_VARIANCE in target_set[OUTPUT_STATES]:
+                    del target_set[OUTPUT_STATES][self.RT_CORRECT_MEAN_INDEX]
 
         try:
             threshold = target_set[FUNCTION_PARAMS][THRESHOLD]
@@ -767,15 +775,6 @@ def _instantiate_attributes_before_function(self, context=None):
 
         super()._instantiate_attributes_before_function(context=context)
 
-    # def _instantiate_attributes_after_function(self, context=None):
-    #
-    #     super()._instantiate_attributes_after_function(context=context)
-    #
-    #     if not isinstance(self.paramsCurrent[FUNCTION], NavarroAndFuss):
-    #         # OUTPUT_STATES is a list, so need to delete the first, so that the index doesn't go out of range
-    #         del self.outputStates[DDM_RT_CORRECT_MEAN]
-    #         del self.outputStates[DDM_RT_CORRECT_VARIANCE]
-
     def _execute(self,
                  variable=None,
                  runtime_params=None,
@@ -792,7 +791,7 @@ def _execute(self,
         Return current decision variable (self.outputState.value) and other output values (self.outputStates[].value
         Arguments:
         # CONFIRM:
-        variable (float): set to self.value (= self.inputValue)
+        variable (float): set to self.value (= self.input_value)
         - params (dict):  runtime_params passed from Mechanism, used as one-time value for current execution:
             + DRIFT_RATE (float)
             + THRESHOLD (float)
@@ -825,66 +824,55 @@ def _execute(self,
 
         # EXECUTE INTEGRATOR SOLUTION (TIME_STEP TIME SCALE) -----------------------------------------------------
         if self.timeScale == TimeScale.TIME_STEP:
-            if self.function_params['integration_type'] == 'diffusion':
-                result = self.function(self.variable, context=context)
-                return np.array([result, [0.0], [0.0], [0.0]])
-            else:
-                raise MechanismError(
-                    "Invalid integration_type: '{}'. For the DDM mechanism, integration_type must be set"
-                    " to 'DIFFUSION'".format(self.function_params['integration_type']))
+
+            result = self.function(self.variable, context=context)
+            if INITIALIZING not in context:
+                logger.info('{0} {1} is at {2}'.format(type(self).__name__, self.name, result))
+            if abs(result) >= self.threshold:
+                logger.info('{0} {1} has reached threshold {2}'.format(type(self).__name__, self.name, self.threshold))
+                self.is_finished = True
+
+            return np.array([result, [0.0], [0.0], [0.0]])
+
 
         # EXECUTE ANALYTIC SOLUTION (TRIAL TIME SCALE) -----------------------------------------------------------
         elif self.timeScale == TimeScale.TRIAL:
 
-            # # Get length of self.outputValue from OUTPUT_STATES
-            # # Note: use paramsCurrent here (instead of outputStates), as during initialization the execute method
-            # #       is run (to evaluate self.outputValue) before outputStates have been instantiated
-            # self.outputValue = [None] * len(self.paramsCurrent[OUTPUT_STATES])
-
-            # # TEST PRINT:
-            # print ("\nDDM RUN")
-            # print ("stimulus: {}".format(self.inputState.value))
-            # print ("control signal: {}\n".format(self.parameterStates[DRIFT_RATE].value))
-
-            # - convolve inputState.value (signal) w/ driftRate param value (attentional contribution to the process)
-            drift_rate = float((self.variable * self.parameterStates[DRIFT_RATE].value))
-
-            starting_point = float(self.parameterStates[STARTING_POINT].value)
-            threshold = float(self.parameterStates[THRESHOLD].value)
-            noise = float(self.parameterStates[NOISE].value)
-            t0 = float(self.parameterStates[NON_DECISION_TIME].value)
-
-            result = self.function(params={DRIFT_RATE: drift_rate,
-                                           STARTING_POINT: starting_point,
-                                           THRESHOLD: threshold,
-                                           NOISE: noise,
-                                           NON_DECISION_TIME: t0},
+            result = self.function(variable=self.variable,
+                                   params=runtime_params,
                                    context=context)
 
             if isinstance(self.function.__self__, BogaczEtAl):
                 return_value = np.array([[0.0], [0.0], [0.0], [0.0]])
-                return_value[DDM_Output.RESPONSE_TIME.value], return_value[DDM_Output.P_LOWER_MEAN.value] = result
-                return_value[DDM_Output.P_UPPER_MEAN.value] = 1 - return_value[DDM_Output.P_LOWER_MEAN.value]
+                return_value[self.RESPONSE_TIME_INDEX], return_value[self.PROBABILITY_LOWER_THRESHOLD_INDEX] = result
+                return_value[self.PROBABILITY_UPPER_THRESHOLD_INDEX] = \
+                                                               1 - return_value[self.PROBABILITY_LOWER_THRESHOLD_INDEX]
 
             elif isinstance(self.function.__self__, NavarroAndFuss):
                 return_value = np.array([[0], [0], [0], [0], [0], [0]])
-                return_value[DDM_Output.RESPONSE_TIME.value] = result[NF_Results.MEAN_DT.value]
-                return_value[DDM_Output.P_LOWER_MEAN.value] = result[NF_Results.MEAN_ER.value]
-                return_value[DDM_Output.P_UPPER_MEAN.value] = 1 - result[NF_Results.MEAN_ER.value]
-                return_value[DDM_Output.RT_CORRECT_MEAN.value] = result[NF_Results.MEAN_CORRECT_RT.value]
-                return_value[DDM_Output.RT_CORRECT_VARIANCE.value] = result[NF_Results.MEAN_CORRECT_VARIANCE.value]
+                return_value[self.RESPONSE_TIME_INDEX] = result[NF_Results.MEAN_DT.value]
+                return_value[self.PROBABILITY_LOWER_THRESHOLD_INDEX] = result[NF_Results.MEAN_ER.value]
+                return_value[self.PROBABILITY_UPPER_THRESHOLD_INDEX] = 1 - result[NF_Results.MEAN_ER.value]
+                return_value[self.RT_CORRECT_MEAN_INDEX] = result[NF_Results.MEAN_CORRECT_RT.value]
+                return_value[self.RT_CORRECT_VARIANCE_INDEX]= result[NF_Results.MEAN_CORRECT_VARIANCE.value]
                 # CORRECT_RT_SKEW = results[DDMResults.MEAN_CORRECT_SKEW_RT.value]
 
+            else:
+                raise DDMError("PROGRAM ERROR: Unrecognized analytic fuction ({}) for DDM".
+                               format(self.function.__self__))
+
             # Convert ER to decision variable:
-            if random() < return_value[DDM_Output.P_LOWER_MEAN.value]:
-                return_value[DDM_Output.DECISION_VARIABLE.value] = np.atleast_1d(-1 * threshold)
+            threshold = float(self.function_object.threshold)
+            if random.random() < return_value[self.PROBABILITY_LOWER_THRESHOLD_INDEX]:
+                return_value[self.DECISION_VARIABLE_INDEX] = np.atleast_1d(-1 * threshold)
             else:
-                return_value[DDM_Output.DECISION_VARIABLE.value] = threshold
+                return_value[self.DECISION_VARIABLE_INDEX] = threshold
 
             return return_value
 
         else:
-            raise MechanismError("time_scale not specified for DDM")
+            raise MechanismError("PROGRAM ERROR: unrecognized or unspecified time_scale ({}) for DDM".
+                                 format(self.timeScale))
 
             # def _out_update(self, particle, drift, noise, time_step_size, decay):
             #     ''' Single update for OU (special case l=0 is DDM)'''
@@ -914,4 +902,4 @@ def _execute(self,
             #     returns output
             #     Returns: value
             #     """
-            #     # IMPLEMENTATION NOTE:  TBI when time_step is implemented for DDM
\ No newline at end of file
+            #     # IMPLEMENTATION NOTE:  TBI when time_step is implemented for DDM
diff --git a/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/IntegratorMechanism.py b/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/IntegratorMechanism.py
index fe3a2f212cb..3b7937ff2e8 100644
--- a/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/IntegratorMechanism.py
+++ b/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/IntegratorMechanism.py
@@ -24,8 +24,8 @@
 -------------------------------
 
 An IntegratorMechanism can be created directly by calling its constructor, or using the
-:py:func:`mechanism <Mechanism.mechanism>` function and specifying :keyword:`IntegratorMechanism` as its `mech_spec`
-argument.  Its function is specified in the :keyword:`function` argument, which can be parameterized by calling its
+`mechanism() <Mechanism.mechanism>` function and specifying INTEGRATOR_MECHANISM as its **mech_spec**
+argument.  Its function is specified in the **function** argument, which can be parameterized by calling its
 constructor with parameter values::
 
     my_time_averaging_mechanism = IntegratorMechanism(function=Integrator(integration_type=ADAPTIVE, rate=0.5))
@@ -159,13 +159,13 @@ class IntegratorMechanism(ProcessingMechanism_Base):
 
     name : str : default IntegratorMechanism-<index>
         the name of the mechanism.
-        Specified in the :keyword:`name` argument of the constructor for the mechanism;
+        Specified in the :keyword:**name** argument of the constructor for the mechanism;
         if not is specified, a default is assigned by `MechanismRegistry`
         (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
 
     prefs : Optional[PreferenceSet or specification dict : Mechanism.classPreferences]
         the `PreferenceSet` for mechanism.
-        Specified in the `prefs` argument of the constructor for the mechanism;
+        Specified in the **prefs** argument of the constructor for the mechanism;
         if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
         (see :doc:`PreferenceSet <LINK>` for details).
 
@@ -180,44 +180,43 @@ class IntegratorMechanism(ProcessingMechanism_Base):
         kpReportOutputPref: PreferenceEntry(True, PreferenceLevel.INSTANCE)}
 
     # Sets template for variable (input)
-    variableClassDefault = [[0]]
+    variableClassDefault = None
 
     paramClassDefaults = Mechanism_Base.paramClassDefaults.copy()
     paramClassDefaults.update({
         # TIME_SCALE: TimeScale.TRIAL,
         OUTPUT_STATES:[PREDICTION_MECHANISM_OUTPUT]
+
     })
 
     # Set default input_value to default bias for SigmoidLayer
     paramNames = paramClassDefaults.keys()
 
-    from PsyNeuLink.Components.Functions.Function import Integrator
+    from PsyNeuLink.Components.Functions.Function import AdaptiveIntegrator
 
     @tc.typecheck
     def __init__(self,
                  default_input_value=None,
-                 function=Integrator(rate=0.5,
-                                     integration_type=ADAPTIVE),
+                 size:tc.optional(int)=None,
+                 function=AdaptiveIntegrator(rate=0.5),
                  time_scale=TimeScale.TRIAL,
                  params=None,
                  name=None,
                  prefs:is_pref_set=None,
                  context=None):
         """Assign type-level preferences, default input value (SigmoidLayer_DEFAULT_BIAS) and call super.__init__
-
-        :param default_input_value: (value)
-        :param params: (dict)
-        :param name: (str)
-        :param prefs: (PreferenceSet)
         """
 
-
         # Assign args to params and functionParams dicts (kwConstants must == arg names)
-        params = self._assign_args_to_param_dicts(function=function, params=params)
+        self.variableClassDefault = default_input_value or [[0]]
+        params = self._assign_args_to_param_dicts(function=function,
+                                                  params=params)
 
         # if default_input_value is NotImplemented:
         #     default_input_value = SigmoidLayer_DEFAULT_NET_INPUT
 
+        self.size = size
+
         super(IntegratorMechanism, self).__init__(variable=default_input_value,
                                   params=params,
                                   name=name,
diff --git a/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/LCA.py b/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/LCA.py
new file mode 100644
index 00000000000..88cbe654b6b
--- /dev/null
+++ b/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/LCA.py
@@ -0,0 +1,389 @@
+# Princeton University licenses this file to You under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.  You may obtain a copy of the License at:
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed
+# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and limitations under the License.
+
+# NOTES:
+#  * COULD NOT IMPLEMENT integrator_function in paramClassDefaults (see notes below)
+#  * NOW THAT NOISE AND TIME_CONSTANT ARE PROPRETIES THAT DIRECTLY REFERERNCE integrator_function,
+#      SHOULD THEY NOW BE VALIDATED ONLY THERE (AND NOT IN TransferMechanism)??
+#  * ARE THOSE THE ONLY TWO integrator PARAMS THAT SHOULD BE PROPERTIES??
+
+# ****************************************  LCA *************************************************
+
+"""
+
+Overview
+--------
+
+An LCA is a subclass of `RecurrentTransferMechanism` that implements a single-layered leaky competitive accumulator 
+network, in which each element is connected to every other element with mutually inhibitory weights.  All of the
+inhibitory weights have the same value, specified by its `inhibition <LCA.inhibition>` parameter.  In the case that 
+it has two elements, the value of its `inhibition <LCA.inhibition>` parameter is equal to its `decay 
+<RecurrentTransferMechanism.decay>` parameter, and the two are of sufficient magnitude, it implements a close 
+approximation of a `DDM` mechanism 
+(see `Usher & McClelland, 2001; <http://psycnet.apa.org/?&fa=main.doiLanding&doi=10.1037/0033-295X.108.3.550>`_ and 
+`Bogacz et al (2006) <https://www.ncbi.nlm.nih.gov/pubmed/17014301>`_).
+  
+.. _Recurrent_Transfer_Creation:
+
+Creating an LCA
+---------------
+
+An LCA can be created directly by calling its constructor, or using the `mechanism() <Mechanism.mechanism>` function 
+and specifying LCA as its **mech_spec** argument.  The set of mutually inhibitory connections are implemented as a 
+recurrent `MappingProjection` with a `matrix <LCA.matrix>` of uniform negative weights specified by 
+the **inhibition** argument of the LCA's constructor.  The default format of its `variable <LCA.variable>`, and default 
+values of its `inhibition <LCA.inhibition>`, `decay <RecurrentTransferMechanism.decay>` and 
+`noise <TransferMechanism.noise>` parameters implement an approximation of a `DDM`. 
+
+.. _LCA_Structure:
+
+Structure
+---------
+
+The distinguishing feature of an LCA is its `matrix <LCA.matrix>` of uniform negative weights.  It also has, in 
+addition to its `primary outputState <OutputState_Primary>` (which contains the current value of the elements of the
+LCA) and the outputStates of a RecurrentTransferMechanism, it has two additional outputStates: MAX_VS_NEXT and 
+MAX_VS_AVG.  Both are two element arrays that track the element of the LCA with the currently highest value relative 
+to the value of the others.  The two elements of the MAX_VS_NEXT outputState contain, respectively, the index of the 
+LCA element with the greatest value, and the difference between its value and the next highest one;  MAX_VS_AVG 
+contains the index of the LCA element with the greatest value, and the difference between its value and the average 
+of all the others.  For an LCA with only two elements, MAX_VS_NEXT implements a close approximation of the 
+`threshold <DDM.threshold>` parameter of a `DDM`  
+(see `Usher & McClelland, 2001; <http://psycnet.apa.org/?&fa=main.doiLanding&doi=10.1037/0033-295X.108.3.550>`_ and 
+`Bogacz et al (2006) <https://www.ncbi.nlm.nih.gov/pubmed/17014301>`_).
+For an LCA with more than two elements, MAX_VS_NEXT and 
+MAX_VS_AVERAGE implement threshold approximations with different properties 
+(see `McMillen & Holmes, 2006 <http://www.sciencedirect.com/science/article/pii/S0022249605000891>`_).
+ 
+.. _LCA_Execution:
+
+Execution
+---------
+
+The execution of an LCA is identical to that of `RecurrentTransferMechanism`.
+
+.. _LCA_Class_Reference:
+
+Class Reference
+---------------
+
+
+"""
+
+from PsyNeuLink.Components.Functions.Function import Logistic, max_vs_next, max_vs_avg
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.RecurrentTransferMechanism import *
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
+from PsyNeuLink.Components.States.OutputState import StandardOutputStates, PRIMARY_OUTPUT_STATE
+
+
+class LCAError(Exception):
+    def __init__(self, error_value):
+        self.error_value = error_value
+
+    def __str__(self):
+        return repr(self.error_value)
+
+MAX_VS_NEXT = 'max_vs_next'
+MAX_VS_AVG = 'max_vs_avg'
+
+# This is a convenience class that provides list of standard_output_state names in IDE
+class LCA_OUTPUT():
+        RESULT=RESULT
+        MEAN=MEAN
+        MEDIAN=MEDIAN
+        STANDARD_DEV=STANDARD_DEV
+        VARIANCE=VARIANCE
+        ENERGY=ENERGY
+        ENTROPY=ENTROPY
+        MAX_VS_NEXT=MAX_VS_NEXT
+        MAX_VS_AVG=MAX_VS_AVG
+# THIS WOULD HAVE BEEN NICE, BUT IDE DOESN'T EXECUTE IT, SO NAMES DON'T SHOW UP
+# for item in [item[NAME] for item in DDM_standard_output_states]:
+#     setattr(DDM_OUTPUT.__class__, item, item)
+
+
+# THIS WOULD HAVE BEEN NICE, BUT IDE DOESN'T EXECUTE IT, SO NAMES DON'T SHOW UP
+# for item in [item[NAME] for item in DDM_standard_output_states]:
+#     setattr(DDM_OUTPUT.__class__, item, item)
+
+
+# IMPLEMENTATION NOTE:  IMPLEMENTS OFFSET PARAM BUT IT IS NOT CURRENTLY BEING USED
+class LCA(RecurrentTransferMechanism):
+    """
+    LCA(                                   \
+        default_input_value=None,          \
+        function=Logistic,                 \
+        initial_value=None,                \
+        decay=1.0,                         \
+        inhibition=1.0,                    \
+        noise=0.1,                         \
+        time_constant=1.0,                 \
+        range=(float:min, float:max),      \
+        time_scale=TimeScale.TIME_STEP,    \
+        params=None,                       \
+        name=None,                         \
+        prefs=None)
+
+    Implements LCA subclass of `RecurrentTransferMechanism`.
+
+    COMMENT:
+        Description
+        -----------
+            LCA is a Subtype of the RecurrentTransferMechanism Subtype of the TransferMechanism 
+            Subtype of the ProcessingMechanisms Type of the Mechanism Category of the Component class.
+            It implements a RecurrentTransferMechanism with a set of uniform recurrent inhibitory weights.
+            In all other respects, it is identical to a RecurrentTransferMechanism. 
+    COMMENT
+
+    Arguments
+    ---------
+
+    default_input_value : number, list or np.ndarray : default Transfer_DEFAULT_BIAS
+        specifies the input to the mechanism to use if none is provided in a call to its
+        `execute <Mechanism.Mechanism_Base.execute>` or `run <Mechanism.Mechanism_Base.run>` method;
+        also serves as a template to specify the length of `variable <TransferMechanism.variable>` for
+        `function <TransferMechanism.function>`, and the `primary outputState <OutputState_Primary>`
+        of the mechanism.
+
+    function : TransferFunction : default Linear
+        specifies the function used to transform the input;  can be `Linear`, `Logistic`, `Exponential`,
+        or a custom function.
+
+    inhibition : number : default 1.0
+        specifies the magnitude of the (uniform) negative weights used for the 
+        `matrix <LCA.matrix>` parameter of the `recurrent_projection <LCA.recurrent_projection>`. 
+
+    decay : number : default 1.0
+        specifies the amount by which to decrement its `previous_input <TransferMechanism.previous_input>`
+        in each round of execution.
+
+    initial_value :  value, list or np.ndarray : default Transfer_DEFAULT_BIAS
+        specifies the starting value for time-averaged input (only relevant if
+        `time_constant <TransferMechanism.time_constant>` is not 1.0).
+        :py:data:`Transfer_DEFAULT_BIAS <LINK->SHOULD RESOLVE TO VALUE>`
+
+    noise : float or function : default 0.0
+        a stochastically-sampled value added to the result of the `function <TransferMechanism.function>`:
+        if it is a float, it must be in the interval [0,1] and is used to scale the variance of a zero-mean Gaussian;
+        if it is a function, it must return a scalar value.
+
+    time_constant : float : default 1.0
+        the time constant for exponential time averaging of input when the mechanism is executed with `time_scale`
+        set to `TimeScale.TIME_STEP`::
+
+         result = (time_constant * current input) + (1-time_constant * result on previous time_step)
+
+    range : Optional[Tuple[float, float]]
+        specifies the allowable range for the result of `function <TransferMechanism.function>`:
+        the first item specifies the minimum allowable value of the result, and the second its maximum allowable value;
+        any element of the result that exceeds the specified minimum or maximum value is set to the value of
+        `range <TransferMechanism.range>` that it exceeds.
+
+    params : Optional[Dict[param keyword, param value]]
+        a `parameter dictionary <ParameterState_Specifying_Parameters>` that can be used to specify the parameters for
+        the mechanism, its function, and/or a custom function and its parameters.  Values specified for parameters in
+        the dictionary override any assigned to those parameters in arguments of the constructor.
+
+    time_scale :  TimeScale : TimeScale.TRIAL
+        specifies whether the mechanism is executed using the `TIME_STEP` or `TRIAL` `TimeScale`.
+        This must be set to `TimeScale.TIME_STEP` for the `time_constant <TransferMechanism.time_constant>`
+        parameter to have an effect.
+
+    name : str : default TransferMechanism-<index>
+        a string used for the name of the mechanism.
+        If not is specified, a default is assigned by `MechanismRegistry`
+        (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
+
+    prefs : Optional[PreferenceSet or specification dict : Mechanism.classPreferences]
+        the `PreferenceSet` for mechanism.
+        If it is not specified, a default is assigned using `classPreferences` defined in __init__.py
+        (see :doc:`PreferenceSet <LINK>` for details).
+
+    .. context=componentType+INITIALIZING):
+            context : str : default ''None''
+                   string used for contextualization of instantiation, hierarchical calls, executions, etc.
+
+    Attributes
+    ----------
+
+    variable : value
+        the input to mechanism's `function <LCA.function>`.
+
+    function : Function
+        the function used to transform the input.
+
+    matrix : 2d np.array
+        the `matrix <MappingProjection.matrix>` parameter of the `recurrent_projection` for the mechanism,
+        with a uniform set of negative weights, the magnitude of which are determined by the 
+        `inhibition <LCA.inhibition>` attribute.  
+
+    recurrent_projection : MappingProjection
+        a `MappingProjection` that projects from the mechanism's `primary outputState <OutputState_Primary>` 
+        back to it `primary inputState <Mechanism_InputStates>`.
+
+    inhibition : number : default 1.0
+        determines the magnitude of the (uniform) negative weights for the `matrix <LCA.matrix>` parameter
+        of the `recurrent_projection <LCA.recurrent_projection>`. 
+
+    decay : float : default 1.0
+        determines the amount by which to multiply the `previous_input <TransferMechanism.previous_input>` value
+        in each round of execution (acts, in effect like the weight on a self-connection).
+
+    COMMENT:
+       THE FOLLOWING IS THE CURRENT ASSIGNMENT
+    COMMENT
+    initial_value :  value, list or np.ndarray : Transfer_DEFAULT_BIAS
+        determines the starting value for time-averaged input
+        (only relevant if `time_constant <TransferMechanism.time_constant>` parameter is not 1.0).
+        :py:data:`Transfer_DEFAULT_BIAS <LINK->SHOULD RESOLVE TO VALUE>`
+
+    noise : float or function : default 0.0
+        a stochastically-sampled value added to the output of the `function <TransferMechahnism.function>`:
+        if it is a float, it must be in the interval [0,1] and is used to scale the variance of a zero-mean Gaussian;
+        if it is a function, it must return a scalar value.
+
+    time_constant : float
+        the time constant for exponential time averaging of input
+        when the mechanism is executed using the `TIME_STEP` `TimeScale`::
+
+          result = (time_constant * current input) + (1-time_constant * result on previous time_step)
+
+    range : Tuple[float, float]
+        determines the allowable range of the result: the first value specifies the minimum allowable value
+        and the second the maximum allowable value;  any element of the result that exceeds minimum or maximum
+        is set to the value of `range <TransferMechanism.range>` it exceeds.  If `function <TransferMechanism.function>`
+        is `Logistic`, `range <TransferMechanism.range>` is set by default to (0,1).
+
+    previous_input : 1d np.array of floats
+        the value of the input on the previous round of execution, including the value of `recurrent_projection`. 
+
+    value : 2d np.array [array(float64)]
+        result of executing `function <TransferMechanism.function>`; same value as fist item of
+        `output_values <TransferMechanism.output_values>`.    
+
+    COMMENT:
+        CORRECTED:
+        value : 1d np.array
+            the output of `function <LCA.function>`;  also assigned to :keyword:`value` of the TRANSFER_RESULT 
+            outputState and the first item of :keyword:`output_values`.
+    COMMENT
+
+    outputStates : Dict[str, OutputState]
+        an OrderedDict with the following `outputStates <OutputState>`:
+        * `TRANSFER_RESULT`, the :keyword:`value` of which is the **result** of `function <TransferMechanism.function>`;
+        * `TRANSFER_MEAN`, the :keyword:`value` of which is the mean of the result;
+        * `TRANSFER_VARIANCE`, the :keyword:`value` of which is the variance of the result;
+        * `ENERGY`, the :keyword:`value` of which is the energy of the result, 
+          calculated using the `Stability` Function with the ENERGY metric;
+        * `ENTROPY`, the :keyword:`value` of which is the entropy of the result,
+          calculated using the `Stability` Function with the ENTROPY metric; 
+          note:  this is only present if the mechanism's :keyword:`function` is bounded between 0 and 1 
+          (e.g., the `Logistic` function);
+        * `MAX_VS_NEXT`, the :keyword:`value` of which is a two element array: the first is the 
+          index of the element of RESULT with the highest value, and the second the difference between that
+          and the next highest one in RESULT;
+        * `MAX_VS_AVG`, the :keyword:`value` of which is a two element array: the first is the 
+          index of the element of RESULT with the highest value, and the second the difference between that
+          and the average of the value of all its other elements;
+
+    output_values : List[array(float64), float, float]
+        a list with the following items:
+        * **result** of the `function <LCA.function>` calculation (value of TRANSFER_RESULT outputState);
+        * **mean** of the result (:keyword:`value` of TRANSFER_MEAN outputState)
+        * **variance** of the result (:keyword:`value` of TRANSFER_VARIANCE outputState);
+        * **energy** of the result (:keyword:`value` of ENERGY outputState);
+        * **entropy** of the result (if the ENTROPY outputState is present);
+        * **max_vs_next** of the result (:keyword:`value` of MAX_VS_NEXT outputState);
+        * **max_vs_avg** of the result (:keyword:`value` of MAX_VS_AVG outputState).
+
+    time_scale :  TimeScale
+        specifies whether the mechanism is executed using the `TIME_STEP` or `TRIAL` `TimeScale`.
+
+    name : str : default TransferMechanism-<index>
+        the name of the mechanism.
+        Specified in the **name** argument of the constructor for the projection;
+        if not is specified, a default is assigned by `MechanismRegistry`
+        (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
+
+    prefs : PreferenceSet or specification dict : Mechanism.classPreferences
+        the `PreferenceSet` for mechanism.
+        Specified in the **prefs** argument of the constructor for the mechanism;
+        if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
+        (see :doc:`PreferenceSet <LINK>` for details).
+
+    Returns
+    -------
+    instance of LCA : LCA
+
+    """
+    componentType = LCA
+
+    paramClassDefaults = RecurrentTransferMechanism.paramClassDefaults.copy()
+
+    # paramClassDefaults[OUTPUT_STATES].append({NAME:MAX_VS_NEXT})
+    # paramClassDefaults[OUTPUT_STATES].append({NAME:MAX_VS_AVG})
+    standard_output_states = RecurrentTransferMechanism.standard_output_states.copy()
+    standard_output_states.extend([{NAME:MAX_VS_NEXT,
+                                    CALCULATE:max_vs_next},
+                                   {NAME:MAX_VS_AVG,
+                                    CALCULATE:max_vs_avg}])
+
+    @tc.typecheck
+    def __init__(self,
+                 default_input_value=None,
+                 size:tc.optional(int)=None,
+                 input_states:tc.optional(tc.any(list, dict))=None,
+                 matrix=None,
+                 function=Logistic,
+                 initial_value=None,
+                 decay:tc.optional(tc.any(int, float))=1.0,
+                 inhibition:tc.optional(tc.any(int, float))=1.0,
+                 noise:is_numeric_or_none=0.1,
+                 time_constant:is_numeric_or_none=1.0,
+                 range=None,
+                 output_states:tc.optional(tc.any(list, dict))=[RESULT],
+                 time_scale=TimeScale.TRIAL,
+                 params=None,
+                 name=None,
+                 prefs:is_pref_set=None,
+                 context=componentType+INITIALIZING):
+        """Instantiate LCA
+        """
+
+        if matrix is not None:
+            warnings.warn("Matrix arg for LCA is not used; matrix was assigned using inhibition arg")
+        size = size or len(self.variableClassDefault)
+        matrix = np.full((size, size), -inhibition) * get_matrix(HOLLOW_MATRIX,size,size)
+
+        # Assign args to params and functionParams dicts (kwConstants must == arg names)
+        params = self._assign_args_to_param_dicts(input_states=input_states,
+                                                  inhibition=inhibition,
+                                                  output_states=output_states,
+                                                  params=params)
+
+        if not isinstance(self.standard_output_states, StandardOutputStates):
+            self.standard_output_states = StandardOutputStates(self,
+                                                               self.standard_output_states,
+                                                               indices=PRIMARY_OUTPUT_STATE)
+
+
+        super().__init__(default_input_value=default_input_value,
+                         size=size,
+                         input_states=input_states,
+                         matrix=matrix,
+                         function=function,
+                         initial_value=initial_value,
+                         decay=decay,
+                         noise=noise,
+                         time_constant=time_constant,
+                         range=range,
+                         output_states=output_states,
+                         time_scale=time_scale,
+                         params=params,
+                         name=name,
+                         prefs=prefs,
+                         context=context)
diff --git a/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/ObjectiveMechanism.py b/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/ObjectiveMechanism.py
deleted file mode 100644
index 92125c53459..00000000000
--- a/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/ObjectiveMechanism.py
+++ /dev/null
@@ -1,738 +0,0 @@
-# Princeton University licenses this file to You under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.  You may obtain a copy of the License at:
-#     http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed
-# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and limitations under the License.
-
-# *********************************************  ObjectiveMechanism *******************************************************
-
-"""
-
-Overview
---------
-
-An ObjectiveMechanism is a `ProcessingMechanism` that monitors the `outputStates <OutputState>` of one or more other
-ProcessingMechanisms specified in its `monitor <ObjectiveMechanism.monitor>` attribute, and evaluates them using its
-`function <ObjectiveMechanism.function>`. The result of the evaluation is placed in the ObjectiveMechanism's
-`primary outputState <OutputState_Primary>`.  ObjectiveMechanisms are typically used closely (and often created
-automatically) with `AdaptiveMechanisms <AdpativeMechanism>`.
-
-.. _Comparator_Creation:
-
-Creating a ComparatorMechanism
-------------------------------
-
-An ObjectiveMechanism can be created directly by calling its constructor.  ObjectiveMechanisms are also created
-automatically when other PsyNeuLink components are created (such as `LearningMechanisms <LearningMechanism_Creation>`
-and `ControlMechanisms <ControlMechanism_Creation>`.
-
-.. _ObjectiveMechanism_Structure:
-
-Structure
----------
-
-An ObjectiveMechanism has one `inputState <InputState>` for each of the values that are specified
-to be monitored in its `monitored_values` attribute.  When an ObjectiveMechanism is created, an inputState is created
-for each of those values, and assigned a `MappingProjection` from the outputState to which the value belongs.  The
-ObjectiveMechanism's `function  <ObjectiveMechanism.function>` uses these values to compute an `objective (or "loss")
-function <https://en.wikipedia.org/wiki/Loss_function>`_, that is assigned as the value of its outputState.
-
-.. _ObjectiveMechanism_Monitored_OutputStates:
-
-
-Monitored Values
-~~~~~~~~~~~~~~~~
-
-The values to be monitored by an ObjectiveMechanism are specified in the :keyword:`monitored_values` argument of its
-constructor.  These can be specified in a variety of ways, each of which must eventually resolve to an outputState, the
-value of which is to be monitored.  Those outputStates are listed in the ObjectiveMechanism's `monitored_values`
-attribute.
-
-The number of items in `monotired_values` must match the length of the `default_input_value
-<ObjectiveMechanism.Additional_Attributes>` if it is specified.  Note that some forms of
-specification may depend on specifications made for the outputState referenced, the mechanism to which it belongs,
-and/or the process or system to which that mechanism belongs. These interactions (and the precedence afforded to
-each) are described below.
-
-If an outputState is specified at the time the ObjectiveMechanism is created, or the specification can be resolved
-to an outputState, a MappingProjection is automatically created from it to the corresponding inputState
-using `AUTO_ASSIGN_MATRIX` as its `matrix <MapppingProjection.matrix>` parameter.  If the outputState can't be
-determined, no MappingProjection is assigned, and this must be done by some other means;  any values in
-`monitored_values` that are not associated with an outputState at the time the ObjectiveMechanism is executed are
-ignored.
-
-The specification of item in `monitored_values` can take any of the following forms:
-
-* **OutputState**:  a reference to the `outputState <OutputState>` of a mechanism.  This will create an inputState
-  using the outputState's value as the template for its variable.
-COMMENT: TBI
-    Note that an outputState can be *excluded* from being monitored by assigning `None` as the value of its
-    `monitoring_status` attribute.  This specification takes precedence over any others;  that is, it will suppress
-    monitoring of that outputState, irrespective of any other specifications that might otherwise apply to that
-    outputState, including those described below.
-COMMENT
-..
-* **Mechanism**: by default, the mechanism's `primary outputState <OutputState_Primary>` will be used.  However,
-  if the mechanism has any outputStates specified in its `monitored_states` attribute, those will be used (except for
-  any that specify `None` as their `monitoring_status`). This specification takes precedence over any of the other
-  types listed below:  if it is `None`, then none of that mechanism's outputStates will be monitored; if it
-  specifies outputStates to be monitored, those will be monitored even if they do not satisify any of the conditions
-  described in the specifications below.
-..
-* **InputState**:  this creates a "placemarker" inputState, that will later be assigned to an outputState to be
-  monitored and a projection from it.  It can be any of the following:
-
-  * **existing inputState**:  its name, value, and parameters will be used to create an identical
-    inputState for the ObjectiveMechanism;
-  |
-  * `specification dictionary <InputState_Creation>` **for an inputState**:  the specifications will be used to
-    create an inputState for the ObjectiveMechanism;
-  |
-  * **value**: a default inputState will be created using that value;
-  |
-  * **string**: a default inputState will be created using the string as its name, and a scalar as its value.
-
-COMMENT: TBI
-    .. _ObjectiveMechanism_OutputState_Tuple:
-
-    * **MonitoredOutputState Tuple**  tuple can be used wherever an outputState can be specified, to determine how
-      its value is combined with others by the ObjectiveMechanism's `function <ObjectiveMechanism.function>`. Each
-      tuple must have the three following items in the order listed:
-
-          * an outputState or mechanism, the name of one, or a specification dictionary for one;
-          ..
-          * a weight (int) - multiplies the value of the outputState.
-          ..
-          * an exponent (int) - exponentiates the value of the outputState;
-COMMENT
-
-Additional Attributes
-~~~~~~~~~~~~~~~~~~~~~
-
-* `default_input_value`
-   This specifies the format of each value monitored by the ObjectiveMechanism and the variable for the corresponding
-   inputState.  These values take precedence over the specification of values in `monitored_values`, and can be used
-   to override the defaults assumed there. If `default_input_value` is specified, it must have the same number of items
-   as `monitored_values`. If  `default_input_value` is `None` (the default), then the specifications in
-   `monitored_values` are used.  The use of `default_input_value` to override defaults used by `monitored_values`
-   can be helpful in some situations (see `example <ObjectiveMechanism_Default_Input_Value_Example>` below).
-
-
-.. _ObjectiveMechanism_Function:
-
-Function
-~~~~~~~~
-
-The ObjectiveMechanism's `function` can be customized to implement a wide variety of
-`objective (or "loss") functions <https://en.wikipedia.org/wiki/Loss_function>`_.  The default is the
-`LinearCombination` function, which simply sums the values of the outputStates listed in `monitored_values`.
-However, this can easily be configured to calculate differnces, ratios, etc. (see
-`example <ObjectiveMechanism_Weights_and_Exponents_Example>` below).  It can also be replaced with any
-`CombinationFunction`, or any python function that takes a 2d array with an arbitrary number of
-items or a number equal to the number of items in the ObjectiveMechanism's variable (and its number of
-inputStates), and returns a 1d array.
-
-.. _ObjectiveMechanism_Execution:
-
-Execution
----------
-
-When an ObjectiveMechanism is executed, it updates its inputStates with the values of the outputStates listed in
-its `monitored_values` attribute, and then uses its `function <ObjectiveMechanism.function>` to
-evaluate these.  The result is assigned as to its `value <ObjectiveMechanism.value>` attribute as the value of its
-`primary outputState <OutputState_Primary>`.
-
-.. _ObjectiveMechanism_Class_Reference:
-
-Examples
---------
-
-.. _ObjectiveMechanism_Default_Input_Value_Example:
-
-*Formatting inputState values*
-
-The use of default_input_value to override a specification in `monitored_values` can be useful in some situations.
-For example, for `Reinforcement Learning <Reinforcement>`, an ObjectiveMechanism is used to monitor an action
-selection mechanism.  In the example below, the latter uses a `TransferMechanism` with the `SoftMax` function (and the
-`PROB <Softmax.PROB>` as its output format) to select the action.  This generates a vector with a single non-zero
-value, which designates the predicted reward for the selected action.  Because the output is a vector,
-by default the inputState of the ObjectiveMechanism created to monitor it will also be a vector.  However, the
-ObjectiveMechanism requires that this be a single value, that it can compare with the value of the reward mechanism.
-This can be dealt with by using `default_input_value` in the construction of the ObjectiveMechanism, to force
-the inputState for the ObjectiveMechanism to have a single value, as in the example below::
-
-    my_action_select_mech = TransferMechanism(default_input_value = [0,0,0],
-                                function=SoftMax(output=PROB))
-
-    my_reward_mech = TransferMechanism(default_input_value = [0])
-
-    my_objective_mech = ObjectiveMechanism(default_input_value = [[0],[0]],
-                                          monitored_values = [my_action_select_mech, my_reward_mech])
-
-Note that the outputState for the `my_action_selection` and `my_reward_mech` are specified
-in `monitored_values`.  If that were the only specification, the inputState created for `my_action_select_mech`
-would be a vector of length 3.  This is overridden by specifying `default_input_value` as an array with two
-single-value arrays (one corresponding to `my_action_select_mech` and the other to `my_reward_mech`).  This forces
-the inputState for `my_action_select_mech` to have only a single element which, in turn, will cause a
-MappingProjection to be created from  `my_action_select_mech` to the ObjectiveMechanism's inputState using a
-`FULL_CONNECTIVITY_MATRIX` (the one used for `AUTO_ASSIGN_MATRIX` when the sender and receiver have values of
-different lengths).  This produces the desired effect, since the action selected is the only non-zero value in the
-output of `my_action_select_mech`, and so the `FULL_CONNECTIVITY_MATRIX` will combine it with zeros (the other values
-in the vector), and so its value will be assigned as the value of the corresponding inputState in the
-ObjectiveMechanism.  Another option would have been to customize the ObjectiveMechanism's
-`function <ObjectiveMechanism.function>` to convert the output of `my_action_select_mech` to a length 1 vector, though
-this would have been more involved.  The next example describes a simple case of customizing the ObjectiveMechanism's
-`function <ObjectiveMechanism.function>`, however more sophisticated ones are possible, just as the one just suggested.
-
-.. _ObjectiveMechanism_Weights_and_Exponents_Example:
-
-*Customizing the ObjectiveMechanism's function*
-
-The simplest way to customize the `function <ObjectiveMechanism.function>` of an ObjectiveMechanism is to
-parameterize its default function (`LinearCombination`).  In the example below, the ObjectiveMechanism used in the
-`previous example <ObjectiveMechanism_Default_Input_Value_Example>` is further customized to subtract the value
-of the action selected from the value of the reward::
-
-    my_objective_mech = ObjectiveMechanism(default_input_value = [[0],[0]],
-                                          monitored_values = [my_action_select_mech, my_reward_mech],
-                                          function=LinearCombination(weights=[[-1], [1]]))
-
-This is done by specifying the `weights <LinearCombination.weights>` parameter of the `LinearCombination` function,
-with two values [-1] and [1] corresponding to the two items in `monitored_values` (and `default_input_value`).  This
-will multiply the value from `my_action_select_mech` by -1 before adding it to (and thus
-subtracting it from) the value of `my_reward_mech`.  Similarly, the `operation <LinearCombination.operation>`
-and `exponents <LinearCombination.exponents>` parameters of `LinearCombination` can be used together to multiply and
-divide quantities.
-
-
-Class Reference
----------------
-
-"""
-
-# from PsyNeuLink.Components.Mechanisms.MonitoringMechanisms.MonitoringMechanism import *
-from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ProcessingMechanism import *
-from PsyNeuLink.Components.States.InputState import InputState
-from PsyNeuLink.Components.Functions.Function import LinearCombination
-
-OBJECT = 0
-WEIGHT = 1
-EXPONENT = 2
-ROLE = 'role'
-NAMES = 'names'
-MONITORED_VALUES = 'monitored_values'
-MONITORED_VALUE_NAME_SUFFIX = '_Monitor'
-DEFAULT_MONITORED_VALUE = [0]
-
-OBJECTIVE_RESULT = "ObjectiveResult"
-
-
-class ObjectiveMechanismError(Exception):
-    def __init__(self, error_value):
-        self.error_value = error_value
-
-    def __str__(self):
-        return repr(self.error_value)
-
-
-class ObjectiveMechanism(ProcessingMechanism_Base):
-    """
-    ObjectiveMechanism(           \
-    default_input_value=None,     \
-    monitored_values=None,        \
-    names=None,                   \
-    function=LinearCombination,   \
-    params=None,                  \
-    name=None,                    \
-    prefs=None)
-
-    Implements the ObjectiveMechanism subclass of `ProcessingMechanism`.
-
-    COMMENT:
-        Description:
-            ObjectiveMechanism is a subtype of the ProcessingMechanism Type of the Mechanism Category of the
-                Component class
-            It's function uses the LinearCombination Function to compare two input variables
-            COMPARISON_OPERATION (functionParams) determines whether the comparison is subtractive or divisive
-            The function returns an array with the Hadamard (element-wise) differece/quotient of target vs. sample,
-                as well as the mean, sum, sum of squares, and mean sum of squares of the comparison array
-
-        Class attributes:
-            + componentType (str): ComparatorMechanism
-            + classPreference (PreferenceSet): Comparator_PreferenceSet, instantiated in __init__()
-            + classPreferenceLevel (PreferenceLevel): PreferenceLevel.SUBTYPE
-            + variableClassDefault (value):  Comparator_DEFAULT_STARTING_POINT // QUESTION: What to change here
-            + paramClassDefaults (dict): {TIME_SCALE: TimeScale.TRIAL,
-                                          FUNCTION_PARAMS:{COMPARISON_OPERATION: SUBTRACTION}}
-            + paramNames (dict): names as above
-
-        Class methods:
-            None
-
-        MechanismRegistry:
-            All instances of ComparatorMechanism are registered in MechanismRegistry, which maintains an
-              entry for the subclass, a count for all instances of it, and a dictionary of those instances
-    COMMENT
-
-    Arguments
-    ---------
-
-    default_input_value : Optional[List[array] or 2d np.array]
-        specifies the format of the values monitored by the ObjectiveMechanism;  each item corresponds to the value
-        of an outputState monitored, and to the value of the corresponding inputState of the ObjectiveMechanism.  It
-        must have the same length as the number items in monitored_values.  The values specified here take precedence
-        over those in :keyword:`monitored_values`;  if none are provided, the ones in :keyword:`monitored_values`
-        will be used.
-
-    monitored_values : [List[value, InputState, OutputState, Mechanism, string, or MonitoredOutputStateOption]
-        specifies the values that will will be monitored, and evaluated by the `function <ObjectiveMechanism>`
-        (see `monitored_values` for details of specification).  The number of items must equal the length
-        of `default_input_value` if that is specified.
-
-    names: List[str]
-        specifies the names to use for the inputStates created for the list in
-        `monitored_values <ObjectiveMechanism.monitor>`.  If specified,
-        the number of items in the list must equal the number of items in `monitored_values`, and takes precedence
-        over any names specified there.
-
-    function: Function, function or method
-        specifies the function used to evaluate the values listed in :keyword:`monitored_values`
-        (see `function <LearningMechanism.function>` for details.
-
-    role: Optional[LEARNING, CONTROL]
-        specifies if the ObjectiveMechanism is being used for learning or control (see `role` for details).
-
-    params : Optional[Dict[param keyword, param value]]
-        a `parameter dictionary <ParameterState_Specifying_Parameters>` that can be used to specify the parameters for
-        the mechanism, its function, and/or a custom function and its parameters. Values specified for parameters in
-        the dictionary override any assigned to those parameters in arguments of the
-        constructor.
-
-    COMMENT:
-        [TBI]
-        time_scale :  TimeScale : TimeScale.TRIAL
-            specifies whether the mechanism is executed on the TIME_STEP or TRIAL time scale.
-            This must be set to :keyword:`TimeScale.TIME_STEP` for the ``rate`` parameter to have an effect.
-    COMMENT
-
-    name : str : default ComparatorMechanism-<index>
-        a string used for the name of the mechanism.
-        If not is specified, a default is assigned by `MechanismRegistry`
-        (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
-
-    prefs : Optional[PreferenceSet or specification dict : Mechanism.classPreferences]
-        the `PreferenceSet` for mechanism.
-        If it is not specified, a default is assigned using `classPreferences` defined in __init__.py
-        (see :doc:`PreferenceSet <LINK>` for details).
-
-
-    Attributes
-    ----------
-
-    COMMENT:
-    default_input_value : Optional[List[array] or 2d np.array]
-    COMMENT
-
-    monitored_values : [List[OutputState, Mechanism, InputState, dict, value, or str]
-        determines  the values monitored, and evaluated by `function <ObjectiveMechanism>`.  Once the
-        ObjectiveMechanism is fully instantiated, each item in the list refers to an outputState containing the
-        value to be monitored, with a `MappingProjection` from it to the corresponding inputState.
-
-    function : CombinationFunction or function : default LinearCombination
-        the function used to compare evaluate the values monitored by the ObjectiveMechanism.  The function can be
-        any PsyNeuLink `CombinationFunction` or a python function that takes a 2d array with an arbitrary number of
-        items or a number equal to the number of items in the ObjectiveMechanism's variable (and its number of
-        inputStates), and returns a 1d array.
-
-    role : None, LEARNING or CONTROL
-        specifies whether the ObjectiveMechanism is being used for learning in a process or system (in conjunction
-        with a `LearningMechanism`), or for control in a system (in conjunction with a `ControlMechanism`).
-
-    value : 1d np.array
-        the output of the evaluation carried out by the ObjectiveMechanism's `function <ObjectiveMechanism.function>`.
-
-    outputValue : 2d np.array
-        1st and only item is same as `value <ObjectiveMechanisms.value>`.
-
-    name : str : default ComparatorMechanism-<index>
-        the name of the mechanism.
-        Specified in the `name` argument of the constructor for the mechanism;
-        if not is specified, a default is assigned by `MechanismRegistry`
-        (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
-
-    prefs : PreferenceSet or specification dict : Mechanism.classPreferences
-        the `PreferenceSet` for mechanism.
-        Specified in the `prefs` argument of the constructor for the mechanism;
-        if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
-        (see :doc:`PreferenceSet <LINK>` for details).
-
-
-    """
-
-    componentType = OBJECTIVE_MECHANISM
-
-    classPreferenceLevel = PreferenceLevel.SUBTYPE
-    # These will override those specified in TypeDefaultPreferences
-    classPreferences = {
-        kwPreferenceSetName: 'ObjectiveCustomClassPreferences',
-        kpReportOutputPref: PreferenceEntry(False, PreferenceLevel.INSTANCE)}
-
-    # variableClassDefault = [[0],[0]]  # By default, ObjectiveMechanism compares two 1D np.array inputStates
-    variableClassDefault = None
-
-    # ObjectiveMechanism parameter and control signal assignments):
-    paramClassDefaults = Mechanism_Base.paramClassDefaults.copy()
-    paramClassDefaults.update({
-        TIME_SCALE: TimeScale.TRIAL,
-        FUNCTION: LinearCombination,
-        OUTPUT_STATES:[{NAME:OBJECTIVE_RESULT}]})
-        # MODIFIED 12/7/16 NEW:
-
-    paramNames = paramClassDefaults.keys()
-
-    # FIX:  TYPECHECK MONITOR TO LIST OR ZIP OBJECT
-    @tc.typecheck
-    def __init__(self,
-                 default_input_value=None,
-                 monitored_values=None,
-                 names:tc.optional(list)=None,
-                 function=LinearCombination,
-                 # role:tc.optional(str)=None,
-                 params=None,
-                 name=None,
-                 prefs:is_pref_set=None,
-                 context=None):
-
-        # if default_input_value is None:
-        #     default_input_value = self.variableClassDefault
-
-        # Assign args to params and functionParams dicts (kwConstants must == arg names)
-        params = self._assign_args_to_param_dicts(monitored_values=monitored_values,
-                                                  names=names,
-                                                  function=function,
-                                                  params=params)
-
-        self._learning_role = None
-
-        super().__init__(variable=default_input_value,
-                         params=params,
-                         name=name,
-                         prefs=prefs,
-                         context=self)
-
-        # IMPLEMENATION NOTE: THIS IS HERE UNTIL Composition IS IMPLEMENTED,
-        # SO THAT SYSTEMS AND PROCESSES CAN FIND THE OBJECTIVE MECHANISSMS SERVING AS TARGETS
-
-    def _validate_variable(self, variable, context=None):
-        """Validate that if default_input_value is specified the number of values matches the number of monitored_values
-
-        """
-        # IMPLEMENTATION NOTE:  use self.user_params (i.e., values specified in constructor)
-        #                       since params have not yet been validated and so self.params is not yet available
-        if variable is not None and len(variable) != len(self.user_params[MONITORED_VALUES]):
-                raise ObjectiveMechanismError("The number of items specified for the default_input_value arg ({}) of {} "
-                                     "must match the number of items specified for its monitored_values arg ({})".
-                                     format(len(variable), self.name, len(self.user_params[MONITORED_VALUES])))
-
-        super()._validate_variable(variable=variable, context=context)
-
-
-    def _validate_params(self, request_set, target_set=None, context=None):
-        """Validate `monitored_values`, `role` and `names <ObjectiveMechanism.names>` arguments
-
-        """
-
-        super()._validate_params(request_set=request_set,
-                                 target_set=target_set,
-                                 context=context)
-
-        if ROLE in target_set and target_set[ROLE] and not target_set[ROLE] in {LEARNING, CONTROL}:
-            raise ObjectiveMechanismError("\'role\'arg ({}) of {} must be either \'LEARNING\' or \'CONTROL\'".
-                                 format(target_set[ROLE], self.name))
-
-        if NAMES in target_set and target_set[NAMES]:
-            if len(target_set[NAMES]) != len(target_set[MONITORED_VALUES]):
-                raise ObjectiveMechanismError("The number of items in \'names\'arg ({}) must equal of the number in the "
-                                     "\`monitored_values\` arg for {}".
-                                     format(len(target_set[NAMES]), len(target_set[MONITORED_VALUES]), self.name))
-
-            for name in target_set[NAMES]:
-                if not isinstance(name, str):
-                    raise ObjectiveMechanismError("it in \'names\'arg ({}) of {} is not a string".
-                                         format(target_set[NAMES], self.name))
-
-        #region VALIDATE MONITORED VALUES
-        # FIX: IS THE FOLLOWING STILL TRUE:
-        # Note: this must be validated after OUTPUT_STATES (and therefore call to super._validate_params)
-        #       as it can reference entries in that param
-        if MONITORED_VALUES in target_set:
-            try:
-                if not target_set[MONITORED_VALUES] or target_set[MONITORED_VALUES] is NotImplemented:
-                    pass
-                # It is a MonitoredOutputStatesOption specification
-                elif isinstance(target_set[MONITORED_VALUES], MonitoredOutputStatesOption):
-                    # Put in a list (standard format for processing by _instantiate_monitored_output_states)
-                    target_set[MONITORED_VALUES] = [target_set[MONITORED_VALUES]]
-                # It is NOT a MonitoredOutputStatesOption specification, so assume it is a list of Mechanisms or States
-                else:
-                    # Validate each item of MONITORED_VALUES
-                    for item in target_set[MONITORED_VALUES]:
-                        validate_monitored_value(self, item, context=context)
-                    # FIX: PRINT WARNING (IF VERBOSE) IF WEIGHTS or EXPONENTS IS SPECIFIED,
-                    # FIX:  INDICATING THAT IT WILL BE IGNORED;
-                    # FIX:  weights AND exponents ARE SPECIFIED IN TUPLES
-                    # FIX:  WEIGHTS and EXPONENTS ARE VALIDATED IN
-                    # FIX:           SystemContro.Mechanism_instantiate_monitored_output_states
-                    # # Validate WEIGHTS if it is specified
-                    # try:
-                    #     num_weights = len(target_set[FUNCTION_PARAMS][WEIGHTS])
-                    # except KeyError:
-                    #     # WEIGHTS not specified, so ignore
-                    #     pass
-                    # else:
-                    #     # Insure that number of weights specified in WEIGHTS
-                    #     #    equals the number of states instantiated from MONITOR_FOR_CONTROL
-                    #     num_monitored_states = len(target_set[MONITOR_FOR_CONTROL])
-                    #     if not num_weights != num_monitored_states:
-                    #         raise MechanismError("Number of entries ({0}) in WEIGHTS of kwFunctionParam for EVC "
-                    #                        "does not match the number of monitored states ({1})".
-                    #                        format(num_weights, num_monitored_states))
-            except KeyError:
-                pass
-
-
-    def _instantiate_input_states(self, context=None):
-        """Instantiate input state for each value specified in `monitored_values` arg and instantiate self.variable
-
-        """
-        num_values = len(self.monitored_values)
-        values = [None] * num_values
-        names = self.names or [None] * num_values
-
-        # If default_input_value arg (assigned to variable in __init__) was used to specify the size of inputStates,
-        #   pass those values for use in instantiating inputStates
-        if self.variable is not None:
-            input_state_sizes = self.variable
-        else:
-            input_state_sizes = values
-        for i, monitored_value, name in zip(range(num_values), self.monitored_values, names):
-            values[i] = self._instantiate_input_state_for_monitored_value(input_state_sizes[i],
-                                                                          monitored_value,
-                                                                          name,
-                                                                          context=context)
-
-        # If self.variable was not specified, construct from values of inputStates
-        if self.variable is None:
-            # If all items of self.variable are numeric and of the same length, convert to ndarray
-            dim_axis_0 = len(values)
-            dim_axis_1 = len(values[0])
-            if all((is_numeric(values[i]) and len(values[i])==dim_axis_1) for i in range(dim_axis_0)):
-                self.variable = np.zeros((dim_axis_0,dim_axis_1), dtype=float)
-            # Otherwise, just use list of values returned from instantiation above
-            else:
-                self.variable = values.copy()
-
-        self.variableClassDefault = self.variable.copy()
-        self.inputValue = list(self.variable)
-
-    def _instantiate_input_state_for_monitored_value(self, variable, monitored_value, name=None, context=None):
-        """Instantiate inputState with projection from monitoredOutputState
-
-        Validate specification for value to be monitored (using call to validate_monitored_value)
-        Instantiate the inputState (assign name if specified, and value of monitored_state)
-        Re-specify corresponding item of variable to match the value of the new inputState
-        Update self.inputState and self.inputStates
-        Call _instantiate_monitoring_projection() to instantiate MappingProjection to inputState
-            if an outputState has been specified.
-
-        Parameters
-        ----------
-        monitored_value (value, InputState, OutputState, Mechanisms, str, dict, or MonitoredOutputStateOption)
-        name (str)
-        context (str)
-
-        Returns
-        -------
-
-        """
-        from PsyNeuLink.Components.Mechanisms.Mechanism import Mechanism
-        from PsyNeuLink.Components.States.InputState import InputState
-        from PsyNeuLink.Components.States.OutputState import OutputState
-
-        call_for_projection = False
-        value = DEFAULT_MONITORED_VALUE
-        input_state_name = name
-        input_state_params = None
-
-        # If monitored_value is a value:
-        # - create default InputState using monitored_value as its variable specification
-        if _is_value_spec(monitored_value):
-            value = monitored_value
-            input_state_name = name or self.name + MONITORED_VALUE_NAME_SUFFIX
-
-        # If monitored_value is an InputState:
-        # - use InputState's variable, params, and name
-        elif isinstance(monitored_value, InputState):
-            value = monitored_value.variable
-            # Note: name specified in names argument of constructor takes precedence over existing name of inputState
-            input_state_name = name or monitored_value.name
-            input_state_params = monitored_value.params
-
-        # If monitored_value is a specification dictionary for an InputState:
-        elif isinstance(monitored_value, InputState):
-            try:
-                value = monitored_value[VARIABLE]
-                # Note: name specified in a specification dictionary takes precedence over names argument in constructor
-                input_state_name = monitored_value[NAME]
-                input_state_params = monitored_value[INPUT_STATE_PARAMS]
-            except KeyError:
-                if (value == DEFAULT_MONITORED_VALUE and
-                            input_state_name is name and
-                            input_state_params is None):
-                    raise ObjectiveMechanismError("Specification dictionary in monitored_values arg for {}"
-                                         "did not contain any entries relevant to an inputState".format(self.name))
-                else:
-                    pass
-
-        # elif isinstance(monitored_value, tuple):
-        #     monitored_value = monitored_value[0]
-        #     # FIX:
-        #     # IF IT IS A STRING, LOOK FOR OUTPUTSTATE OR MECHANISM WITH THAT NAME AND REASSIGN??
-
-        # If monitored_value is an OutputState:
-        # - match inputState to the value of the outputState's value
-        # - and specify the need for a projection from it
-        elif isinstance(monitored_value, OutputState):
-            value = monitored_value.value
-            input_state_name = name or monitored_value.owner.name + MONITORED_VALUE_NAME_SUFFIX
-            call_for_projection = True
-            
-        # If monitored_value is a Mechanism:
-        # - match inputState to the value of its primary outputState
-        # - and specify the need for a projection from it
-        elif isinstance(monitored_value, Mechanism):
-            value = monitored_value.outputState.value
-            input_state_name = name or monitored_value.name + MONITORED_VALUE_NAME_SUFFIX
-            call_for_projection = True
-            
-        # # If monitored_value is a MonitoredOutputStatesOption:
-        # # FIX: NOT SURE WHAT TO DO HERE YET
-        # elif isinstance(montiored_value, MonitoredOutputStateOption):
-        #     value = ???
-        #     call_for_projection = True
-
-        # If monitored_value is a string:
-        # - use as name of inputState
-        # - instantiate InputState with defalut value (1d array with single scalar item??)
-
-        elif isinstance(monitored_value, str):
-            input_state_name = monitored_value
-            value = DEFAULT_MONITORED_VALUE
-
-        # Format the item of self.variable that corresponds to the inputState
-        # Give precedence to item specified in self.variable for inputState's variable
-        if variable is not None:
-            input_state_variable = variable
-        # Otherwise, set to value derived from monitored_value above
-        else:
-            input_state_variable = value
-
-        from PsyNeuLink.Components.States.State import _instantiate_state
-        input_state = _instantiate_state(owner=self,
-                                         state_type=InputState,
-                                         state_name=input_state_name,
-                                         state_spec=input_state_variable,
-                                         state_params=input_state_params,
-                                         constraint_value=input_state_variable,
-                                         constraint_value_name='ObjectiveMechanism inputState value',
-                                         context=context)
-
-        #  Update inputState and inputStates
-        try:
-            self.inputStates[input_state.name] = input_state
-        except AttributeError:
-            self.inputStates = OrderedDict({input_state_name:input_state})
-            self.inputState = list(self.inputStates.values())[0]
-
-        self.inputValue = list(state.value for state in self.inputStates.values())
-
-        # IMPLEMENTATION NOTE: THIS IS A PLACEMARKER FOR A METHOD TO BE IMPLEMENTED IN THE Composition CLASS
-        if call_for_projection:
-            _instantiate_monitoring_projection(sender=monitored_value, receiver=input_state, matrix=AUTO_ASSIGN_MATRIX)
-
-        return input_state.value
-
-    def add_monitored_values(self, states_spec, context=None):
-        """Validate specification and then add inputState to ObjectiveFunction + MappingProjection to it from state
-
-        Use by other objects to add a state or list of states to be monitored by EVC
-        states_spec can be a Mechanism, OutputState or list of either or both
-        If item is a Mechanism, each of its outputStates will be used
-
-        Args:
-            states_spec (Mechanism, MechanimsOutputState or list of either or both:
-            context:
-        """
-        states_spec = list(states_spec)
-        validate_monitored_value(self, states_spec, context=context)
-        self._instantiate_monitored_output_states(states_spec, context=context)
-
-
-def validate_monitored_value(objective_mech, state_spec, context=None):
-    """Validate specification for monitored_value arg
-
-    Validate that each item of monitored_value arg is an inputState, OutputState, mechanism, string,
-    or a MonitoredOutpuStatesOption value.
-    
-    Called by both self._validate_variable(), self.add_monitored_value(), and EVCMechanism._get_monitored_states()
-    """
-    state_spec_is_OK = False
-
-    if _is_value_spec(state_spec):
-        state_spec_is_OK = True
-
-    from PsyNeuLink.Components.States.OutputState import OutputState
-    if isinstance(state_spec, (InputState, OutputState, Mechanism)):
-        state_spec_is_OK = True
-
-    if isinstance(state_spec, dict):
-        state_spec_is_OK = True
-
-    if isinstance(state_spec, str):
-        # Will be used as the name of the inputState
-        state_spec_is_OK = True
-
-    if isinstance(state_spec, MonitoredOutputStatesOption):
-        state_spec_is_OK = True
-
-    if not state_spec_is_OK:
-        raise ObjectiveMechanismError("Specification of state to be monitored ({0}) by {1} is not "
-                             "a value, Mechanism, OutputState, string, dict, or a value of MonitoredOutputStatesOption".
-                             format(state_spec, self.name))
-
-
-def _objective_mechanism_role(mech, role):
-    if isinstance(mech, ObjectiveMechanism):
-        if mech._role is role:
-            return True
-        else:
-            return False
-    else:
-        return False
-
-
-def _is_value_spec(spec):
-    if isinstance(spec, (int, float, list, np.ndarray)):
-        return True
-    else:
-        return False
-
-
-# IMPLEMENTATION NOTE: THIS IS A PLACEMARKER FOR A METHOD TO BE IMPLEMENTED IN THE Composition CLASS
-def _instantiate_monitoring_projection(sender, receiver, matrix=DEFAULT_MATRIX):
-    from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
-    MappingProjection(sender=sender,
-                      receiver=receiver,
-                      matrix=matrix,
-                      name = sender.name + ' monitor')
\ No newline at end of file
diff --git a/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/ObjectiveMechanisms/ComparatorMechanism.py b/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/ObjectiveMechanisms/ComparatorMechanism.py
new file mode 100644
index 00000000000..fde57758361
--- /dev/null
+++ b/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/ObjectiveMechanisms/ComparatorMechanism.py
@@ -0,0 +1,430 @@
+# Princeton University licenses this file to You under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.  You may obtain a copy of the License at:
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed
+# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and limitations under the License.
+
+# *********************************************  ComparatorMechanism ***************************************************
+
+"""
+
+Overview
+--------
+
+A ComparatorMechanism is a subclass of `ObjectiveMechanism` that receives two inputs (a sample and a target), compares 
+them using its `function <ComparatorMechanism.function>`, and generates as its output an 
+`error_signal <Comparator.error_signal>` that is the discrepancy between them.
+
+.. _ComparatorMechanism_Creation:
+
+Creating a ComparatorMechanism
+------------------------------
+
+ComparatorMechanisms are generally created automatically when other PsyNeuLink components are created (such as 
+`LearningMechanisms <LearningMechanism_Creation>`.  A ComparatorMechanism can also be created directly by calling 
+its constructor.  Its **sample** and **target** arguments are used to specify the outputStates that provide the 
+sample and target inputs, respectively (see `ObjectiveMechanism_Monitored_States` for details concerning their 
+specification, which are special versions of an ObjectiveMechanism's **monitored_values** argument).  When the 
+ComparatorMechanism is created, two inputStates are created, one each for its sample and target inputs (and named, 
+by default, *SAMPLE* and *TARGET*). Each is assigned a MappingProjection from the corresponding outputState specified 
+in the **sample** and **target** arguments.
+
+It is important to recognize that the value of the *SAMPLE* and *TARGET* inputStates must have the same length and type, 
+so that they can be compared using the ComparatorMechanism's `function <ComparatorMechanism.function>`.  By default, 
+they use the format of the outputStates specified in the **sample** and **target** arguments, respectively,
+and the `MappingProjection` to each uses an `IDENTITY_MATRIX`.  Therefore, for the default configuration, the 
+outputStates specified in the **sample** and **target** arguments must have values of the same length and type.
+If these differ, the **input_states** argument can be used to explicitly specify the format of the ComparatorMechanism's 
+*SAMPLE* and *TARGET* inputStates, to insure they are compatible with one another (as well as to customize their 
+names, if desired).  If the **input_states** argument is used, *both* the sample and target inputStates must be 
+specified.  Any of the formats for `specifying inputStates <InputState_Specification>` can be used in the argument. 
+If values are assigned for the inputStates, they must be of equal length and type.  Their types must be 
+also be compatible with the value of the outputStates specified in the **sample** and **target** arguments.  However,
+the length specified for an inputState can differ from its corresponding OutputState;  in that case, by default, the
+MappingProjection created uses a `FULL_CONNECTIVITY` matrix.  Thus, outputStates of differing lengths can be mapped 
+to the sample and target inputStates of a ComparatorMechanism (see the `example <ComparatorMechanism_Example>` below),
+so long as the latter are of the same length.  If a projection other than a `FULL_CONNECTIVITY` matrix is needed, 
+this can be specified using the *PROJECTION* entry of a `state specification dictionary <LINK>`
+for the inputState in the **input_states** argument.
+
+.. _ComparatorMechanism_Structure:
+
+Structure
+---------
+
+A ComparatorMechanism has two `inputStates <ComparatorMechanism.input_states>`, each of which receives a 
+`MappingProjection` from a corresponding outputState specified in the **sample** and **target** 
+arguments of its constructor.  The inputStates are listed in the mechanism's 
+`input_states <ComparatorMechanism.input_States>` attribute.  The outputStates from which they receive their 
+projections (specified in the **sample** and **target** arguments) are listed in the mechanism's 
+`sample` and `target` attributes as well as in its `monitored_values <Comparator.monitored_values>` attribute.  
+The ComparatorMechanism's `function <ComparatorMechanism.function>` compares the value of the sample and target 
+inputStates.  By default, it uses a `LinearCombination` function, assigning the sample inputState a 
+`weight <LinearCombination.weight>` of *-1* and the target a `weight <LinearCombination.weight>` of *1*, so that the 
+sample is subtracted from the target.  However, the `function <ComparatorMechanism.function>` can be customized, so 
+long as it is replaced with one that takes two arrays with the same format as its inputs, and generates a similar 
+array as its result.  The result is assigned as the value of the Comparator mechanism's *ERROR_SIGNAL* 
+(`primary <OutputState_Primary>`) outputState.  
+
+.. _ComparatorMechanism_Function:
+
+Execution
+---------
+
+When an ComparatorMechanism is executed, it updates its input_states with the values of the outputStates specified
+in its **sample** and **target** arguments, and then uses its `function <ComparatorMechanism.function>` to
+compare these.  By default, the result is assigned as to the `value <ComparatorMechanism.value>` of its *ERROR_SIGNAL*
+`output_state <ComparatorMechanism.output_state>`, and as the first item of the mechanism's 
+`output_value <ComparatorMechanism.output_value>` attribute. 
+
+.. _ComparatorMechanism_Example:
+
+Example
+-------
+
+.. _ComparatorMechanism_Default_Input_Value_Example:
+
+*Formatting inputState values*
+
+The **input_states** argument can be used to specify a particular format for the SAMPLE and/or TARGET inputStates
+of a ComparatorMechanism.  This can be useful when one or both of these must be different than the format of the
+outputState(s) specified in the **sample** and **target** arguments. For example, for `Reinforcement Learning 
+<Reinforcement>`, a ComparatorMechanism is used to monitor an action selection mechanism (the sample), and compare 
+this with a reinforcement signal (the target).  In the example below, the action selection mechanism is a
+`TransferMechanism` that uses the `SoftMax` function (and the `PROB <Softmax.PROB>` as its output format) to select 
+an action.  This generates a vector with a single non-zero value (the selected action). Because the output is a vector,
+specifying it as the ComparatorMechanism's **sample** argument will generate a corresponding inputState with a vector
+as its value.  This will not match the reward signal specified in the ComparatorMechanism's **target** argument, the
+value of which is a single scalar.  This can be dealt with by explicitly specifying the format for the SAMPLE and
+TARGET inputStates in the **input_states** argument of the ComparatorMechanism's constructor, as follows:
+
+    my_action_selection_mech = TransferMechanism(size=5,
+                                                 function=SoftMax(output=PROB))
+
+    my_reward_mech = TransferMechanism(default_input_value = [0])
+
+    my_comparator_mech = ComparatorMechanism(sample=my_action_selection_mech,
+                                             target=my_reward_mech,
+                                             input_states = [[0],[0]])
+
+Note that ``my_action_selection_mechanism`` is specified to take an array of length 5 as its input, and therefore
+generate one of the same length as its `primary output <OutputState_Primary>`.  Since it is assigned as the **sample** 
+of the ComparatorMechanism, by default this will create a *SAMPLE* inputState of length 5, that will not match the 
+length of the *TARGET* inputState (which is 1).  This is taken care of, by specifying the **input_states** argument 
+as an array with two single-value arrays (corresponding to the *SAMPLE* and *TARGET* inputStates). (In this
+example, the **sample** and **target** arguments are specified as mechanisms since, by default, each has only a single
+(`primary <OutputState_Primary>`) outputState, that will be used;  if either had more than one outputState, and
+one of those was desired, it would have had to be specified explicitly in the **sample** or **target** argument).    
+
+.. _ComparatorMechanism_Class_Reference:
+
+Class Reference
+---------------
+
+"""
+
+from PsyNeuLink.Components.Functions.Function import LinearCombination
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ProcessingMechanism import *
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ObjectiveMechanisms.ObjectiveMechanism \
+    import ObjectiveMechanism, MONITORED_VALUES, ERROR_SIGNAL
+from PsyNeuLink.Components.States.InputState import InputState
+from PsyNeuLink.Components.States.OutputState import OutputState, StandardOutputStates, PRIMARY_OUTPUT_STATE
+
+
+SSE = 'SSE'
+MSE = 'MSE'
+
+class COMPARATOR_OUTPUT():
+    SSE = SSE
+    MSE = MSE
+
+
+class ComparatorMechanismError(Exception):
+    def __init__(self, error_value):
+        self.error_value = error_value
+
+    def __str__(self):
+        return repr(self.error_value)
+
+
+class ComparatorMechanism(ObjectiveMechanism):
+    """
+    ComparatorMechanism(                                \
+        sample,                                         \
+        target,                                         \
+        input_states=[SAMPLE,TARGET]                    \
+        function=LinearCombination(weights=[[-1],[1]],  \
+        input_states=[ERROR_SIGNAL]                     \
+        params=None,                                    \
+        name=None,                                      \
+        prefs=None)
+
+    Implements the ComparatorMechanism subclass of `ObjectiveMechanism`.
+
+    COMMENT:
+        Description:
+            ComparatorMechanism is a subtype of the ObjectiveMechanism Subtype of the ProcssingMechanism Type 
+            of the Mechanism Category of the Component class.
+            By default, it's function uses the LinearCombination Function to compare two input variables.
+            COMPARISON_OPERATION (functionParams) determines whether the comparison is subtractive or divisive
+            The function returns an array with the Hadamard (element-wise) differece/quotient of target vs. sample,
+                as well as the mean, sum, sum of squares, and mean sum of squares of the comparison array
+
+        Class attributes:
+            + componentType (str): ComparatorMechanism
+            + classPreference (PreferenceSet): Comparator_PreferenceSet, instantiated in __init__()
+            + classPreferenceLevel (PreferenceLevel): PreferenceLevel.SUBTYPE
+            + variableClassDefault (value):  Comparator_DEFAULT_STARTING_POINT // QUESTION: What to change here
+            + paramClassDefaults (dict): {TIME_SCALE: TimeScale.TRIAL,
+                                          FUNCTION_PARAMS:{COMPARISON_OPERATION: SUBTRACTION}}
+            + paramNames (dict): names as above
+
+        Class methods:
+            None
+
+        MechanismRegistry:
+            All instances of ComparatorMechanism are registered in MechanismRegistry, which maintains an
+              entry for the subclass, a count for all instances of it, and a dictionary of those instances
+    COMMENT
+
+    Arguments
+    ---------
+
+    sample : OutputState, Mechanism, value, or string
+        specifies the value to compare with the `target` by the `function <ComparatorMechanism.function>`.
+
+    target : OutputState, Mechanism, value, or string
+        specifies the value with which the `sample` is compared by the `function <ComparatorMechanism.function>`.
+
+    input_states :  List[InputState, value, str or dict] or Dict[] : default [SAMPLE, TARGET]
+        specifies the names and/or formats to use for the values of the sample and target inputStates;  
+        by default they are named *SAMPLE* and *TARGET*, and their formats are match the value of the outputStates
+        specified in the **sample** and **target** arguments, respectively.
+
+    function: Function, function or method : default Distance(metric=DIFFERENCE)
+        specifies the `function <Comparator.function>` used to compare the `sample` with the `target`.
+         
+    output_states :  List[OutputState, value, str or dict] or Dict[] : default [ERROR_SIGNAL]  
+        specifies the outputStates for the mechanism;
+
+    params : Optional[Dict[param keyword, param value]]
+        a `parameter dictionary <ParameterState_Specifying_Parameters>` that can be used to specify the parameters for
+        the mechanism, its function, and/or a custom function and its parameters. Values specified for parameters in
+        the dictionary override any assigned to those parameters in arguments of the
+        constructor.
+
+    COMMENT:
+        [TBI]
+        time_scale :  TimeScale : TimeScale.TRIAL
+            specifies whether the mechanism is executed on the TIME_STEP or TRIAL time scale.
+            This must be set to :keyword:`TimeScale.TIME_STEP` for the ``rate`` parameter to have an effect.
+    COMMENT
+
+    name : str : default ComparatorMechanism-<index>
+        a string used for the name of the mechanism.
+        If not is specified, a default is assigned by `MechanismRegistry`
+        (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
+
+    prefs : Optional[PreferenceSet or specification dict : Mechanism.classPreferences]
+        the `PreferenceSet` for mechanism.
+        If it is not specified, a default is assigned using `classPreferences` defined in __init__.py
+        (see :doc:`PreferenceSet <LINK>` for details).
+
+
+    Attributes
+    ----------
+
+    COMMENT:
+    default_input_value : Optional[List[array] or 2d np.array]
+    COMMENT
+
+    sample : OutputState
+        determines the value to compare with the `target` by the `function <ComparatorMechanism.function>`.
+
+    target : OutputState
+        determines the value with which `sample` is compared by the `function <ComparatorMechanism.function>`.
+        
+    input_states : ContentAddressableList[InputState, InputState]
+        contains the two inputStates named, by default, *SAMPLE* and *TARGET*, each of which receives a 
+        `MappingProjection` from the outputStates referenced by the `sample` and `target` attributes.
+
+    function : CombinationFunction, function or method
+        used to compare the `sample` with the `target`.  It can be any PsyNeuLink `CombinationFunction`,
+        or a python function that takes a 2d array with two items and returns a 1d array of the same length
+        as the two input items.
+
+\    value : 1d np.array
+        the result of the comparison carried out by the `function <ComparatorMechanism.function>`.
+
+    output_state : OutputState
+        contains the 'primary <OutputState_Primary>` outputState of the ComparatorMechanism; the default is  
+        its *ERROR_SIGNAL* outputState, the value of which is equal to the `value <ComparatorMechanism.value>` 
+        attribute of the ComparatorMechanism.
+
+
+    output_states : ContentAddressableList[OutputState]
+        contains, by default, only the *ERROR_SIGNAL* (primary) outputState of the ComparatorMechanism.
+
+    output_values : 2d np.array
+        contains one item that is the value of the *ERROR_SIGNAL* outputState.
+
+    name : str : default ComparatorMechanism-<index>
+        the name of the mechanism.
+        Specified in the **name** argument of the constructor for the mechanism;
+        if not is specified, a default is assigned by `MechanismRegistry`
+        (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
+
+    prefs : PreferenceSet or specification dict : Mechanism.classPreferences
+        the `PreferenceSet` for mechanism.
+        Specified in the **prefs** argument of the constructor for the mechanism;
+        if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
+        (see :doc:`PreferenceSet <LINK>` for details).
+
+
+    """
+    componentType = COMPARATOR_MECHANISM
+
+    classPreferenceLevel = PreferenceLevel.SUBTYPE
+    # These will override those specified in TypeDefaultPreferences
+    classPreferences = {
+        kwPreferenceSetName: 'ComparatorCustomClassPreferences',
+        kpReportOutputPref: PreferenceEntry(False, PreferenceLevel.INSTANCE)}
+
+    # variableClassDefault = [[0],[0]]  # By default, ComparatorMechanism compares two 1D np.array input_states
+    variableClassDefault = None
+
+    # ComparatorMechanism parameter and control signal assignments):
+    paramClassDefaults = Mechanism_Base.paramClassDefaults.copy()
+    paramClassDefaults.update({
+        TIME_SCALE: TimeScale.TRIAL,
+        MONITORED_VALUES: None})
+
+    paramNames = paramClassDefaults.keys()
+
+    standard_output_states = ObjectiveMechanism.standard_output_states.copy()
+    standard_output_states.extend([{NAME:SSE,
+                                    CALCULATE:lambda x: np.sum(x*x)},
+                                   {NAME:MSE,
+                                    CALCULATE:lambda x: np.sum(x*x)/len(x)}])
+
+    @tc.typecheck
+    def __init__(self,
+                 sample:tc.optional(tc.any(OutputState, Mechanism_Base, dict, is_numeric, str))=None,
+                 target:tc.optional(tc.any(OutputState, Mechanism_Base, dict, is_numeric, str))=None,
+                 input_states=[SAMPLE, TARGET],
+                 function=LinearCombination(weights=[[-1], [1]]),
+                 output_states:tc.optional(tc.any(list, dict))=[ERROR_SIGNAL, MSE],
+                 params=None,
+                 name=None,
+                 prefs:is_pref_set=None,
+                 context=None):
+
+        # Parse items of input_states arg for validation (in _validate_params)
+        input_states = input_states or [None] * 2
+        from PsyNeuLink.Components.States.State import _parse_state_spec
+        sample_input = _parse_state_spec(owner=self,
+                                         state_type=InputState,
+                                         state_spec=input_states[0],
+                                         name=SAMPLE,
+                                         value=None)
+        target_input = _parse_state_spec(owner=self,
+                                         state_type=InputState,
+                                         state_spec=input_states[1],
+                                         name=TARGET,
+                                         value=None)
+
+        # IMPLEMENTATION NOTE: The following prevents the default from being updated by subsequent assignment
+        #                     (in this case, to [ERROR_SIGNAL, {NAME= MSE}]), but fails to expose default in IDE
+        # output_states = output_states or [ERROR_SIGNAL, MSE]
+
+        # Create a StandardOutputStates object from the list of stand_output_states specified for the class
+        if not isinstance(self.standard_output_states, StandardOutputStates):
+            self.standard_output_states = StandardOutputStates(self,
+                                                               self.standard_output_states,
+                                                               indices=PRIMARY_OUTPUT_STATE)
+
+        super().__init__(monitored_values=[sample, target],
+                         input_states = [sample_input, target_input],
+                         function=function,
+                         output_states=output_states.copy(), # prevent default from getting overwritten by later assign
+                         params=params,
+                         name=name,
+                         prefs=prefs,
+                         context=self)
+
+    def _validate_params(self, request_set, target_set=None, context=None):
+        """If sample and target values are specified, validate that they are compatible 
+        """
+
+        if INPUT_STATES in request_set:
+            input_states = request_set[INPUT_STATES]
+
+            # Validate that there are exactly two input_states (for sample and target)
+            num_input_states = len(input_states)
+            if num_input_states != 2:
+                raise ComparatorMechanismError("{} arg is specified for {} ({}), so it must have exactly 2 items, "
+                                               "one each for {} and {}".
+                                               format(INPUT_STATES,
+                                                      self.__class__.__name__,
+                                                      len(input_states),
+                                                      SAMPLE,
+                                                      TARGET))
+
+            # Validate that input_states are specified as dicts
+            if not all(isinstance(input_state,dict) for input_state in input_states):
+                raise ComparatorMechanismError("PROGRAM ERROR: all items in input_state args must be converted to dicts"
+                                               " by calling State._parse_state_spec() before calling super().__init__")
+
+            # Validate length of variable for sample = target
+            if VARIABLE in input_states[0]:
+                # input_states arg specified in standard state specification dict format
+                lengths = [len(input_state[VARIABLE]) for input_state in input_states]
+            else:
+                # input_states arg specified in {<STATE_NAME>:<STATE SPECIFICATION DICT>} format
+                lengths = [len(list(input_state_dict.values())[0][VARIABLE]) for input_state_dict in input_states]
+
+            if lengths[0] != lengths[1]:
+                raise ComparatorMechanismError("Length of value specified for {} inputState of {} ({}) must be "
+                                               "same as length of value specified for {} ({})".
+                                               format(SAMPLE,
+                                                      self.__class__.__name__,
+                                                      lengths[0],
+                                                      TARGET,
+                                                      lengths[1]))
+
+        elif SAMPLE in request_set and TARGET in request_set:
+
+            sample = request_set[SAMPLE]
+            if isinstance(sample, InputState):
+                sample_value = sample.value
+            elif isinstance(sample, Mechanism):
+                sample_value = sample.input_value[0]
+            elif is_value_spec(sample):
+                sample_value = sample
+            else:
+                sample_value = None
+
+            target = request_set[TARGET]
+            if isinstance(target, InputState):
+                target_value = target.value
+            elif isinstance(target, Mechanism):
+                target_value = target.input_value[0]
+            elif is_value_spec(target):
+                target_value = target
+            else:
+                target_value = None
+
+            if sample is not None and target is not None:
+                if not iscompatible(sample, target, **{kwCompatibilityLength: True,
+                                                       kwCompatibilityNumeric: True}):
+                    raise ComparatorMechanismError("The length of the sample ({}) must be the same as for the target ({})"
+                                                   "for {} {}".
+                                                   format(len(sample),
+                                                          len(target),
+                                                          self.__class__.__name__,
+                                                          self.name))
+
+        super()._validate_params(request_set=request_set,
+                                 target_set=target_set,
+                                 context=context)
diff --git a/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/ObjectiveMechanisms/ObjectiveMechanism.py b/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/ObjectiveMechanisms/ObjectiveMechanism.py
new file mode 100644
index 00000000000..6c92a146d5b
--- /dev/null
+++ b/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/ObjectiveMechanisms/ObjectiveMechanism.py
@@ -0,0 +1,717 @@
+# Princeton University licenses this file to You under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.  You may obtain a copy of the License at:
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed
+# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and limitations under the License.
+
+# ADD TO DOCUMENTATION: [per ComparatorMechanism]
+#  input_states is used to name and/or specify the value of the input_states; it can be:
+#      just a list of strings (used as names)
+#      just a list of values (used as values of inputState, overrides use of monitored_values such as for RL)
+#      if both, use dict with NAME and VARIABLE entries
+#      # if input_states must == monitored_values
+#      if none are specified, they will autojatically be created based on the monitored_values spec
+#  monitored_values no longer takes an inputState specification (that must be in input_states)
+#      if it is a name or value, no projection is called for (assumes that it is a TARGET, and that the projection
+#                    will be implemented by Composition
+
+# *********************************************  ObjectiveMechanism ****************************************************
+
+"""
+
+Overview
+--------
+
+An ObjectiveMechanism is a `ProcessingMechanism` that monitors the `outputStates <OutputState>` of one or more other
+ProcessingMechanisms specified in its `monitor <ObjectiveMechanism.monitor>` attribute, and evaluates them using its
+`function <ObjectiveMechanism.function>`. The result of the evaluation is placed in the ObjectiveMechanism's
+`primary outputState <OutputState_Primary>`.  ObjectiveMechanisms are typically used closely with (and often created
+automatically by) `AdaptiveMechanisms <AdaptiveMechanism>`.
+
+.. _ObjectiveMechanism_Creation:
+
+Creating an ObjectiveMechanism
+------------------------------
+
+ObjectiveMechanisms are often created automatically when other PsyNeuLink components are created (in particular, 
+AdaptiveMechanisms such as `LearningMechanisms <LearningMechanism_Creation>` and 
+`ControlMechanisms <ControlMechanism_Creation>`.  An ObjectiveMechanism can also be created directly by calling its 
+constructor.  Its **monitored_values** argument is used to specify the outputStates to be monitored.  Any of the forms 
+used for `specifying outputStates <OutputState_Specification>` can be used, as well as a value of 
+MonitoredOutputStateOption.  When an ObjectiveMechanism is created, an inputState is created for each of the 
+outputStates specified in its **monitored_values** argument, and a `MappingProjection` is assigned from each of those 
+to the corresponding inputState.  By default, the value of each inputState uses the format of its corresponding 
+outputState, and the MappingProjection between them uses an `IDENTITY_MATRIX`.  However, the **input_states** argument 
+can be used to customize the inputStates, and/or to specify their names. Any of the forms used for 
+`specifying inputStates <InputState_Specification>` can be used in the **input_states** argument, however the number
+of inputStates specified must equal the number of outputStates specified in the **monitored_values** argument (see the 
+`examples <ObjectiveMechanism_Examples>` below). The value of each must also be of the same type as the value of the 
+corresponding outputState, however their lengths can differ;  in that case, by default, the MappingProjection created 
+uses a  `FULL_CONNECTIVITY` matrix, although this too can be customized using the *PROJECTION* entry of a 
+`state specification dictionary <LINK>` for the inputState in the **input_states** argument.
+
+
+.. _ObjectiveMechanism_Structure:
+
+Structure
+---------
+
+An ObjectiveMechanism has one `inputState <InputState>` for each of the outputStates specified in its
+**monitored_values** argument, each pair of which is connected by a `MappingProjection`.  The monitored outputStates
+are listed in the ObjectiveMechanism's `monitored_values <ObjectiveMechanism.monitored_values>` attribute, and  
+the inputStates to which they project are listed in the ObjectiveMechanism's 
+`input_states <ObjectiveMechanism.input_states>` attribute.  The ObjectiveMechanism's 
+`function <ObjectiveMechanism.function>` uses these values to compute an 
+`objective (or "loss") function <https://en.wikipedia.org/wiki/Loss_function>`_, that is assigned as the value of its 
+*ERROR_SIGNAL* (`primary <OutputState_Primary>`) outputState.  By default, it uses a `LinearCombination` function to 
+sum the values of its inputStates.  However, the `function <ComparatorMechanism.function>` can be customized to 
+calculate other quantities (differences, ratios, etc. -- see 
+`example <ObjectiveMechanism_Weights_and_Exponents_Example>` below). It can also be replaced with any Python function
+or method, so long as it takes a 2d array as its input, and generates a 1d array as its result.  
+
+.. _ObjectiveMechanism_Monitored_Values:
+
+Monitored Values
+~~~~~~~~~~~~~~~~
+
+These can be specified in a variety of ways, each of which must eventually resolve to an outputState, the
+value of which is to be monitored.
+
+Note that some forms of
+specification may depend on specifications made for the outputState referenced, the mechanism to which it belongs,
+and/or the process or system to which that mechanism belongs. These interactions (and the precedence afforded to
+each) are described below.
+
+If an outputState is specified at the time the ObjectiveMechanism is created, or the specification can be resolved
+to an outputState, a MappingProjection is automatically created from it to the corresponding inputState
+using `AUTO_ASSIGN_MATRIX` as its `matrix <MapppingProjection.matrix>` parameter.  If the outputState can't be
+determined, no MappingProjection is assigned, and this must be done by some other means;  any values in
+`monitored_values` that are not associated with an outputState at the time the ObjectiveMechanism is executed are
+ignored.
+
+The specification of item in `monitored_values` can take any of the following forms:
+
+* **OutputState**:  a reference to the `outputState <OutputState>` of a mechanism.  This will create a
+  `MappingProjection` from it to the corresponding inputState in `input_states <ObjectiveMechanism.input_states>`.
+COMMENT: TBI
+    Note that an outputState can be *excluded* from being monitored by assigning `None` as the value of its
+    `monitoring_status` attribute.  This specification takes precedence over any others;  that is, it will suppress
+    monitoring of that outputState, irrespective of any other specifications that might otherwise apply to that
+    outputState, including those described below.
+COMMENT
+..
+* **Mechanism**: by default, the mechanism's `primary outputState <OutputState_Primary>` will be used.  However,
+  if the mechanism has any outputStates specified in its `monitored_states` attribute, those will be used (except for
+  any that specify `None` as their `monitoring_status`). This specification takes precedence over any of the other
+  types listed below:  if it is `None`, then none of that mechanism's outputStates will be monitored; if it
+  specifies outputStates to be monitored, those will be monitored even if they do not satisfy any of the conditions
+  described in the specifications below.
+..
+
+COMMENT: TBI
+    .. _ObjectiveMechanism_OutputState_Tuple:
+
+    * **MonitoredOutputState Tuple**  tuple can be used wherever an outputState can be specified, to determine how
+      its value is combined with others by the ObjectiveMechanism's `function <ObjectiveMechanism.function>`. Each
+      tuple must have the three following items in the order listed:
+
+          * an outputState or mechanism, the name of one, or a specification dictionary for one;
+          ..
+          * a weight (int) - multiplies the value of the outputState.
+          ..
+          * an exponent (int) - exponentiates the value of the outputState;
+COMMENT
+
+* **string**, **value** or **dict**: These can be used as placemarkers for a monitored_state that will be instantiated
+  later (for example, for the TARGET input of a Composition).  If a string is specified, it is used as the
+  default name of the corresponding inputState (specified in the `input_states <ObjectiveMechanism.input_states>`
+  attribute of the ObjectiveMechanism) If a value is specified, it is used as the default value for the corresponding
+  inputState.  If a dict is specified, it must have a single entry, the key of which will be used as a string
+  specification -- i.e., as the name of the inputState -- and the value as its value specification. 
+
+.. _ObjectiveMechanism_Function:
+
+Function
+~~~~~~~~
+
+The ObjectiveMechanism's `function` can be customized to implement a wide variety of
+`objective (or "loss") functions <https://en.wikipedia.org/wiki/Loss_function>`_.  The default is the
+`LinearCombination` function, which simply sums the values of the outputStates listed in `monitored_values`.
+However, this can easily be configured to calculate differences, ratios, etc. (see
+`example <ObjectiveMechanism_Weights_and_Exponents_Example>` below).  It can also be replaced with any
+`CombinationFunction`, or any python function that takes a 2d array with an arbitrary number of
+items or a number equal to the number of items in the ObjectiveMechanism's variable (and its number of
+input_states), and returns a 1d array.
+
+.. _ObjectiveMechanism_Execution:
+
+Execution
+---------
+
+When an ObjectiveMechanism is executed, it updates its input_states with the values of the outputStates listed in
+its `monitored_values` attribute, and then uses its `function <ObjectiveMechanism.function>` to
+evaluate these.  The result is assigned as to its `value <ObjectiveMechanism.value>` attribute as the value of its
+`primary outputState <OutputState_Primary>`.
+
+
+.. _ObjectiveMechanism_Examples:
+
+Examples
+--------
+
+.. _ObjectiveMechanism_Default_Input_Value_Example:
+
+*Formatting inputState values*
+
+The use of default_input_value to override a specification in `monitored_values` can be useful in some situations.
+For example, for `Reinforcement Learning <Reinforcement>`, an ObjectiveMechanism is used to monitor an action
+selection mechanism.  In the example below, the latter uses a `TransferMechanism` with the `SoftMax` function (and the
+`PROB <Softmax.PROB>` as its output format) to select the action.  This generates a vector with a single non-zero
+value, which designates the predicted reward for the selected action.  Because the output is a vector,
+by default the inputState of the ObjectiveMechanism created to monitor it will also be a vector.  However, the
+ObjectiveMechanism requires that this be a single value, that it can compare with the value of the reward mechanism.
+This can be dealt with by using `default_input_value` in the constructor of the ObjectiveMechanism, to force
+the inputState for the ObjectiveMechanism to have a single value, as in the example below::
+
+    my_action_select_mech = TransferMechanism(default_input_value = [0,0,0],
+                                function=SoftMax(output=PROB))
+
+    my_reward_mech = TransferMechanism(default_input_value = [0])
+
+    my_objective_mech = ObjectiveMechanism(default_input_value = [[0],[0]],
+                                          monitored_values = [my_action_select_mech, my_reward_mech])
+
+Note that the outputState for the `my_action_selection` and `my_reward_mech` are specified
+in `monitored_values`.  If that were the only specification, the inputState created for `my_action_select_mech`
+would be a vector of length 3.  This is overridden by specifying `default_input_value` as an array with two
+single-value arrays (one corresponding to `my_action_select_mech` and the other to `my_reward_mech`).  This forces
+the inputState for `my_action_select_mech` to have only a single element which, in turn, will cause a
+MappingProjection to be created from  `my_action_select_mech` to the ObjectiveMechanism's inputState using a
+`FULL_CONNECTIVITY_MATRIX` (the one used for `AUTO_ASSIGN_MATRIX` when the sender and receiver have values of
+different lengths).  This produces the desired effect, since the action selected is the only non-zero value in the
+output of `my_action_select_mech`, and so the `FULL_CONNECTIVITY_MATRIX` will combine it with zeros (the other values
+in the vector), and so its value will be assigned as the value of the corresponding inputState in the
+ObjectiveMechanism.  Another option would have been to customize the ObjectiveMechanism's
+`function <ObjectiveMechanism.function>` to convert the output of `my_action_select_mech` to a length 1 vector, though
+this would have been more involved.  The next example describes a simple case of customizing the ObjectiveMechanism's
+`function <ObjectiveMechanism.function>`, however more sophisticated ones are possible, just as the one just suggested.
+
+.. _ObjectiveMechanism_Weights_and_Exponents_Example:
+
+*Customizing the ObjectiveMechanism's function*
+
+The simplest way to customize the `function <ObjectiveMechanism.function>` of an ObjectiveMechanism is to
+parameterize its default function (`LinearCombination`).  In the example below, the ObjectiveMechanism used in the
+`previous example <ObjectiveMechanism_Default_Input_Value_Example>` is further customized to subtract the value
+of the action selected from the value of the reward::
+
+    my_objective_mech = ObjectiveMechanism(default_input_value = [[0],[0]],
+                                          monitored_values = [my_action_select_mech, my_reward_mech],
+                                          function=LinearCombination(weights=[[-1], [1]]))
+
+This is done by specifying the `weights <LinearCombination.weights>` parameter of the `LinearCombination` function,
+with two values [-1] and [1] corresponding to the two items in `monitored_values` (and `default_input_value`).  This
+will multiply the value from `my_action_select_mech` by -1 before adding it to (and thus
+subtracting it from) the value of `my_reward_mech`.  Similarly, the `operation <LinearCombination.operation>`
+and `exponents <LinearCombination.exponents>` parameters of `LinearCombination` can be used together to multiply and
+divide quantities.
+
+.. _ObjectiveMechanism_Class_Reference:
+
+Class Reference
+---------------
+
+"""
+
+from PsyNeuLink.Components.Functions.Function import LinearCombination
+# from PsyNeuLink.Components.Mechanisms.MonitoringMechanisms.MonitoringMechanism import *
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ProcessingMechanism import *
+from PsyNeuLink.Components.States.InputState import InputState
+from PsyNeuLink.Components.States.OutputState import standard_output_states, PRIMARY_OUTPUT_STATE
+
+ROLE = 'role'
+MONITORED_VALUES = 'monitored_values'
+MONITORED_VALUE_NAME_SUFFIX = '_Monitor'
+ERROR_SIGNAL = 'error_signal'
+
+# This is a convenience class that provides list of standard_output_state names in IDE
+class OBJECTIVE_OUTPUT():
+        ERROR_SIGNAL=ERROR_SIGNAL
+
+
+class ObjectiveMechanismError(Exception):
+    def __init__(self, error_value):
+        self.error_value = error_value
+
+    def __str__(self):
+        return repr(self.error_value)
+
+
+class ObjectiveMechanism(ProcessingMechanism_Base):
+    """
+    ObjectiveMechanism(               \
+        monitored_values,             \
+        input_states=None,            \
+        function=LinearCombination,   \
+        output_states=[ERROR_SIGNAL], \
+        params=None,                  \
+        name=None,                    \
+        prefs=None)
+
+    Implements the ObjectiveMechanism subclass of `ProcessingMechanism`.
+
+    COMMENT:
+        Description:
+            ObjectiveMechanism is a subtype of the ProcessingMechanism Type of the Mechanism Category of the
+                Component class
+            It's function uses the LinearCombination Function to compare two input variables
+            COMPARISON_OPERATION (functionParams) determines whether the comparison is subtractive or divisive
+            The function returns an array with the Hadamard (element-wise) differece/quotient of target vs. sample,
+                as well as the mean, sum, sum of squares, and mean sum of squares of the comparison array
+
+        Class attributes:
+            + componentType (str): ObjectiveMechanism
+            + classPreference (PreferenceSet): Comparator_PreferenceSet, instantiated in __init__()
+            + classPreferenceLevel (PreferenceLevel): PreferenceLevel.SUBTYPE
+            + variableClassDefault (value):  Comparator_DEFAULT_STARTING_POINT // QUESTION: What to change here
+            + paramClassDefaults (dict): {TIME_SCALE: TimeScale.TRIAL,
+                                          FUNCTION_PARAMS:{COMPARISON_OPERATION: SUBTRACTION}}
+            + paramNames (dict): names as above
+
+        Class methods:
+            None
+
+        MechanismRegistry:
+            All instances of ObjectiveMechanism are registered in MechanismRegistry, which maintains an
+              entry for the subclass, a count for all instances of it, and a dictionary of those instances
+    COMMENT
+
+    Arguments
+    ---------
+
+    monitored_values : List[OutputState, Mechanism, string, value, dict, MonitoredOutputStateOption] or Dict[]
+        specifies the values that will will be monitored, and evaluated by the `function <ObjectiveMechanism>`
+        (see `monitored_values` for details of specification).
+
+    input_states :  List[InputState, value, str or dict] or Dict[] : default None
+        specifies the names and/or formats to use for the values of the inputStates that receive the input from the
+        outputStates specified in the **monitored_values** argument; if specified, there must be one for each item 
+        specified in the **monitored_values** argument.
+
+    COMMENT:
+    names: List[str]
+        specifies the names to use for the input_states created for the list in
+        `monitored_values <ObjectiveMechanism.monitor>`.  If specified,
+        the number of items in the list must equal the number of items in `monitored_values`, and takes precedence
+        over any names specified there.
+    COMMENT
+
+    function: CombinationFunction, ObjectiveFunction, function or method : default LinearCombination
+        specifies the function used to evaluate the values listed in :keyword:`monitored_values`
+        (see `function <LearningMechanism.function>` for details.
+
+    output_states :  List[OutputState, value, str or dict] or Dict[] : default [ERROR_SIGNAL]  
+        specifies the outputStates for the mechanism;
+
+    role: Optional[LEARNING, CONTROL]
+        specifies if the ObjectiveMechanism is being used for learning or control (see `role` for details).
+
+    params : Optional[Dict[param keyword, param value]]
+        a `parameter dictionary <ParameterState_Specifying_Parameters>` that can be used to specify the parameters for
+        the mechanism, its function, and/or a custom function and its parameters. Values specified for parameters in
+        the dictionary override any assigned to those parameters in arguments of the
+        constructor.
+
+    COMMENT:
+        [TBI]
+        time_scale :  TimeScale : TimeScale.TRIAL
+            specifies whether the mechanism is executed on the TIME_STEP or TRIAL time scale.
+            This must be set to :keyword:`TimeScale.TIME_STEP` for the ``rate`` parameter to have an effect.
+    COMMENT
+
+    name : str : default ObjectiveMechanism-<index>
+        a string used for the name of the mechanism.
+        If not is specified, a default is assigned by `MechanismRegistry`
+        (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
+
+    prefs : Optional[PreferenceSet or specification dict : Mechanism.classPreferences]
+        the `PreferenceSet` for mechanism.
+        If it is not specified, a default is assigned using `classPreferences` defined in __init__.py
+        (see :doc:`PreferenceSet <LINK>` for details).
+
+
+    Attributes
+    ----------
+
+    COMMENT:
+    default_input_value : Optional[List[array] or 2d np.array]
+    COMMENT
+
+    monitored_values : ContentAddressableList[OutputState]
+        determines  the values monitored, and evaluated by `function <ObjectiveMechanism>`.  Each item in the list 
+        refers to an outputState containing the value to be monitored, with a `MappingProjection` from it to the 
+        corresponding inputState listed in the `input_states <ComparatorMechanism.input_states>` attribute.
+
+    input_states : ContentAddressableList[InputState]
+        contains the inputStates of the ObjectiveMechanism, each of which receives a `MappingProjection` from the 
+        outputStates specified in its `monitored_values <ObjectiveMechanism.monitored_values>` attribute.
+
+    function : CombinationFunction, ObjectiveFunction, function, or method
+        the function used to compare evaluate the values monitored by the ObjectiveMechanism.  The function can be
+        any PsyNeuLink `CombinationFunction` or a python function that takes a 2d array with an arbitrary number of
+        items or a number equal to the number of items in the ObjectiveMechanism's variable (and its number of
+        input_states), and returns a 1d array.
+
+    role : None, LEARNING or CONTROL
+        specifies whether the ObjectiveMechanism is being used for learning in a process or system (in conjunction
+        with a `LearningMechanism`), or for control in a system (in conjunction with a `ControlMechanism`).
+
+    value : 1d np.array
+        the output of the evaluation carried out by the ObjectiveMechanism's `function <ObjectiveMechanism.function>`.
+
+    output_state : OutputState
+        contains the 'primary <OutputState_Primary>` outputState of the ObjectiveMechanism; the default is  
+        its *ERROR_SIGNAL* outputState, the value of which is equal to the `value <ObjectiveMechanism.value>` 
+        attribute of the ObjectiveMechanism.
+
+    output_states : ContentAddressableList[OutputState]
+        contains, by default, only the *ERROR_SIGNAL* (primary) outputState of the ObjectiveMechanism.
+
+    output_values : 2d np.array
+        contains one item that is the value of the *ERROR_SIGNAL* outputState.
+
+    name : str : default ObjectiveMechanism-<index>
+        the name of the mechanism.
+        Specified in the **name** argument of the constructor for the mechanism;
+        if not is specified, a default is assigned by `MechanismRegistry`
+        (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
+
+    prefs : PreferenceSet or specification dict : Mechanism.classPreferences
+        the `PreferenceSet` for mechanism.
+        Specified in the **prefs** argument of the constructor for the mechanism;
+        if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
+        (see :doc:`PreferenceSet <LINK>` for details).
+
+
+    """
+
+    componentType = OBJECTIVE_MECHANISM
+
+    classPreferenceLevel = PreferenceLevel.SUBTYPE
+    # These will override those specified in TypeDefaultPreferences
+    classPreferences = {
+        kwPreferenceSetName: 'ObjectiveCustomClassPreferences',
+        kpReportOutputPref: PreferenceEntry(False, PreferenceLevel.INSTANCE)}
+
+    # variableClassDefault = [[0],[0]]  # By default, ObjectiveMechanism compares two 1D np.array input_states
+    variableClassDefault = None
+
+    # ObjectiveMechanism parameter and control signal assignments):
+    paramClassDefaults = Mechanism_Base.paramClassDefaults.copy()
+    paramClassDefaults.update({
+        MONITORED_VALUES: None,
+        TIME_SCALE: TimeScale.TRIAL,
+        FUNCTION: LinearCombination,
+        })
+
+    paramNames = paramClassDefaults.keys()
+
+    standard_output_states = standard_output_states.copy()
+
+    # FIX:  TYPECHECK MONITOR TO LIST OR ZIP OBJECT
+    @tc.typecheck
+    def __init__(self,
+                 monitored_values:tc.any(list, dict),
+                 input_states=None,
+                 function=LinearCombination,
+                 output_states:tc.optional(tc.any(list, dict))=[ERROR_SIGNAL],
+                 params=None,
+                 name=None,
+                 prefs:is_pref_set=None,
+                 context=None):
+
+        # Assign args to params and functionParams dicts (kwConstants must == arg names)
+        params = self._assign_args_to_param_dicts(monitored_values=monitored_values,
+                                                  input_states=input_states,
+                                                  output_states=output_states,
+                                                  function=function,
+                                                  params=params)
+        self._learning_role = None
+
+        from PsyNeuLink.Components.States.OutputState import StandardOutputStates
+        if not isinstance(self.standard_output_states, StandardOutputStates):
+            self.standard_output_states = StandardOutputStates(self,
+                                                               self.standard_output_states,
+                                                               indices=PRIMARY_OUTPUT_STATE)
+
+        super().__init__(
+                         variable=None,
+                         input_states=input_states,
+                         output_states=output_states,
+                         params=params,
+                         name=name,
+                         prefs=prefs,
+                         context=self)
+
+    def _validate_variable(self, variable, context=None):
+        """Validate that if default_input_value is specified the number of values matches the number of monitored_values
+
+        """
+        # IMPLEMENTATION NOTE:  use self.user_params (i.e., values specified in constructor)
+        #                       since params have not yet been validated and so self.params is not yet available
+        if variable is not None and len(variable) != len(self.user_params[MONITORED_VALUES]):
+            raise ObjectiveMechanismError("The number of items specified for the default_input_value arg ({}) of {} "
+                                          "must match the number of items specified for its monitored_values arg ({})".
+                                          format(len(variable), self.name, len(self.user_params[MONITORED_VALUES])))
+
+        super()._validate_variable(variable=variable, context=context)
+
+    def _validate_params(self, request_set, target_set=None, context=None):
+        """Validate `role`, `monitored_values`, amd `input_states <ObjectiveMechanism.input_states>` arguments
+
+        """
+
+        super()._validate_params(request_set=request_set,
+                                 target_set=target_set,
+                                 context=context)
+
+        if ROLE in target_set and target_set[ROLE] and not target_set[ROLE] in {LEARNING, CONTROL}:
+            raise ObjectiveMechanismError("\'role\'arg ({}) of {} must be either \'LEARNING\' or \'CONTROL\'".
+                                          format(target_set[ROLE], self.name))
+
+        if (INPUT_STATES in target_set and
+                    target_set[INPUT_STATES] is not None and
+                not all(input_state is None for input_state in target_set[INPUT_STATES])):
+            if MONITORED_VALUES in target_set:
+                monitored_values = target_set[MONITORED_VALUES]
+            elif hasattr(self, 'monitored_values'):
+                monitored_values = self.monitored_values
+            else:
+                raise ObjectiveMechanismError("PROGRAM ERROR: monitored_values not instantiated as param or attirb")
+
+            if len(target_set[INPUT_STATES]) != len(monitored_values):
+                raise ObjectiveMechanismError("The number of items in the \'{}\'arg for {} ({}) "
+                                              "must equal of the number in the \'{}\' arg ({})".
+                                     format(INPUT_STATES,
+                                            self.name,
+                                            len(target_set[INPUT_STATES]),
+                                            MONITORED_VALUES,
+                                        len(target_set[MONITORED_VALUES])))
+
+            #FIX: IS THIS HANDLED BY _instantiate_input_states??
+            for state_spec in target_set[INPUT_STATES]:
+                if not isinstance(state_spec, (str, InputState, Mechanism, dict)):
+                    raise ObjectiveMechanismError("Item in \'{}\'arg for {} is not a "
+                                                  "valid specificaton for an InputState".
+                                                  format(INPUT_STATES,
+                                                         self.name,
+                                                         target_set[INPUT_STATES]))
+
+        #region VALIDATE MONITORED VALUES
+        # FIX: IS THE FOLLOWING STILL TRUE:
+        # Note: this must be validated after OUTPUT_STATES (and therefore call to super._validate_params)
+        #       as it can reference entries in that param
+        if MONITORED_VALUES in target_set:
+            try:
+                if not target_set[MONITORED_VALUES] or target_set[MONITORED_VALUES] is NotImplemented:
+                    pass
+                # It is a MonitoredOutputStatesOption specification
+                elif isinstance(target_set[MONITORED_VALUES], MonitoredOutputStatesOption):
+                    # Put in a list (standard format for processing by _instantiate_monitored_output_states)
+                    target_set[MONITORED_VALUES] = [target_set[MONITORED_VALUES]]
+                # It is NOT a MonitoredOutputStatesOption specification, so assume it is a list of Mechanisms or States
+                else:
+                    # Validate each item of MONITORED_VALUES
+                    for item in target_set[MONITORED_VALUES]:
+                        _validate_monitored_value(self, item, context=context)
+
+                    # FIX: PRINT WARNING (IF VERBOSE) IF WEIGHTS or EXPONENTS IS SPECIFIED,
+                    # FIX:  INDICATING THAT IT WILL BE IGNORED;
+                    # FIX:  weights AND exponents ARE SPECIFIED IN TUPLES
+                    # FIX:  WEIGHTS and EXPONENTS ARE VALIDATED IN
+                    # FIX:           System.ControlMechanism_instantiate_monitored_output_states
+                    # # Validate WEIGHTS if it is specified
+                    # try:
+                    #     num_weights = len(target_set[FUNCTION_PARAMS][WEIGHTS])
+                    # except KeyError:
+                    #     # WEIGHTS not specified, so ignore
+                    #     pass
+                    # else:
+                    #     # Insure that number of weights specified in WEIGHTS
+                    #     #    equals the number of states instantiated from MONITOR_FOR_CONTROL
+                    #     num_monitored_states = len(target_set[MONITOR_FOR_CONTROL])
+                    #     if not num_weights != num_monitored_states:
+                    #         raise MechanismError("Number of entries ({0}) in WEIGHTS of kwFunctionParam for EVC "
+                    #                        "does not match the number of monitored states ({1})".
+                    #                        format(num_weights, num_monitored_states))
+
+            except KeyError:
+                pass
+
+    # IMPLEMENTATION NOTE: FOR Composition, BREAK THIS UP SO THAT monitored_values CAN BE IMPLEMENTED
+    #                      ONE AT TIME (IN A CALL TO _instantiate_monitored_value THAT INSTANTATEs
+    #                      ADDS an input_state FOR EACH ITEM IN monitored_values
+    #                      (AKIN _instantiate_control_signal)
+    def _instantiate_input_states(self, context=None):
+        """Instantiate input state for each value specified in `monitored_values` arg and instantiate self.variable
+        
+        Parse specifications for input_states, using monitored_values where relevant, and instantiate input_states. 
+        Re-specify corresponding items of variable to match the values of the inputStates in input_states.
+        Update self.input_state and self.input_states.
+        Call _instantiate_monitoring_projection() to instantiate MappingProjection to inputState
+            if an outputState has been specified.
+        """
+
+        from PsyNeuLink.Components.States.State import _parse_state_spec
+        from PsyNeuLink.Components.States.OutputState import OutputState
+
+        # Parse monitored_values
+        monitored_values = []
+        for value in self.monitored_values:
+            monitored_value_dict = {}
+            monitored_value = _parse_state_spec(owner=self,
+                                                state_type=OutputState,
+                                                state_spec=value)
+            if isinstance(monitored_value, dict):
+                monitored_value_dict = monitored_value
+            elif isinstance(monitored_value, State):
+                monitored_value_dict[NAME] = monitored_value.name
+                monitored_value_dict[VARIABLE] = monitored_value.variable
+                monitored_value_dict[VALUE] = monitored_value.variable
+                # monitored_value_dict[PARAMS] = monitored_value.params
+            else:
+                raise ObjectiveMechanismError("PROGRAM ERROR: call to State._parse_state_spec() for {} of {} "
+                                              "should have returned dict or State, but returned {} instead".
+                                              format(OUTPUT_STATE, self.name, type(monitored_value)))
+            monitored_value_dict[OUTPUT_STATE]=value
+            monitored_value_dict[NAME] = monitored_value_dict[NAME] + MONITORED_VALUE_NAME_SUFFIX
+            monitored_values.append(monitored_value_dict)
+
+        # If input_states were not specified, assign value of monitored_valued for each
+        #    (to invoke a default assignment for each input_state)
+        if self.input_states is None:
+            self._input_states = [m[VALUE] for m in monitored_values]
+
+        # Parse input_states into a state specification dict, passing monitored_values as defaults from monitored_value
+        for i, input_state, monitored_value in zip(range(len(self.input_states)),
+                                                   self.input_states,
+                                                   monitored_values):
+
+            # Parse input_state to determine its specifications and assign values from monitored_values
+            #    to any missing specifications, including any projections requested.
+            self._input_states[i] = _parse_state_spec(self,
+                                                      state_type=InputState,
+                                                      state_spec=input_state,
+                                                      name=monitored_values[i][NAME],
+                                                      value=monitored_values[i][VALUE])
+
+        constraint_value = []
+        for input_state in self.input_states:
+            constraint_value.append(input_state[VARIABLE])
+        self.variable = constraint_value
+
+        super()._instantiate_input_states(context=context)
+
+        # self.variableClassDefault = self.variable.copy()
+
+        # Get any projections specified in input_states arg, else set to default (AUTO_ASSIGN_MATRIX)
+        input_state_projection_specs = []
+        for i, state in enumerate(self._input_states):
+            input_state_projection_specs.append(state.params[STATE_PROJECTIONS] or [AUTO_ASSIGN_MATRIX])
+
+        # IMPLEMENTATION NOTE:  THIS SHOULD BE MOVED TO COMPOSITION ONCE THAT IS IMPLEMENTED
+        _instantiate_monitoring_projections(owner=self,
+                                            sender_list=[mon_val[OUTPUT_STATE] for mon_val in monitored_values],
+                                            receiver_list=self.input_states,
+                                            receiver_projection_specs=input_state_projection_specs,
+                                            context=context)
+
+def _validate_monitored_value(objective_mech, state_spec, context=None):
+    """Validate specification for monitored_value arg
+
+    Validate that each item of monitored_value arg is: 
+        * OutputState
+        * Mechanism, 
+        * string, or 
+        * MonitoredOutpuStatesOption value.
+    
+    Called by both self._validate_variable(), self.add_monitored_value(), and EVCMechanism._get_monitored_states()
+    """
+    from PsyNeuLink.Components.States.OutputState import OutputState
+    if not isinstance(state_spec, (str, OutputState, Mechanism, MonitoredOutputStatesOption, dict)):
+        raise ObjectiveMechanismError("Specification of {} arg for {} ({}) must be"
+                             "an OutputState, Mechanism, or a MonitoredOutputStatesOption value".
+                             format(MONITORED_VALUES, objective_mech.name, state_spec))
+
+
+def _objective_mechanism_role(mech, role):
+    if isinstance(mech, ObjectiveMechanism):
+        if mech._role is role:
+            return True
+        else:
+            return False
+    else:
+        return False
+
+
+# IMPLEMENTATION NOTE: THIS IS A PLACEMARKER FOR A METHOD TO BE IMPLEMENTED IN THE Composition CLASS
+#                      ??MAYBE INTEGRATE INTO State MODULE (IN _instantate_state)
+@tc.typecheck
+def _instantiate_monitoring_projections(owner,
+                                        sender_list:tc.any(list, ContentAddressableList),
+                                        receiver_list:tc.any(list, ContentAddressableList),
+                                        receiver_projection_specs:tc.optional(list)=None,
+                                        context=None):
+
+    from PsyNeuLink.Components.States.OutputState import OutputState
+    from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
+
+    receiver_projection_specs = receiver_projection_specs or [DEFAULT_MATRIX] * len(sender_list)
+
+    if len(sender_list) != len(receiver_list):
+        raise ObjectiveMechanismError("PROGRAM ERROR: Number of senders ({}) does not equal number of receivers ({}) "
+                                     "in call to instantiate monitoring projections for {}".
+                                     format(len(sender_list), len(receiver_list), owner.name))
+
+    if len(receiver_projection_specs) != len(receiver_list):
+        raise ObjectiveMechanismError("PROGRAM ERROR: Number of projection specs ({}) "
+                                     "does not equal number of receivers ({}) "
+                                     "in call to instantiate monitoring projections for {}".
+                                     format(len(receiver_projection_specs), len(receiver_list), owner.name))
+
+    # Instantiate inputState with projection from outputState specified by sender
+    for sender, receiver, recvr_projs in zip(sender_list, receiver_list, receiver_projection_specs):
+
+        # IMPLEMENTATION NOTE:  If there is more than one projection specified for a receiver, only the 1st is used;
+        #                           (there should only be one if a 2-item tuple was used to specify the inputState,
+        #                            however other forms of specifications could produce more)
+        if len(recvr_projs) > 1 and owner.verbosePref:
+            warnings.warn("{} projections were specified for inputState ({}) of {} ;"
+                          "only the first ({}) will be used".
+                          format(len(recvr_projs), receiver.name, owner.name))
+        projection_spec = recvr_projs[0]
+
+        # IMPLEMENTATION NOTE:  This may not handle situations properly in which the outputState is specified
+        #                           by a 2-item tuple (i.e., with a projection specification as its second item)
+        if isinstance(sender, OutputState):
+            # Projection has been specified for receiver and initialization begun, so call deferred_init()
+            if receiver.path_afferents:
+                if not receiver.path_afferents[0].value is DEFERRED_INITIALIZATION:
+                    raise ObjectiveMechanismError("PROGRAM ERROR: {} of {} already has an afferent projection "
+                                                  "implemented and initialized ({})".
+                                                  format(receiver.name, owner.name, receiver.aferents[0].name))
+                if not receiver.path_afferents[0].function_params[MATRIX] is projection_spec:
+                    raise ObjectiveMechanismError("PROGRAM ERROR: Projection specification for {} of {} ({}) "
+                                                  "does not match matrix already assigned ({})".
+                                                  format(receiver.name,
+                                                         owner.name,
+                                                         projection_spec,
+                                                         receiver.path_afferents[0].function_params[MATRIX]))
+                receiver.path_afferents[0].init_args[SENDER] = sender
+                receiver.path_afferents[0]._deferred_init()
+            else:
+                MappingProjection(sender=sender,
+                                  receiver=receiver,
+                                  matrix=projection_spec,
+                                  name = sender.name + ' monitor')
diff --git a/tests/__init__.py b/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/ObjectiveMechanisms/__init__.py
similarity index 100%
rename from tests/__init__.py
rename to PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/ObjectiveMechanisms/__init__.py
diff --git a/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/ProcessingMechanism.py b/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/ProcessingMechanism.py
index 3c9cda0976e..08f0dd6fbeb 100644
--- a/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/ProcessingMechanism.py
+++ b/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/ProcessingMechanism.py
@@ -78,11 +78,13 @@ class ProcessingMechanism_Base(Mechanism_Base):
     #     kp<pref>: <setting>...}
 
     # variableClassDefault = defaultControlAllocation
-    # This must be a list, as there may be more than one (e.g., one per controlSignal)
+    # This must be a list, as there may be more than one (e.g., one per control_signal)
     variableClassDefault = defaultControlAllocation
 
     def __init__(self,
                  variable=None,
+                 input_states=None,
+                 output_states=None,
                  params=None,
                  name=None,
                  prefs=None,
@@ -99,6 +101,8 @@ def __init__(self,
         self.system = None
 
         super().__init__(variable=variable,
+                         input_states=input_states,
+                         output_states=output_states,
                          params=params,
                          name=name,
                          prefs=prefs,
diff --git a/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/RecurrentTransferMechanism.py b/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/RecurrentTransferMechanism.py
new file mode 100644
index 00000000000..b89d620acf4
--- /dev/null
+++ b/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/RecurrentTransferMechanism.py
@@ -0,0 +1,479 @@
+# Princeton University licenses this file to You under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.  You may obtain a copy of the License at:
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed
+# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and limitations under the License.
+
+# NOTES:
+#  * COULD NOT IMPLEMENT integrator_function in paramClassDefaults (see notes below)
+#  * NOW THAT NOISE AND TIME_CONSTANT ARE PROPRETIES THAT DIRECTLY REFERERNCE integrator_function,
+#      SHOULD THEY NOW BE VALIDATED ONLY THERE (AND NOT IN TransferMechanism)??
+#  * ARE THOSE THE ONLY TWO integrator PARAMS THAT SHOULD BE PROPERTIES??
+
+# ****************************************  RecurrentTransferMechanism *************************************************
+
+"""
+
+Overview
+--------
+
+A RecurrentTransferMechanism is a subclass of `TransferMechanism` that implements a single-layered recurrent 
+network, in which each element is connected to every other element (instantiated in a recurrent MappingProjection
+referenced by the mechanism's `matrix <RecurrentTransferMechanism.matrix>` parameter).  It also allows its
+previous input to be decayed, and reports the energy and, if appropriate, the entropy of its output.
+  
+.. _Recurrent_Transfer_Creation:
+
+Creating a RecurrentTransferMechanism
+-------------------------------------
+
+A RecurrentTransferMechanism can be created directly by calling its constructor, or using the 
+`mechanism() <Mechanism.mechanism>` function and specifying RECURRENT_TRANSFER_MECHANISM as its 
+**mech_spec** argument.  The recurrent projection is created using the **matrix** argument of the mechanism's 
+constructor, which must specify either a square matrix or a `MappingProjection` that uses one (the default is 
+`FULL_CONNECTIVITY_MATRIX`).  In all other respects, a RecurrentTransferMechanism is specified in the same way as a 
+standard `TransferMechanism`.
+
+.. _Recurrent_Transfer_Structure:
+
+Structure
+---------
+
+The distinguishing feature of a RecurrentTransferMechanism is its `matrix <RecurrentTransferMechanism.matrix>` 
+parameter, which specifies a self-projecting MappingProjection;  that is, one that projects from the mechanism's 
+`primary outputState <OutputState_Primary>` back to it `primary inputState <Mechanism_InputStates>`.  
+In all other respects the mechanism is identical to a standard `TransferMechanism`.  
+
+In addition, a RecurrentTransferMechanism also has a `decay` <RecurrentTransferMechanism.decay>' parameter, 
+that decrements its `previous_input <TransferMechanism.previous_input>` value by the specified factor in each 
+`round of execution <LINK>`.  It also has two additional outputStates:  an ENERGY outputState and, if its 
+`function <TransferMechanisms.function>` is bounded between 0 and 1 (e.g., a `Logistic` function), an ENTROPY
+outputState, that each report the  respective values of the vector in it its 
+`primary (RESULTS) outputState <OutputState_Primary>`.
+ 
+.. _Recurrent_Transfer_Execution:
+
+Execution
+---------
+
+When a RecurrentTransferMechanism executes, it includes in its input the value of its 
+`primary outputState <OutputState_Primary>` from the last :ref:`round of execution <LINK>`.
+
+Like a `TransferMechanism`, the function used to update each element can be assigned using its
+`function <TransferMechanism.function>` parameter.  When a RecurrentTransferMechanism is executed,
+if its `decay <RecurrentTransferMechanism.decay>` parameter is specified (and is not 1.0), it 
+decays the value of its `previous_input <TransferMechanism.previous_input>` parameter by the
+specified factor.  It then transforms its input (including from the recurrent projection) using the specified 
+function and parameters (see `Transfer_Execution`), and returns the results in its outputStates.
+
+.. _Recurrent_Transfer_Class_Reference:
+
+Class Reference
+---------------
+
+
+"""
+
+from PsyNeuLink.Components.Functions.Function import get_matrix, Stability
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import *
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
+from PsyNeuLink.Components.States.OutputState import StandardOutputStates, PRIMARY_OUTPUT_STATE
+
+
+class RecurrentTransferError(Exception):
+    def __init__(self, error_value):
+        self.error_value = error_value
+
+    def __str__(self):
+        return repr(self.error_value)
+
+DECAY = 'decay'
+
+# This is a convenience class that provides list of standard_output_state names in IDE
+class RECURRENT_OUTPUT():
+        RESULT=RESULT
+        MEAN=MEAN
+        MEDIAN=MEDIAN
+        STANDARD_DEV=STANDARD_DEV
+        VARIANCE=VARIANCE
+        ENERGY=ENERGY
+        ENTROPY=ENTROPY
+# THIS WOULD HAVE BEEN NICE, BUT IDE DOESN'T EXECUTE IT, SO NAMES DON'T SHOW UP
+# for item in [item[NAME] for item in DDM_standard_output_states]:
+#     setattr(DDM_OUTPUT.__class__, item, item)
+
+
+# IMPLEMENTATION NOTE:  IMPLEMENTS OFFSET PARAM BUT IT IS NOT CURRENTLY BEING USED
+class RecurrentTransferMechanism(TransferMechanism):
+    """
+    RecurrentTransferMechanism(        \
+    default_input_value=None,          \
+    function=Linear,                   \
+    matrix=FULL_CONNECTIVITY_MATRIX,   \
+    initial_value=None,                \
+    decay=None,                        \
+    noise=0.0,                         \
+    time_constant=1.0,                 \
+    range=(float:min, float:max),      \
+    time_scale=TimeScale.TRIAL,        \
+    params=None,                       \
+    name=None,                         \
+    prefs=None)
+
+    Implements RecurrentTransferMechanism subclass of `TransferMechaism`.
+
+    COMMENT:
+        Description
+        -----------
+            RecurrentTransferMechanism is a Subtype of the TransferMechanism Subtype of the ProcessingMechanisms Type 
+            of the Mechanism Category of the Component class.
+            It implements a TransferMechanism with a recurrent projection (default matrix: FULL_CONNECTIVITY_MATRIX).
+            In all other respects, it is identical to a TransferMechanism.
+    COMMENT
+
+    Arguments
+    ---------
+
+    default_input_value : number, list or np.ndarray : default Transfer_DEFAULT_BIAS
+        specifies the input to the mechanism to use if none is provided in a call to its
+        `execute <Mechanism.Mechanism_Base.execute>` or `run <Mechanism.Mechanism_Base.run>` method;
+        also serves as a template to specify the length of `variable <TransferMechanism.variable>` for
+        `function <TransferMechanism.function>`, and the `primary outputState <OutputState_Primary>`
+        of the mechanism.
+
+    function : TransferFunction : default Linear
+        specifies the function used to transform the input;  can be `Linear`, `Logistic`, `Exponential`,
+        or a custom function.
+
+    matrix : list, np.ndarray, np.matrix, function keyword, or MappingProjection : default FULL_CONNECTIVITY_MATRIX
+        specifies the matrix to use for creating a `recurrent MappingProjection <Recurrent_Transfer_Structure>`, 
+        or a MappingProjection to use. 
+
+    decay : number : default 1.0
+        specifies the amount by which to decrement its `previous_input <TransferMechanism.previous_input>`
+        in each round of execution.
+
+    initial_value :  value, list or np.ndarray : default Transfer_DEFAULT_BIAS
+        specifies the starting value for time-averaged input (only relevant if
+        `time_constant <TransferMechanism.time_constant>` is not 1.0).
+        :py:data:`Transfer_DEFAULT_BIAS <LINK->SHOULD RESOLVE TO VALUE>`
+
+    noise : float or function : default 0.0
+        a stochastically-sampled value added to the result of the `function <TransferMechanism.function>`:
+        if it is a float, it must be in the interval [0,1] and is used to scale the variance of a zero-mean Gaussian;
+        if it is a function, it must return a scalar value.
+
+    time_constant : float : default 1.0
+        the time constant for exponential time averaging of input when the mechanism is executed with `time_scale`
+        set to `TimeScale.TIME_STEP`::
+
+         result = (time_constant * current input) + (1-time_constant * result on previous time_step)
+
+    range : Optional[Tuple[float, float]]
+        specifies the allowable range for the result of `function <TransferMechanism.function>`:
+        the first item specifies the minimum allowable value of the result, and the second its maximum allowable value;
+        any element of the result that exceeds the specified minimum or maximum value is set to the value of
+        `range <TransferMechanism.range>` that it exceeds.
+
+    params : Optional[Dict[param keyword, param value]]
+        a `parameter dictionary <ParameterState_Specifying_Parameters>` that can be used to specify the parameters for
+        the mechanism, its function, and/or a custom function and its parameters.  Values specified for parameters in
+        the dictionary override any assigned to those parameters in arguments of the constructor.
+
+    time_scale :  TimeScale : TimeScale.TRIAL
+        specifies whether the mechanism is executed using the `TIME_STEP` or `TRIAL` `TimeScale`.
+        This must be set to `TimeScale.TIME_STEP` for the `time_constant <TransferMechanism.time_constant>`
+        parameter to have an effect.
+
+    name : str : default TransferMechanism-<index>
+        a string used for the name of the mechanism.
+        If not is specified, a default is assigned by `MechanismRegistry`
+        (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
+
+    prefs : Optional[PreferenceSet or specification dict : Mechanism.classPreferences]
+        the `PreferenceSet` for mechanism.
+        If it is not specified, a default is assigned using `classPreferences` defined in __init__.py
+        (see :doc:`PreferenceSet <LINK>` for details).
+
+    .. context=componentType+INITIALIZING):
+            context : str : default ''None''
+                   string used for contextualization of instantiation, hierarchical calls, executions, etc.
+
+    Attributes
+    ----------
+
+    variable : value
+        the input to mechanism's `function <RecurrentTransferMechanism.variable>`.
+
+    function : Function
+        the function used to transform the input.
+
+    matrix : 2d np.array
+        the `matrix <MappingProjection.matrix>` parameter of the `recurrent_projection` for the mechanism.
+
+    recurrent_projection : MappingProjection
+        a `MappingProjection` that projects from the mechanism's `primary outputState <OutputState_Primary>` 
+        back to it `primary inputState <Mechanism_InputStates>`.
+
+    decay : float : default 1.0
+        determines the amount by which to multiply the `previous_input <TransferMechanism.previous_input>` value
+        in each round of execution.
+
+    COMMENT:
+       THE FOLLOWING IS THE CURRENT ASSIGNMENT
+    COMMENT
+    initial_value :  value, list or np.ndarray : Transfer_DEFAULT_BIAS
+        determines the starting value for time-averaged input
+        (only relevant if `time_constant <TransferMechanism.time_constant>` parameter is not 1.0).
+        :py:data:`Transfer_DEFAULT_BIAS <LINK->SHOULD RESOLVE TO VALUE>`
+
+    noise : float or function : default 0.0
+        a stochastically-sampled value added to the output of the `function <TransferMechahnism.function>`:
+        if it is a float, it must be in the interval [0,1] and is used to scale the variance of a zero-mean Gaussian;
+        if it is a function, it must return a scalar value.
+
+    time_constant : float
+        the time constant for exponential time averaging of input
+        when the mechanism is executed using the `TIME_STEP` `TimeScale`::
+
+          result = (time_constant * current input) + (1-time_constant * result on previous time_step)
+
+    range : Tuple[float, float]
+        determines the allowable range of the result: the first value specifies the minimum allowable value
+        and the second the maximum allowable value;  any element of the result that exceeds minimum or maximum
+        is set to the value of `range <TransferMechanism.range>` it exceeds.  If `function <TransferMechanism.function>`
+        is `Logistic`, `range <TransferMechanism.range>` is set by default to (0,1).
+
+    previous_input : 1d np.array of floats
+        the value of the input on the previous round of execution, including the value of `recurrent_projection`. 
+
+    value : 2d np.array [array(float64)]
+        result of executing `function <TransferMechanism.function>`; same value as fist item of
+        `output_values <TransferMechanism.output_values>`.    
+
+    COMMENT:
+        CORRECTED:
+        value : 1d np.array
+            the output of ``function``;  also assigned to ``value`` of the TRANSFER_RESULT outputState
+            and the first item of ``output_values``.
+    COMMENT
+
+    outputStates : Dict[str, OutputState]
+        an OrderedDict with the following `outputStates <OutputState>`:
+        * `TRANSFER_RESULT`, the :keyword:`value` of which is the **result** of `function <TransferMechanism.function>`;
+        * `TRANSFER_MEAN`, the :keyword:`value` of which is the mean of the result;
+        * `TRANSFER_VARIANCE`, the :keyword:`value` of which is the variance of the result;
+        * `ENERGY`, the :keyword:`value` of which is the energy of the result, 
+          calculated using the `Stability` Function with the ENERGY metric;
+        * `ENTROPY`, the :keyword:`value` of which is the entropy of the result,
+          calculated using the `Stability` Function with the ENTROPY metric; 
+          note:  this is only present if the mechanism's :keyword:`function` is bounded between 0 and 1 
+          (e.g., the `Logistic` function).
+
+    output_values : List[array(float64), float, float]
+        a list with the following items:
+        * **result** of the ``function`` calculation (value of TRANSFER_RESULT outputState);
+        * **mean** of the result (``value`` of TRANSFER_MEAN outputState)
+        * **variance** of the result (``value`` of TRANSFER_VARIANCE outputState);
+        * **energy** of the result (``value`` of ENERGY outputState);
+        * **entropy** of the result (if the ENTROPY outputState is present).
+
+    time_scale :  TimeScale
+        specifies whether the mechanism is executed using the `TIME_STEP` or `TRIAL` `TimeScale`.
+
+    name : str : default TransferMechanism-<index>
+        the name of the mechanism.
+        Specified in the **name** argument of the constructor for the projection;
+        if not is specified, a default is assigned by `MechanismRegistry`
+        (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
+
+    prefs : PreferenceSet or specification dict : Mechanism.classPreferences
+        the `PreferenceSet` for mechanism.
+        Specified in the **prefs** argument of the constructor for the mechanism;
+        if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
+        (see :doc:`PreferenceSet <LINK>` for details).
+
+    Returns
+    -------
+    instance of RecurrentTransferMechanism : RecurrentTransferMechanism
+
+    """
+    componentType = RECURRENT_TRANSFER_MECHANISM
+
+    paramClassDefaults = TransferMechanism.paramClassDefaults.copy()
+
+    standard_output_states = TransferMechanism.standard_output_states.copy()
+    standard_output_states.extend([{NAME:ENERGY}, {NAME:ENTROPY}])
+
+    @tc.typecheck
+    def __init__(self,
+                 default_input_value=None,
+                 size:tc.optional(int)=None,
+                 input_states:tc.optional(tc.any(list, dict))=None,
+                 matrix=FULL_CONNECTIVITY_MATRIX,
+                 function=Linear,
+                 initial_value=None,
+                 decay:is_numeric_or_none=None,
+                 noise:is_numeric_or_none=0.0,
+                 time_constant:is_numeric_or_none=1.0,
+                 range=None,
+                 output_states:tc.optional(tc.any(list, dict))=[RESULT],
+                 time_scale=TimeScale.TRIAL,
+                 params=None,
+                 name=None,
+                 prefs:is_pref_set=None,
+                 context=componentType+INITIALIZING):
+        """Instantiate RecurrentTransferMechanism
+        """
+
+        # Assign args to params and functionParams dicts (kwConstants must == arg names)
+        params = self._assign_args_to_param_dicts(input_states=input_states,
+                                                  matrix=matrix,
+                                                  decay=decay,
+                                                  output_states=output_states,
+                                                  params=params)
+        self.size = size
+
+        if not isinstance(self.standard_output_states, StandardOutputStates):
+            self.standard_output_states = StandardOutputStates(self,
+                                                               self.standard_output_states,
+                                                               indices=PRIMARY_OUTPUT_STATE)
+
+        super().__init__(default_input_value=default_input_value,
+                         size=size,
+                         input_states=input_states,
+                         function=function,
+                         initial_value=initial_value,
+                         noise=noise,
+                         time_constant=time_constant,
+                         range=range,
+                         output_states=output_states,
+                         time_scale=time_scale,
+                         params=params,
+                         name=name,
+                         prefs=prefs,
+                         context=context)
+
+    def _validate_params(self, request_set, target_set=None, context=None):
+        """Validate shape and size of matrix and decay.
+        """
+
+        super()._validate_params(request_set=request_set, target_set=target_set, context=context)
+
+        # Validate MATRIX
+        if MATRIX in target_set:
+
+            matrix_param = target_set[MATRIX]
+            size = len(self.variable[0])
+
+            if isinstance(matrix_param, MappingProjection):
+                matrix = matrix_param.matrix
+
+            elif isinstance(matrix_param, str):
+                matrix = get_matrix(matrix_param, size, size)
+
+            else:
+                matrix = matrix_param
+
+            rows = np.array(matrix).shape[0]
+            cols = np.array(matrix).shape[1]
+
+            # Shape of matrix must be square
+            if rows != cols:
+                if (matrix_param, MappingProjection):
+                    # if __name__ == '__main__':
+                    err_msg = ("{} param of {} must be square to be used as recurrent projection for {}".
+                               format(MATRIX, matrix_param.name, self.name))
+                else:
+                    err_msg = "{} param for must be square".format(MATRIX, self.name)
+                raise RecurrentTransferError(err_msg)
+
+            # Size of matrix must equal length of variable:
+            if rows != size:
+                if (matrix_param, MappingProjection):
+                    # if __name__ == '__main__':
+                    err_msg = ("Size of {} param for {} ({}) must be same as variable for {} ({})"
+                               "to be used as recurrent projection for {}".
+                               format(MATRIX, matrix_param.name, rows, self.name, size))
+                else:
+                    err_msg = ("Size of {} param for {} ({}) must same as its variable ({})".
+                               format(MATRIX, self.name, rows, size))
+                raise RecurrentTransferError(err_msg)
+
+
+        if DECAY in target_set and target_set[DECAY] is not None:
+
+            decay = target_set[DECAY]
+            if not (0.0 <= decay and decay <= 1.0):
+                raise RecurrentTransferError("{} argument for {} ({}) must be from 0.0 to 1.0".
+                                             format(DECAY, self.name, decay))
+
+    def _instantiate_attributes_after_function(self, context=None):
+        """Instantiate recurrent_projection, matrix, and the functions for the ENERGY and ENTROPY outputStates
+        """
+
+        super()._instantiate_attributes_after_function(context=context)
+
+        if isinstance(self.matrix, MappingProjection):
+            self.recurrent_projection = self.matrix
+
+        else:
+            self.recurrent_projection = _instantiate_recurrent_projection(self, self.matrix, context=context)
+
+        self._matrix = self.recurrent_projection.matrix
+
+        if ENERGY in self.output_states.names:
+            energy = Stability(self.variable[0],
+                               metric=ENERGY,
+                               transfer_fct=self.function,
+                               matrix=self.recurrent_projection._parameter_states[MATRIX])
+            self.output_states[ENERGY]._calculate = energy.function
+
+        if ENTROPY in self.output_states.names:
+            if self.function_object.bounds == (0,1) or range == (0,1):
+                entropy = Stability(self.variable[0],
+                                    metric=ENTROPY,
+                                    transfer_fct=self.function,
+                                    matrix=self.recurrent_projection._parameter_states[MATRIX])
+                self.output_states[ENTROPY]._calculate = entropy.function
+            else:
+                del self.output_states[ENTROPY]
+
+    def _execute(self,
+                 variable=None,
+                 runtime_params=None,
+                 clock=CentralClock,
+                 time_scale = TimeScale.TRIAL,
+                 context=None):
+        """Implement decay
+        """
+
+        if self.decay is not None and self.decay != 1.0:
+            self.previous_input *= self.decay
+
+        return super()._execute(variable=variable,
+                                runtime_params=runtime_params,
+                                clock=CentralClock,
+                                time_scale=time_scale,
+                                context=context)
+
+
+# IMPLEMENTATION NOTE:  THIS SHOULD BE MOVED TO COMPOSITION ONCE THAT IS IMPLEMENTED
+@tc.typecheck
+def _instantiate_recurrent_projection(mech:Mechanism_Base,
+                                      matrix:is_matrix=FULL_CONNECTIVITY_MATRIX,
+                                      context=None):
+    """Instantiate a MappingProjection from mech to itself
+
+    """
+
+    if isinstance(matrix, str):
+        size = len(mech.variable[0])
+        matrix = get_matrix(matrix, size, size)
+
+    return MappingProjection(sender=mech,
+                             receiver=mech,
+                             matrix=matrix,
+                             name = mech.name + ' recurrent projection')
+
diff --git a/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/TransferMechanism.py b/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/TransferMechanism.py
index 45ad4f33e2b..fad0498b335 100644
--- a/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/TransferMechanism.py
+++ b/PsyNeuLink/Components/Mechanisms/ProcessingMechanisms/TransferMechanism.py
@@ -36,9 +36,9 @@
 Creating a TransferMechanism
 -----------------------------
 
-A TransferMechanism can be created directly by calling its constructor, or using the :py:func:`mechanism`
-function and specifying `TRANSFER_MECHANISM` as its `mech_spec` argument.  Its function is specified in the
-:keyword:`function` argument, which can be simply the name of the class (first example below), or a call to its
+A TransferMechanism can be created directly by calling its constructor, or using the `mechanism() <Mechanism.mechanism>`
+function and specifying TRANSFER_MECHANISM as its **mech_spec** argument.  Its function is specified in the
+**function** argument, which can be simply the name of the class (first example below), or a call to its
 constructor which can include arguments specifying the function's parameters (second example)::
 
     my_linear_transfer_mechanism = TransferMechanism(function=Linear)
@@ -83,14 +83,14 @@
 .. _Transfer_Results:
 
     * **result** of `function <TransferMechanism.function>` is assigned to the mechanism's
-      `value <TransferMechanism.value>` attribute, the :keyword:`value` of its `TRANSFER_RESULT` outputState,
-      and to the 1st item of the mechanism's `outputValue <TransferMechanism.outputValue>` attribute;
+      `value <TransferMechanism.value>` attribute, the :keyword:`value` of its TRANSFER_RESULT outputState,
+      and to the 1st item of the mechanism's `output_values <TransferMechanism.output_values>` attribute;
     ..
-    * **mean** of the result is assigned to the the :keyword:`value` of the mechanism's `TRANSFER_MEAN` outputState,
-      and to the 2nd item of its `outputValue <TransferMechanism.outputValue>` attribute;
+    * **mean** of the result is assigned to the the :keyword:`value` of the mechanism's TRANSFER_MEAN outputState,
+      and to the 2nd item of its `output_values <TransferMechanism.output_values>` attribute;
     ..
-    * **variance** of the result is assigned to the :keyword:`value` of the mechanism's `TRANSFER_VARIANCE` outputState,
-      and to the 3rd item of its `outputValue <TransferMechanism.outputValue>` attribute.
+    * **variance** of the result is assigned to the :keyword:`value` of the mechanism's TRANSFER_VARIANCE outputState,
+      and to the 3rd item of its `output_values <TransferMechanism.output_values>` attribute.
 
 COMMENT
 
@@ -103,30 +103,16 @@
 
 # from numpy import sqrt, random, abs, tanh, exp
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ProcessingMechanism import *
-from PsyNeuLink.Components.Functions.Function import Linear, TransferFunction, Integrator, NormalDist
+from PsyNeuLink.Components.Functions.Function import Linear, TransferFunction, AdaptiveIntegrator, NormalDist
+# from PsyNeuLink.Components.States.OutputState import *
+from PsyNeuLink.Components.States.OutputState \
+    import StandardOutputStates, standard_output_states, PRIMARY_OUTPUT_STATE
 
 # TransferMechanism parameter keywords:
 RANGE = "range"
 TIME_CONSTANT = "time_constant"
 INITIAL_VALUE = 'initial_value'
 
-# TransferMechanism outputs (used to create and name outputStates):
-TRANSFER_RESULT = "transfer_result"
-TRANSFER_MEAN = "transfer_mean "
-TRANSFER_VARIANCE = "transfer_variance"
-TRANSFER_DIFFERENTIAL = "transfer_differential"
-
-# TransferMechanism output indices (used to index output values):
-class Transfer_Output(AutoNumber):
-    """Indices of the `outputValue <TransferMechanism.outputValue>` attribute of the TransferMechanism containing the
-    values described below."""
-    RESULT = ()
-    """Result of the TransferMechanism's `function <TransferMechanism.function>`."""
-    MEAN = ()
-    """Mean of the elements in the :keyword`value` of the RESULT outputState."""
-    VARIANCE = ()
-    """Variance of the elements in the :keyword`value` of the RESULT outputState."""
-
 # TransferMechanism default parameter values:
 Transfer_DEFAULT_LENGTH= 1
 Transfer_DEFAULT_GAIN = 1
@@ -134,6 +120,17 @@ class Transfer_Output(AutoNumber):
 Transfer_DEFAULT_OFFSET = 0
 # Transfer_DEFAULT_RANGE = np.array([])
 
+# This is a convenience class that provides list of standard_output_state names in IDE
+class TRANSFER_OUTPUT():
+        RESULT=RESULT
+        MEAN=MEAN
+        MEDIAN=MEDIAN
+        STANDARD_DEV=STANDARD_DEV
+        VARIANCE=VARIANCE
+# THIS WOULD HAVE BEEN NICE, BUT IDE DOESN'T EXECUTE IT, SO NAMES DON'T SHOW UP
+# for item in [item[NAME] for item in DDM_standard_output_states]:
+#     setattr(DDM_OUTPUT.__class__, item, item)
+
 
 class TransferError(Exception):
     def __init__(self, error_value):
@@ -254,7 +251,14 @@ class TransferMechanism(ProcessingMechanism_Base):
     ----------
 
     variable : value: default Transfer_DEFAULT_BIAS
-        the input to mechanism's ``function``.  :py:data:`Transfer_DEFAULT_BIAS <LINK->SHOULD RESOLVE TO VALUE>`
+        the input to mechanism's `function <TransferMechanism.function>`.
+        COMMENT:
+            :py:data:`Transfer_DEFAULT_BIAS <LINK->SHOULD RESOLVE TO VALUE>`
+        COMMENT
+
+    size : int
+        length of the first (and only) item in `variable <TransferMechanism.variable>`
+        (i.e., the vector transformed by `function <TransferMechanism.function>`.
 
     function : Function :  default Linear
         the function used to transform the input.
@@ -284,15 +288,18 @@ class TransferMechanism(ProcessingMechanism_Base):
         is set to the value of `range <TransferMechanism.range>` it exceeds.  If `function <TransferMechanism.function>`
         is `Logistic`, `range <TransferMechanism.range>` is set by default to (0,1).
 
+    previous_input : float
+        the value of the `variable <TransferMechanism.variable>` on the `previous round of execution <LINK>`.
+
     value : 2d np.array [array(float64)]
         result of executing `function <TransferMechanism.function>`; same value as fist item of
-        `outputValue <TransferMechanism.outputValue>`.
+        `output_values <TransferMechanism.output_values>`.
 
     COMMENT:
         CORRECTED:
         value : 1d np.array
             the output of ``function``;  also assigned to ``value`` of the TRANSFER_RESULT outputState
-            and the first item of ``outputValue``.
+            and the first item of ``output_values``.
     COMMENT
 
     outputStates : Dict[str, OutputState]
@@ -301,24 +308,24 @@ class TransferMechanism(ProcessingMechanism_Base):
         * `TRANSFER_MEAN`, the :keyword:`value` of which is the mean of the result;
         * `TRANSFER_VARIANCE`, the :keyword:`value` of which is the variance of the result;
 
-    outputValue : List[array(float64), float, float]
+    output_values : List[array(float64), float, float]
         a list with the following items:
-        * **result** of the ``function`` calculation (value of `TRANSFER_RESULT` outputState);
-        * **mean** of the result (``value`` of `TRANSFER_MEAN` outputState)
-        * **variance** of the result (``value`` of `TRANSFER_VARIANCE` outputState)
+        * **result** of the ``function`` calculation (value of TRANSFER_RESULT outputState);
+        * **mean** of the result (``value`` of TRANSFER_MEAN outputState)
+        * **variance** of the result (``value`` of TRANSFER_VARIANCE outputState)
 
     time_scale :  TimeScale : defaul tTimeScale.TRIAL
         specifies whether the mechanism is executed using the `TIME_STEP` or `TRIAL` `TimeScale`.
 
     name : str : default TransferMechanism-<index>
         the name of the mechanism.
-        Specified in the `name` argument of the constructor for the projection;
+        Specified in the **name** argument of the constructor for the projection;
         if not is specified, a default is assigned by `MechanismRegistry`
         (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
 
     prefs : PreferenceSet or specification dict : Mechanism.classPreferences
         the `PreferenceSet` for mechanism.
-        Specified in the `prefs` argument of the constructor for the mechanism;
+        Specified in the **prefs** argument of the constructor for the mechanism;
         if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
         (see :doc:`PreferenceSet <LINK>` for details).
 
@@ -339,53 +346,52 @@ class TransferMechanism(ProcessingMechanism_Base):
 
     # TransferMechanism parameter and control signal assignments):
     paramClassDefaults = Mechanism_Base.paramClassDefaults.copy()
-    paramClassDefaults.update({
-        # TIME_SCALE: TimeScale.TRIAL,
-        NOISE: None,
-        INPUT_STATES: None,
-        OUTPUT_STATES:[
-            {NAME:TRANSFER_RESULT},
-            {NAME:TRANSFER_MEAN,
-             CALCULATE:lambda x: np.mean(x)},
-            {NAME:TRANSFER_VARIANCE,
-             CALCULATE:lambda x: np.var(x)}],
-        # INTEGRATOR_FUNCTION: Integrator
-        })
+    paramClassDefaults.update({NOISE: None})
+
+    standard_output_states = standard_output_states.copy()
 
     paramNames = paramClassDefaults.keys()
 
     @tc.typecheck
     def __init__(self,
-                 default_input_value=None,
+                 default_input_value=Transfer_DEFAULT_BIAS,
+                 size:tc.optional(int)=None,
+                 input_states:tc.optional(tc.any(list, dict))=None,
                  function=Linear,
                  initial_value=None,
                  noise=0.0,
                  time_constant=1.0,
                  range=None,
+                 output_states:tc.optional(tc.any(list, dict))=[RESULT],
                  time_scale=TimeScale.TRIAL,
                  params=None,
                  name=None,
                  prefs:is_pref_set=None,
                  context=componentType+INITIALIZING):
         """Assign type-level preferences, default input value (Transfer_DEFAULT_BIAS) and call super.__init__
-
-        :param default_input_value: (value)
-        :param params: (dict)
-        :param name: (str)
-        :param prefs: (PreferenceSet)
         """
+
         # Assign args to params and functionParams dicts (kwConstants must == arg names)
         params = self._assign_args_to_param_dicts(function=function,
                                                   initial_value=initial_value,
+                                                  input_states=input_states,
+                                                  output_states=output_states,
                                                   noise=noise,
                                                   time_constant=time_constant,
                                                   time_scale=time_scale,
                                                   range=range,
                                                   params=params)
-        if default_input_value is None:
-            default_input_value = Transfer_DEFAULT_BIAS
+        # if default_input_value is None:
+        #     default_input_value = Transfer_DEFAULT_BIAS
+        self.size = size
+
         self.integrator_function=None
 
+        if not isinstance(self.standard_output_states, StandardOutputStates):
+            self.standard_output_states = StandardOutputStates(self,
+                                                               self.standard_output_states,
+                                                               indices=PRIMARY_OUTPUT_STATE)
+
         super(TransferMechanism, self).__init__(variable=default_input_value,
                                                 params=params,
                                                 name=name,
@@ -415,9 +421,9 @@ def _validate_params(self, request_set, target_set=None, context=None):
                 transfer_function_class = transfer_function
                 transfer_function_name = transfer_function.__name__
 
-            if not transfer_function_class.componentType is TRANFER_FUNCTION_TYPE:
+            if not transfer_function_class.componentType is TRANSFER_FUNCTION_TYPE:
                 raise TransferError("Function {} specified as FUNCTION param of {} must be a {}".
-                                    format(transfer_function_name, self.name, TRANFER_FUNCTION_TYPE))
+                                    format(transfer_function_name, self.name, TRANSFER_FUNCTION_TYPE))
 
         # Validate INITIAL_VALUE
         if INITIAL_VALUE in target_set:
@@ -496,6 +502,7 @@ def _validate_noise(self, noise):
                 for i in self.variable:
                     new_noise.append(self.noise)
                 noise = new_noise
+
         elif isinstance(noise, float):
             self.noise_function = False
         else:
@@ -518,6 +525,7 @@ def _instantiate_attributes_before_function(self, context=None):
         super()._instantiate_attributes_before_function(context=context)
 
         self.initial_value = self.initial_value or self.variableInstanceDefault
+        self.size = len(self.variable[0])
 
     def _execute(self,
                 variable=None,
@@ -527,7 +535,7 @@ def _execute(self,
                 context=None):
         """Execute TransferMechanism function and return transform of input
 
-        Execute TransferMechanism function on input, and assign to outputValue:
+        Execute TransferMechanism function on input, and assign to output_values:
             - Activation value for all units
             - Mean of the activation values across units
             - Variance of the activation values across units
@@ -539,7 +547,7 @@ def _execute(self,
         Arguments:
 
         # CONFIRM:
-        variable (float): set to self.value (= self.inputValue)
+        variable (float): set to self.value (= self.input_value)
         - params (dict):  runtime_params passed from Mechanism, used as one-time value for current execution:
             + NOISE (float)
             + TIME_CONSTANT (float)
@@ -582,7 +590,6 @@ def _execute(self,
 
         #endregion
 
-
         #region EXECUTE TransferMechanism FUNCTION ---------------------------------------------------------------------
 
         # FIX: NOT UPDATING self.previous_input CORRECTLY
@@ -593,15 +600,14 @@ def _execute(self,
 
             if not self.integrator_function:
 
-                self.integrator_function = Integrator(
-                                            self.inputState.value,
+                self.integrator_function = AdaptiveIntegrator(
+                                            self.variable,
                                             initializer = self.previous_input,
-                                            integration_type= ADAPTIVE,
                                             noise = self.noise,
                                             rate = self.time_constant
                                             )
 
-            current_input = self.integrator_function.execute(self.inputState.value,
+            current_input = self.integrator_function.execute(self.variable,
                                                         # Should we handle runtime params?
                                                              # params={INITIALIZER: self.previous_input,
                                                              #         INTEGRATION_TYPE: ADAPTIVE,
@@ -611,19 +617,22 @@ def _execute(self,
                                                              # name=Integrator.componentName + '_for_' + self.name
                                                              )
 
-
         elif time_scale is TimeScale.TRIAL:
-
             if self.noise_function:
                 if isinstance(noise, (list, np.ndarray)):
                     new_noise = []
                     for n in noise:
                         new_noise.append(n())
                     noise = new_noise
+                elif isinstance(variable, (list, np.ndarray)):
+                    new_noise = []
+                    for v in variable[0]:
+                        new_noise.append(noise())
+                    noise = new_noise
                 else:
                     noise = noise()
 
-            current_input = self.inputState.value + noise
+            current_input = self.input_state.value + noise
         else:
             raise MechanismError("time_scale not specified for TransferMechanism")
 
diff --git a/PsyNeuLink/Components/Process.py b/PsyNeuLink/Components/Process.py
index a56177f7e31..a2ea30c99aa 100644
--- a/PsyNeuLink/Components/Process.py
+++ b/PsyNeuLink/Components/Process.py
@@ -7,7 +7,7 @@
 
 
 # *********************************************  Process ***************************************************************
-#
+
 
 # *****************************************    PROCESS CLASS    ********************************************************
 
@@ -20,7 +20,7 @@
          * :ref:`Process_Pathway`
          * :ref:`Process_Mechanisms`
          * :ref:`Process_Projections`
-         * :ref:`Process_Input_And_Ouput`
+         * :ref:`Process_Input_And_Output`
          * :ref:`Process_Learning`
       * :ref:`Process_Execution`
       * :ref:`Process_Class_Reference`
@@ -144,7 +144,7 @@
     `IDENTITY_MATRIX` is used for the projection;  if the formats do not match, or
     `learning has been specified <Process_Learning>` either for the projection or the process,
     then a `FULL_CONNECTIVITY_MATRIX` is used. If the mechanism is the `ORIGIN` mechanism (i.e., first in the
-    `pathway`), a `ProcessInputState <Process_Input_And_Ouput>` will be used as the sender,
+    `pathway`), a `ProcessInputState <Process_Input_And_Output>` will be used as the sender,
     and an `IDENTITY_MATRIX` is used for the projection.
 
 
@@ -158,18 +158,18 @@
 When a process is created, a set of `ProcessInputStates <processInputStates>` and `MappingProjections
 <MappingProjection>` are automatically generated to transmit the process' input to its `ORIGIN` mechanism, as follows:
 
-* if the number of items in the `input` is the same as the number of `ORIGIN` inputStates:
+* if the number of items in the `input` is the same as the number of `ORIGIN` input_states:
     a MappingProjection is created for each value of the input to an inputState of the `ORIGIN` mechanism;
 
 * if the `input` has only one item but the `ORIGIN` mechanism has more than one inputState:
-    a single ProcessInputState is created with projections to each of the `ORIGIN` mechanism inputStates;
+    a single ProcessInputState is created with projections to each of the `ORIGIN` mechanism input_states;
 
 * if the `input` has more than one item but the `ORIGIN` mechanism has only one inputState:
     a ProcessInputState is created for each input item, and all project to the `ORIGIN` mechanism's inputState;
 
 * otherwise, if both the `input` and `ORIGIN` mechanism have more than one inputState, but the numbers are not equal:
     an error message is generated indicating that the there is an ambiguous mapping from the Process'
-    input value to `ORIGIN` mechanism's inputStates.
+    input value to `ORIGIN` mechanism's input_states.
 
 The output of a process is a 2d np.array containing the values of its `TERMINAL` mechanism's outputStates.
 
@@ -324,15 +324,19 @@
 
 import math
 import re
-from collections import Iterable
 
 import PsyNeuLink.Components
+import PsyNeuLink.Components
+from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.LearningMechanisms.LearningMechanism import LearningMechanism
 from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.LearningMechanisms.LearningMechanism import LearningMechanism
 from PsyNeuLink.Components.Mechanisms.Mechanism import *
-from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ObjectiveMechanism import ObjectiveMechanism
-from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection, _is_learning_spec
-from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
-from PsyNeuLink.Components.Projections.Projection import _is_projection_spec, _is_projection_subclass, _add_projection_to
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ObjectiveMechanisms.ObjectiveMechanism import \
+    ObjectiveMechanism
+from PsyNeuLink.Components.Projections.ModulatoryProjections.LearningProjection import LearningProjection, \
+    _is_learning_spec
+from PsyNeuLink.Components.Projections.Projection import _is_projection_spec, _is_projection_subclass, \
+    _add_projection_to
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
 from PsyNeuLink.Components.ShellClasses import *
 from PsyNeuLink.Components.States.ParameterState import ParameterState
 from PsyNeuLink.Components.States.State import _instantiate_state_list, _instantiate_state
@@ -622,7 +626,7 @@ class Process_Base(Process):
 
     processInputStates : Optional[List[ProcessInputState]]
         used to represent the input to the process, and transmit this to the inputState(s) of its `ORIGIN`
-        mechanism.  Each processInputState sends a MappingProjection to one or more inputStates of the
+        mechanism.  Each processInputState sends a MappingProjection to one or more input_states of the
         `ORIGIN` mechanism.
 
     input :  Optional[List[value] or ndarray]
@@ -639,7 +643,7 @@ class Process_Base(Process):
                   to receive the initial input to the Process.  However, this behavior can be modified with the process'
                   `clamp_input` attribute.
 
-    inputValue :  2d np.array : default ``variableInstanceDefault``
+    input_value :  2d np.array : default ``variableInstanceDefault``
         same as the :keyword:`variable` attribute of the process; contains the values of the ProcessInputStates in its
         `processInputStates` attribute.
 
@@ -668,28 +672,28 @@ class Process_Base(Process):
     outputState : State
         the `primary outputState <OutputState_Primary>` of the `TERMINAL` mechanism in the process.
 
-      .. _mech_tuples : List[MechanismTuple]
+      .. _mechs : List[MechanismTuple]
              :class:`MechanismTuple` for all mechanisms in the process, listed in the order specified in pathway.
              MechanismTuples are of the form: (mechanism, runtime_params, phase) where runtime_params is dictionary
              of {argument keyword: argument values} entries and phase is an int.
              Note:  the list includes monitoring mechanisms (used for learning).
 
       .. _allMechanisms : MechanismList
-             Contains all mechanisms in the system (based on _mech_tuples).
+             Contains all mechanisms in the system (based on _mechs).
 
-      .. _origin_mech_tuples : List[MechanismTuple]
+      .. _origin_mechs : List[MechanismTuple]
              Contains a tuple for the `ORIGIN` mechanism of the process.
              (Note:  the use of a list is for compatibility with the MechanismList object)
 
-      .. _terminal_mech_tuples : List[MechanismTuple]
+      .. _terminal_mechs : List[MechanismTuple]
              Contains a tuple for the `TERMINAL` mechanism of the process.
              (Note:  the use of a list is for compatibility with the MechanismList object)
 
-      .. _target_mech_tuples : List[MechanismTuple]
+      .. _target_mechs : List[MechanismTuple]
              Contains a tuple for the `TARGET` mechanism of the process.
              (Note:  the use of a list is for compatibility with the MechanismList object)
 
-      .. _monitoring_mech_tuples : List[MechanismTuple]
+      .. _monitoring_mechs : List[MechanismTuple]
              `MechanismTuples <Mechanism.MechanismTuples>` for all `MonitoringMechanism <MonitoringMechanisms>` in the
              process (used for learning).
 
@@ -712,7 +716,7 @@ class Process_Base(Process):
                   methods that are also used for systems.
 
         COMMENT:
-            based on _origin_mech_tuples;  process.input contains the input to `ORIGIN` mechanism.
+            based on _origin_mechs;  process.input contains the input to `ORIGIN` mechanism.
         COMMENT
 
     terminalMechanisms : MechanismList
@@ -722,13 +726,13 @@ class Process_Base(Process):
                   methods that are also used for systems.
 
         COMMENT:
-            based on _terminal_mech_tuples; process.output contains the output of the `TERMINAL` mechanism.
+            based on _terminal_mechs; process.output contains the output of the `TERMINAL` mechanism.
         COMMENT
 
     monitoringMechanisms : MechanismList
         a list of all of the monitoring mechanisms in the process.
 
-        .. based on _monitoring_mech_tuples
+        .. based on _monitoring_mechs
 
     targetMechanisms : MechanismList
         a list with the `TARGET` mechanism of the process.
@@ -737,7 +741,7 @@ class Process_Base(Process):
                   methods that are also used for systems.
 
         COMMENT:
-            based on _target_mech_tuples
+            based on _target_mechs
         COMMENT
 
     systems : List[System]
@@ -785,13 +789,13 @@ class Process_Base(Process):
 
     name : str : default Process-<index>
         the name of the process.
-        Specified in the `name` argument of the constructor for the process;
+        Specified in the **name** argument of the constructor for the process;
         if not is specified, a default is assigned by ProcessRegistry
         (see :ref:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
 
     prefs : PreferenceSet or specification dict : Process.classPreferences
         the `PreferenceSet` for the process.
-        Specified in the `prefs` argument of the constructor for the process;  if it is not specified, a default is
+        Specified in the **prefs** argument of the constructor for the process;  if it is not specified, a default is
         assigned using `classPreferences` defined in __init__.py (see :ref:`PreferenceSet <LINK>` for details).
 
 
@@ -811,11 +815,7 @@ class Process_Base(Process):
     #     kpReportOutputPref: PreferenceEntry(False, PreferenceLevel.INSTANCE)}
     # Use inputValueSystemDefault as default input to process
 
-    # # MODIFIED 10/2/16 OLD:
-    # variableClassDefault = inputValueSystemDefault
-    # MODIFIED 10/2/16 NEW:
     variableClassDefault = None
-    # MODIFIED 10/2/16 END
 
     paramClassDefaults = Component.paramClassDefaults.copy()
     paramClassDefaults.update({TIME_SCALE: TimeScale.TRIAL,
@@ -894,7 +894,7 @@ def _validate_variable(self, variable, context=None):
     def _validate_params(self, request_set, target_set=None, context=None):
         """Validate initial_values args
            Note: validation of target (for learning) is deferred until _instantiate_target since,
-                 if it doesn't have a TARGET mechanism (see _check_for_target_mechanism), 
+                 if it doesn't have a TARGET mechanism (see _check_for_target_mechanism),
                  it will not need a target.
         """
 
@@ -938,7 +938,7 @@ def _instantiate_function(self, context=None):
             super(Process_Base, self)._instantiate_function(context=context)
         # Otherwise, just set Process output info to the corresponding info for the last mechanism in the pathway
         else:
-            self.value = self.pathway[-1][OBJECT_ITEM].outputState.value
+            self.value = self.pathway[-1].output_state.value
 
 # DOCUMENTATION:
 #         Uses paramClassDefaults[PATHWAY] == [Mechanism_Base.defaultMechanism] as default
@@ -963,7 +963,7 @@ def _instantiate_pathway(self, context):
         Iterate through Pathway, assigning Projections to Mechanisms:
             - first Mechanism in Pathway:
                 if it does NOT already have any projections:
-                    assign projection(s) from ProcessInputState(s) to corresponding Mechanism.inputState(s):
+                    assign projection(s) from ProcessInputState(s) to corresponding Mechanism.input_state(s):
                 if it DOES already has a projection, and it is from:
                     (A) the current Process input, leave intact
                     (B) another Process input, if verbose warn
@@ -986,11 +986,9 @@ def _instantiate_pathway(self, context):
         :return:
         """
         pathway = self.paramsCurrent[PATHWAY]
-        self._mech_tuples = []
-        self._monitoring_mech_tuples = []
-        self._target_mech_tuples = []
-
-        from PsyNeuLink.Globals.Run import _get_unique_id
+        self._mechs = []
+        self._monitoring_mechs = []
+        self._target_mechs = []
 
         self._standardize_config_entries(pathway=pathway, context=context)
 
@@ -999,23 +997,23 @@ def _instantiate_pathway(self, context):
 
         # Identify origin and terminal mechanisms in the process and
         #    and assign the mechanism's status in the process to its entry in the mechanism's processes dict
-        self.firstMechanism = pathway[0][OBJECT_ITEM]
+        self.firstMechanism = pathway[0]
         self.firstMechanism.processes[self] = ORIGIN
-        self._origin_mech_tuples = [pathway[0]]
-        self.originMechanisms = MechanismList(self, self._origin_mech_tuples)
+        self._origin_mechs = [pathway[0]]
+        self.originMechanisms = MechanismList(self, self._origin_mechs)
 
         # Assign last mechanism in pathwway to lastMechanism attribute
         i = -1
-        while not isinstance(pathway[i][OBJECT_ITEM],Mechanism_Base):
+        while not isinstance(pathway[i],Mechanism_Base):
             i -=1
-        self.lastMechanism = pathway[i][OBJECT_ITEM]
+        self.lastMechanism = pathway[i]
 
         if self.lastMechanism is self.firstMechanism:
             self.lastMechanism.processes[self] = SINGLETON
         else:
             self.lastMechanism.processes[self] = TERMINAL
-        self._terminal_mech_tuples = [pathway[-1]]
-        self.terminalMechanisms = MechanismList(self, self._terminal_mech_tuples)
+        self._terminal_mechs = [pathway[-1]]
+        self.terminalMechanisms = MechanismList(self, self._terminal_mechs)
 
         # # Assign process outputState to last mechanisms in pathway
         # self.outputState = self.lastMechanism.outputState
@@ -1036,95 +1034,72 @@ def _instantiate_pathway(self, context):
         else:
             self._learning_enabled = False
 
-        self._allMechanisms = MechanismList(self, self._mech_tuples)
-        self.monitoringMechanisms = MechanismList(self, self._monitoring_mech_tuples)
-        self.targetMechanisms = MechanismList(self, self._target_mech_tuples)
+        self._allMechanisms = MechanismList(self, self._mechs)
+        self.monitoringMechanisms = MechanismList(self, self._monitoring_mechs)
+        self.targetMechanisms = MechanismList(self, self._target_mechs)
 
     def _standardize_config_entries(self, pathway, context=None):
 
         from PsyNeuLink.Components.Mechanisms.Mechanism import _is_mechanism_spec
 # FIX: SHOULD MOVE VALIDATION COMPONENTS BELOW TO Process._validate_params
-        # Convert all entries to (item, params, phaseSpec) tuples, padded with None for absent params and/or phaseSpec
+        self.runtime_params_dict = {}
+
+        # Kristen modified 5/24
+        # in  ALL mechanism tuples, the middle entry is set to zero (formerly used for specifying runtime params)
+        # rightmost entry is set to zero (formerly phase spec)
+        # if _is_mechanism_spec, runtime_params_dict[mechanism] is set to actual runtime params
+
         for i in range(len(pathway)):
             config_item = pathway[i]
+            # if this element of the pathway is a tuple
             if isinstance(config_item, tuple):
-                if len(config_item) is 3:
-                    # Check that first item is either a mechanism or projection specification
-                    if not _is_mechanism_spec(config_item[0]) or _is_projection_spec(config_item[0]):
-                        raise ProcessError("First item of tuple ({}) in entry {} of pathway for {}"
-                                           " is neither a mechanism nor a projection specification".
-                                           format(config_item[0], i, self.name))
-                    # Check that second item is a dict (presumably of params)
-                    if config_item[1] is not None and not isinstance(config_item[1], dict):
-                        raise ProcessError("Second item of tuple ({}) in entry {} of pathway for {}"
-                                           " must be a params dict".
-                                           format(config_item[1], i, self.name))
-                    # Check that third item is a int (presumably a phase spec)
-                    if not isinstance(config_item[2], numbers.Number):
-                        raise ProcessError("Third item of tuple ({}) in entry {} of pathway for {}"
-                                           " must be a phase value".
-                                           format(config_item[2], i, self.name))
-                    pathway[i] = MechanismTuple(config_item[0], config_item[1], config_item[2])
-
-                # If the tuple has only one item, check that it is a Mechanism or Projection specification
+                # and the tuple has 1 item
                 if len(config_item) is 1:
+                    # if the tuple contains either a mechanism or a projection
                     if _is_mechanism_spec(config_item[0]) or _is_projection_spec(config_item[0]):
-                        # Pad with None
-                        pathway[i] = MechanismTuple(config_item[0],
-                                                          None,
-                                                          DEFAULT_PHASE_SPEC)
+
+                        # Replace it with just the mech or proj
+                        pathway[i] = config_item
+                        # if it's a mechanism, set the runtime params to None
+                        if _is_mechanism_spec(config_item[0]):
+                            self.runtime_params_dict[config_item[0]] = None
+                    # otherwise the tuple is not valid
                     else:
                         raise ProcessError("First item of tuple ({}) in entry {} of pathway for {}"
                                            " is neither a mechanism nor a projection specification".
                                            format(config_item[0], i, self.name))
                 # If the tuple has two items
                 if len(config_item) is 2:
-                    # Mechanism
-                    #     check whether second item is a params dict or a phaseSpec
-                    #     and assign it to the appropriate position in the tuple, padding other with None
-                    second_tuple_item = config_item[1]
+
+                    # Replace it with just the mech or proj
+                    pathway[i] = config_item[0]
+
+                    # If it's a mechanism
                     if _is_mechanism_spec(config_item[0]):
-                        if isinstance(second_tuple_item, dict):
-                            pathway[i] = MechanismTuple(config_item[0],
-                                                              second_tuple_item,
-                                                              DEFAULT_PHASE_SPEC)
-                        # If the second item is a number, assume it is meant as a phase spec and move it to third item
-                        elif isinstance(second_tuple_item, (int, float)):
-                            pathway[i] = MechanismTuple(config_item[0],
-                                                              None,
-                                                              second_tuple_item)
-                        else:
-                            raise ProcessError("Second item of tuple ((}) in item {} of pathway for {}"
-                                               " is neither a params dict nor phaseSpec (int or float)".
-                                               format(second_tuple_item, i, self.name))
-                    # Projection
-                    #     check that second item is a projection spec for a LearningProjection
-                    #     if so, leave it there, and pad third item with None
-                    elif _is_projection_spec(config_item[0]):
-                        if (_is_projection_spec(second_tuple_item) and
-                                _is_projection_subclass(second_tuple_item, LEARNING_PROJECTION)):
-                            pathway[i] = MechanismTuple(config_item[0],
-                                                              second_tuple_item,
-                                                              DEFAULT_PHASE_SPEC)
+                        # and its second element is a dict
+                        if isinstance(config_item[1], dict):
+                            # set the mechanism's runtime params to be the second element
+                            self.runtime_params_dict[config_item[0]] = config_item[1]
+                        # if the second element is not a dict, then it's not valid
                         else:
                             raise ProcessError("Second item of tuple ({}) in item {} of pathway for {}"
-                                               " should be 'LearningProjection' or absent".
-                                               format(second_tuple_item, i, self.name))
+                                               " is not a params dict.".
+                                               format(config_item[1], i, self.name))
+                    # if the first element is not a mechanism, then it's not valid
                     else:
-                        raise ProcessError("First item of tuple ({}) in item {} of pathway for {}"
-                                           " is neither a mechanism nor a projection spec".
-                                           format(config_item[0], i, self.name))
-                # tuple should not have more than 3 items
-                if len(config_item) > 3:
-                    raise ProcessError("The tuple for item {} of pathway for {} has more than three items {}".
+                        raise ProcessError("Projection cannot have a runtime params dict".format(config_item[0],
+                                                                                                 i, self.name))
+                # config_item should not have more than 2 elements
+                if len(config_item) > 2:
+                    raise ProcessError("The tuple for item {} of pathway for {} has more than two items {}".
                                        format(i, self.name, config_item))
             else:
-                # Convert item to tuple, padded with None
+                # If the item is a mechanism or a projection
                 if _is_mechanism_spec(pathway[i]) or _is_projection_spec(pathway[i]):
-                    # Pad with None for param and DEFAULT_PHASE_SPEC for phase
-                    pathway[i] = MechanismTuple(pathway[i],
-                                                      None,
-                                                      DEFAULT_PHASE_SPEC)
+                    # if it's a mechanism, set runtime params to None
+                    if _is_mechanism_spec(pathway[i]):
+                        self.runtime_params_dict[pathway[i]] = None
+
                 else:
                     raise ProcessError("Item of {} of pathway for {}"
                                        " is neither a mechanism nor a projection specification".
@@ -1139,14 +1114,13 @@ def _parse_and_instantiate_mechanism_entries(self, pathway, context=None):
 
         previous_item_was_projection = False
 
-        from PsyNeuLink.Components.Projections.Projection import Projection_Base
         for i in range(len(pathway)):
-            item, params, phase_spec = pathway[i]
+            item = pathway[i]
 
             # Get max phaseSpec for Mechanisms in pathway
-            if not phase_spec:
-                phase_spec = 0
-            self._phaseSpecMax = int(max(math.floor(float(phase_spec)), self._phaseSpecMax))
+            # if not phase_spec:
+            #     phase_spec = 0
+            # self._phaseSpecMax = int(max(math.floor(float(phase_spec)), self._phaseSpecMax))
 
             # VALIDATE PLACEMENT OF PROJECTION ENTRIES  ----------------------------------------------------------
 
@@ -1182,21 +1156,38 @@ def _parse_and_instantiate_mechanism_entries(self, pathway, context=None):
                     raise ProcessError("Entry {0} ({1}) is not a recognized form of Mechanism specification".
                                        format(i, mech))
                 # Params in mech tuple must be a dict or None
-                if params and not isinstance(params, dict):
-                    raise ProcessError("Params entry ({0}) of tuple in item {1} of pathway for {2} is not a dict".
-                                          format(params, i, self.name))
+                # if params and not isinstance(params, dict):
+                #     raise ProcessError("Params entry ({0}) of tuple in item {1} of pathway for {2} is not a dict".
+                #                           format(params, i, self.name))
                 # Replace Pathway entry with new tuple containing instantiated Mechanism object and params
-                pathway[i] = MechanismTuple(mech, params, phase_spec)
+                pathway[i] = mech
+
 
             # Entry IS already a Mechanism object
-            # Add entry to _mech_tuples and name to mechanismNames list
-            mech.phaseSpec = phase_spec
+            # Add entry to _mechs and name to mechanismNames list
+            # mech.phaseSpec = phase_spec
             # Add Process to the mechanism's list of processes to which it belongs
             if not self in mech.processes:
                 mech.processes[self] = INTERNAL
-                self._mech_tuples.append(pathway[i])
+                self._mechs.append(pathway[i])
             # self.mechanismNames.append(mech.name)
 
+            # FIX: ADD RECURRENT PROJECTION AND MECHANISM
+            # IMPLEMENTATION NOTE:  THIS IS A TOTAL HACK TO ALLOW RECURRENT MECHANISMS IN THE CURRENT SYSTEM
+            #                       SHOULD BE HANDLED MORE APPROPRIATELY IN COMPOSITION
+            # If this is the last mechanism in the pathway, and it has a recurrent projection,
+            #    add that to the pathway so that it can be identified and assigned for learning if so specified
+            if i+1 == len(pathway):
+                if any(any(proj.receiver.owner is mech
+                           for proj in state.efferents)
+                       for state in mech.output_states):
+                    for state in mech.output_states:
+                        for proj in state.efferents:
+                            if proj.receiver.owner is mech:
+                                pathway.append(proj)
+                                pathway.append(pathway[i-2])
+
+
         # Validate initial values
         # FIX: CHECK WHETHER ALL MECHANISMS DESIGNATED AS INITALIZE HAVE AN INITIAL_VALUES ENTRY
         if self.initial_values:
@@ -1234,7 +1225,7 @@ def _parse_and_instantiate_projection_entries(self, pathway, context=None):
                                  {MATRIX: matrix_spec}}
 
         for i in range(len(pathway)):
-                item, params, phase_spec = pathway[i]
+                item = pathway[i]
 
                 #region FIRST ENTRY
 
@@ -1243,23 +1234,25 @@ def _parse_and_instantiate_projection_entries(self, pathway, context=None):
                 # Note: does not include learning (even if specified for the process)
                 if i == 0:
                     # Relabel for clarity
-                    mechanism = item
+                    mech = item
 
                     # Check if first Mechanism already has any projections and, if so, issue appropriate warning
-                    if mechanism.inputState.receivesFromProjections:
-                        self._issue_warning_about_existing_projections(mechanism, context)
+                    if mech.input_state.path_afferents:
+                        self._issue_warning_about_existing_projections(mech, context)
 
                     # Assign input projection from Process
-                    self._assign_process_input_projections(mechanism, context=context)
+                    self._assign_process_input_projections(mech, context=context)
                     continue
                 #endregion
 
                 #region SUBSEQUENT ENTRIES
 
                 # Item is a Mechanism
+                item = item
                 if isinstance(item, Mechanism):
 
-                    preceding_item = pathway[i-1][OBJECT_ITEM]
+                    preceding_item = pathway[i-1]
+
 
                     # PRECEDING ITEM IS A PROJECTION
                     if isinstance(preceding_item, Projection):
@@ -1270,13 +1263,13 @@ def _parse_and_instantiate_projection_entries(self, pathway, context=None):
                             try:
                                 learning_projections = list(projection for
                                                         projection in
-                                                        preceding_item.parameterStates[MATRIX].receivesFromProjections
+                                                        preceding_item._parameter_states[MATRIX].mod_afferents
                                                         if isinstance(projection, LearningProjection))
 
                             # preceding_item doesn't have a parameterStates attrib, so assign one with self.learning
                             except AttributeError:
                                 # Instantiate parameterStates Ordered dict with ParameterState and self.learning
-                                preceding_item.parameterStates = _instantiate_state_list(
+                                preceding_item._parameter_states = _instantiate_state_list(
                                                                                 owner=preceding_item,
                                                                                 state_list=[(MATRIX,
                                                                                              self.learning)],
@@ -1289,7 +1282,7 @@ def _parse_and_instantiate_projection_entries(self, pathway, context=None):
                             # preceding_item has parameterStates but not (yet!) one for MATRIX, so instantiate it
                             except KeyError:
                                 # Instantiate ParameterState for MATRIX
-                                preceding_item.parameterStates[MATRIX] = _instantiate_state(
+                                preceding_item._parameter_states[MATRIX] = _instantiate_state(
                                                                                 owner=preceding_item,
                                                                                 state_type=ParameterState,
                                                                                 state_name=MATRIX,
@@ -1303,7 +1296,7 @@ def _parse_and_instantiate_projection_entries(self, pathway, context=None):
                                 if not learning_projections:
                                     # Add learningProjection to projection if it doesn't have one
                                     _add_projection_to(preceding_item,
-                                                      preceding_item.parameterStates[MATRIX],
+                                                      preceding_item._parameter_states[MATRIX],
                                                       projection_spec=self.learning)
                         continue
 
@@ -1314,7 +1307,7 @@ def _parse_and_instantiate_projection_entries(self, pathway, context=None):
     # FIX: POTENTIAL PROBLEM - EVC *CAN* HAVE MULTIPLE PROJECTIONS FROM (DIFFERENT outputStates OF) THE SAME MECHANISM
 
                     # PRECEDING ITEM IS A MECHANISM
-                    projection_list = item.inputState.receivesFromProjections
+                    projection_list = item.input_state.path_afferents
                     projection_found = False
                     for projection in projection_list:
                         # Current mechanism DOES receive a projection from the preceding item
@@ -1323,12 +1316,12 @@ def _parse_and_instantiate_projection_entries(self, pathway, context=None):
                             if self.learning:
                                 # Make sure projection includes a learningSignal and add one if it doesn't
                                 try:
-                                    matrix_param_state = projection.parameterStates[MATRIX]
+                                    matrix_param_state = projection._parameter_states[MATRIX]
 
                                 # projection doesn't have a parameterStates attrib, so assign one with self.learning
                                 except AttributeError:
                                     # Instantiate parameterStates Ordered dict with ParameterState for self.learning
-                                    projection.parameterStates = _instantiate_state_list(
+                                    projection._parameter_states = _instantiate_state_list(
                                                                                 owner=preceding_item,
                                                                                 state_list=[(MATRIX,
                                                                                              self.learning)],
@@ -1342,7 +1335,7 @@ def _parse_and_instantiate_projection_entries(self, pathway, context=None):
                                 #    so instantiate it with self.learning
                                 except KeyError:
                                     # Instantiate ParameterState for MATRIX
-                                    projection.parameterStates[MATRIX] = _instantiate_state(
+                                    projection._parameter_states[MATRIX] = _instantiate_state(
                                                                                 owner=preceding_item,
                                                                                 state_type=ParameterState,
                                                                                 state_name=MATRIX,
@@ -1355,7 +1348,7 @@ def _parse_and_instantiate_projection_entries(self, pathway, context=None):
                                 # Check if projection's matrix param has a learningSignal
                                 else:
                                     if not (any(isinstance(projection, LearningProjection) for
-                                                projection in matrix_param_state.receivesFromProjections)):
+                                                projection in matrix_param_state.mod_afferents)):
                                         _add_projection_to(projection,
                                                           matrix_param_state,
                                                           projection_spec=self.learning)
@@ -1404,18 +1397,18 @@ def _parse_and_instantiate_projection_entries(self, pathway, context=None):
 
 
                     # Get sender for projection
-                    sender_mech=pathway[i-1][OBJECT_ITEM]
-                    
+                    sender_mech=pathway[i-1]
+
                     # Get receiver for projection
                     try:
-                        receiver_mech=pathway[i+1][OBJECT_ITEM]
+                        receiver_mech=pathway[i+1]
                     except IndexError:
                        # There are no more entries in the pathway
                        #    so the projection had better project to a mechanism already in the pathway;
                        #    otherwise, raise and exception
                        try:
                            receiver_mech = item.receiver.owner
-                           if not receiver_mech in [mech_tuple[0] for mech_tuple in pathway]:
+                           if not receiver_mech in [object_item for object_item in pathway]:
                                raise AttributeError
                        except AttributeError:
                            raise ProcessError("The last entry in the pathway for {} is a project specification {}, "
@@ -1433,19 +1426,19 @@ def _parse_and_instantiate_projection_entries(self, pathway, context=None):
                         if item.value is DEFERRED_INITIALIZATION:
                             # Check sender arg
                             try:
-                                sender_arg = item.init_args[kwSenderArg]
+                                sender_arg = item.init_args[SENDER]
                             except AttributeError:
                                 raise ProcessError("PROGRAM ERROR: Value of {} is {} but it does not have init_args".
                                                    format(item, DEFERRED_INITIALIZATION))
                             except KeyError:
                                 raise ProcessError("PROGRAM ERROR: Value of {} is {} "
                                                    "but init_args does not have entry for {}".
-                                                   format(item.init_args[NAME], DEFERRED_INITIALIZATION, kwSenderArg))
+                                                   format(item.init_args[NAME], DEFERRED_INITIALIZATION, SENDER))
                             else:
                                 # If sender is not specified for the projection,
                                 #    assign mechanism that precedes in pathway
                                 if sender_arg is None:
-                                    item.init_args[kwSenderArg] = sender_mech
+                                    item.init_args[SENDER] = sender_mech
                                 elif sender_arg is not sender_mech:
                                     raise ProcessError("Sender of projection ({}) specified in item {} of"
                                                        " pathway for {} is not the mechanism ({}) "
@@ -1488,27 +1481,28 @@ def _parse_and_instantiate_projection_entries(self, pathway, context=None):
                         projection = item
 
                         # TEST
-                        if params:
-                            projection.matrix = params
+                        # if params:
+                        #     projection.matrix = params
 
                     # projection spec is a MappingProjection class reference
                     elif inspect.isclass(item) and issubclass(item, MappingProjection):
-                        if params:
-                            # Note:  If self.learning is specified, it has already been added to projection_params above
-                            projection_params = params
+                        # if params:
+                        #     # Note:  If self.learning is specified, it has already been added to projection_params above
+                        #     projection_params = params
                         projection = MappingProjection(sender=sender_mech,
                                              receiver=receiver_mech,
-                                             params=projection_params)
+                                             # params=projection_params
+                                                       )
 
                     # projection spec is a matrix spec, a keyword for one, or a (matrix, LearningProjection) tuple
                     # Note: this is tested above by call to _is_projection_spec()
                     elif (isinstance(item, (np.matrix, str, tuple)) or
                               (isinstance(item, np.ndarray) and item.ndim == 2)):
                         # If a LearningProjection is explicitly specified for this projection, use it
-                        if params:
-                            matrix_spec = (item, params)
+                        # if params:
+                        #     matrix_spec = (item, params)
                         # If a LearningProjection is not specified for this projection but self.learning is, use that
-                        elif self.learning:
+                        if self.learning:
                             matrix_spec = (item, self.learning)
                         # Otherwise, do not include any LearningProjection
                         else:
@@ -1522,12 +1516,13 @@ def _parse_and_instantiate_projection_entries(self, pathway, context=None):
                     # Reassign Pathway entry
                     #    with Projection as OBJECT item and original params as PARAMS item of the tuple
                     # IMPLEMENTATION NOTE:  params is currently ignored
-                    pathway[i] = MechanismTuple(projection, params, DEFAULT_PHASE_SPEC)
+                    pathway[i] = projection
+
 
     def _issue_warning_about_existing_projections(self, mechanism, context=None):
 
         # Check where the projection(s) is/are from and, if verbose pref is set, issue appropriate warnings
-        for projection in mechanism.inputState.receivesFromProjections:
+        for projection in mechanism.input_state.all_afferents:
 
             # Projection to first Mechanism in Pathway comes from a Process input
             if isinstance(projection.sender, ProcessInputState):
@@ -1541,9 +1536,9 @@ def _issue_warning_about_existing_projections(self, mechanism, context=None):
                               format(mechanism.name, self.name, projection.sender.owner.name))
                     return
 
-            # (C) Projection to first Mechanism in Pathway comes from one in the Process' _mech_tuples;
+            # (C) Projection to first Mechanism in Pathway comes from one in the Process' _mechs;
             #     so warn if verbose pref is set
-            if projection.sender.owner in list(item[0] for item in self._mech_tuples):
+            if projection.sender.owner in self._mechs:
                 if self.prefs.verbosePref:
                     print("WARNING: first mechanism ({0}) in pathway for {1} receives "
                           "a (recurrent) projection from another mechanism {2} in {1}".
@@ -1594,17 +1589,17 @@ def _assign_process_input_projections(self, mechanism, context=None):
         """Create projection(s) for each item in Process input to inputState(s) of the specified Mechanism
 
         For each item in Process input:
-        - create process_input_state, as sender for MappingProjection to the mechanism.inputState
+        - create process_input_state, as sender for MappingProjection to the mechanism.input_state
         - create the MappingProjection (with process_input_state as sender, and mechanism as receiver)
 
         If len(Process.input) == len(mechanism.variable):
-            - create one projection for each of the mechanism.inputState(s)
+            - create one projection for each of the mechanism.input_state(s)
         If len(Process.input) == 1 but len(mechanism.variable) > 1:
-            - create a projection for each of the mechanism.inputStates, and provide Process.input[value] to each
+            - create a projection for each of the mechanism.input_states, and provide Process.input[value] to each
         If len(Process.input) > 1 but len(mechanism.variable) == 1:
-            - create one projection for each Process.input[value] and assign all to mechanism.inputState
+            - create one projection for each Process.input[value] and assign all to mechanism.input_state
         Otherwise,  if len(Process.input) != len(mechanism.variable) and both > 1:
-            - raise exception:  ambiguous mapping from Process input values to mechanism's inputStates
+            - raise exception:  ambiguous mapping from Process input values to mechanism's input_states
 
         :param mechanism:
         :return:
@@ -1616,8 +1611,8 @@ def _assign_process_input_projections(self, mechanism, context=None):
         if self.variable is None:
             self.variable = []
             seen = set()
-            mech_list = list(mech_tuple[OBJECT_ITEM] for mech_tuple in self._mech_tuples)
-            for mech in mech_list:
+            # mech_list = list(object_item for object_item in self._mechs)
+            for mech in self._mechs:
                 # Skip repeat mechansims (don't add another element to self.variable)
                 if mech in seen:
                     continue
@@ -1630,15 +1625,15 @@ def _assign_process_input_projections(self, mechanism, context=None):
         # Get number of Process inputs
         num_process_inputs = len(process_input)
 
-        # Get number of mechanism.inputStates
+        # Get number of mechanism.input_states
         #    - assume mechanism.variable is a 2D np.array, and that
         #    - there is one inputState for each item (1D array) in mechanism.variable
         num_mechanism_input_states = len(mechanism.variable)
 
-        # There is a mismatch between number of Process inputs and number of mechanism.inputStates:
+        # There is a mismatch between number of Process inputs and number of mechanism.input_states:
         if num_process_inputs > 1 and num_mechanism_input_states > 1 and num_process_inputs != num_mechanism_input_states:
             raise ProcessError("Mismatch between number of input values ({0}) for {1} and "
-                               "number of inputStates ({2}) for {3}".format(num_process_inputs,
+                               "number of input_states ({2}) for {3}".format(num_process_inputs,
                                                                             self.name,
                                                                             num_mechanism_input_states,
                                                                             mechanism.name))
@@ -1650,33 +1645,33 @@ def _assign_process_input_projections(self, mechanism, context=None):
                                                     prefs=self.prefs)
             self.processInputStates.append(process_input_state)
 
-        from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
+        from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
 
-        # If there is the same number of Process input values and mechanism.inputStates, assign one to each
+        # If there is the same number of Process input values and mechanism.input_states, assign one to each
         if num_process_inputs == num_mechanism_input_states:
             for i in range(num_mechanism_input_states):
-                # Insure that each Process input value is compatible with corresponding variable of mechanism.inputState
+                # Insure that each Process input value is compatible with corresponding variable of mechanism.input_state
                 # MODIFIED 4/3/17 NEW:
-                input_state_variable = list(mechanism.inputStates.values())[i].variable
+                input_state_variable = mechanism.input_states[i].variable
                 # MODIFIED 4/3/17 END
                 if not iscompatible(process_input[i], input_state_variable):
                     raise ProcessError("Input value {0} ({1}) for {2} is not compatible with "
                                        "variable for corresponding inputState of {3}".
                                        format(i, process_input[i], self.name, mechanism.name))
-                # Create MappingProjection from Process input state to corresponding mechanism.inputState
+                # Create MappingProjection from Process input state to corresponding mechanism.input_state
                 MappingProjection(sender=self.processInputStates[i],
-                        receiver=list(mechanism.inputStates.items())[i][1],
-                        name=self.name+'_Input Projection',
-                        context=context)
+                                  receiver=mechanism.input_states[i],
+                                  name=self.name+'_Input Projection',
+                                  context=context)
                 if self.prefs.verbosePref:
                     print("Assigned input value {0} ({1}) of {2} to corresponding inputState of {3}".
                           format(i, process_input[i], self.name, mechanism.name))
 
-        # If the number of Process inputs and mechanism.inputStates is unequal, but only a single of one or the other:
-        # - if there is a single Process input value and multiple mechanism.inputStates,
-        #     instantiate a single Process input state with projections to each of the mechanism.inputStates
-        # - if there are multiple Process input values and a single mechanism.inputState,
-        #     instantiate multiple Process input states each with a projection to the single mechanism.inputState
+        # If the number of Process inputs and mechanism.input_states is unequal, but only a single of one or the other:
+        # - if there is a single Process input value and multiple mechanism.input_states,
+        #     instantiate a single Process input state with projections to each of the mechanism.input_states
+        # - if there are multiple Process input values and a single mechanism.input_state,
+        #     instantiate multiple Process input states each with a projection to the single mechanism.input_state
         else:
             for i in range(num_mechanism_input_states):
                 for j in range(num_process_inputs):
@@ -1685,9 +1680,9 @@ def _assign_process_input_projections(self, mechanism, context=None):
                                            "variable ({3}) for inputState {4} of {5}".
                                            format(j, process_input[j], self.name,
                                                   mechanism.variable[i], i, mechanism.name))
-                    # Create MappingProjection from Process buffer_intput_state to corresponding mechanism.inputState
+                    # Create MappingProjection from Process buffer_intput_state to corresponding mechanism.input_state
                     MappingProjection(sender=self.processInputStates[j],
-                            receiver=list(mechanism.inputStates.items())[i][1],
+                            receiver=mechanism.input_states[i],
                             name=self.name+'_Input Projection')
                     if self.prefs.verbosePref:
                         print("Assigned input value {0} ({1}) of {2} to inputState {3} of {4}".
@@ -1743,32 +1738,32 @@ def _instantiate__deferred_inits(self, context=None):
 
         Description:
             For learning:
-                go through _mech_tuples in reverse order of pathway since
+                go through _mechs in reverse order of pathway since
                     LearningProjections are processed from the output (where the training signal is provided) backwards
                 exhaustively check all of components of each mechanism,
-                    including all projections to its inputStates and parameterStates
+                    including all projections to its input_states and parameterStates
                 initialize all items that specified deferred initialization
-                construct a _monitoring_mech_tuples of mechanism tuples (mech, params, phase_spec):
+                construct a _monitoring_mechs of mechanism tuples (mech, params, phase_spec):
                     assign phase_spec for each MonitoringMechanism = self._phaseSpecMax + 1 (i.e., execute them last)
-                add _monitoring_mech_tuples to the Process' _mech_tuples
-                assign input projection from Process to first mechanism in _monitoring_mech_tuples
+                add _monitoring_mechs to the Process' _mechs
+                assign input projection from Process to first mechanism in _monitoring_mechs
 
         IMPLEMENTATION NOTE: assume that the only projection to a projection is a LearningProjection
                              this is implemented to be fully general, but at present may be overkill
                              since the only objects that currently use deferred initialization are LearningProjections
         """
 
-        # For each mechanism in the Process, in backwards order through its _mech_tuples
-        for item in reversed(self._mech_tuples):
-            mech = item[OBJECT_ITEM]
+        # For each mechanism in the Process, in backwards order through its _mechs
+        for item in reversed(self._mechs):
+            mech = item
             mech._deferred_init()
 
             # For each inputState of the mechanism
-            for input_state in mech.inputStates.values():
+            for input_state in mech.input_states:
                 input_state._deferred_init()
                 # Restrict projections to those from mechanisms in the current process
                 projections = []
-                for projection in input_state.receivesFromProjections:
+                for projection in input_state.all_afferents:
                     try:
                         if self in projection.sender.owner.processes:
                             projections.append(projection)
@@ -1777,16 +1772,24 @@ def _instantiate__deferred_inits(self, context=None):
                 self._instantiate__deferred_init_projections(projections, context=context)
 
             # For each parameterState of the mechanism
-            for parameter_state in mech.parameterStates.values():
-                parameter_state._deferred_init()
-                self._instantiate__deferred_init_projections(parameter_state.receivesFromProjections)
-
-        # Label monitoring mechanisms and add _monitoring_mech_tuples to _mech_tuples for execution
-        if self._monitoring_mech_tuples:
+            for parameter_state in mech._parameter_states:
+                parameter_state._deferred_init() # XXX
+                # MODIFIED 5/2/17 OLD:
+                # self._instantiate__deferred_init_projections(parameter_state.path_afferents)
+                # MODIFIED 5/2/17 NEW:
+                # Defer instantiation of ControlProjections to System
+                #   and there should not be any other type of projection to the ParameterState of a Mechanism
+                from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
+                if not all(isinstance(proj, ControlProjection) for proj in parameter_state.mod_afferents):
+                    raise ProcessError("PROGRAM ERROR:  non-ControlProjection found to ParameterState for a Mechanism")
+                # MODIFIED 5/2/17 END
+
+        # Label monitoring mechanisms and add _monitoring_mechs to _mechs for execution
+        if self._monitoring_mechs:
 
             # Add designations to newly created MonitoringMechanisms:
-            for mech_tuple in self._monitoring_mech_tuples:
-                mech = mech_tuple[OBJECT_ITEM]
+            for object_item in self._monitoring_mechs:
+                mech = object_item
                 # If
                 # - mech is a TARGET ObjectiveMechanism, and
                 # - the mech that projects to mech is a TERMINAL for the current process, and
@@ -1794,11 +1797,11 @@ def _instantiate__deferred_inits(self, context=None):
                 # then designate mech as a TARGET
                 if (isinstance(mech, ObjectiveMechanism) and
                         # any(projection.sender.owner.processes[self] == TERMINAL
-                        #     for projection in mech.inputStates[SAMPLE].receivesFromProjections) and
+                        #     for projection in mech.input_states[SAMPLE].path_afferents) and
                         mech._learning_role is TARGET and
                         self.learning
                             ):
-                    mech_tuple[0].processes[self] = TARGET
+                    object_item.processes[self] = TARGET
                 else:
                     # mech must be a LearningMechanism;
                     # If a learning_rate has been specified for the process, assign that to all LearningMechanisms
@@ -1808,10 +1811,10 @@ def _instantiate__deferred_inits(self, context=None):
                         mech.function_object.learning_rate = self.learning_rate
 
                     # Assign its label
-                    mech_tuple[0].processes[self] = MONITORING
+                    object_item.processes[self] = MONITORING
 
-            # Add _monitoring_mech_tuples to _mech_tuples
-            self._mech_tuples.extend(self._monitoring_mech_tuples)
+            # Add _monitoring_mechs to _mechs
+            self._mechs.extend(self._monitoring_mechs)
 
             # IMPLEMENTATION NOTE:
             #   MonitoringMechanisms for learning are assigned _phaseSpecMax;
@@ -1821,45 +1824,40 @@ def _instantiate__deferred_init_projections(self, projection_list, context=None)
 
         # For each projection in the list
         for projection in projection_list:
-            projection._deferred_init()
+            projection._deferred_init() # XXX
 
             # FIX:  WHY DOESN'T THE PROJECTION HANDLE THIS? (I.E., IN ITS deferred_init() METHOD?)
             # For each parameter_state of the projection
             try:
-                for parameter_state in projection.parameterStates.values():
+                for parameter_state in projection._parameter_states:
                     # Initialize each projection to the parameterState (learning or control)
                     # IMPLEMENTATION NOTE:  SHOULD ControlProjections BE IGNORED HERE?
-                    for param_projection in parameter_state.receivesFromProjections:
+                    for param_projection in parameter_state.mod_afferents:
                         param_projection._deferred_init(context=context)
                         if isinstance(param_projection, LearningProjection):
-                            # Get ObjectiveMechanism if there is one, and add to _monitoring_mech_tuples
+                            # Get ObjectiveMechanism if there is one, and add to _monitoring_mechs
                             try:
                                 objective_mechanism = projection.objective_mechanism
                             except AttributeError:
                                 pass
                             else:
-                                # If objective_mechanism is not already in _monitoring_mech_tuples,
+                                # If objective_mechanism is not already in _monitoring_mechs,
                                 #     pack in tuple and add it
-                                if objective_mechanism and not any(objective_mechanism is mech_tuple.mechanism for
-                                                                    mech_tuple in self._monitoring_mech_tuples):
-                                    objective_mech_tuple = MechanismTuple(objective_mechanism,
-                                                                           None,
-                                                                           self._phaseSpecMax+1)
-                                    self._monitoring_mech_tuples.append(objective_mech_tuple)
-                            # Get LearningMechanism and add to _monitoring_mech_tuples; raise exception if not found
+                                if objective_mechanism and not objective_mechanism in self._monitoring_mechs:
+                                    # objective_object_item = objective_mechanism
+                                    self._monitoring_mechs.append(objective_mechanism)
+                            # Get LearningMechanism and add to _monitoring_mechs; raise exception if not found
                             try:
                                 learning_mechanism = projection.learning_mechanism
                             except AttributeError:
                                 raise ProcessError("{} is missing a LearningMechanism".format(param_projection.name))
                             else:
-                                # If learning_mechanism is not already in _monitoring_mech_tuples,
+                                # If learning_mechanism is not already in _monitoring_mechs,
                                 #     pack in tuple and add it
-                                if learning_mechanism and not any(learning_mechanism is mech_tuple.mechanism for
-                                                                    mech_tuple in self._monitoring_mech_tuples):
-                                    learning_mech_tuple = MechanismTuple(learning_mechanism,
-                                                                           None,
-                                                                           self._phaseSpecMax+1)
-                                    self._monitoring_mech_tuples.append(learning_mech_tuple)
+                                if learning_mechanism and not any(learning_mechanism is object_item for
+                                                                    object_item in self._monitoring_mechs):
+                                    # learning_object_item = learning_mechanism
+                                    self._monitoring_mechs.append(learning_mechanism)
 
             # Not all Projection subclasses instantiate parameterStates
             except AttributeError as e:
@@ -1880,21 +1878,21 @@ def _check_for_target_mechanism(self):
              and report assignment if verbose
         """
 
-        from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ObjectiveMechanism import ObjectiveMechanism
+        from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ObjectiveMechanisms.ObjectiveMechanism import ObjectiveMechanism
         def trace_learning_objective_mechanism_projections(mech):
             """Recursively trace projections to Objective mechanisms;
                    return TARGET ObjectiveMechanism if one is found upstream;
                    return None if no TARGET ObjectiveMechanism is found.
             """
-            for input_state in mech.inputStates.values():
-                for projection in input_state.receivesFromProjections:
+            for input_state in mech.input_states.values():
+                for projection in input_state.path_afferents:
                     sender = projection.sender.owner
                     # If projection is not from another ObjectiveMechanism, ignore
                     if not isinstance(sender, (ObjectiveMechanism)):
                         continue
                     if isinstance(sender, ObjectiveMechanism) and sender._learning_role is TARGET:
                         return sender
-                    if sender.inputStates:
+                    if sender.input_states:
                         target_mech = trace_learning_objective_mechanism_projections(sender)
                         if target_mech:
                             return target_mech
@@ -1907,10 +1905,10 @@ def trace_learning_objective_mechanism_projections(mech):
             raise ProcessError("PROGRAM ERROR: _check_for_target_mechanism should only be called"
                                " for a process if it has a learning specification")
 
-        target_mechs = list(mech_tuple.mechanism
-                           for mech_tuple in self._mech_tuples
-                            if (isinstance(mech_tuple.mechanism, ObjectiveMechanism) and
-                                mech_tuple.mechanism._learning_role is TARGET))
+        target_mechs = list(object_item
+                           for object_item in self._mechs
+                            if (isinstance(object_item, ObjectiveMechanism) and
+                                object_item._learning_role is TARGET))
 
         if not target_mechs:
 
@@ -1918,7 +1916,7 @@ def trace_learning_objective_mechanism_projections(mech):
             #   (in case terminal mechanism of process is part of another process that has learning implemented)
             #    in which case, shouldn't assign target ObjectiveMechanism, but rather just a LearningMechanism)
             try:
-                target_mech = trace_learning_objective_mechanism_projections(self._monitoring_mech_tuples[0][0])
+                target_mech = trace_learning_objective_mechanism_projections(self._monitoring_mechs[0][0])
             except IndexError:
                 raise ProcessError("Learning specified for {} but no ObjectiveMechanisms or LearningMechanisms found"
                                    .format(self.name))
@@ -1944,7 +1942,7 @@ def trace_learning_objective_mechanism_projections(mech):
 
         else:
             self.targetMechanism = target_mechs[0]
-            self._target_mech_tuples.append(MechanismTuple(target_mechs[0], None, None))
+            self._target_mechs.append(target_mechs[0])
             if self.prefs.verbosePref:
                 print("\'{}\' assigned as TARGET ObjectiveMechanism for output of \'{}\'".
                       format(self.targetMechanism.name, self.name))
@@ -1958,7 +1956,7 @@ def _instantiate_target_input(self):
         target = np.atleast_1d(self.target)
 
         # Create ProcessInputState for target and assign to targetMechanism's target inputState
-        target_mech_target = self.targetMechanism.inputStates[TARGET]
+        target_mech_target = self.targetMechanism.input_states[TARGET]
 
         # Check that length of process' target input matches length of targetMechanism's target input
         if len(target) != len(target_mech_target.variable):
@@ -1972,7 +1970,7 @@ def _instantiate_target_input(self):
         self.targetInputStates.append(target_input_state)
 
         # Add MappingProjection from target_input_state to MonitoringMechanism's target inputState
-        from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
+        from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
         MappingProjection(sender=target_input_state,
                 receiver=target_mech_target,
                 name=self.name+'_Input Projection to '+target_mech_target.name)
@@ -1993,6 +1991,8 @@ def execute(self,
                 time_scale=None,
                 # time_scale=TimeScale.TRIAL,
                 runtime_params=None,
+                termination_processing=None,
+                termination_learning=None,
                 context=None
                 ):
         """Execute the mechanisms specified in the process` `pathway` attribute.
@@ -2066,7 +2066,7 @@ def execute(self,
             self._report_process_initiation(separator=True)
 
         # Execute each Mechanism in the pathway, in the order listed, except those used for learning
-        for mechanism, params, phase_spec in self._mech_tuples:
+        for mechanism in self._mechs:
             if (isinstance(mechanism, LearningMechanism) or
                     (isinstance(mechanism, ObjectiveMechanism) and mechanism._role is LEARNING)):
                 continue
@@ -2075,7 +2075,7 @@ def execute(self,
             mechanism.execute(clock=clock,
                               time_scale=self.timeScale,
                               # time_scale=time_scale,
-                              runtime_params=params,
+                              # runtime_params=params,
                               context=context)
             if report_output:
                 # FIX: USE clamp_input OPTION HERE, AND ADD HARD_CLAMP AND SOFT_CLAMP
@@ -2096,13 +2096,13 @@ def execute(self,
             self._report_process_completion(separator=True)
 
         # FIX:  SHOULD THIS BE JUST THE VALUE OF THE PRIMARY OUTPUTSTATE, OR OF ALL OF THEM?
-        return self.outputState.value
+        return self.output_state.value
 
     def _execute_learning(self, target=None, clock=CentralClock, context=None):
     # def _execute_learning(self, clock=CentralClock, time_scale=TimeScale.TRIAL, context=None):
-        """ Update each LearningProjection for mechanisms in _mech_tuples of process
+        """ Update each LearningProjection for mechanisms in _mechs of process
 
-        # Begin with projection(s) to last Mechanism in _mech_tuples, and work backwards
+        # Begin with projection(s) to last Mechanism in _mechs, and work backwards
 
         """
 
@@ -2144,8 +2144,7 @@ def _execute_learning(self, target=None, clock=CentralClock, context=None):
         # # MODIFIED 3/22/17 END
 
         # THEN, execute Objective and LearningMechanisms
-        for mechanism, params, phase_spec in self._monitoring_mech_tuples:
-
+        for mechanism in self._monitoring_mechs:
             # # MODIFIED 3/22/17 NEW:
             # # If learning_rate was specified for process and this is a LearningMechanism
             # if process_learning_rate_spec_dict is not None and isinstance(mechanism, LearningMechanism):
@@ -2159,23 +2158,23 @@ def _execute_learning(self, target=None, clock=CentralClock, context=None):
 
             mechanism.execute(clock=clock,
                               time_scale=self.timeScale,
-                              runtime_params=params,
+                              # runtime_params=params,
                               context=context)
 
 
         # FINALLY, execute LearningProjections to MappingProjections in the process' pathway
-        for item in self._mech_tuples:
-            mech = item.mechanism
-            params = item.params
+        for item in self._mechs:
+            mech = item
+            # params = item.params
 
             # IMPLEMENTATION NOTE:
-            #    This implementation restricts learning to parameterStates of projections to inputStates
+            #    This implementation restricts learning to parameterStates of projections to input_states
             #    That means that other parameters (e.g. object or function parameters) are not currenlty learnable
 
             # For each inputState of the mechanism
-            for input_state in mech.inputStates.values():
+            for input_state in mech.input_states:
                 # For each projection in the list
-                for projection in input_state.receivesFromProjections:
+                for projection in input_state.path_afferents:
 
                     # Skip learning if projection is an input from the Process or a system
                     # or comes from a mechanism that belongs to another process
@@ -2187,12 +2186,15 @@ def _execute_learning(self, target=None, clock=CentralClock, context=None):
 
                     # For each parameter_state of the projection
                     try:
-                        for parameter_state in projection.parameterStates.values():
+                        for parameter_state in projection._parameter_states:
                             # Call parameter_state.update with LEARNING in context to update LearningSignals
                             # Note: do this rather just calling LearningSignals directly
                             #       since parameter_state.update() handles parsing of LearningProjection-specific params
                             context = context + SEPARATOR_BAR + LEARNING
-                            parameter_state.update(params=params, time_scale=TimeScale.TRIAL, context=context)
+
+                            # NOTE: This will need to be updated when runtime params are reenabled
+                            # parameter_state.update(params=params, time_scale=TimeScale.TRIAL, context=context)
+                            parameter_state.update(time_scale=TimeScale.TRIAL, context=context)
 
                     # Not all Projection subclasses instantiate parameterStates
                     except AttributeError as e:
@@ -2297,7 +2299,8 @@ def _report_process_initiation(self, input=None, separator=False):
 
         print("\n\'{}\' executing with:\n- pathway: [{}]".
               format(append_type_to_name(self),
-                     re.sub('[\[,\],\n]','',str(self.mechanismNames))))
+              # format(self.name,
+                     re.sub(r'[\[,\],\n]','',str(self.mechanismNames))))
         # # MODIFIED 2/17/17 OLD:
         # variable = [list(i) for i in self.variable]
         # print("- variable: {1}".format(self, re.sub('[\[,\],\n]','',
@@ -2323,12 +2326,16 @@ def _report_process_completion(self, separator=False):
 
         print("\n\'{}' completed:\n- output: {}".
               format(append_type_to_name(self),
-                     re.sub('[\[,\],\n]','',str([float("{:0.3}".format(float(i))) for i in self.outputState.value]))))
+                     re.sub(r'[\[,\],\n]','',str([float("{:0.3}".format(float(i))) for i in self.output_state.value]))))
 
         if self.learning:
-            from PsyNeuLink.Components.Projections.LearningProjection import TARGET_MSE
-            print("\n- MSE: {:0.3}".
-                  format(float(self.targetMechanism.outputStates[TARGET_MSE].value)))
+            from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ObjectiveMechanisms.ComparatorMechanism \
+                import MSE
+            for mech in self.targetMechanisms:
+                if not MSE in mech.output_states:
+                    continue
+                print("\n- MSE: {:0.3}".
+                      format(float(mech.output_states[MSE].value)))
 
         elif separator:
             print("\n\n****************************************\n")
@@ -2357,18 +2364,18 @@ def show(self, options=None):
         #     print ("\t\t{}".format(mech_name))
 
         print ("\n\tMechanisms:")
-        for mech_tuple in self._mech_tuples:
-            print ("\t\t{} (phase: {})".format(mech_tuple.mechanism.name, mech_tuple.phase))
+        for object_item in self._mechs:
+            print ("\t\t{} (phase: {})".format(object_item.name, object_item.phase))
 
 
         print ("\n\tOrigin mechanism: ".format(self.name))
-        for mech_tuple in self.originMechanisms.mech_tuples_sorted:
-            print("\t\t{} (phase: {})".format(mech_tuple.mechanism.name, mech_tuple.phase))
+        for object_item in self.originMechanisms.mechs_sorted:
+            print("\t\t{} (phase: {})".format(object_item.name, object_item.phase))
 
         print ("\n\tTerminal mechanism: ".format(self.name))
-        for mech_tuple in self.terminalMechanisms.mech_tuples_sorted:
-            print("\t\t{} (phase: {})".format(mech_tuple.mechanism.name, mech_tuple.phase))
-            for output_state_name in mech_tuple.mechanism.outputStates:
+        for object_item in self.terminalMechanisms.mechs_sorted:
+            print("\t\t{} (phase: {})".format(object_item.name, object_item.phase))
+            for output_state_name in object_item.output_states:
                 print("\t\t\t{0}".format(output_state_name))
 
         print ("\n---------------------------------------------------------")
@@ -2396,18 +2403,18 @@ def variableInstanceDefault(self, value):
             pass
         self._variableInstanceDefault = value
 
-    @property
-    def inputValue(self):
-        return self.variable
+    # @property
+    # def input_value(self):
+    #     return self.variable
 
     @property
-    def outputState(self):
-        return self.lastMechanism.outputState
+    def output_state(self):
+        return self.lastMechanism.output_state
 
     @property
     def output(self):
         # FIX: THESE NEED TO BE PROPERLY MAPPED
-        return np.array(list(item.value for item in self.lastMechanism.outputStates.values()))
+        return np.array(list(item.value for item in self.lastMechanism.output_states.values()))
 
     @property
     def numPhases(self):
@@ -2418,13 +2425,13 @@ class ProcessInputState(OutputState):
 
     Each instance encodes one of the following:
     - an item of the `input <Process.input>` to the process (a 1d array in the 2d input array) and provides it to a
-        `MappingProjection` that projects to one or more `inputStates <Mechanism.Mechanism_Base.inputStates>` of the
+        `MappingProjection` that projects to one or more `input_states <Mechanism.Mechanism_Base.input_states>` of the
         `ORIGIN` mechanism in the process.
     - a `target <Process.target>` to the process (also a 1d array) and provides it to a `MappingProjection` that
          projects to the `TARGET` mechanism of the process.
 
     (See :ref:`Process_Input_And_OuputProcess` for an explanation of the mapping from processInputStates to
-    `ORIGIN` mechanism inputStates when there is more than one process input value and/or mechanism inputState)
+    `ORIGIN` mechanism input_states when there is more than one process input value and/or mechanism inputState)
 
     .. Declared as a sublcass of OutputState so that it is recognized as a legitimate sender to a Projection
        in Projection._instantiate_sender()
@@ -2442,13 +2449,13 @@ def __init__(self, owner=None, variable=None, name=None, prefs=None):
         else:
             self.name = owner.name + "_" + name
         self.prefs = prefs
-        self.sendsToProjections = []
+        self.efferents = []
         self.owner = owner
         self.value = variable
         # MODIFIED 2/17/17 NEW:
         # self.owner.input = self.value
         # MODIFIED 2/17/17 END
-        # self.receivesFromProjections = []
+        # self.path_afferents = []
         # from PsyNeuLink.Components.States.OutputState import PRIMARY_OUTPUT_STATE
         # from PsyNeuLink.Components.Functions.Function import Linear
         # self.index = PRIMARY_OUTPUT_STATE
@@ -2496,15 +2503,15 @@ def _get_tuple_for_process(self, process):
         """Return first process tuple containing specified process from list of process_tuples
         """
         # FIX:
-        # if list(item[MECHANISM] for item in self.mech_tuples).count(mech):
+        # if list(item[MECHANISM] for item in self.mechs).count(mech):
         #     if self.owner.verbosePref:
-        #         print("PROGRAM ERROR:  {} found in more than one mech_tuple in {} in {}".
+        #         print("PROGRAM ERROR:  {} found in more than one object_item in {} in {}".
         #               format(append_type_to_name(mech), self.__class__.__name__, self.owner.name))
         return next((ProcessTuple for ProcessTuple in self.process_tuples if ProcessTuple.process is process), None)
 
     @property
     def process_tuples_sorted(self):
-        """Return list of mech_tuples sorted by mechanism name"""
+        """Return list of mechs sorted by mechanism name"""
         return sorted(self.process_tuples, key=lambda process_tuple: process_tuple[0].name)
 
     @property
@@ -2528,9 +2535,9 @@ def processNames(self):
         # MODIFIED 11/1/16 END
 
     @property
-    def _mech_tuples(self):
-        return self.__mech_tuples__
+    def _mechs(self):
+        return self.__mechs__
 
-    @_mech_tuples.setter
-    def _mech_tuples(self, value):
-        self.__mech_tuples__ = value
\ No newline at end of file
+    @_mechs.setter
+    def _mechs(self, value):
+        self.__mechs__ = value
diff --git a/PsyNeuLink/Components/Projections/ControlProjection.py b/PsyNeuLink/Components/Projections/ModulatoryProjections/ControlProjection.py
similarity index 63%
rename from PsyNeuLink/Components/Projections/ControlProjection.py
rename to PsyNeuLink/Components/Projections/ModulatoryProjections/ControlProjection.py
index 990c8bf814b..19cdfebbb58 100644
--- a/PsyNeuLink/Components/Projections/ControlProjection.py
+++ b/PsyNeuLink/Components/Projections/ModulatoryProjections/ControlProjection.py
@@ -14,12 +14,11 @@
 Overview
 --------
 
-A ControlProjection is a subclass of `Projection` that projects to the `parameterState <ParameterState>`
-of a `ProcessingMechanism`. It takes the value of an `outputState <OutputState> of another mechanism (e.g., usually a
-`ControlMechanism <ControlMechanism>`), and uses it to  modify the value of the parameter associated with the
-parameterState to which it projects.  All of the ControlProjections in a system, along with its other 
-`control components <ControlMechanism>`, can be displayed using the system's `show_graph` method with its 
-**show_control** argument assigned :keyword:`True`.
+A ControlProjection is a subclass of `ModulatoryProjection` that projects to the `parameterState <ParameterState>` of
+a `ProcessingMechanism`. It takes the value of a `ControlSignal` of a `ControlMechanism`, and uses it to  modify the
+value of the parameter associated with the ParameterState to which it projects.  All of the ControlProjections in a
+system, along with its other `control components <ControlMechanism>`, can be displayed using the system's `show_graph`
+method with its **show_control** argument assigned as :keyword:`True`.
 
 .. _ControlProjection_Creation:
 
@@ -27,41 +26,56 @@
 ----------------------------
 
 A ControlProjection can be created using any of the standard ways to `create a projection <Projection_Creation>`,
-or by including it in the `specification of a parameter <ParameterState_Specifying_Parameters>` for a mechanism,
-MappingProjection, or the :keyword:`function` of either of these.  If a ControlProjection is created using its
-constructor on its own, the `receiver <ControlProjection.receiver>` argument must be specified.  If it is included in a
-parameter specification, the parameterState for the parameter being specified will be assigned as the
+or by including it in a tuple that `specifies a parameter <ParameterState_Specifying_Parameters>` for a `Mechanism`,
+`MappingProjection`, or the `function <Component.function>` of either of these.  If a ControlProjection is created
+using its constructor on its own, the `receiver <ControlProjection.receiver>` argument must be specified.  If it is
+included in a parameter specification, the ParameterState for the parameter being specified will be assigned as the
 ControlProjection's `receiver <ControlProjection.receiver>`.  If its `sender <ControlProjection.sender>` is not
 specified, its assignment depends on the `receiver <ControlProjection.receiver>`.  If the receiver belongs to a
-mechanism that is part of a system, then the ControlProjection's `sender <ControlProjection.sender>` is assigned to an
-outputState of the system's `controller`. Otherwise, the `sender <ControlProjection.sender>` is assigned to
-the outputState of a `DefaultControlMechanism`.
+Mechanism that is part of a System, then the ControlProjection's `sender <ControlProjection.sender>` is assigned to a
+`ControlSignal` of the System's `controller`.
+
+.. _ControlProjection_Deferred_Initialization:
+
+Deferred Initialization
+~~~~~~~~~~~~~~~~~~~~~~~
+
+When a ControlProjection is created, its full initialization is :ref:`deferred <Component_Deferred_Init>` until its
+`sender <ControlProjection.sender>` and `receiver <ControlProjection.receiver>` have been fully specified.  This allows
+a ControlProjection to be created before its `sender` (and/or `receiver`) have been created (e.g., before them in a
+script), by calling its constructor without specifying its **sender** or **receiver** arguments. However, for the
+ControlProjection to be operational, initialization must be completed by calling its `deferred_init` method.  This is
+not necessary if the parameter(s) to be controlled are specified in the **control_signals** argument of a `System`, or
+in a tuple specifying the parameter of a `Mechanism` (or its `function <Mechanism.function`) that is part of a System
+-- in those cases, deferred initialization is completed automatically.
 
 .. _ControlProjection_Structure:
 
 Structure
 ---------
 
-A ControlProjection has the same structure as a `Projection`.  Its `sender <ControlProjection.sender>`
-can be the outputState of any mechanism, but is generally a `ControlMechanism <ControlMechanism>` and commonly the
-`ControlSignal` of an `EVCMechanism`.  Its `receiver <ControlProjection.receiver>` is always the `parameterState
-<ParameterState>` of a mechanism or `MappingProjection`, that is associated with a parameter of either the
-parameterState's owner or it owner's :keyword:`function`.  The `function <ControlProjection.function>` of a
-ControlProjection is, by default, the identity function;  that is, it uses the :keyword:`value` of its `sender
-<ControlProjection.sender>` to modify the value of the parameter that it controls.
+The `sender <ControlProjection.sender>` of a ControlProjection is a `ControlSignal` of a `ControlMechanism`.  Its
+`receiver <ControlProjection.receiver>` is the `ParameterState` of a `Mechanism` or `MappingProjection`, that is
+associated with a parameter of the ParameterState's owner or its owner's `function <Component.function>`.  The
+`function <ControlProjection.function>` of a ControlProjection is, by default, the identity function;  that is,
+it conveys the `value <ControlSignal.value>` of its `sender <ControlProjection.sender>` to
+its `receiver <ControlProjection.receiver>`, for use in modifying the value of the parameter that it controls.
 
 .. _ControlProjection_Execution:
 
 Execution
 ---------
 
-A ControlProjection uses its `function <ControlProjection.function>` to assign its `value <ControlProjection.value>`
-from the one received  from its `sender <ControlProjection.sender>`.  This is used by the paramaterState to which the
-ControlProjection projects to modify the corresponding parameter of its owner or owner's :keyword:`function`.
+A ControlProjection cannot be executed directly.  It is executed when the `ParameterState` to which it projects is
+updated.  Note that this only occurs when the ProcessingMechanism to which the `ParameterState` belongs is executed
+(see :ref:`Lazy Evaluation <LINK>` for an explanation of "lazy" updating). When a ControlProjection is executed, its
+`function <ControlProjection.function>` assigns the value of the `ControlSignal` from which it projects as its own
+`value <ControlProjection.value>`. This is used by the `ParameterState` to which the ControlProjection projects to
+modify the parameter for which it is responsible.
 
 .. note::
    The changes in a parameter in response to the execution of a ControlProjection are not applied until the
-   mechanism that receives the projection are next executed; see :ref:`Lazy Evaluation` for an explanation of "lazy"
+   Mechanism that receives the projection are next executed; see :ref:`Lazy Evaluation` for an explanation of "lazy"
    updating).
 
 .. _ControlProjection_Class_Reference:
@@ -72,12 +86,15 @@
 
 """
 
-from PsyNeuLink.Components import DefaultController
 from PsyNeuLink.Components.Functions.Function import *
 from PsyNeuLink.Components.Projections.Projection import *
+from PsyNeuLink.Components.Projections.ModulatoryProjections.ModulatoryProjection import ModulatoryProjection_Base
+from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.ControlMechanism import ControlMechanism_Base
+
+parameter_keywords.update({CONTROL_PROJECTION, CONTROL})
+projection_keywords.update({CONTROL_PROJECTION, CONTROL})
 
-projection_keywords.update({CONTROL_PROJECTION})
-parameter_keywords.update({CONTROL_PROJECTION})
+CONTROL_SIGNAL_PARAMS = 'control_signal_params'
 
 class ControlProjectionError(Exception):
     def __init__(self, error_value):
@@ -86,14 +103,15 @@ def __init__(self, error_value):
     def __str__(self):
         return repr(self.error_value)
 
-class ControlProjection(Projection_Base):
+class ControlProjection(ModulatoryProjection_Base):
     """
-    ControlProjection( \
-     sender=None,      \
-     receiver=None,    \
-     function=Linear   \
-     params=None,      \
-     name=None,        \
+    ControlProjection(     \
+     sender=None,          \
+     receiver=None,        \
+     function=Linear       \
+     control_signal_params \
+     params=None,          \
+     name=None,            \
      prefs=None)
 
      Implements a projection that controls the parameter of a mechanism or its :keyword:`function`.
@@ -120,7 +138,7 @@ class ControlProjection(Projection_Base):
             + paramClassDefaults:
                 FUNCTION:Linear,
                 FUNCTION_PARAMS:{SLOPE: 1, INTERCEPT: 0},  # Note: this implements identity function
-                PROJECTION_SENDER: DefaultController, # ControlProjection (assigned to class ref in __init__ module)
+                PROJECTION_SENDER: ControlMechanism_Base
                 PROJECTION_SENDER_VALUE: [defaultControlAllocation],
                 CONTROL_SIGNAL_COST_OPTIONS:ControlSignalCostOptions.DEFAULTS,
                 ALLOCATION_SAMPLES: DEFAULT_ALLOCATION_SAMPLES,
@@ -134,8 +152,11 @@ class ControlProjection(Projection_Base):
     sender : Optional[Mechanism or OutputState]
         specifies the source of the input for the ControlProjection;  usually an `outputState <OutputState>` of a
         `ControlMechanism <ControlMechanism>`, and commonly the `ControlSignal` of an `EVCMechanism`.  If it is not
-        specified, an outputState of the `DefaultControlMechanism` for the system to which the receiver belongs will
-        be assigned.
+        specified, the ControlProjection will 
+        COMMENT:
+        remain in DEFER_INITIALIZATION status, and will 
+        COMMENT
+        be ignored during execution.
 
     receiver : Optional[Mechanism or ParameterState]
         specifies the parameterState associated with the parameter to be controlled.  This must be specified,
@@ -144,6 +165,11 @@ class ControlProjection(Projection_Base):
     function : TransferFunction : default Linear
         specifies the function used to convert the :keyword:`value` of the ControlProjection's
         `sender <ControlProjection.sender>`  to its own `value <ControlProjection.value>`.
+        
+    control_signal_params : Dict[param keyword, param value]
+        a `parameter dictionary <ParameterState_Specifying_Parameters>` that can be used to specify the parameters for
+        the `ControlSignal` that is the sender of the projection (see `ControlSignal_Structure` for a description
+        of ControlSignal parameters). 
 
     params : Optional[Dict[param keyword, param value]]
         a `parameter dictionary <ParameterState_Specifying_Parameters>` that can be used to specify the parameters for
@@ -182,13 +208,13 @@ class ControlProjection(Projection_Base):
 
     name : str : default ControlProjection-<index>
         the name of the ControlProjection.
-        Specified in the `name` argument of the constructor for the projection;
+        Specified in the **name** argument of the constructor for the projection;
         if not is specified, a default is assigned by ProjectionRegistry
         (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
 
     prefs : PreferenceSet or specification dict : Projection.classPreferences
         the `PreferenceSet` for projection.
-        Specified in the `prefs` argument of the constructor for the projection;
+        Specified in the **prefs** argument of the constructor for the projection;
         if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
         (see :doc:`PreferenceSet <LINK>` for details).
 
@@ -207,7 +233,7 @@ class ControlProjection(Projection_Base):
 
     paramClassDefaults = Projection_Base.paramClassDefaults.copy()
     paramClassDefaults.update({
-        PROJECTION_SENDER: DefaultController,
+        PROJECTION_SENDER: ControlMechanism_Base,
         PROJECTION_SENDER_VALUE: defaultControlAllocation})
 
     @tc.typecheck
@@ -215,7 +241,7 @@ def __init__(self,
                  sender=None,
                  receiver=None,
                  function=Linear,
-                 control_signal:tc.optional(dict)=None,
+                 control_signal_params:tc.optional(dict)=None,
                  params=None,
                  name=None,
                  prefs:is_pref_set=None,
@@ -223,18 +249,22 @@ def __init__(self,
 
         # Assign args to params and functionParams dicts (kwConstants must == arg names)
         params = self._assign_args_to_param_dicts(function=function,
-                                                  control_signal=control_signal
-                                                  )
+                                                  control_signal_params=control_signal_params,
+                                                  params=params)
 
         # If receiver has not been assigned, defer init to State.instantiate_projection_to_state()
-        if not receiver:
+        if (sender is None or sender.value is DEFERRED_INITIALIZATION or
+                    receiver is None or receiver.value is DEFERRED_INITIALIZATION):
             # Store args for deferred initialization
             self.init_args = locals().copy()
             self.init_args['context'] = self
             self.init_args['name'] = name
-            # Delete this as it has breen moved to params dict (so it will not be passed to Projection.__init__)
-            del self.init_args[CONTROL_SIGNAL]
-
+            # Delete this as it has been moved to params dict (so it will not be passed to Projection.__init__)
+            del self.init_args[CONTROL_SIGNAL_PARAMS]
+            if sender:
+                self.init_args[SENDER] = sender
+            if receiver:
+                self.init_args[RECEIVER] = receiver
             # Flag for deferred initialization
             self.value = DEFERRED_INITIALIZATION
             return
@@ -243,16 +273,16 @@ def __init__(self,
         # Note: pass name of mechanism (to override assignment of componentName in super.__init__)
         # super(ControlSignal_Base, self).__init__(sender=sender,
         super(ControlProjection, self).__init__(sender=sender,
-                                            receiver=receiver,
-                                            params=params,
-                                            name=name,
-                                            prefs=prefs,
-                                            context=self)
+                                                receiver=receiver,
+                                                params=params,
+                                                name=name,
+                                                prefs=prefs,
+                                                context=self)
 
 
     def _instantiate_sender(self, params=None, context=None):
-# FIX: NEEDS TO BE BETTER INTEGRATED WITH super()._instantiate_sender
-        """Check if DefaultController is being assigned and if so configures it for the requested ControlProjection
+
+        """Check if DefaultController is being assigned and if so configure it for the requested ControlProjection
 
         If self.sender is a Mechanism, re-assign to <Mechanism>.outputState
         Insure that sender.value = self.variable
@@ -260,49 +290,31 @@ def _instantiate_sender(self, params=None, context=None):
         This method overrides the corresponding method of Projection, before calling it, to check if the
             DefaultController is being assigned as sender and, if so:
             - creates projection-dedicated inputState and outputState in DefaultController
-            - puts them in DefaultController's inputStates and outputStates attributes
+            - puts them in DefaultController's input_states and outputStates attributes
             - lengthens variable of DefaultController to accommodate the ControlProjection
             - updates value of the DefaultController (in response to the new variable)
         Notes:
-            * the default function of the DefaultController simply maps the inputState value to the outputState
+            * the default function of the DefaultControlMechanism simply maps the inputState value to the outputState
             * the params arg is assumed to be a dictionary of params for the ControlSignal of the ControlMechanism
 
         :return:
         """
 
+        # A Process can't be the sender of a ControlMechanism
         if isinstance(self.sender, Process):
-            raise ProjectionError("Illegal attempt to add a ControlProjection from a Process {0} "
+            raise ProjectionError("PROGRAM ERROR: attempt to add a ControlProjection from a Process {0} "
                                   "to a mechanism {0} in pathway list".format(self.name, self.sender.name))
 
-        # If sender is a class:
-        # - assume it is Mechanism or State class ref (as validated in _validate_params)
-        # - implement default sender of the corresponding type
-        if inspect.isclass(self.sender):
-            # self.sender = self.paramsCurrent[PROJECTION_SENDER](self.paramsCurrent[PROJECTION_SENDER_VALUE])
-# FIX 6/28/16:  IF CLASS IS ControlMechanism SHOULD ONLY IMPLEMENT ONCE;  THEREAFTER, SHOULD USE EXISTING ONE
-            self.sender = self.sender(self.paramsCurrent[PROJECTION_SENDER_VALUE])
-
-# FIX:  THE FOLLOWING CAN BE CONDENSED:
-# FIX:      ONLY TEST FOR ControlMechanism_Base (TO IMPLEMENT PROJECTION)
-# FIX:      INSTANTATION OF OutputState WILL BE HANDLED IN CALL TO super._instantiate_sender
-# FIX:      (CHECK TO BE SURE THAT THIS DOES NOT MUCK UP _instantiate_control_projection FOR ControlMechanism)
-        # If sender is a Mechanism (rather than a State) object, get (or instantiate) its State
-        #    (Note:  this includes ControlMechanism)
+        # If sender is specified as a Mechanism, validate that it is a ControlMechanism
         if isinstance(self.sender, Mechanism):
             # If sender is a ControlMechanism, call it to instantiate its ControlSignal projection
-            from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.ControlMechanism import ControlMechanism_Base
-            from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.ControlSignal import ControlSignalError
-            if isinstance(self.sender, ControlMechanism_Base):
-                try:
-                    params = params or self.control_signal
-                    self.sender._instantiate_control_projection(self, params=params, context=context)
-                except ControlSignalError as error_msg:
-                    raise FunctionError("Error in attempt to specify ControlSignal for {} of {}".
-                                        format(self.name, self.receiver.owner.name, error_msg))
+            if not isinstance(self.sender, ControlMechanism_Base):
+                raise ControlProjectionError("Mechanism specified as sender for {} ({}) must be a "
+                                                  "ControlMechanism (but it is a {})".
+                                    format(self.name, self.sender.name, self.sender.__class__.__name__))
 
         # Call super to instantiate sender
-
-        super(ControlProjection, self)._instantiate_sender(context=context)
+        super()._instantiate_sender(context=context)
 
 
     def _instantiate_receiver(self, context=None):
diff --git a/PsyNeuLink/Components/Projections/GatingProjection.py b/PsyNeuLink/Components/Projections/ModulatoryProjections/GatingProjection.py
similarity index 57%
rename from PsyNeuLink/Components/Projections/GatingProjection.py
rename to PsyNeuLink/Components/Projections/ModulatoryProjections/GatingProjection.py
index 9c4b6c3efbf..cf27b935a45 100644
--- a/PsyNeuLink/Components/Projections/GatingProjection.py
+++ b/PsyNeuLink/Components/Projections/ModulatoryProjections/GatingProjection.py
@@ -14,50 +14,51 @@
 Overview
 --------
 
-A GatingProjection is a subclass of `Projection` that projects to the `inputState <InputState>` or 
-`outputState <OutputState>` of a `ProcessingMechanism`. It takes the value of an `outputState <OutputState> of 
-another mechanism (e.g., usually a `GatingMechanism <GatingMechanism>`), and uses it to modulate the value of the 
-state to which it projects.
+A GatingProjection is a subclass of `ModulatoryProjection` that modulates the function of the `InputState` or
+`OutputState` of a `ProcessingMechanism`. It takes the value of a `GatingSignal` of a `GatingMechanism`, and uses it
+to modulate the value of the state to which it projects.
 
 .. _GatingProjection_Creation:
 
 Creating a GatingProjection
 ----------------------------
 
-A GatingProjection can be created using any of the standard ways to `create a projection <Projection_Creation>`,
-or by including it in the specification of an inputState or outputState .  If a GatingProjection is created using its
-constructor on its own, the `receiver <GatingProjection.receiver>` argument must be specified.  It can be specified
-as a particular inputState or outputState of a mechanism, or simply as a mechanism.  In the latter case, 
-the mechanism's `primary inputState <Mechanism_InputStates>` will be used. If the GatingProjection is included in an
-inputState or outputState specification, that state will be assigned as the GatingProjection's 
-`receiver <GatingProjection.receiver>`.  If GatingProjection's `sender <GatingProjection.sender>` is not
-specified, the `sender <GatingProjection.sender>` is assigned to the outputState of a `DefaultGatingMechanism`.
+A GatingProjection can be created using any of the standard ways to `create a projection <Projection_Creation>`, or
+by including it in the specification of an `InputState` or `OutputState` .  If a GatingProjection is created using its
+constructor on its own, the **receiver** argument must be specified.  It can be specified as a particular InputState
+or OutputState of a Mechanism, or simply as a `Mechanism`.  In the latter case, the Mechanism's
+`primary InputState <Mechanism_InputStates>` will be used. If the GatingProjection is included in an InputState or
+OutputState specification, that state will be assigned as the GatingProjection's `receiver <GatingProjection.receiver>`.
+If a GatingProjection's `sender <GatingProjection.sender>` is not specified, the `sender <GatingProjection.sender>`
+is assigned to the OutputState of a `DefaultGatingMechanism`.
 
 .. _GatingProjection_Structure:
 
 Structure
 ---------
 
-A GatingProjection has the same structure as a `Projection`.  Its `sender <GatingProjection.sender>`
-can be the outputState of any mechanism, but is generally a `GatingMechanism <GatingMechanism>`.  Its 
-`receiver <GatingProjection.receiver>` is the `inputState <InputState>` or `outputState <OutputState>`  
-of a `ProcessingMechanism`.  The `function <GatingProjection.function>` of a
-GatingProjection is, by default, the identity function;  that is, it uses the :keyword:`value` of its `sender
-<GatingProjection.sender>` to modulate the value of the state to which it projects.
+The `sender <GatingProjection.sender>` of a GatingProjection is a `GatingSignal` of a
+`GatingMechanism <GatingMechanism>`.  Its `receiver <GatingProjection.receiver>` is the `InputState` or `OutputState`
+of a `ProcessingMechanism`.  The `function <GatingProjection.function>` of a GatingProjection is, by default, the
+identity function;  that is, it conveys the `value <GatingSignal.value>` of its `sender <GatingProjection.sender>` to
+its `receiver <GatingProjection.receiver>`, for use in modifying the value of the State that it gates.
 
 .. _GatingProjection_Execution:
 
 Execution
 ---------
 
-A GatingProjection uses its `function <GatingProjection.function>` to assign its `value <GatingProjection.value>`
-from the one received from its `sender <GatingProjection.sender>`.  This is used by the inputState or outputState to 
-which the GatingProjection projects to modulate its own value.
+A GatingProjection cannot be executed directly.  It is executed when the `InputState` or `OutputState` to which it
+projects is updated.  Note that this only occurs when the ProcessingMechanism to which the State belongs (see
+:ref:`Lazy Evaluation <LINK>` for an explanation of "lazy" updating).
+When a GatingProjection is executed, its `function <GatingProjection.function>` assigns the value of the `GatingSignal`
+from which it projects as its own `value <GatingProjection.value>`. This is used by the InputState or OutputState
+to which the GatingProjection projects to modulate its own `value <State.value>`.
 
 .. note::
-   The changes in an inputState or outputState's value in response to the execution of a GatingProjection are not 
-   applied until the mechanism to which the state belongs is next executed; see :ref:`Lazy Evaluation` for an 
-   explanation of "lazy" updating).
+   The changes in an InputState or OutputState's `value <State.value >`in response to the execution of a
+   GatingProjection are not applied until the Mechanism to which the State belongs is next executed;
+   see :ref:`Lazy Evaluation` for an explanation of "lazy" updating).
 
 .. _GatingProjection_Class_Reference:
 
@@ -67,12 +68,16 @@
 
 """
 
-from PsyNeuLink.Components import DefaultGatingMechanism
+# from PsyNeuLink.Components import DefaultGatingMechanism
 from PsyNeuLink.Components.Functions.Function import *
 from PsyNeuLink.Components.Projections.Projection import *
+from PsyNeuLink.Components.Projections.ModulatoryProjections.ModulatoryProjection import ModulatoryProjection_Base
+from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.GatingMechanisms.GatingMechanism import GatingMechanism
 
-projection_keywords.update({GATING_PROJECTION})
-parameter_keywords.update({GATING_PROJECTION})
+
+parameter_keywords.update({GATING_PROJECTION, GATING})
+projection_keywords.update({GATING_PROJECTION, GATING})
+GATING_SIGNAL_PARAMS = 'gating_signal_params'
 
 class GatingProjectionError(Exception):
     def __init__(self, error_value):
@@ -81,7 +86,7 @@ def __init__(self, error_value):
     def __str__(self):
         return repr(self.error_value)
 
-class GatingProjection(Projection_Base):
+class GatingProjection(ModulatoryProjection_Base):
     """
     GatingProjection(  \
      sender=None,      \
@@ -124,7 +129,7 @@ class GatingProjection(Projection_Base):
     Arguments
     ---------
 
-    sender : Optional[Mechanism or OutputState]
+    sender : Optional[Mechanism or GatingSignal]
         specifies the source of the input for the GatingProjection;  usually an `outputState <OutputState>` of a
         `GatingMechanism <GatingMechanism>`.  If it is not specified, an outputState of the `DefaultGatingMechanism` 
         for the system to which the receiver belongs will be assigned.
@@ -174,13 +179,13 @@ class GatingProjection(Projection_Base):
 
     name : str : default GatingProjection-<index>
         the name of the GatingProjection.
-        Specified in the `name` argument of the constructor for the projection;
+        Specified in the **name** argument of the constructor for the projection;
         if not is specified, a default is assigned by ProjectionRegistry
         (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
 
     prefs : PreferenceSet or specification dict : Projection.classPreferences
         the `PreferenceSet` for projection.
-        Specified in the `prefs` argument of the constructor for the projection;
+        Specified in the **prefs** argument of the constructor for the projection;
         if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
         (see :doc:`PreferenceSet <LINK>` for details).
 
@@ -199,15 +204,16 @@ class GatingProjection(Projection_Base):
 
     paramClassDefaults = Projection_Base.paramClassDefaults.copy()
     paramClassDefaults.update({
-        PROJECTION_SENDER: DefaultGatingMechanism,
-        PROJECTION_SENDER_VALUE: defaultGatingSignal})
+        PROJECTION_SENDER: GatingMechanism,
+        PROJECTION_SENDER_VALUE: defaultGatingPolicy})
 
     @tc.typecheck
     def __init__(self,
                  sender=None,
                  receiver=None,
-                 function=Linear,
-                 gating_signal:tc.optional(dict)=None,
+                 function=Linear(params={FUNCTION_OUTPUT_TYPE:FunctionOutputType.RAW_NUMBER}),
+                 # function=Linear,
+                 gating_signal_params:tc.optional(dict)=None,
                  params=None,
                  name=None,
                  prefs:is_pref_set=None,
@@ -215,17 +221,17 @@ def __init__(self,
 
         # Assign args to params and functionParams dicts (kwConstants must == arg names)
         params = self._assign_args_to_param_dicts(function=function,
-                                                  gating_signal=gating_signal
-                                                  )
+                                                  gating_signal_params=gating_signal_params,
+                                                  params=params)
 
         # If receiver has not been assigned, defer init to State.instantiate_projection_to_state()
-        if not receiver:
+        if sender is None or receiver is None:
             # Store args for deferred initialization
             self.init_args = locals().copy()
             self.init_args['context'] = self
             self.init_args['name'] = name
             # Delete this as it has breen moved to params dict (so it will not be passed to Projection.__init__)
-            del self.init_args[GATING_SIGNAL]
+            del self.init_args[GATING_SIGNAL_PARAMS]
 
             # Flag for deferred initialization
             self.value = DEFERRED_INITIALIZATION
@@ -239,87 +245,64 @@ def __init__(self,
                          name=name,
                          prefs=prefs,
                          context=self)
-
+        TEST = True
 
     def _instantiate_sender(self, params=None, context=None):
-# FIX: NEEDS TO BE BETTER INTEGRATED WITH super()._instantiate_sender
-        """Check if DefaultGatingMechanism is being assigned and if so configures it for the requested GatingProjection
-
-        If self.sender is a Mechanism, re-assign to <Mechanism>.outputState
-        Insure that sender.value = self.variable
-
-        This method overrides the corresponding method of Projection, before calling it, to check if the
-            DefaultGatingMechanism is being assigned as sender and, if so:
-            - creates a projection-dedicated inputState and outputState in DefaultGatingMechanism
-            - puts them in DefaultGatingMechanism's inputStates and outputStates attributes
-            - lengthens variable of DefaultGatingMechanism to accommodate the GatingProjection channel
-            - updates value of DefaultGatingMechanism (in resposne to new variable)
-        Notes:
-            * the default function of the DefaultGatingMechanism simply maps the inputState value to the outputState
-            * the params arg is assumed to be a dictionary of params for the gating_signal of the GatingMechanism
-
-        :return:
+        """Check that sender is not a process and that, if specified as a Mechanism, it is a GatingMechanism 
         """
 
+        # A Process can't be the sender of a GatingProjection
         if isinstance(self.sender, Process):
-            raise ProjectionError("Illegal attempt to add a GatingProjection from a Process {0} "
-                                  "to a mechanism {0} in pathway list".format(self.name, self.sender.name))
-
-        # If sender is a class:
-        # - assume it is Mechanism or State class ref (as validated in _validate_params)
-        # - implement default sender of the corresponding type
-        if inspect.isclass(self.sender):
-            # self.sender = self.paramsCurrent[PROJECTION_SENDER](self.paramsCurrent[PROJECTION_SENDER_VALUE])
-# FIX 6/28/16:  IF CLASS IS GatingMechanism SHOULD ONLY IMPLEMENT ONCE;  THEREAFTER, SHOULD USE EXISTING ONE
-            self.sender = self.sender(self.paramsCurrent[PROJECTION_SENDER_VALUE])
-
-# FIX:  THE FOLLOWING CAN BE CONDENSED:
-# FIX:      ONLY TEST FOR GatingMechanism_Base (TO IMPLEMENT PROJECTION)
-# FIX:      INSTANTATION OF OutputState WILL BE HANDLED IN CALL TO super._instantiate_sender
-# FIX:      (CHECK TO BE SURE THAT THIS DOES NOT MUCK UP _instantiate_gating_projection FOR GatingMechanism)
-        # If sender is a Mechanism (rather than a State) object, get (or instantiate) its State
-        #    (Note:  this includes GatingMechanism)
+            raise ProjectionError("PROGRAM ERROR: attempt to add a {} from a Process {0} "
+                                  "to a mechanism {0} in pathway list".
+                                  format(GATING_PROJECTION, self.name, self.sender.name))
+
+        # If sender is specified as a Mechanism, validate that it is a GatingMechanism
         if isinstance(self.sender, Mechanism):
-            # If sender is a GatingMechanism, call it to instantiate its GatingSignal projection
-            from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.GatingMechanisms.GatingMechanism import GatingMechanism_Base
-            from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.GatingMechanisms.GatingSignal import GatingSignalError
-            if isinstance(self.sender, GatingMechanism_Base):
-                try:
-                    params = params or self.gating_signal
-                    self.sender._instantiate_gating_projection(self, params=params, context=context)
-                except GatingSignalError as error_msg:
-                    raise FunctionError("Error in attempt to specify GatingSignal for {} of {}".
-                                        format(self.name, self.receiver.owner.name, error_msg))
+            if not isinstance(self.sender, GatingMechanism):
+                raise GatingProjectionError("Mechanism specified as sender for {} ({}) must be a {} (but it is a {})".
+                                    format(GATING_MECHANISM,self.name, self.sender.name,self.sender.__class__.__name__))
 
         # Call super to instantiate sender
-
         super()._instantiate_sender(context=context)
 
     def _validate_params(self, request_set, target_set=None, context=None):
 
-        super()._validate_params(self, request_set=request_set, target_set=target_set, context=context)
+        super()._validate_params(request_set=request_set, target_set=target_set, context=context)
 
         if INITIALIZING in context:
+            from PsyNeuLink.Components.States.InputState import InputState
+            from PsyNeuLink.Components.States.OutputState import OutputState
             if not isinstance(self.receiver, (InputState, OutputState, Mechanism)):
                 raise GatingProjectionError("Receiver specified for {} {} is not a "
                                             "Mechanism, InputState or OutputState".
                                             format(self.receiver, self.name))
 
     def _instantiate_receiver(self, context=None):
-        # FIX: THIS NEEDS TO BE PUT BEFORE _instantiate_function SINCE THAT USES self.receiver
-        """Handle situation in which self.receiver was specified as a Mechanism (rather than State)
-
-        Overrides Projection._instantiate_receiver, to determine if the receiver is specified as a Mechanism,
-            and, if so, assigns receiver as its primary inputState (the default receiver for a GatingMechanism);
-            otherwise, passes control to Projection._instantiate_receiver for validation
-
+        """Assign state if receiver is Mechanism, and match output to param being modulated
         """
+        # If receiver specification was a Mechanism, re-assign to the mechanism's primary inputState
         if isinstance(self.receiver, Mechanism):
-            self.receiver = self.receiver.inputState
+            # If Mechanism is specified as receiver, assign GatingProjection to primary inputState as the default
+            self.receiver = self.receiver.input_states[0]
 
-        super(ControlProjection, self)._instantiate_receiver(context=context)
+        # # Match type of GatingProjection.value to type to the parameter being modulated
+        # modulated_param = self.sender.modulation
+        # function = self.receiver.function_object
+        # function_param = function.params[modulated_param]
+        # function_param_value = function.params[function_param]
+        # gating_projection_function = self.function.__self__
+        # gating_projection_function.functionOutputType = type(function_param_value)
+        # # ASSIGN FUNCTION TYPE TO FUNCTION HERE
+
+        super()._instantiate_receiver(context=context)
 
     def execute(self, params=None, clock=CentralClock, time_scale=None, context=None):
+    # def execute(self, params=None, clock=CentralClock, time_scale=TimeScale.TRIAL, context=None):
         self.variable = self.sender.value
         self.value = self.function(variable=self.variable, params=params, time_scale=time_scale, context=context)
         return self.value
+
+    @property
+    def gating_policy(self):
+        return self.sender.value
diff --git a/PsyNeuLink/Components/Projections/LearningProjection.py b/PsyNeuLink/Components/Projections/ModulatoryProjections/LearningProjection.py
similarity index 85%
rename from PsyNeuLink/Components/Projections/LearningProjection.py
rename to PsyNeuLink/Components/Projections/ModulatoryProjections/LearningProjection.py
index b2f72d37065..8506380e0ec 100644
--- a/PsyNeuLink/Components/Projections/LearningProjection.py
+++ b/PsyNeuLink/Components/Projections/ModulatoryProjections/LearningProjection.py
@@ -14,11 +14,11 @@
 Overview
 --------
 
-A LearningProjection is a subclass of `Projection` that projects from a `LearningMechanism` to the
+A LearningProjection is a subclass of `ModulatoryProjection` that projects from a `LearningMechanism` to the
 MATRIX `parameterState <ParameterState>` of a `MappingProjection`, and modifies the value of the
 `matrix <MappingProjection.matrix>` parameter of that MappingProjection.  All of the LearningProjections in a system, 
 along with its other `learning components <LearningMechanism>`, can be displayed using the system's `show_graph` method 
-with its **show_learning** argument assigned :keyword:`True`.
+with its **show_learning** argument assigned as :keyword:`True`.
 
 .. _LearningProjection_Creation:
 
@@ -29,19 +29,24 @@
 or by including it in a tuple that specifies the `matrix <Mapping_Matrix_Specification>` parameter for a
 `MappingProjection`.  LearningProjections are also created automatically, along with the other
 `components required for learning <LearningMechanism_Learning_Configurations>`, when learning is specified for a
-`process <Process_Learning>` or a `system <System_Execution_Learning>`.
+`Process <Process_Learning>` or a `System <System_Execution_Learning>`.
 
 If a LearningProjection is created using its constructor on its own, the `receiver <ControlProjection.receiver>`
 argument must be specified.  If it is included in the `matrix specification <Mapping_Matrix_Specification>` for a
-MappingProjection, the parameterState for the MappingProjection's MATRIX will be assigned as the
+`MappingProjection`, the *MATRIX* `ParameterState` for the MappingProjection will be assigned as the
 LearningProjection's `receiver <LearningProjection.receiver>`.  If its `sender <LearningProjection.sender>` is not
 specified, its assignment depends on the `receiver <LearningProjection.receiver>`.  If the receiver belongs to a
-MappingProjection that projects between two mechanisms that are both in the same `process <Process_Learning>` or
-`system <System_Execution_Learning>`, then the LearningProjection's `sender <LearningProjection.sender>` is assigned
-to the `LEARNING_SIGNAL <LearningMechanism_Learning_Signal>` outputState of the `LearningMechanism` for the
-MappingProjection. If there is none, it is `created <LearningMechanism_Creation>` along with any other components
+MappingProjection that projects between two mechanisms that are both in the same `Process <Process_Learning>` or
+`System <System_Execution_Learning>`, then the LearningProjection's `sender <LearningProjection.sender>` is assigned
+to a `LearningSignal` of the `LearningMechanism` for the MappingProjection. If there is none, it is
+`created <LearningMechanism_Creation>` along with any other components
 needed to implement learning for the MappingProjection (see `LearningMechanism_Learning_Configurations`).
 
+.. _LearningProjection_Deferred_Initialization:
+
+Deferred Initialization
+~~~~~~~~~~~~~~~~~~~~~~~
+
 When a LearningProjection is created, its full initialization is :ref:`deferred <Component_Deferred_Init>` until its
 `sender <LearningProjection.sender>` and `receiver <LearningProjection.receiver>` have been fully specified.  This
 allows a LearningProjection to be created before its `sender` and/or `receiver` have been created (e.g., before them
@@ -56,28 +61,32 @@
 Structure
 ---------
 
-The `sender <LearningProjection.sender>` of a LearningProjection is the
-`LEARNING_SIGNAL <LearingMechanisms.Learning_Signal>` outputState of a LearningMechanism. Its
-`receiver <LearningProjection.receiver>` is the `MATRIX` parameterState of a MappingProjection,
-Its `function simply conveys the `learning_signal <LearningProjection.learning_signal>` received from its
-`sender <LearningProjection.sender>` to the `receiver <LearningProjection.receiver>`, possibly modulated by
-the `learning_rate <LearningProjection.learning_rate>`.
-
+The `sender <LearningProjection.sender>` of a LearningProjection is a `LearningSignal` of a `LearningMechanism`.  Its
+`receiver <LearningProjection.receiver>` is the *MATRIX* `ParameterState` of a `MappingProjection`,  The
+`function <LearningProjection.function>` of a LearningProjection is, by default, the identity function;  that is,
+it conveys the `value <LearningSignal.value>` of its `sender <LearningProjection.sender>` to
+its `receiver <LearningProjection.receiver>`, for use in modifying the `matrix <MappingProjection.matrix>` parameter
+of the `MappingProjection` being learned.
 
 .. _LearningProjection_Execution:
 
 Execution
 ---------
 
-A LearningProjection cannot be executed directly.  It is executed when its
-`learned_projection <LearningProjection.learned_projection>` is executed the MATRIX parameterState
-for the `learned_projection <LearningProjection.learned_projection>` is updated.  Note that these events only occur
-when the ProcessingMechanism to which the `learned_projection <LearningProjection.learned_projection>` projects is
+A LearningProjection cannot be executed directly.  It is executed when the *MATRIX* ParameterState to which it
+projects is updated.  This occurs when the `learned_projection <LearningProjection.learned_projection>` (i.e.,
+`MappingProjection` to which the *MATRIX* ParameterState belongs) is updated. Note that these events occur only
+when the ProcessingMechanism that receives the `learned_projection <LearningProjection.learned_projection>` is
 executed (see :ref:`Lazy Evaluation <LINK>` for an explanation of "lazy" updating). When the LearningProjection is
-executed, it gets the `learning_signal` from its `sender <LearningProjection.sender>`
-and conveys this to its `receiver <LearningProjection.receiver>`, modified only by the `learning_rate
-<LearningProjection.learning_rate>` if that is specified.  Additional attributes are described under
-`Class Reference <LearningProjection_Class_Reference>` below.
+executed, it gets the `learning_signal <LearningProjection.learning_signal>` from its
+`sender <LearningProjection.sender>` and conveys this to its `receiver <LearningProjection.receiver>`,
+possibly modified by its `learning_rate <LearningProjection.learning_rate>` parameter if that is specified.
+
+.. note::
+   The changes in the `matrix <MappingProjection.maitrix>` parameter of a `MappingProjection` in response to the
+   execution of a LearningProjection are not applied until the `Mechanism` that receives MappingProjection are next
+   executed; see :ref:`Lazy Evaluation` for an explanation of "lazy" updating).
+
 
 .. _LearningProjection_Class_Reference:
 
@@ -86,29 +95,28 @@
 
 """
 
-from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.LearningMechanisms.LearningMechanism import \
-    LearningMechanism, ACTIVATION_INPUT, ACTIVATION_OUTPUT, ERROR_SIGNAL
-
-from PsyNeuLink.Components.Functions.Function import BackPropagation, Logistic
-from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ObjectiveMechanism import ObjectiveMechanism
-from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ObjectiveMechanism import _objective_mechanism_role
-from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ProcessingMechanism import ProcessingMechanism_Base
-from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
+from PsyNeuLink.Components.Functions.Function import Linear, BackPropagation
+from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.LearningMechanisms.LearningMechanism \
+    import LearningMechanism, ERROR_SIGNAL
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ObjectiveMechanisms.ObjectiveMechanism import \
+    ObjectiveMechanism
+from PsyNeuLink.Components.Projections.ModulatoryProjections.ModulatoryProjection import ModulatoryProjection_Base
 from PsyNeuLink.Components.Projections.Projection import *
 from PsyNeuLink.Components.Projections.Projection import _is_projection_spec
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
 from PsyNeuLink.Components.States.OutputState import OutputState
 from PsyNeuLink.Components.States.ParameterState import ParameterState
 
 # Params:
 
-parameter_keywords.update({LEARNING_PROJECTION})
-projection_keywords.update({LEARNING_PROJECTION})
+parameter_keywords.update({LEARNING_PROJECTION, LEARNING})
+projection_keywords.update({LEARNING_PROJECTION, LEARNING})
 
 def _is_learning_spec(spec):
     """Evaluate whether spec is a valid learning specification
 
-    Return :keyword:`true` if spec is LEARNING or a valid projection_spec (see Projection._is_projection_spec
-    Otherwise, return :keyword:`False`
+    Return `True` if spec is LEARNING or a valid projection_spec (see Projection._is_projection_spec.
+    Otherwise, return `False`
 
     """
     if spec is LEARNING:
@@ -122,13 +130,6 @@ def _is_learning_spec(spec):
 WT_MATRIX_SENDER_DIM = 0
 WT_MATRIX_RECEIVERS_DIM = 1
 
-TARGET_ERROR = "TARGET_ERROR"
-TARGET_ERROR_MEAN = "TARGET_ERROR_MEAN"
-TARGET_ERROR_SUM = "TARGET_ERROR_SUM"
-TARGET_SSE = "TARGET_SSE"
-TARGET_MSE = "TARGET_MSE"
-
-
 DefaultTrainingMechanism = ObjectiveMechanism
 
 class LearningProjectionError(Exception):
@@ -140,7 +141,7 @@ def __str__(self):
 
 
 
-class LearningProjection(Projection_Base):
+class LearningProjection(ModulatoryProjection_Base):
     """
     LearningProjection(               \
                  sender=None,         \
@@ -174,7 +175,7 @@ class LearningProjection(Projection_Base):
                                                  # Note:  assumes MappingProjection.function is LinearCombination
                     default
                     + FUNCTION_PARAMS: {OPERATION: SUM},
-                    + PARAMETER_MODULATION_OPERATION: ModulationOperation.ADD,
+                    + PARAMETER_MODULATION_OPERATION: Modulation.ADD,
                     + PROJECTION_TYPE: LEARNING_PROJECTION
 
             + paramNames (dict)
@@ -187,8 +188,8 @@ class LearningProjection(Projection_Base):
 
     Arguments
     ---------
-    sender : Optional[LearningMechanism or LEARNING_SIGNAL OutputState of one]
-        the source of the `error_signal` for the LearningProjection. If it is not specified, one will be
+    sender : Optional[LearningMechanism or LearningSignal]
+        the source of the `learning_signal` for the LearningProjection;  If it is not specified, one will be
         `automatically created <LearningProjection_Automatic_Creation>` that is appropriate for the
         LearningProjection's `errorSource <LearningProjection.errorSource>`.
 
@@ -201,7 +202,7 @@ class LearningProjection(Projection_Base):
         `function <LearningMechanism.function>` attribute).
 
     learning_rate : Optional[float or int]
-        if specified, it is applied mulitiplicatively to `learning_signal` received from the `LearningMechanism`
+        if specified, it is applied mulitiplicatively to `learning_signal` received from the `LearningSignal`
         from which it projects (see `learning_rate <LearningProjection.learning_rate>` for additional details).
 
     params : Optional[Dict[param keyword, param value]]
@@ -256,7 +257,7 @@ class LearningProjection(Projection_Base):
        `function <MappingProjection.function>` of the receiver is `LinearCombination`.  By default it passes
        the following dictionary of specifications to the `receiver <LearningProjection.receiver>`::
            FUNCTION_PARAMS: {OPERATION: SUM,
-                             PARAMETER_MODULATION_OPERATION: ModulationOperation.ADD,
+                             PARAMETER_MODULATION_OPERATION: Modulation.ADD,
                              PROJECTION_TYPE: LEARNING_PROJECTION}
 
     learning_rate : Optional[float]
@@ -275,13 +276,13 @@ class LearningProjection(Projection_Base):
 
     name : str : default LearningProjection-<index>
         the name of the LearningProjection.
-        Specified in the `name` argument of the constructor for the projection;
+        Specified in the **name** argument of the constructor for the projection;
         if not is specified, a default is assigned by ProjectionRegistry
         (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
 
     prefs : PreferenceSet or specification dict : Projection.classPreferences
         the `PreferenceSet` for projection.
-        Specified in the `prefs` argument of the constructor for the projection;
+        Specified in the **prefs** argument of the constructor for the projection;
         if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
         (see :doc:`PreferenceSet <LINK>` for details).
 
@@ -306,7 +307,6 @@ class LearningProjection(Projection_Base):
                                WEIGHT_CHANGE_PARAMS:  # Determine how weight changes are applied to weight matrix
                                    {                  # Note:  assumes MappingProjection.function is LinearCombination
                                        FUNCTION_PARAMS: {OPERATION: SUM},
-                                       PARAMETER_MODULATION_OPERATION: ModulationOperation.ADD,
                                        PROJECTION_TYPE: LEARNING_PROJECTION}
                                })
 
@@ -356,7 +356,10 @@ def _validate_params(self, request_set, target_set=None, context=None):
             # VALIDATE SENDER
             sender = self.sender
             if isinstance(sender, LearningMechanism):
-                sender = self.sender = sender.outputState
+                if len(sender.learning_signals) > 1:
+                    raise LearningProjectionError("PROGRAM ERROR: {} has more than one LearningSignal "
+                                                  "which is not currently supported".format(sender.name))
+                sender = self.sender = sender.learning_signals[0]
 
             if any(s in {OutputState, LearningMechanism} for s in {sender, type(sender)}):
                 # If it is the outputState of a MonitoringMechanism, check that it is a list or 1D np.array
@@ -381,7 +384,7 @@ def _validate_params(self, request_set, target_set=None, context=None):
             receiver = self.receiver
             if isinstance(receiver, MappingProjection):
                 try:
-                    receiver = self.receiver = receiver.parameterStates[MATRIX]
+                    receiver = self.receiver = receiver._parameter_states[MATRIX]
                 except KeyError:
                     raise LearningProjectionError("The MappingProjection {} specified as the receiver for {} "
                                                   "has no MATRIX parameter state".format(receiver.name, self.name))
@@ -473,7 +476,7 @@ def _instantiate_receiver(self, context=None):
 
         # Check if learning_mechanism receives a projection from an ObjectiveMechanism;
         #    if it does, assign it to the objective_mechanism attribute for the projection being learned
-        candidate_objective_mech = learning_mechanism.inputStates[ERROR_SIGNAL].receivesFromProjections[0].sender.owner
+        candidate_objective_mech = learning_mechanism.input_states[ERROR_SIGNAL].path_afferents[0].sender.owner
         if isinstance(candidate_objective_mech, ObjectiveMechanism) and candidate_objective_mech._role is LEARNING:
             learned_projection.objective_mechanism = candidate_objective_mech
         learned_projection.learning_mechanism = learning_mechanism
@@ -505,9 +508,12 @@ def execute(self, input=None, clock=CentralClock, time_scale=None, params=None,
         if not INITIALIZING in context and self.reportOutputPref:
             print("\n{} weight change matrix: \n{}\n".format(self.name, self.weight_change_matrix))
 
-        # # TEST PRINT
+        # TEST PRINT
         # print("\n@@@ WEIGHT CHANGES FOR {} TRIAL {}:\n{}".format(self.name, CentralClock.trial, self.value))
-        # # print("\n@@@ WEIGHT CHANGES CALCULATED FOR {} TRIAL {}".format(self.name, CentralClock.trial))
+        # print("\n@@@ WEIGHT CHANGES CALCULATED FOR {} TRIAL {}".format(self.name, CentralClock.trial))
+        # TEST DEBUG MULTILAYER
+        # print("\n{}\n@@@ WEIGHT CHANGES FOR {} TRIAL {}:\n{}".
+        #       format(self.__class__.__name__.upper(), self.name, CentralClock.trial, self.value))
 
         return self.value
 
diff --git a/PsyNeuLink/Components/Projections/ModulatoryProjections/ModulatoryProjection.py b/PsyNeuLink/Components/Projections/ModulatoryProjections/ModulatoryProjection.py
new file mode 100644
index 00000000000..bf86c1f273d
--- /dev/null
+++ b/PsyNeuLink/Components/Projections/ModulatoryProjections/ModulatoryProjection.py
@@ -0,0 +1,173 @@
+# Princeton University licenses this file to You under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.  You may obtain a copy of the License at:
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed
+# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and limitations under the License.
+
+
+# ******************************************  ModulatoryProjection *****************************************************
+
+"""
+.. _ModulatoryProjection_Overview:
+
+Overview
+--------
+
+A ModulatoryProjection is a subclass of `Projection <Projection>` that takes the value of a
+`ModulatorySignal <ModulatorySignal>` belonging to an `AdaptiveMechanism <AdaptiveMechanism>`, and uses that to
+modulate the function of the `state <State>` to which it projects.  There are three types of ModulatoryProjections,
+that modulate different types of components and their states:
+
+* `LearningProjection`
+    This takes the `value <LearningSignal.value>` of a `LearningSignal` belonging to a LearningMechanism,
+    and conveys it to the *MATRIX* `ParameterState` of a `MappingProjection`, for use by its
+    `function <ParameterState.function>` in modulating the value of the MappingProjection's
+    `matrix <MappingProjection.matrix>` parameter.
+..
+* `GatingProjection`
+    This takes the `value <GatingSignal.value>` of a `GatingSignal` belonging to a GatingMechanism,
+    and conveys it to the `inputState <InputState>` or `outputState <OutputState>` of a `ProcessingMechanism`.
+    for use by the state's :keyword:`funtion` in modulating its :keyword:`value`.
+..
+* `ControlProjection`
+    This takes the `value of a <ControlSignal.value> of a `ControlSignal` belonging to a ControlMechanism,
+    and conveys it to the `ParameterState` for the parameter of a `Mechanism` or its
+    `function <Mechanism.function>`, for use in modulating the value of the parameter.
+
+.. _Projection_Creation:
+
+Creating a ModulatoryProjection
+-------------------------------
+
+A ModulatoryProjection is a base class, and cannot be instantiated directly.  However, the three types of
+ModulatoryProjections listed above can be created directly, by calling the constructor for the desired type.
+More commonly, however, ModulatoryProjections are either specified `in context <Projection_In_Context_Specification>`,
+or are `created automatically <Projection_Automatic_Creation>`, the details of which are described in the documentation
+for each type.
+
+.. _ModulatoryProjection_Structure:
+
+Structure
+---------
+
+A ModulatoryProjection has the same basic structure as a `Projection`, augmented by type-specific attributes
+and methods described under each type of ModulatoryProjection.  The ModulatoryProjections received by a `State`
+are listed in the State's `mod_afferents` attribute.
+
+.. _ModulatoryProjection_Execution:
+
+Execution
+---------
+
+.. _ModulatoryProjection_Class_Reference:
+
+A ModulatoryProjection, like any projection, cannot be executed directly.  It is executed when the `state <State>` to 
+which it projects — its `receiver <Projection.receiver>` — is updated;  that occurs when the state's owner mechanism 
+is executed.  When a ModulatoryProjection executes, it conveys both the value of the `ModulatorySignal` from which it
+projects, and the ModulatorySignal's `modulation <ModulatorySignal.modulation>` attribute, to the state that receives
+the projection.  The state assigns the value to the parameter of the state's function specified by the `modulation`
+attribute, and then calls the function to determine the `value <State.value>` of the state.
+
+.. note::
+   The change made to the parameter of the state's function in response to the execution of a ModulatoryProjection
+   are not applied until the state is updated which, in turn, does not occur until the mechanism to which the state 
+   belongs is next executed; see :ref:`Lazy Evaluation` for an explanation of "lazy" updating).
+
+.. _ModulatoryProjection_Class_Reference:
+
+Class Reference
+---------------
+
+"""
+
+from PsyNeuLink.Components.Projections.Projection import Projection_Base
+from PsyNeuLink.Globals.Keywords import MODULATORY_PROJECTION
+
+MODULATORY_SIGNAL_PARAMS = 'modulatory_signal_params'
+
+class ModulatoryProjection_Base(Projection_Base):
+    """
+    ModulatoryProjection(                   \
+                 receiver=None,             \
+                 sender=None,               \
+                 params=None,               \
+                 name=None,                 \
+                 prefs=None)
+
+    Implements a projection that modulates the value of a `state <State>`.
+
+    Arguments
+    ---------
+    receiver : Optional[State or Mechanism]
+        specifies the state to which the ModulatoryProjection projects.
+
+    sender : Optional[OutputState or Mechanism]
+        specifies the component from which the ModulatoryProjection projects.
+
+    params : Optional[Dict[param keyword, param value]]
+        a `parameter dictionary <ParameterState_Specifying_Parameters>` that specifies the parameters for the
+        projection, its function, and/or a custom function and its parameters. By default, it contains an entry for
+        the projection's default `function <LearningProjection.function>` and parameter assignments.  Values specified
+        for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
+
+    name : str : default LearningProjection-<index>
+        a string used for the name of the LearningProjection.
+        If not is specified, a default is assigned by ProjectionRegistry
+        (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
+
+    prefs : Optional[PreferenceSet or specification dict : Projection.classPreferences]
+        the `PreferenceSet` for the LearningProjection.
+        If it is not specified, a default is assigned using `classPreferences` defined in __init__.py
+        (see :doc:`PreferenceSet <LINK>` for details).
+
+
+    Attributes
+    ----------
+
+    receiver : MATRIX ParameterState of a MappingProjection
+        the state to which the ModulatoryProjection projects, the `function <State.function>` of which is modulated
+        by it.
+
+    sender : LEARNING_SIGNAL OutputState of a LearningMechanism
+        the `ModulatorySignal` from which the ModulatoryProjection projects.
+
+    variable : 2d np.array
+        value received from the `ModulatorySignal` that is the projection's `sender <ModulatoryProjection.sender`.
+
+    function : Function : default Linear
+        assigns the value received from the ModulatoryProjection's `sender <ModualatoryProjection.sender>` to
+        its `value <ModulatoryProjection.value>`.
+
+    value : 2d np.array
+        value used to modulate the function of the state that is its `receiver <ModulatoryProjection.receiver>`.
+
+    name : str : default ModulatoryProjection-<index>
+        the name of the ModulatoryProjection.
+        Specified in the **name** argument of the constructor for the projection;
+        if not is specified, a default is assigned by ProjectionRegistry
+        (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
+
+    prefs : PreferenceSet or specification dict : Projection.classPreferences
+        the `PreferenceSet` for projection.
+        Specified in the **prefs** argument of the constructor for the projection;
+        if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
+        (see :doc:`PreferenceSet <LINK>` for details).
+
+    """
+    componentCategory = MODULATORY_PROJECTION
+
+    def __init__(self,
+                 receiver,
+                 sender=None,
+                 params=None,
+                 name=None,
+                 prefs=None,
+                 context=None):
+
+        super().__init__(receiver=receiver,
+                         sender=sender,
+                         params=params,
+                         name=name,
+                         prefs=prefs,
+                         context=context)
diff --git a/PsyNeuLink/Components/Projections/ModulatoryProjections/__init__.py b/PsyNeuLink/Components/Projections/ModulatoryProjections/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/PsyNeuLink/Components/Projections/MappingProjection.py b/PsyNeuLink/Components/Projections/PathwayProjections/MappingProjection.py
similarity index 82%
rename from PsyNeuLink/Components/Projections/MappingProjection.py
rename to PsyNeuLink/Components/Projections/PathwayProjections/MappingProjection.py
index 388c79853b7..be9d7ab14bb 100644
--- a/PsyNeuLink/Components/Projections/MappingProjection.py
+++ b/PsyNeuLink/Components/Projections/PathwayProjections/MappingProjection.py
@@ -126,16 +126,6 @@
   input, that is then provided to its `receiver <MappingProjection.receiver>`.  It can be specified using a number of
   different formats, as described `above <Mapping_Matrix_Specification>`.
 
-.. _Mapping_Parameter_Modulation_Operation:
-
-* `param_modulation_operation <MappingProjection.param_modulation_operation>`
-
-  Used to determine how the value of any projections to the `parameterState <ParameterState>` for the
-  `matrix <MappingProjection.matrix>` influence it.  For example, this is used for a `LearningProjection` to apply
-  weight changes to the `matrix <MappingProjection.matrix>` during learning. The
-  :keyword:`param_modulation_operation` attribute must be assigned a value of `ModulationOperation` and the operation
-  is always applied in an element-wise (Hadamard) manner. The default operation is ADD.
-
 .. _Projection_Execution:
 
 Execution
@@ -159,6 +149,7 @@
 """
 
 from PsyNeuLink.Components.Projections.Projection import *
+from PsyNeuLink.Components.Projections.PathwayProjections.PathwayProjection import PathwayProjection_Base
 from PsyNeuLink.Components.Functions.Function import *
 
 parameter_keywords.update({MAPPING_PROJECTION})
@@ -170,13 +161,12 @@ def __init__(self, error_value):
         self.error_value = error_value
 
 
-class MappingProjection(Projection_Base):
+class MappingProjection(PathwayProjection_Base):
     """
     MappingProjection(                                      \
         sender=None,                                        \
         receiver=None,                                      \
         matrix=DEFAULT_MATRIX,                              \
-        param_modulation_operation=ModulationOperation.ADD, \
         params=None,                                        \
         name=None,                                          \
         prefs=None)
@@ -237,10 +227,6 @@ class MappingProjection(Projection_Base):
         the matrix used by `function <MappingProjection.function>` (default: `LinearCombination`) to transform the
         value of the `sender <MappingProjection.sender>`.
 
-    param_modulation_operation : ModulationOperation : default ModulationOperation.ADD
-        specifies the operation used to combine the value of any projections to the matrix's parameterState with the
-        `matrix <MappingProjection.matrix>` itself.  Most commonly used with `LearningProjections <LearningProjection>`.
-
     params : Optional[Dict[param keyword, param value]]
         a `parameter dictionary <ParameterState_Specifying_Parameters>` that can be used to specify the parameters for
         the projection, its function, and/or a custom function and its parameters. By default, it contains an entry for
@@ -268,10 +254,6 @@ class MappingProjection(Projection_Base):
     receiver: InputState
         identifies the destination of the projection.
 
-    param_modulation_operation : ModulationOperation
-        determines the operation used to combine the value of any projections to the matrix's parameterState with the
-        `matrix <MappingProjection.matrix>` itself.
-
     monitoringMechanism : MonitoringMechanism
         source of error signal for that determine changes to the `matrix <MappingProjection.matrix>` when
         `learning <LearningProjection>` is used.
@@ -286,13 +268,13 @@ class MappingProjection(Projection_Base):
 
     name : str : default MappingProjection-<index>
         the name of the MappingProjection.
-        Specified in the `name` argument of the constructor for the projection;
+        Specified in the **name** argument of the constructor for the projection;
         if not is specified, a default is assigned by ProjectionRegistry
         (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
 
     prefs : PreferenceSet or specification dict : Projection.classPreferences
         the `PreferenceSet` for projection.
-        Specified in the `prefs` argument of the constructor for the projection;
+        Specified in the **prefs** argument of the constructor for the projection;
         if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
         (see :doc:`PreferenceSet <LINK>` for details).
 
@@ -314,29 +296,36 @@ def __init__(self,
                  sender=None,
                  receiver=None,
                  matrix=DEFAULT_MATRIX,
-                 param_modulation_operation=ModulationOperation.ADD,
                  params=None,
                  name=None,
                  prefs:is_pref_set=None,
                  context=None):
 
+        # if matrix is DEFAULT_MATRIX:
+        #     initializer = get_matrix(matrix)
+        #     initial_rate = initializer * 0.0
+        #     matrix={VALUE:DEFAULT_MATRIX,
+        #             FUNCTION:ConstantIntegrator(owner=self._parameter_states[MATRIX],
+        #                                         initializer=get_matrix(DEFAULT_MATRIX),
+        #                                         rate=initial_rate)}
+
         # Assign args to params and functionParams dicts (kwConstants must == arg names)
-        params = self._assign_args_to_param_dicts(function_params={MATRIX: matrix},
-                                                  param_modulation_operation=param_modulation_operation,
-                                                  params=params)
+        # Assign matrix to function_params for use as matrix param of MappingProjection.function
+        params = self._assign_args_to_param_dicts(
+                function_params={MATRIX: matrix},
+                params=params)
 
         self.learning_mechanism = None
+        self.has_learning_projection = False
 
         # If sender or receiver has not been assigned, defer init to State.instantiate_projection_to_state()
-        # if sender is NotImplemented or receiver is NotImplemented:
-        if not sender or not receiver:
+        if sender is None or receiver is None:
             # Store args for deferred initialization
             self.init_args = locals().copy()
             self.init_args['context'] = self
             self.init_args['name'] = name
             # Delete these as they have been moved to params dict (and will not be recognized by Projection.__init__)
             del self.init_args['matrix']
-            del self.init_args['param_modulation_operation']
 
             # Flag for deferred initialization
             self.value = DEFERRED_INITIALIZATION
@@ -344,32 +333,44 @@ def __init__(self,
 
         # Validate sender (as variable) and params, and assign to variable and paramsInstanceDefaults
         super(MappingProjection, self).__init__(sender=sender,
-                                      receiver=receiver,
-                                      params=params,
-                                      name=name,
-                                      prefs=prefs,
-                                      context=self)
+                                                receiver=receiver,
+                                                params=params,
+                                                name=name,
+                                                prefs=prefs,
+                                                context=self)
 
-        self.has_learning_projection = False
+    # def _instantiate_sender(self, context=None):
+            # # IMPLEMENT: HANDLE MULTIPLE SENDER -> RECEIVER MAPPINGS, EACH WITH ITS OWN MATRIX:
+            # #            - kwMATRIX NEEDS TO BE A 3D np.array, EACH 3D ITEM OF WHICH IS A 2D WEIGHT MATRIX
+            # #            - MAKE SURE len(self.sender.value) == len(self.receiver.input_states.items())
+            # # for i in range (len(self.sender.value)):
+            # #            - CHECK EACH MATRIX AND ASSIGN??
 
-    def _instantiate_receiver(self, context=None):
-        """Handle situation in which self.receiver was specified as a Mechanism (rather than State)
+    def _instantiate_parameter_states(self, context=None):
 
-        If receiver is specified as a Mechanism, it is reassigned to the (primary) inputState for that Mechanism
-        If the Mechanism has more than one inputState, assignment to other inputStates must be done explicitly
-            (i.e., by: _instantiate_receiver(State)
+        super()._instantiate_parameter_states(context=context)
 
-        """
-        # MODIFIED 4/21/17 OLD: [MOVED TO PROJECTION INIT]
-        # # Assume that if receiver was specified as a Mechanism, it should be assigned to its (primary) inputState
-        # if isinstance(self.receiver, Mechanism):
-        #     if (len(self.receiver.inputStates) > 1 and
-        #             (self.prefs.verbosePref or self.receiver.prefs.verbosePref)):
-        #         print("{0} has more than one inputState; {1} was assigned to the first one".
-        #               format(self.receiver.owner.name, self.name))
-        #     self.receiver = self.receiver.inputState
-        # MODIFIED 4/21/17 END
+        # FIX: UPDATE FOR LEARNING
+        # FIX: UPDATE WITH MODULATION_MODS
+        # FIX: MOVE THIS TO MappingProjection.__init__;
+        # FIX: AS IT IS, OVER-WRITES USER ASSIGNMENT OF FUNCTION IN params dict FOR MappingProjection
+        matrix = get_matrix(self._parameter_states[MATRIX].value)
+        initial_rate = matrix * 0.0
+
+        self._parameter_states[MATRIX].function_object = AccumulatorIntegrator(owner=self._parameter_states[MATRIX],
+                                                                            initializer=matrix,
+                                                                            # rate=initial_rate
+                                                                               )
 
+        self._parameter_states[MATRIX]._function = self._parameter_states[MATRIX].function_object.function
+
+    def _instantiate_receiver(self, context=None):
+        """Determine matrix needed to map from sender to receiver
+        
+        Assign specification to self.matrix_spec attribute
+        Assign matrix to self.matrix attribute
+        
+        """
         self.reshapedWeightMatrix = False
 
         # Get sender and receiver lengths
@@ -409,7 +410,7 @@ def _instantiate_receiver(self, context=None):
             else:
                 projection_string = 'projection'
 
-            if self._matrix_spec in {IDENTITY_MATRIX, OFF_DIAGNOAL_MATRIX}:
+            if self._matrix_spec in {IDENTITY_MATRIX, HOLLOW_MATRIX}:
                 # Identity matrix is not reshapable
                 raise ProjectionError("Output length ({}) of \'{}{}\' from {} to mechanism \'{}\'"
                                       " must equal length of it inputState ({}) to use {}".
@@ -433,7 +434,7 @@ def _instantiate_receiver(self, context=None):
                                  receiver_len,
                                  self.receiver.owner.name))
 
-                self.matrix = get_matrix(self._matrix_spec, mapping_input_len, receiver_len, context=context)
+                self._matrix = get_matrix(self._matrix_spec, mapping_input_len, receiver_len, context=context)
 
                 # Since matrix shape has changed, output of self.function may have changed, so update self.value
                 self._update_value()
@@ -463,32 +464,43 @@ def execute(self, input=None, clock=CentralClock, time_scale=None, params=None,
 
             # Assume that if monitoringMechanism attribute is assigned,
             #    both a LearningProjection and parameterState[MATRIX] to receive it have been instantiated
-            matrix_parameter_state = self.parameterStates[MATRIX]
+            matrix_parameter_state = self._parameter_states[MATRIX]
 
-            # Assign current MATRIX to parameter state's baseValue, so that it is updated in call to execute()
-            matrix_parameter_state.baseValue = self.matrix
+            # Assign current MATRIX to parameter state's base_value, so that it is updated in call to execute()
+            # # MODIFIED 6/1/17 OLD:
+            # matrix_parameter_state.base_value = self.matrix
+            # MODIFIED 6/1/17 NEW:
+            setattr(self, '_'+MATRIX, self.matrix)
+            # MODIFIED 6/1/17 END
 
+            # FIX: UPDATE FOR LEARNING BEGIN
+            #    ??DELETE ONCE INTEGRATOR FUNCTION IS IMPLEMENTED
             # Pass params for parameterState's function specified by instantiation in LearningProjection
             weight_change_params = matrix_parameter_state.paramsCurrent
 
-            # Update parameter state: combines weightChangeMatrix from LearningProjection with matrix baseValue
+            # Update parameter state: combines weightChangeMatrix from LearningProjection with matrix base_value
             matrix_parameter_state.update(weight_change_params, context=context)
 
             # MODIFIED 2/21/17 OLD: [REPLACE WITH ASSIGNMENT UNDER @property value IN ParmaterState??]
             # Update MATRIX
             self.matrix = matrix_parameter_state.value
             # MODIFIED 2/21/17 END
+            # FIX: UPDATE FOR LEARNING END
 
             # # TEST PRINT
             # print("\n### WEIGHTS CHANGED FOR {} TRIAL {}:\n{}".format(self.name, CentralClock.trial, self.matrix))
             # # print("\n@@@ WEIGHTS CHANGED FOR {} TRIAL {}".format(self.name, CentralClock.trial))
+            # TEST DEBUG MULTILAYER
+            # print("\n{}\n### WEIGHTS CHANGED FOR {} TRIAL {}:\n{}".
+            #       format(self.__class__.__name__.upper(), self.name, CentralClock.trial, self.matrix))
 
 
         return self.function(self.sender.value, params=params, context=context)
 
     @property
     def matrix(self):
-        return self.function.__self__.matrix
+        # return self.function.__self__.matrix
+        return self.function_object.matrix
 
     @matrix.setter
     def matrix(self, matrix):
@@ -498,14 +510,19 @@ def matrix(self, matrix):
             raise MappingError("Matrix parameter for {} ({}) MappingProjection must be "
                                "an np.matrix, a 2d np.array, or a correspondingly configured list".
                                format(self.name, matrix))
-        self.function.__self__.matrix = matrix
+
+        # FIX: Hack to prevent recursion in calls to setter and assign_params
+        self.function.__self__.paramValidationPref = PreferenceEntry(False, PreferenceLevel.INSTANCE)
+
+        # self.function.__self__.matrix = matrix
+        self.function_object.matrix = matrix
 
     @property
     def _matrix_spec(self):
         """Returns matrix specification in self.paramsCurrent[FUNCTION_PARAMS][MATRIX]
 
         Returns matrix param for MappingProjection, getting second item if it is
-         a ParamValueprojection or unnamed (matrix, projection) tuple
+         an unnamed (matrix, projection) tuple
         """
         return self._get_param_value_from_tuple(self.paramsCurrent[FUNCTION_PARAMS][MATRIX])
 
@@ -514,24 +531,20 @@ def _matrix_spec(self, value):
         """Assign matrix specification for self.paramsCurrent[FUNCTION_PARAMS][MATRIX]
 
         Assigns matrix param for MappingProjection, assigning second item if it is
-         a ParamValueProjection or unnamed (matrix, projection) tuple
+         a 2-item tuple or unnamed (matrix, projection) tuple
         """
 
-        # Specification is a ParamValueProjection tuple, so allow
-        if isinstance(self.paramsCurrent[FUNCTION_PARAMS][MATRIX], ParamValueProjection):
-            self.paramsCurrent[FUNCTION_PARAMS][MATRIX].value =  value
-
         # Specification is a two-item tuple, so validate that 2nd item is:
         # a projection keyword, projection subclass, or instance of a projection subclass
-        elif (isinstance(self.paramsCurrent[FUNCTION_PARAMS][MATRIX], tuple) and
-                      len(self.paramsCurrent[FUNCTION_PARAMS][MATRIX]) is 2 and
-                  (self.paramsCurrent[FUNCTION_PARAMS][MATRIX][1] in {MAPPING_PROJECTION,
-                                                                      CONTROL_PROJECTION,
-                                                                      LEARNING_PROJECTION}
-                   or isinstance(self.paramsCurrent[FUNCTION_PARAMS][MATRIX][1], Projection) or
-                       (inspect.isclass(self.paramsCurrent[FUNCTION_PARAMS][MATRIX][1]) and
-                            issubclass(self.paramsCurrent[FUNCTION_PARAMS][MATRIX][1], Projection)))
-              ):
+        if (isinstance(self.paramsCurrent[FUNCTION_PARAMS][MATRIX], tuple) and
+                    len(self.paramsCurrent[FUNCTION_PARAMS][MATRIX]) is 2 and
+                (self.paramsCurrent[FUNCTION_PARAMS][MATRIX][1] in {MAPPING_PROJECTION,
+                                                                    CONTROL_PROJECTION,
+                                                                    LEARNING_PROJECTION}
+                 or isinstance(self.paramsCurrent[FUNCTION_PARAMS][MATRIX][1], Projection) or
+                     (inspect.isclass(self.paramsCurrent[FUNCTION_PARAMS][MATRIX][1]) and
+                          issubclass(self.paramsCurrent[FUNCTION_PARAMS][MATRIX][1], Projection)))
+            ):
             # # MODIFIED 4/8/17 OLD:
             # self.paramsCurrent[FUNCTION_PARAMS][MATRIX] = (value, self.paramsCurrent[FUNCTION_PARAMS][MATRIX][1])
             # MODIFIED 4/8/17 NEW:
diff --git a/PsyNeuLink/Components/Projections/PathwayProjections/PathwayProjection.py b/PsyNeuLink/Components/Projections/PathwayProjections/PathwayProjection.py
new file mode 100644
index 00000000000..0510c5864d3
--- /dev/null
+++ b/PsyNeuLink/Components/Projections/PathwayProjections/PathwayProjection.py
@@ -0,0 +1,72 @@
+# Princeton University licenses this file to You under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.  You may obtain a copy of the License at:
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed
+# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and limitations under the License.
+
+
+# ******************************************  PathwayProjection *****************************************************
+
+"""
+.. _PathwayProjection_Overview:
+
+Overview
+--------
+
+ModulatoryProjections allow information to be passed between mechanisms.  A projection takes its input from the
+`outputState <OutputState>` of one mechanism (its `sender <Projection.sender>`), and does whatever conversion is
+needed to transmit that information to the `inputState <InputState>` of another mechanism (its
+`receiver <Projection.receiver>`).  There are three types of projections that serve difference purposes:
+
+* `MappingProjection`
+    XXXX.
+..
+
+
+.. _Projection_Creation:
+
+Creating a PathwayProjection
+---------------------------------
+
+A PathwayProjection can be created on its own, by calling the constructor for the desired type of projection.  More
+commonly, however, projections are either specified `in context <Projection_In_Context_Specification>`, or
+are `created automatically <Projection_Automatic_Creation>`, as described below.
+
+
+
+.. _PathwayProjection_Structure:
+
+Structure
+---------
+
+.. _PathwayProjection_Execution:
+
+Execution
+---------
+
+.. _PathwayProjection_Class_Reference:
+
+"""
+
+from PsyNeuLink.Components.Projections.Projection import Projection_Base
+from PsyNeuLink.Globals.Keywords import TRANSMISSIVE_PROJECTION
+
+class PathwayProjection_Base(Projection_Base):
+
+    componentCategory = TRANSMISSIVE_PROJECTION
+
+    def __init__(self,
+                 receiver,
+                 sender=None,
+                 params=None,
+                 name=None,
+                 prefs=None,
+                 context=None):
+
+        super().__init__(receiver=receiver,
+                         sender=sender,
+                         params=params,
+                         name=name,
+                         prefs=prefs,
+                         context=context)
diff --git a/PsyNeuLink/Components/Projections/PathwayProjections/__init__.py b/PsyNeuLink/Components/Projections/PathwayProjections/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/PsyNeuLink/Components/Projections/Projection.py b/PsyNeuLink/Components/Projections/Projection.py
index b1e02ba645d..5d6bb716c87 100644
--- a/PsyNeuLink/Components/Projections/Projection.py
+++ b/PsyNeuLink/Components/Projections/Projection.py
@@ -82,6 +82,8 @@
       |
       * CONTROL_PROJECTION -- a `ControlProjection` with the `DefaultControlMechanism` as its :keyword:`sender`.
       |
+      * GATING_PROJECTION -- a `GatingProjection` with the `DefaultGatingMechanism` as its :keyword:`sender`.
+      |
       * LEARNING_PROJECTION -- a `LearningProjection`.  At present, this can only be used together with the
         specification of a MappingProjection (see `tuple <Mapping_Matrix_Specification>` format).  If the
         :keyword:`receiver` of the MappingProjection projects to a `MonitoringMechanism <MonitoringMechanism>`,
@@ -138,7 +140,7 @@
 ~~~~~~
 
 This must be an `OutputState`.  The projection is assigned to the outputState's
-`sendsToProjections <State.State_Base.sendsToProjections>` list, and outputState's `value
+`efferents <State.State_Base.efferents>` list, and outputState's `value
 <OutputState.OutputState.value>` is used as the :keyword:`variable` for projection's `function <Projection.function>`.
 A sender can be specified as:
 
@@ -160,27 +162,31 @@
     :py:const:`DefaultProcessingMechanism <Components.__init__.DefaultProcessingMechanism LINK>`
     is used, and its `primary outputState <OutputState_Primary>` is assigned as the `sender <Projection.sender>`.
   ..
+  * `ControlProjection`: if the projection's `receiver <Projection.receiver>` belongs to a system, then the system's
+    `controller` is used as the mechanism for the `sender <Projection.sender>`, an outputState is added to the
+    ControlMechanism, and assigned as the projection's `sender <Projection.sender>`.  If the receiver does not
+    belong to a system, the ControlProjection will be ignored.
+  ..
   COMMENT:
-     CONFIRM THIS IS TRUE
+  * `GatingProjection`:  DOCUMENT
   COMMENT
-  * `ControlProjection`: if the projection's `receiver <Projection.receiver>` belongs to a system, then the system's
-    `controller` is used as the mechanism for the `sender <Projection.sender>.  Otherwise, the `DefaultControlMechanism`
-    is used.  In either case, an outputState is added to the ControlMechanism and assigned as the
-    projection's `sender <Projection.sender>`.
   ..
   * `LearningProjection`: if it is to a MappingProjection that projects to the `TERMINAL` mechanism of a process,
     then a `ComparatorMechanism` is created, and its `primary outputState <OutputState_Primary>` is assigned as the
     `sender <Projection.sender>`.  Otherwise, a `WeightedErrorMechanism` is created and its
     `primary outputState <OutputState_Primary>` is assigned as the `sender <Projection.sender>`.
 
+
+
 .. _Projection_Receiver:
 
 Receiver
 ~~~~~~~~
 
 This must be an :doc:`InputState` or a :doc:`ParameterState`.  The projection is assigned to the receiver's
-`receivesFromProjections <State.State_Base.receivesFromProjections>` list, and the output of the projection's
-`function <Projection.function>` is transmitted to its receiver.  A `receiver <Projection.receiver>` can be specified as:
+`path_afferents <State.State_Base.path_afferents>` list, and the output of the projection's
+`function <Projection.function>` is transmitted to its receiver.  A `receiver <Projection.receiver>`
+can be specified as:
 
   * an existing **inputState**;
   ..
@@ -198,7 +204,7 @@
     If the ``receiver`` of a projection is specified as a projection or mechanism, the type of state created and added
     to the mechanism depends on the type of projection:
         MappingProjection:
-            receiver = <Mechanism>.inputState
+            receiver = <Mechanism>.input_state
         ControlProjection:
             sender = <Mechanism>.outputState
             receiver = <Mechanism>.parameterState if there is a corresponding parameter; otherwise, an error occurs
@@ -236,14 +242,11 @@
 
 projection_keywords = set()
 
-PROJECTION_SPEC_KEYWORDS = {AUTO_ASSIGN_MATRIX,
-                            DEFAULT_MATRIX,
-                            IDENTITY_MATRIX,
-                            FULL_CONNECTIVITY_MATRIX,
-                            OFF_DIAGNOAL_MATRIX,
-                            RANDOM_CONNECTIVITY_MATRIX,
-                            LEARNING_PROJECTION,
-                            CONTROL_PROJECTION}
+PROJECTION_SPEC_KEYWORDS = {MAPPING_PROJECTION,
+                            LEARNING, LEARNING_PROJECTION,
+                            CONTROL, CONTROL_PROJECTION,
+                            GATING, GATING_PROJECTION}
+
 
 class ProjectionError(Exception):
     def __init__(self, error_value):
@@ -355,13 +358,13 @@ class Projection_Base(Projection):
 
     name : str : default <Projection subclass>-<index>
         the name of the projection.
-        Specified in the `name` argument of the constructor for the projection;  if not is specified,
+        Specified in the **name** argument of the constructor for the projection;  if not is specified,
         a default is assigned by ProjectionRegistry based on the projection's subclass
         (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
 
     prefs : PreferenceSet or specification dict : Projection.classPreferences
         the `PreferenceSet` for the projection.
-        Specified in the `prefs` argument of the constructor for the projection;  if it is not specified, a default is
+        Specified in the **prefs** argument of the constructor for the projection;  if it is not specified, a default is
         assigned using `classPreferences` defined in __init__.py
         (see :doc:`PreferenceSet <LINK>` for details).
 
@@ -415,7 +418,7 @@ def __init__(self,
         * If sender and/or receiver is a Mechanism, the appropriate State is inferred as follows:
             MappingProjection:
                 sender = <Mechanism>.outputState
-                receiver = <Mechanism>.inputState
+                receiver = <Mechanism>.input_state
             ControlProjection:
                 sender = <Mechanism>.outputState
                 receiver = <Mechanism>.paramsCurrent[<param>] IF AND ONLY IF there is a single one
@@ -461,7 +464,7 @@ def __init__(self,
                           context=context)
 
 # FIX: 6/23/16 NEEDS ATTENTION *******************************************************A
-#      NOTE: SENDER IS NOT YET KNOWN FOR DEFAULT controlSignal
+#      NOTE: SENDER IS NOT YET KNOWN FOR DEFAULT control_signal
 #      WHY IS self.sender IMPLEMENTED WHEN sender IS NOT??
 
         self.sender = sender
@@ -490,11 +493,11 @@ def __init__(self,
         # MODIFIED 4/21/17 NEW: [MOVED FROM MappingProjection._instantiate_receiver]
         # Assume that if receiver was specified as a Mechanism, it should be assigned to its (primary) inputState
         if isinstance(self.receiver, Mechanism):
-            if (len(self.receiver.inputStates) > 1 and
+            if (len(self.receiver.input_states) > 1 and
                     (self.prefs.verbosePref or self.receiver.prefs.verbosePref)):
                 print("{0} has more than one inputState; {1} was assigned to the first one".
                       format(self.receiver.owner.name, self.name))
-            self.receiver = self.receiver.inputState
+            self.receiver = self.receiver.input_state
         # MODIFIED 4/21/17 END
 
 
@@ -615,72 +618,63 @@ def _validate_params(self, request_set, target_set=None, context=None):
                                                  self.paramClassDefaults[PROJECTION_SENDER]))
 
     def _instantiate_attributes_before_function(self, context=None):
-
         self._instantiate_sender(context=context)
+        self._instantiate_parameter_states(context=context)
+
+    def _instantiate_parameter_states(self, context=None):
 
         from PsyNeuLink.Components.States.ParameterState import _instantiate_parameter_states
         _instantiate_parameter_states(owner=self, context=context)
 
+
     def _instantiate_sender(self, context=None):
         """Assign self.sender to outputState of sender and insure compatibility with self.variable
 
         Assume self.sender has been assigned in _validate_params, from either sender arg or PROJECTION_SENDER
-        Validate, set self.variable, and assign projection to sender's sendsToProjections attribute
+        Validate, set self.variable, and assign projection to sender's efferents attribute
 
         If self.sender is a Mechanism, re-assign it to <Mechanism>.outputState
         If self.sender is a State class reference, validate that it is a OutputState
-        Assign projection to sender's sendsToProjections attribute
+        Assign projection to sender's efferents attribute
         If self.value / self.variable is None, set to sender.value
-
-        Notes:
-        * ControlProjection initially overrides this method to check if sender is DefaultControlMechanism;
-            if so, it assigns a ControlProjection-specific inputState and outputState to it
-        [TBI: * LearningProjection overrides this method to check if sender is kwDefaultSender;
-            if so, it instantiates a default MonitoringMechanism and a projection to it from receiver's outputState]
-
-        :param context: (str)
-        :return:
         """
 
         from PsyNeuLink.Components.States.OutputState import OutputState
         from PsyNeuLink.Components.States.ParameterState import ParameterState
 
-        # If sender is a class, instantiate it:
-        # - assume it is Mechanism or State (as validated in _validate_params)
-        # - implement default sender of the corresponding type
-        if inspect.isclass(self.sender):
-            if issubclass(self.sender, OutputState):
-                # MODIFIED 9/12/16 NEW:
-                # self.paramsCurrent['function_params']['matrix']
-                # FIX: ASSIGN REFERENCE VALUE HERE IF IT IS A MAPPING_PROJECTION??
-                # MODIFIED 9/12/16 END
-                self.sender = self.paramsCurrent[PROJECTION_SENDER](self.paramsCurrent[PROJECTION_SENDER_VALUE])
-            else:
-                raise ProjectionError("Sender ({0}) for {1} must be a OutputState".
-                                      format(self.sender.__name__, self.name))
-
-        # # If sender is a Mechanism (rather than a State), get relevant outputState and assign it to self.sender
+        # # IMPLEMENTATION NOTE:  The following supported instantiation of a default sender type by a projection, for
+        # #                       projections that did not yet have their sender specified;  however, this should now
+        # #                       be covered by deferred_init(): sender is assigned in that call or by the time is made.
+        # # If sender is a class, instantiate it:
+        # # - assume it is a Mechanism or State (should have been validated in _validate_params)
+        # # - implement default sender of the corresponding type
+        # if inspect.isclass(self.sender):
+        #     if issubclass(self.sender, OutputState):
+        #         # MODIFIED 9/12/16 NEW:
+        #         # self.paramsCurrent['function_params']['matrix']
+        #         # FIX: ASSIGN REFERENCE VALUE HERE IF IT IS A MAPPING_PROJECTION??
+        #         # MODIFIED 9/12/16 END
+        #         self.sender = self.paramsCurrent[PROJECTION_SENDER](self.paramsCurrent[PROJECTION_SENDER_VALUE])
+        #     else:
+        #         raise ProjectionError("Sender ({0}) for {1} must be a OutputState".
+        #                               format(self.sender.__name__, self.name))
+
+        # If sender is specified as a Mechanism (rather than a State),
+        #     get relevant outputState and assign it to self.sender
+        # IMPLEMENTATION NOTE: Assume that sender should be the primary OutputState; if that is not the case,
+        #                      sender should either be explicitly assigned, or handled in an override of the
+        #                      method by the relevant subclass prior to calling super
         if isinstance(self.sender, Mechanism):
-
-            # # IMPLEMENT: HANDLE MULTIPLE SENDER -> RECEIVER MAPPINGS, EACH WITH ITS OWN MATRIX:
-            # #            - kwMATRIX NEEDS TO BE A 3D np.array, EACH 3D ITEM OF WHICH IS A 2D WEIGHT MATRIX
-            # #            - MAKE SURE len(self.sender.value) == len(self.receiver.inputStates.items())
-            # # for i in range (len(self.sender.value)):
-            # #            - CHECK EACH MATRIX AND ASSIGN??
-            # # FOR NOW, ASSUME SENDER HAS ONLY ONE OUTPUT STATE, AND THAT RECEIVER HAS ONLY ONE INPUT STATE
-            self.sender = self.sender.outputState
+            self.sender = self.sender.output_state
 
         # At this point, self.sender should be a OutputState
-        # MODIFIED 2/10/17 OLD: 2/21/17
         if not isinstance(self.sender, OutputState):
-        # # MODIFIED 2/10/17 NEW: [ADDED ParameterState TO ACCOMODATE LEARNING PROJECTION FOR BACKPROPAGATION]
-        # if not isinstance(self.sender, (OutputState, ParameterState)):
-        # MODIFIED 2/10/17 END
-            raise ProjectionError("Sender for MappingProjection must be a Mechanism or State")
+            raise ProjectionError("Sender specified for {} ({}) must be a Mechanism or an OutputState".
+                                  format(self.name, self.sender))
 
-        # Assign projection to sender's sendsToProjections list attribute
-        # MODIFIED 8/4/16 OLD:  SHOULD CALL _add_projection_from
-        self.sender.sendsToProjections.append(self)
+        # Assign projection to sender's efferents list attribute
+        if not self in self.sender.efferents:
+            self.sender.efferents.append(self)
 
         # Validate projection's variable (self.variable) against sender.outputState.value
         if iscompatible(self.variable, self.sender.value):
@@ -706,18 +700,17 @@ def _instantiate_attributes_after_function(self, context=None):
         self._instantiate_receiver(context=context)
 
     def _instantiate_receiver(self, context=None):
-        """Call receiver's owner to add projection to its receivesFromProjections list
+        """Call receiver's owner to add projection to its afferents list
 
         Notes:
         * Assume that subclasses implement this method in which they:
           - test whether self.receiver is a Mechanism and, if so, replace with State appropriate for projection
           - calls this method (as super) to assign projection to the Mechanism
-        * Constraint that self.value is compatible with receiver.inputState.value
+        * Constraint that self.value is compatible with receiver.input_state.value
             is evaluated and enforced in _instantiate_function, since that may need to be modified (see below)
         * Verification that projection has not already been assigned to receiver is handled by _add_projection_to;
             if it has, a warning is issued and the assignment request is ignored
 
-
         :param context: (str)
         :return:
         """
@@ -742,7 +735,7 @@ def _instantiate_receiver(self, context=None):
             raise ProjectionError("Unrecognized receiver specification ({0}) for {1}".format(self.receiver, self.name))
 
     def _update_parameter_states(self, runtime_params=None, time_scale=None, context=None):
-        for state_name, state in self.parameterStates.items():
+        for state_name, state in self._parameter_states.items():
 
             state.update(params=runtime_params, time_scale=time_scale, context=context)
 
@@ -761,7 +754,7 @@ def _update_parameter_states(self, runtime_params=None, time_scale=None, context
 
             # Get its type
             param_type = type(param_template[state_name])
-            # If param is a tuple, get type of parameter itself (= 1st item;  2nd is projection or ModulationOperation)
+            # If param is a tuple, get type of parameter itself (= 1st item;  2nd is projection or Modulation)
             if param_type is tuple:
                 param_type = type(param_template[state_name][0])
 
@@ -773,17 +766,20 @@ def add_to(self, receiver, state, context=None):
         _add_projection_to(receiver=receiver, state=state, projection_spec=self, context=context)
 
 
-def _is_projection_spec(spec):
+def _is_projection_spec(spec, include_matrix_keywords=True):
     """Evaluate whether spec is a valid Projection specification
 
     Return :keyword:`true` if spec is any of the following:
     + Projection class (or keyword string constant for one):
     + Projection object:
+    + 2-item tuple of which the second is a projection_spec (checked recursively with thi method):
     + specification dict containing:
         + PROJECTION_TYPE:<Projection class> - must be a subclass of Projection
+    + Matrix keyword (if include_matrix_keywords is set to `True`)
 
     Otherwise, return :keyword:`False`
     """
+
     if inspect.isclass(spec) and issubclass(spec, Projection):
         return True
     if isinstance(spec, Projection):
@@ -792,16 +788,24 @@ def _is_projection_spec(spec):
         return True
     if isinstance(spec, str) and spec in PROJECTION_SPEC_KEYWORDS:
         return True
-    if get_matrix(spec) is not None:
-        return True
+    if include_matrix_keywords:
+        if isinstance(spec, str) and spec in MATRIX_KEYWORD_SET:
+            return True
+        from PsyNeuLink.Components.Functions.Function import get_matrix
+        if get_matrix(spec) is not None:
+            return True
     if isinstance(spec, tuple) and len(spec) == 2:
         # Call recursively on first item, which should be a standard projection spec
         if _is_projection_spec(spec[0]):
             # IMPLEMENTATION NOTE: keywords must be used to refer to subclass, to avoid import loop
-            if _is_projection_subclass(spec[1], CONTROL_PROJECTION):
+            if _is_projection_subclass(spec[1], MAPPING_PROJECTION):
                 return True
             if _is_projection_subclass(spec[1], LEARNING_PROJECTION):
                 return True
+            if _is_projection_subclass(spec[1], CONTROL_PROJECTION):
+                return True
+            if _is_projection_subclass(spec[1], GATING_PROJECTION):
+                return True
     return False
 
 def _is_projection_subclass(spec, keyword):
@@ -839,30 +843,103 @@ def _is_projection_subclass(spec, keyword):
             return True
     return False
 
+# IMPLEMENTATION NOTE: MOVE THIS TO ModulatorySignals WHEN THAT IS IMPLEMENTED
+@tc.typecheck
+def _validate_receiver(sender_mech:Mechanism,
+                       projection:Projection,
+                       expected_owner_type:type,
+                       spec_type=None,
+                       context=None):
+    """Check that projection is to expected_receiver_type and in the same system as the sender_mech (if specified)
+    
+    expected_owner_type must be a Mechanism or a Projection
+    spec_type should be LEARNING_SIGNAL, CONTROL_SIGNAL or GATING_SIGNAL
+
+    """
+    spec_type = " in the {} arg ".format(spec_type) or ""
+
+    if projection.value is DEFERRED_INITIALIZATION:
+        # receiver = projection.init_args['receiver'].owner
+        state = projection.init_args['receiver']
+        receiver = state.owner
+    else:
+        # receiver = projection.receiver.owner
+        state = projection.receiver
+        receiver = state.owner
+
+    if isinstance(receiver, Mechanism):
+        receiver_mech = receiver
+    elif isinstance(receiver, Projection):
+        receiver_mech = receiver.receiver.owner
+    else:
+        raise ProjectionError("receiver of projection ({}) must be a {} or {}".
+                              format(projection.name, MECHANISM, PROJECTION))
+
+    if not isinstance(receiver, expected_owner_type):
+        raise ProjectionError("A {} specified {}for {} ({}) projects to a component other than the {} of a {}".
+                                    format(projection.__class__.__name__,
+                                           spec_type,
+                                           sender_mech.name,
+                                           receiver,
+                                           state.__class__.__name__,
+                                           expected_owner_type.__name__))
+
+    # Check if receiver_mech is in the same system as sender_mech;
+    #    if either has not been assigned a system, return
+
+    # Check whether mech is in the same system as sender_mech
+    receiver_systems = set()
+    # receiver_mech is a ControlMechanism (which has a system but no systems attribute)
+    if hasattr(receiver_mech, 'system') and receiver_mech.system:
+        receiver_systems.update({receiver_mech.system})
+    # receiver_mech is a ProcessingMechanism (which has a systems but system attribute is usually None)
+    elif hasattr(receiver_mech, 'systems') and receiver_mech.systems:
+        receiver_systems.update(set(receiver_mech.systems))
+    else:
+        return
+
+    sender_systems = set()
+    # sender_mech is a ControlMechanism (which has a system but no systems attribute)
+    if hasattr(sender_mech, 'system') and sender_mech.system:
+        sender_systems.update({sender_mech.system})
+    # sender_mech is a ProcessingMechanism (which has a systems but system attribute is usually None)
+    elif hasattr(sender_mech, 'systems')and sender_mech.systems:
+        sender_systems.update(set(sender_mech.systems))
+    else:
+        return
+
+    #  Check that projection is to a (projection to a) mechanisms in the same system as sender_mech
+    if not receiver_systems & sender_systems:
+        raise ProjectionError("A {} specified {}for {} projects to a component that is not in the same system".
+                                    format(projection.__class__.__name__,
+                                           spec_type,
+                                           sender_mech.name))
+
+# IMPLEMENTATION NOTE:  THIS SHOULD BE MOVED TO COMPOSITION ONCE THAT IS IMPLEMENTED
 def _add_projection_to(receiver, state, projection_spec, context=None):
-    """Assign an "incoming" Projection to a receiver InputState or ParameterState of a Function object
+    """Assign an "incoming" Projection to a receiver InputState or ParameterState of a Component object
 
     Verify that projection has not already been assigned to receiver;
         if it has, issue a warning and ignore the assignment request.
 
     Requirements:
-       * receiver must be an appropriate Function object (currently, a Mechanism or a Projection);
+       * receiver must be an appropriate Component object (currently, a Mechanism or a Projection);
        * state must be a specification of an InputState or ParameterState;
        * specification of InputState can be any of the following:
                 - INPUT_STATE - assigns projection_spec to (primary) inputState;
                 - InputState object;
-                - index for Mechanism.inputStates OrderedDict;
-                - name of inputState (i.e., key for Mechanism.inputStates OrderedDict));
+                - index for Mechanism.input_states;
+                - name of an existing inputState (i.e., key for Mechanism.input_states);
                 - the keyword kwAddInputState or the name for an inputState to be added;
        * specification of ParameterState must be a ParameterState object
        * projection_spec can be any valid specification of a projection_spec
            (see `State._instantiate_projections_to_state`).
 
     Args:
-        receiver (Mechanism or Projection):
+        receiver (Mechanism or Projection)
+        state (State subclass)
         projection_spec: (Projection, dict, or str)
-        state (State subclass):
-        context:
+        context
 
     """
     # IMPLEMENTATION NOTE:  ADD FULL SET OF ParameterState SPECIFICATIONS
@@ -876,7 +953,7 @@ def _add_projection_to(receiver, state, projection_spec, context=None):
     if not isinstance(state, (int, str, InputState, ParameterState)):
         raise ProjectionError("State specification(s) for {0} (as receivers of {1}) contain(s) one or more items"
                              " that is not a name, reference to an inputState or parameterState object, "
-                             " or an index (for inputStates)".
+                             " or an index (for input_states)".
                              format(receiver.name, projection_spec.name))
 
     # state is State object, so use that
@@ -886,25 +963,25 @@ def _add_projection_to(receiver, state, projection_spec, context=None):
 
     # Generic INPUT_STATE is specified, so use (primary) inputState
     elif state is INPUT_STATE:
-        receiver.inputState._instantiate_projections_to_state(projections=projection_spec, context=context)
+        receiver.input_state._instantiate_projections_to_state(projections=projection_spec, context=context)
         return
 
-    # input_state is index into inputStates OrderedDict, so get corresponding key and assign to input_state
+    # input_state is index into input_states OrderedDict, so get corresponding key and assign to input_state
     elif isinstance(state, int):
         try:
-            key = list(receiver.inputStates.keys)[state]
+            key = receiver.input_states[state]
         except IndexError:
             raise ProjectionError("Attempt to assign projection_spec ({0}) to inputState {1} of {2} "
-                                 "but it has only {3} inputStates".
-                                 format(projection_spec.name, state, receiver.name, len(receiver.inputStates)))
+                                 "but it has only {3} input_states".
+                                 format(projection_spec.name, state, receiver.name, len(receiver.input_states)))
         else:
             input_state = key
 
     # input_state is string (possibly key retrieved above)
-    #    so try as key in inputStates OrderedDict (i.e., as name of an inputState)
+    #    so try as key in input_states OrderedDict (i.e., as name of an inputState)
     if isinstance(state, str):
         try:
-            receiver.inputState[state]._instantiate_projections_to_state(projections=projection_spec, context=context)
+            receiver.input_state[state]._instantiate_projections_to_state(projections=projection_spec, context=context)
         except KeyError:
             pass
         else:
@@ -926,7 +1003,7 @@ def _add_projection_to(receiver, state, projection_spec, context=None):
 
     # validate that projection has not already been assigned to receiver
     if receiver.verbosePref or projection_spec.sender.owner.verbosePref:
-        if projection_spec in receiver.receivesFromProjections:
+        if projection_spec in receiver.all_afferents:
             warnings.warn("Request to assign {} as projection to {} was ignored; it was already assigned".
                           format(projection_spec.name, receiver.owner.name))
 
@@ -937,15 +1014,21 @@ def _add_projection_to(receiver, state, projection_spec, context=None):
                                     constraint_value=projection_spec.value,
                                     constraint_value_name='Projection_spec value for new inputState',
                                     context=context)
-        #  Update inputState and inputStates
-    try:
-        receiver.inputStates[input_state.name] = input_state
+
+    #  Update inputState and input_states
+    if receiver.input_states:
+        receiver.input_states[input_state.name] = input_state
+
     # No inputState(s) yet, so create them
-    except AttributeError:
-        receiver.inputStates = OrderedDict({input_state.name:input_state})
-        receiver.inputState = list(receiver.inputStates)[0]
+    else:
+        receiver.input_states = ContentAddressableList(component_type=State_Base,
+                                                       list=[input_state],
+                                                       name=receiver.name+'.input_states')
+
     input_state._instantiate_projections_to_state(projections=projection_spec, context=context)
 
+
+# IMPLEMENTATION NOTE:  THIS SHOULD BE MOVED TO COMPOSITION ONCE THAT IS IMPLEMENTED
 def _add_projection_from(sender, state, projection_spec, receiver, context=None):
     """Assign an "outgoing" Projection from an OutputState of a sender Mechanism
 
@@ -964,12 +1047,23 @@ def _add_projection_from(sender, state, projection_spec, receiver, context=None)
         state (OutputState, str, or value):
         context:
     """
-    # IMPLEMENTATION NOTE: add verification that projection is not already assigned to sender; if so, warn and ignore
+
 
     from PsyNeuLink.Components.States.State import _instantiate_state
     from PsyNeuLink.Components.States.State import State_Base
     from PsyNeuLink.Components.States.OutputState import OutputState
 
+    # Validate that projection is not already assigned to sender; if so, warn and ignore
+
+    if isinstance(projection_spec, Projection):
+        projection = projection_spec
+        if ((isinstance(sender, OutputState) and projection.sender is sender) or
+                (isinstance(sender, Mechanism) and projection.sender is sender.output_state)):
+            if sender.verbosePref:
+                warnings.warn("Request to assign {} as sender of {}, but it has already been assigned".
+                              format(sender.name, projection.name))
+                return
+
     if not isinstance(state, (int, str, OutputState)):
         raise ProjectionError("State specification for {0} (as sender of {1}) must be the name, reference to "
                               "or index of an outputState of {0} )".format(sender.name, projection_spec))
@@ -981,17 +1075,17 @@ def _add_projection_from(sender, state, projection_spec, receiver, context=None)
 
     # Generic OUTPUT_STATE is specified, so use (primary) outputState
     elif state is OUTPUT_STATE:
-        sender.outputState._instantiate_projections_to_state(projections=projection_spec, context=context)
+        sender.output_state._instantiate_projections_to_state(projections=projection_spec, context=context)
         return
 
     # input_state is index into outputStates OrderedDict, so get corresponding key and assign to output_state
     elif isinstance(state, int):
         try:
-            key = list(sender.outputStates.keys)[state]
+            key = list(sender.output_states.keys)[state]
         except IndexError:
             raise ProjectionError("Attempt to assign projection_spec ({0}) to outputState {1} of {2} "
                                  "but it has only {3} outputStates".
-                                 format(projection_spec.name, state, sender.name, len(sender.outputStates)))
+                                 format(projection_spec.name, state, sender.name, len(sender.output_states)))
         else:
             output_state = key
 
@@ -999,7 +1093,7 @@ def _add_projection_from(sender, state, projection_spec, receiver, context=None)
     #    so try as key in outputStates OrderedDict (i.e., as name of an outputState)
     if isinstance(state, str):
         try:
-            sender.outputState[state]._instantiate_projections_to_state(projections=projection_spec, context=context)
+            sender.output_state[state]._instantiate_projections_to_state(projections=projection_spec, context=context)
         except KeyError:
             pass
         else:
@@ -1008,7 +1102,7 @@ def _add_projection_from(sender, state, projection_spec, receiver, context=None)
                               format(projection_spec.name, state, sender.name))
             # return
 
-    # input_state is either the name for a new inputState or kwAddNewInputState
+    # output_state is either the name for a new outputState or kwAddNewOutputState
     if not state is kwAddOutputState:
         if sender.prefs.verbosePref:
             reassign = input("\nAdd new outputState named {0} to {1} (as sender for {2})? (y/n):".
@@ -1026,11 +1120,14 @@ def _add_projection_from(sender, state, projection_spec, receiver, context=None)
                                      constraint_value=projection_spec.value,
                                      constraint_value_name='Projection_spec value for new inputState',
                                      context=context)
-    #  Update inputState and inputStates
+    #  Update outputState and outputStates
     try:
-        sender.outputStates[output_state.name] = output_state
-    # No inputState(s) yet, so create them
+        sender.output_states[output_state.name] = output_state
+    # No outputState(s) yet, so create them
     except AttributeError:
-        sender.outputStates = OrderedDict({output_state.name:output_state})
-        sender.outputState = list(sender.outputStates)[0]
-    output_state._instantiate_projections_to_state(projections=projection_spec, context=context)
\ No newline at end of file
+        from PsyNeuLink.Components.States.State import State_Base
+        sender.output_states = ContentAddressableList(component_type=State_Base,
+                                                      list=[output_state],
+                                                      name=sender.name+'.output_states')
+
+    output_state._instantiate_projections_to_state(projections=projection_spec, context=context)
diff --git a/PsyNeuLink/Components/ShellClasses.py b/PsyNeuLink/Components/ShellClasses.py
index c0458f36cb8..a92a8a42f47 100644
--- a/PsyNeuLink/Components/ShellClasses.py
+++ b/PsyNeuLink/Components/ShellClasses.py
@@ -23,11 +23,6 @@
   `Mechanism`, `Projection` (and possibly `State`) classes should be extensible:
   developers should be able to create, register and refer to subclasses (plug-ins), without modifying core code
 
-Typechecking
-------------
-* `parameter_spec`
-* `optional_parameter_spec`
-
 """
 
 from PsyNeuLink.Components.Component import *
@@ -96,8 +91,6 @@ def __init__(self,
 
 # ******************************************* MECHANISM ****************************************************************
 
-ParamValueProjection = namedtuple('ParamValueProjection', 'value projection')
-
 
 class Mechanism(ShellClass):
 
@@ -183,46 +176,3 @@ class Function(ShellClass):
 
     def execute(self, variable, params, context):
         raise ShellClassError("Must implement function in {0}".format(self))
-
-
-def optional_parameter_spec(param):
-    """Test whether param is a legal PsyNeuLink parameter specification or `None`
-
-    Calls parameter_spec if param is not `None`
-    Used with typecheck
-
-    Returns
-    -------
-    `True` if it is a legal parameter or `None`.
-    `False` if it is neither.
-
-
-    """
-    if not param:
-        return True
-    return parameter_spec(param)
-
-def parameter_spec(param):
-    """Test whether param is a legal PsyNeuLink parameter specification
-
-    Used with typecheck
-
-    Returns
-    -------
-    `True` if it is a legal parameter.
-    `False` if it is not.
-    """
-    # if isinstance(param, property):
-    #     param = ??
-    # if is_numeric(param):
-    if (isinstance(param, (numbers.Number,
-                           np.ndarray,
-                           list,
-                           tuple,
-                           function_type,
-                           ParamValueProjection,
-                           Projection)) or
-        (inspect.isclass(param) and issubclass(param, Projection)) or
-        param in parameter_keywords):
-        return True
-    return False
diff --git a/PsyNeuLink/Components/States/InputState.py b/PsyNeuLink/Components/States/InputState.py
index b34fb2bea7c..7a492c7a4cc 100644
--- a/PsyNeuLink/Components/States/InputState.py
+++ b/PsyNeuLink/Components/States/InputState.py
@@ -17,11 +17,11 @@
 or system.  If the inputState belongs to an `ORIGIN` mechanism (see
 `role of mechanisms in processes and systems <Mechanism_Role_In_Processes_And_Systems>`), then it receives the input
 specified when that process or system is `run <Run>`.  The projections received by an inputState are
-listed in its `receivesFromProjections <InputState.receivesFromProjections>` attribute. Its
+listed in its `path_afferents <InputState.path_afferents>` attribute. Its
 `function <InputState.function>` combines the values of these inputs, and the result is assigned to an item
 corresponding to the inputState in the owner mechanism's :keyword:`variable <Mechanism.Mechanism_Base.variable>` and
-`inputValue <Mechanism.Mechanism_Base.inputValue>` attributes  (see `Mechanism InputStates <Mechanism_InputStates>`
-for additional details about the role of inputStates in mechanisms).
+`input_value <Mechanism.Mechanism_Base.input_value>` attributes  (see `Mechanism InputStates <Mechanism_InputStates>`
+for additional details about the role of input_states in mechanisms).
 
 
 .. _InputState_Creation:
@@ -33,7 +33,7 @@
 the `pathway` of a process`, the inputState for that mechanism will be created and assigned as the
 `receiver <MappingProjection.MappingProjection.receiver>` of a `MappingProjection` from the  preceding mechanism in the
 pathway;  and a `ControlMechanism <ControlMechanism>` automatically creates an inputState for each mechanism that it
-monitors. PsyNeuLink does not currently support the explicit creation of inputStates (this may be implemented in the
+monitors. PsyNeuLink does not currently support the explicit creation of input_states (this may be implemented in the
 future). However they can modified as described below.
 
 COMMENT:
@@ -46,10 +46,15 @@
 must be specified in the ``owner`` argument of its constructor; if the inputState is specified in the
 INPUT_STATES entry of the parameter dictionary for a mechanism, then the owner is inferred from the context.
 
-If one or more custom inputStates need to be specified when a mechanism is created, or added to an existing mechanism,
+.. _InputState_Specification
+
+InputState Specification
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+If one or more custom input_states need to be specified when a mechanism is created, or added to an existing mechanism,
 they can be specified in an entry of the mechanism's parameter dictionary, using the key :keyword`INPUT_STATES`
-and a value that specifies one or more inputStates. For a single inputState, the value can be any of the
-specifications in the the list below.  To create multiple inputStates, the value of the INPUT_STATES entry
+and a value that specifies one or more input_states. For a single inputState, the value can be any of the
+specifications in the the list below.  To create multiple input_states, the value of the INPUT_STATES entry
 can be either a list, each item of which can be any of the specifications below;  or, it can be an OrderedDict,
 in which the key for each entry is a string specifying the name for the inputState to be created, and its value is
 one of the specifications below:
@@ -82,8 +87,8 @@
       :ref:`projection specification dictionary <Projection_In_Context_Specification>`, or a list containing
       items that are either of those.
     ..
-    * A :any:`ParamValueProjection` tuple.  This creates a default inputState using the ``value`` item as its
-    ``variable``, and assigns the state as the ``receiver`` of the projection item.
+    * A **2-item tuple**.  This creates a default inputState using the first (value) item as its
+      :keyword:`variable`, and assigns the state as the :keyword:`receiver` of the 2nd (projection) item.
 
     .. note::
        In all cases, the resulting ``value`` of the inputState must be compatible with (that is, have the same number
@@ -104,17 +109,17 @@
 COMMENT
 
 COMMENT:
-Assigning inputStates using the INPUT_STATES entry of a mechanism's parameter dictionary adds them to any
+Assigning input_states using the INPUT_STATES entry of a mechanism's parameter dictionary adds them to any
 that are automatically generated for that mechanism;  if the name of one explicitly specified is them same as one
 automatically generated, the name will be suffixed with a numerical index and added (that is, it will *not* replace
 the one automatically generated). InputStates can also be added by using the
-:py:func:`assign_output_state <OutputState.assign_output_state>`. If the mechanism requires multiple inputStates
+:py:func:`assign_output_state <OutputState.assign_output_state>`. If the mechanism requires multiple input_states
 (i.e., it's ``variable`` attribute has more than on item), it assigns the ``value`` of each inputState to an item of
-its ``variable`` (see :ref:`Mechanism Variable <Mechanism_Variable>`). Therefore, the number of inputStates
+its ``variable`` (see :ref:`Mechanism Variable <Mechanism_Variable>`). Therefore, the number of input_states
 specified must equal the number of items in the mechanisms's ``variable``.  An exception is if the mechanism's
 ``variable`` has more than one item, it may still be assigned a single inputState;  in that case, the ``value`` of
 that inputState must have the same number of items as the  mechanisms's ``variable``.  For cases in which there are
-multiple inputStates, the order in which they are specified in the list or OrderedDict must parallel the order of
+multiple input_states, the order in which they are specified in the list or OrderedDict must parallel the order of
 the items to which they will be assined in the mechanism's ``variable``; furthemore, as noted above, the ``value`` for
 each inputState must match (in number and types of elements) the item of ``variable`` to which it will be assigned.
 COMMENT
@@ -127,7 +132,7 @@
 Every inputState is owned by a `mechanism <Mechanism>`. It can receive one or more
 `MappingProjections <MappingProjection>` from other mechanisms, as well as from the process or system to which its
 owner belongs (if it is the `ORIGIN` mechanism for that process or system).  The projections received by an
-inputState are listed in its `receivesFromProjections <InputState.receivesFromProjections>` attribute.
+inputState are listed in its `path_afferents <InputState.path_afferents>` attribute.
 
 Like all PsyNeuLink components, an inputState has the three following core attributes:
 
@@ -153,8 +158,8 @@
 When this occurs, the inputState executes any projections it receives, calls its `function <InputState.function>` to
 aggregate their values, and then assigns the result to the inputState's `value <InputState.value>` attribute.  This,
 in turn, is assigned to the item of the mechanism's `variable <Mechanism.Mechanism_Base.variable>` and
-`inputValue <Mechanism.Mechanism_Base.inputValue>` attributes corresponding to that inputState
-(see `mechanism variable and inputValue attributes <Mechanism_Variable>` for additional details).
+`input_value <Mechanism.Mechanism_Base.input_value>` attributes corresponding to that inputState
+(see `mechanism variable and input_value attributes <Mechanism_Variable>` for additional details).
 
 .. _InputState_Class_Reference:
 
@@ -194,8 +199,8 @@ class InputState(State_Base):
     InputState(                                \
     owner,                                     \
     reference_value=None,                      \
-    value=None,                                \
     function=LinearCombination(operation=SUM), \
+    value=None,                                \
     params=None,                               \
     name=None,                                 \
     prefs=None)
@@ -243,10 +248,8 @@ class InputState(State_Base):
         the value of the item of the owner mechanism's `variable <Mechanism.Mechanism_Base.variable>` attribute to which
         the inputState is assigned; used as the template for the inputState's `value <InputState.value>` attribute.
 
-    value : number, list or np.ndarray
-        specifies the template for the inputState's `variable <InputState.variable>` attribute (since an inputState's
-        `variable <InputState.variable>` and `value <InputState.value>` attributes must have the same format
-        (number and type of elements).
+    variable : number, list or np.ndarray
+        specifies the template for the inputState's `variable <InputState.variable>` attribute.
 
     function : Function or method : default LinearCombination(operation=SUM)
         specifies the function used to aggregate the values of the projections received by the inputState.
@@ -274,7 +277,7 @@ class InputState(State_Base):
     owner : Mechanism
         the mechanism to which the inputState belongs.
 
-    receivesFromProjections : Optional[List[Projection]]
+    afferents : Optional[List[Projection]]
         a list of the projections received by the inputState
         (i.e., for which it is a `receiver <Projection.Projection.receiver>`).
 
@@ -291,7 +294,7 @@ class InputState(State_Base):
 
     name : str : default <State subclass>-<index>
         the name of the inputState.
-        Specified in the `name` argument of the constructor for the outputState.  If not is specified, a default is
+        Specified in the **name** argument of the constructor for the outputState.  If not is specified, a default is
         assigned by the StateRegistry of the mechanism to which the outputState belongs
         (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
 
@@ -303,7 +306,7 @@ class InputState(State_Base):
 
     prefs : PreferenceSet or specification dict : State.classPreferences
         the `PreferenceSet` for the inputState.
-        Specified in the `prefs` argument of the constructor for the projection;  if it is not specified, a default is
+        Specified in the **prefs** argument of the constructor for the projection;  if it is not specified, a default is
         assigned using `classPreferences` defined in __init__.py
         (see :doc:`PreferenceSet <LINK>` for details).
 
@@ -336,24 +339,49 @@ def __init__(self,
                  reference_value=None,
                  variable=None,
                  function=LinearCombination(operation=SUM),
+                 weight=None,
+                 exponent=None,
                  params=None,
                  name=None,
                  prefs:is_pref_set=None,
                  context=None):
 
         # Assign args to params and functionParams dicts (kwConstants must == arg names)
-        params = self._assign_args_to_param_dicts(function=function, params=params)
+        params = self._assign_args_to_param_dicts(function=function,
+                                                  weight=weight,
+                                                  exponent=exponent,
+                                                  params=params)
 
         self.reference_value = reference_value
 
         # Validate sender (as variable) and params, and assign to variable and paramsInstanceDefaults
         # Note: pass name of owner (to override assignment of componentName in super.__init__)
         super(InputState, self).__init__(owner,
-                                                  variable=variable,
-                                                  params=params,
-                                                  name=name,
-                                                  prefs=prefs,
-                                                  context=self)
+                                         variable=variable,
+                                         params=params,
+                                         name=name,
+                                         prefs=prefs,
+                                         context=self)
+
+    def _validate_params(self, request_set, target_set=None, context=None):
+        """Validate weights and exponents
+        
+        This needs to be done here, since paramClassDefault declarations assign None as default
+            (so that they can be ignored if not specified here or in the function)
+        """
+
+        super()._validate_params(request_set=request_set, target_set=target_set, context=context)
+        
+        if WEIGHT in target_set and target_set[WEIGHT] is not None:
+            if not isinstance(target_set[WEIGHT], (int, float)):
+                raise InputStateError("{} parameter of {} for {} ({}) must be an int or float".
+                                      format(WEIGHT, self.name, self.owner.name, target_set[WEIGHT]))
+
+        if EXPONENT in target_set and target_set[EXPONENT] is not None:
+            if not isinstance(target_set[EXPONENT], (int, float)):
+                raise InputStateError("{} parameter of {} for {} ({}) must be an int or float".
+                                      format(EXPONENT, self.name, self.owner.name, target_set[EXPONENT]))
+
 
     def _instantiate_function(self, context=None):
         """Insure that function is LinearCombination and that output is compatible with owner.variable
@@ -394,17 +422,48 @@ def _instantiate_function(self, context=None):
                                                   self.reference_value))
                                                   # self.owner.variable))
 
+    def _execute(self, function_params, context):
+        """Call self.function with self.variable
+
+        If there were no Transmissive projections, ignore and return None
+        """
+
+        # If there were any Transmissive projections:
+        if self._path_proj_values:
+            # Combine projection values
+            combined_values = self.function(variable=self._path_proj_values,
+                                            params=function_params,
+                                            context=context)
+            return combined_values
+
+        # There were no projections
+        else:
+            # mark combined_values as none, so that (after being assigned to self.value)
+            #    it is ignored in execute method (i.e., not combined with base_value)
+            return None
+
+    @property
+    def pathway_projections(self):
+        return self.path_afferents
+
+    @pathway_projections.setter
+    def pathway_projections(self, assignment):
+        self.path_afferents = assignment
+
+
 def _instantiate_input_states(owner, context=None):
     """Call State._instantiate_state_list() to instantiate orderedDict of inputState(s)
 
-    Create OrderedDict of inputState(s) specified in paramsCurrent[INPUT_STATES]
+    Create ContentAddressableList of inputState(s) specified in paramsCurrent[INPUT_STATES]
+
     If INPUT_STATES is not specified, use self.variable to create a default input state
+
     When completed:
-        - self.inputStates contains an OrderedDict of one or more inputStates
-        - self.inputState contains the `primary inputState <Mechanism_InputStates>`:  first or only one in OrderedDict
-        - paramsCurrent[OUTPUT_STATES] contains the same OrderedDict (of one or more inputStates)
+        - self.input_states contains an OrderedDict of one or more input_states
+        - self.input_state contains the `primary inputState <Mechanism_InputStates>`:  first or only one in OrderedDict
+        - paramsCurrent[OUTPUT_STATES] contains the same OrderedDict (of one or more input_states)
         - each inputState corresponds to an item in the variable of the owner's function
-        - the value of all of the inputStates is stored in a list in inputValue
+        - the value of all of the input_states is stored in a list in input_value
         - if there is only one inputState, it is assigned the full value
 
     Note: State._instantiate_state_list()
@@ -412,22 +471,43 @@ def _instantiate_input_states(owner, context=None):
               into individual 1D arrays, one for each input state
 
     (See State._instantiate_state_list() for additional details)
-
-    :param context:
-    :return:
     """
-    owner.inputStates = _instantiate_state_list(owner=owner,
-                                               state_list=owner.paramsCurrent[INPUT_STATES],
-                                               state_type=InputState,
-                                               state_param_identifier=INPUT_STATES,
-                                               constraint_value=owner.variable,
-                                               constraint_value_name="function variable",
-                                               context=context)
-
-    # Check that number of inputStates and their variables are consistent with owner.variable,
+
+    state_list = _instantiate_state_list(owner=owner,
+                                         state_list=owner.input_states,
+                                         state_type=InputState,
+                                         state_param_identifier=INPUT_STATES,
+                                         constraint_value=owner.variable,
+                                         constraint_value_name="function variable",
+                                         context=context)
+
+
+    # FIX: 5/23/17:  SHOULD APPEND THIS TO LIST OF EXISTING INPUT_STATES RATHER THAN JUST ASSIGN;
+    #                THAT WAY CAN USE INCREMENTALLY IN COMPOSITION
+    # if context and 'COMMAND_LINE' in context:
+    #     if owner.input_states:
+    #         owner.input_states.extend(state_list)
+    #     else:
+    #         owner.input_states = state_list
+    # else:
+    #     if owner._input_states:
+    #         owner._input_states.extend(state_list)
+    #     else:
+    #         owner._input_states = state_list
+
+    # FIX: This is a hack to avoid recursive calls to assign_params, in which output_states never gets assigned
+    # FIX: Hack to prevent recursion in calls to setter and assign_params
+    if context and 'COMMAND_LINE' in context:
+        owner.input_states = state_list
+    else:
+        owner._input_states = state_list
+
+
+
+    # Check that number of input_states and their variables are consistent with owner.variable,
     #    and adjust the latter if not
-    for i in range (len(owner.inputStates)):
-        input_state = list(owner.inputStates.values())[i]
+    for i in range (len(owner.input_states)):
+        input_state = owner.input_states[i]
         try:
             variable_item_is_OK = iscompatible(owner.variable[i], input_state.value)
             if not variable_item_is_OK:
@@ -439,25 +519,10 @@ def _instantiate_input_states(owner, context=None):
     if not variable_item_is_OK:
         old_variable = owner.variable
         new_variable = []
-        for state_name, state in owner.inputStates:
+        for state_name, state in owner.input_states:
             new_variable.append(state.value)
         owner.variable = np.array(new_variable)
         if owner.verbosePref:
             warnings.warn("Variable for {} ({}) has been adjusted "
-                          "to match number and format of its inputStates: ({})".
+                          "to match number and format of its input_states: ({})".
                           format(old_variable, append_type_to_name(owner),owner.variable))
-
-
-    # Initialize self.inputValue to correspond to format of owner's variable, and zero it
-# FIX: INSURE THAT ELEMENTS CAN BE FLOATS HERE:  GET AND ASSIGN SHAPE RATHER THAN COPY? XXX
-# FIX:  IS THIS A LIST OR np.array (SHOULD BE A LIST)
-    # ??REPLACE THIS WITH owner.inputValue = list(owner.variable) * 0.0??
-    owner.inputValue = owner.variable.copy() * 0.0
-
-    # Assign self.inputState to first inputState in dict
-    try:
-        owner.inputState = list(owner.inputStates.values())[0]
-    except AttributeError:
-        owner.inputState = None
-
-
diff --git a/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/ControlMechanisms/ControlSignal.py b/PsyNeuLink/Components/States/ModulatorySignals/ControlSignal.py
similarity index 69%
rename from PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/ControlMechanisms/ControlSignal.py
rename to PsyNeuLink/Components/States/ModulatorySignals/ControlSignal.py
index 443145d0111..e21b89f3557 100644
--- a/PsyNeuLink/Components/Mechanisms/AdaptiveMechanisms/ControlMechanisms/ControlSignal.py
+++ b/PsyNeuLink/Components/States/ModulatorySignals/ControlSignal.py
@@ -6,22 +6,21 @@
 # See the License for the specific language governing permissions and limitations under the License.
 
 
-# ******************************************  OutputState *****************************************************
+# ******************************************  ControlSignal *****************************************************
 
 """
 Overview
 --------
 
-A ControlSignal is an `OutputState` specialized for use with an `EVCMechanism`. It is used to modify the
-parameter of a mechanism or of its :keyword:`function` that has been
-`specified for control <ControlMechanism_Specifying_Control>`, in a system that regulates its performance using an
-`EVCMechanism` as its `controller`.  A ControlSignal is associated with a `ControlProjection` to the `parameterState
-<ParameterState>` for the parameter to be controlled.  It receives an `allocation` value specified by the
-EVCMechanism's `function <EVCMechanism.function>`, and uses that to compute an `intensity` that is assigned as the
-`value <ControlProjection.ControlProjection.value>` of its ControlProjection. The parameterState that receives the
-ControlProjection uses that value to modify the :keyword:`value` of the mechanism's (or function's) parameter for
-which it is responsible.  A ControlSignal also calculates a `cost`, based on its `intensity` and/or its time course,
-that is used by the EVCMechanism to adapt its `allocation` in the future.
+A ControlSignal is a type of `ModulatorySignal`, specialized for use with a `ControlMechanism` and a
+`ControlProjection`, to modify the parameter of a `Mechanism` or its `function <Mechanism.function>`, that in turn
+controls the `value <Mechanisms.value>` of that Mechanism. A ControlSignal receives an `allocation` value from the
+ControlMechanism to which it belongs, and uses that to compute an `intensity` that is assigned as the `value
+<ControlProjection.ControlProjection.value>` of its ControlProjection.  The ControlProjection conveys its value to
+the `ParameterState` for the parameter of a `Mechanism` or its `function <Mechanism.function>`, which uses that value
+to `control <ModulatorySignal_Modulation>` the `value <ParameterState.value>` of the parameter.  A ControlSignal also
+calculates a `cost`, based on its `intensity` and/or its time course, that is used by the ControlMechanism to adapt
+its `allocation` in the future.
 
 .. _ControlSignal_Creation:
 
@@ -29,30 +28,65 @@
 ------------------------
 
 A ControlSignal is created automatically whenever the parameter of a mechanism or of its function
-is `specified for control <ControlMechanism_Specifying_Control>` and the mechanism belongs to a system for which
-an `EVCMechanism` is the `controller`.  Although a ControlSignal can be created using its constructor, or any of the
-other ways for `creating an outputState <OutputStates_Creation>`,  this is neither necessary nor advisable,
-as a ControlSignal has dedicated components and requirements for configuration that must be met for it to function
-properly.
+is `specified for control <ControlMechanism_Control_Signals>` and the mechanism belongs to a system for which
+an `ControlMechanism` is the `controller`.  ControlSignals can also be specified in the **control_signals**
+argument of the constructor for a `ControlMechanism`.  Although a ControlSignal can be created directly using its 
+constructor (or any of the other ways for `creating an outputState <OutputStates_Creation>`), this is neither 
+necessary nor advisable, as a ControlSignal has dedicated components and requirements for configuration that must be 
+met for it to function properly.
+
+.. _ControlSignal_Specification:
+
+Specifying ControlSignals
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+When a ControlSignal is specified in context (e.g., the **control_signals** argument of the constructor for a
+`ControlMechanism`, the specification can take any of the following forms:
+
+  * a *ParameterState* of the Mechanism to which the parameter belongs;
+  |
+  * a *tuple*, with the *name* of the parameter as its 1st item. and the *mechanism* to which it belongs as the 2nd;
+    note that this is a convenience format, which is simpler to use than a specification dictionary (see below), 
+    but precludes specification of any `parameters <ControlSignal_Structure>` for the ControlSignal.
+  |
+  * a *specification dictionary*, that must contain at least the following two entries:
+    * *NAME*:str - a string that is the name of the parameter to be controlled;
+    * *MECHANISM*:Mechanism - the Mechanism to which the parameter belongs; 
+      (note: the Mechanism itself should be specified even if the parameter belongs to its function).
+    The dictionary can also contain entries for any other ControlSignal attributes to be specified
+    (e.g., a MODULATION and/or an ALLOCATION_SAMPLES entry; see `below <ControlSignal_Structure>` for a
+    description of ControlSignal attributes).
 
 .. _ControlSignal_Structure:
 
 Structure
 ---------
 
-A ControlSignal is owned by an `EVCMechanism`, and associated with a `ControlProjection` that projects to the
-`parameterState <ParameterState>` associated with the paramter to be controlled.  A ControlSignal has the following
+A ControlSignal is owned by an `ControlMechanism`, and associated with a `ControlProjection` that projects to the
+`parameterState <ParameterState>` associated with the parameter to be controlled.  A ControlSignal has the following
 primary attributes:
 
+.. _ControlSignal_Modulation:
+
+* `modulation <ControlSignal.modulation>` : determines how the ControlProjection is used by the ParameterState to 
+  which it projects to modify its value (see `ModulatorySignals <ModulatorySignals_Modulation>` for an
+  explanation of how the modulation is specified and used to modulate the value of a parameter). The default value 
+  is set to the value of the `modulation <ControlMechanism.modulation>` attribute of the ControlMechanism to which the 
+  ControlSignal belongs;  this the is same for all of the ControlSignals belonging to that ControlMechanism.  
+  However, the `modulation <ControlSignal.modulation>` can be specified individually for a ControlSignal using a 
+  specification dictionary where the ControlSignal is specified, as described `above <ControlSignal_Specification>`.    
+  The `modulation <ControlSignal.modulation>` value of a ControlSignal is used by all of the 
+  `ControlProjections <ControlProjection>` that project from that ControlSignal.
+    
 .. _ControlSignal_Allocation:
 
-* `allocation`: assigned to the ControlSignal by the EVCMechanism to which it belongs, and converted to its
+* `allocation`: assigned to the ControlSignal by the ControlMechanism to which it belongs, and converted to its
   `intensity` by its `function <ControlSignal.function>`. Its value corresponds to the current round of execution of
-  the EVCMechanism to which the ControlSignal belongs.  The value in the previous round of execution can be accessed
-  using the ControlSignal's `last_allocation` attribute.
+  the ControlMechanism to which the ControlSignal belongs.  The value in the previous round of execution can be 
+  accessed using the ControlSignal's `last_allocation` attribute.
 ..
-* `allocation_samples`:  list of the allocation values to be sampled when the `EVCMechanism` to which the
-  ControlSignal belongs determines its `allocation_policy <EVCMechanism.EVCMechanism.allocation_policy>`.
+* `allocation_samples`:  list of the allocation values to be sampled if the `ControlMechanism` to which the
+  ControlSignal belongs determines its `allocation_policy <ControlMechanism.allocation_policy>` by sampling.
 ..
 * `function <ControlSignal.function>`: converts the ControlSignal's `allocation` to its `intensity`.  By default this
   is an identity function (:keyword:`Linear(slope=1, intercept=0))`), that simpy uses the `allocation` as the
@@ -63,8 +97,8 @@
 
 * `intensity`:  the result of the ControlSignal`s `function <ControlSignal.function>` applied to its `allocation`,
   and used to modify the value of the parameter for which the ControlSignal is responsible.  Its value corresponds
-  to the current round of execution of the EVCMechanism to which the ControlSignal belongs.  The value in the previous
-  round of execution can be accessed using the ControlSignal's `lastIntensity` attribute.
+  to the current round of execution of the ControlMechanism to which the ControlSignal belongs.  The value in the 
+  previous round of execution can be accessed using the ControlSignal's `lastIntensity` attribute.
 
 .. _ControlSignal_Costs:
 
@@ -103,32 +137,112 @@
 Execution
 ---------
 
-A ControlSignal cannot be executed directly.  It is executed whenever the `EVCMechanism` to which it belongs is
-executed.  When this occurs, the EVCMechanism provides the ControlSignal with an `allocation`, that is used by its
+XXX NEED EXPLANATION HERE (OR IN PARAMETER STATE) FOR HOW PARAMETER VALUES ARE MODULATED AND ASSIGNED
+    REFERENCE DISCUSSION IN ParameterState
+
+A ControlSignal cannot be executed directly.  It is executed whenever the `ControlMechanism` to which it belongs is
+executed.  When this occurs, the ControlMechanism provides the ControlSignal with an `allocation`, that is used by its
 `function <ControlSignal.function>` to compute its `intensity` for that round of execution.  The `intensity` is used
 by its associated `ControlProjection` to set the :keyword:`value` of the `parameterState <ParameterState>` to which it
 projects. The paramemterState uses that value, in turn, to modify the value of the mechanism or function parameter
 being controlled.  The ControlSignal's `intensity`is also used by its `cost functions <ControlSignal_Cost_Functions>`
-to compute its `cost` attribute. That is used, along with its `allocation_samples` attribute, by the EVCMechanism to
-evaluate the `expected value of control (EVC) <EVCMechanism_EVC>` of the current
-`allocation_policy <EVCMechanism.EVCMechanism.allocation_policy>`, and (possibly) adjust the ControlSignal's
-`allocation` for the next round of execution.
+to compute its `cost` attribute. That is used, along with its `allocation_samples` attribute, by the ControlMechanism
+to evaluate the current `allocation_policy <ControlMechanism.allocation_policy>`, and (possibly) adjust the 
+ControlSignal's `allocation` for the next round of execution.
 
 .. note::
-   The changes in a parameter in response to the execution of an EVCMechanism are not applied until the mechanism
+   The changes in a parameter in response to the execution of a ControlMechanism are not applied until the mechanism
    with the parameter being controlled is next executed; see :ref:`Lazy Evaluation <LINK>` for an explanation of
    "lazy" updating).
 
+.. _ControlSignal_Examples:
+
+Examples
+~~~~~~~~
+
+*Modulate the parameter of a Mechanism's <function <Mechanism.function>*.  The following example assigns a
+ControlSignal to the `bias <Logistic.gain>` parameter of the `Logistic` Function used by a `TransferMechanism`::
+
+    My_Mech = TransferMechanism(function=Logistic(bias=(1.0, ControlSignal)))
+
+Note that the ControlSignal is specified by it class.  This will create a default ControlSignal,
+with a ControlProjection that projects to the TransferMechanism's `ParameterState` for the `bias <Logistic.bias>`
+parameter of its `Logistic` Function.  The default value of a ControlSignal's `modulation <ControlSignal.modulation>`
+attribute is Modulation.MULTIPLICATIVE, so that it will multiply
+
+
+of the Control
+When the TransferMechanism executes, the Logistic Function will use the value of the ControlSignal as its
+gain parameter.
+
+*Specify attributes of a ControlSignal*.  Ordinarily, ControlSignals modify the *MULTIPLICATIVE_PARAM* of a
+ParameterState's `function <ParameterState.function>` to modulate the parameter's value.
+In the example below, this is changed by specifying the `modulation <ControlSignal.modulation>` attribute of a
+`ControlSignal` for the `Logistic` Function of a `TransferMechanism`.  It is changed so that the value of the
+ControlSignal adds to, rather than multiplies, the value of the `gain <Logistic.gain>` parameter of the Logistic
+function::
+
+    My_Mech = TransferMechanism(function=Logistic(gain=(1.0, ControlSignal(modulation=ModulationParam.ADDITIVE))))
+
+Note that the `ModulationParam` specified for the `ControlSignal` pertains to the function of a *ParameterState*
+for the *Logistic* Function (in this case, its `gain <Logistic.gain>` parameter), and *not* the Logistic function
+itself -- that is, the value of the ControlSignal is added to the *gain parameter* of the Logistic function,
+*not* its `variable <Logistic.variable>`).
+
+COMMENT:
+    MOVE THIS EXAMPLE TO EVCMechanism
+
+*Modulate the parameters of several Mechanisms by an EVCMechanism*.  This shows::
+
+    My_Mech_A = TransferMechanism(function=Logistic)
+    My_Mech_B = TransferMechanism(function=Linear,
+                                 output_states=[RESULT, MEAN])
+
+    Process_A = process(pathway=[My_Mech_A])
+    Process_B = process(pathway=[My_Mech_B])
+    My_System = system(processes=[Process_A, Process_B])
+
+    My_EVC_Mechanism = EVCMechanism(system=My_System,
+                                    monitor_for_control=[My_Mech_A.output_states[RESULT],
+                                                         My_Mech_B.output_states[MEAN]],
+                                    control_signals=[(GAIN, My_Mech_A),
+                                                     {NAME: INTERCEPT,
+                                                      MECHANISM: My_Mech_B,
+                                                      MODULATION:ModulationParam.ADDITIVE}],
+                                    name='My EVC Mechanism')
+COMMENT
+
+
+*Modulate the parameters of several Mechanisms in a System*.  This shows::
+
+    My_Mech_A = TransferMechanism(function=Logistic)
+    My_Mech_B = TransferMechanism(function=Linear,
+                                 output_states=[RESULT, MEAN])
+    Process_A = process(pathway=[My_Mech_A])
+    Process_B = process(pathway=[My_Mech_B])
+
+    My_System = system(processes=[Process_A, Process_B],
+                                    monitor_for_control=[My_Mech_A.output_states[RESULT],
+                                                         My_Mech_B.output_states[MEAN]],
+                                    control_signals=[(GAIN, My_Mech_A),
+                                                     {NAME: INTERCEPT,
+                                                      MECHANISM: My_Mech_B,
+                                                      MODULATION:ModulationParam.ADDITIVE}],
+                       name='My Test System')
+
+
 Class Reference
 ---------------
 
 """
 
 # import Components
-from PsyNeuLink.Components.Functions.Function import *
+# FIX: EVCMechanism IS IMPORTED HERE TO DEAL WITH COST FUNCTIONS THAT ARE DEFINED IN EVCMechanism
+#            SHOULD THEY BE LIMITED TO EVC??
+from PsyNeuLink.Components.Functions.Function import _is_modulation_param
 from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.EVCMechanism import *
-from PsyNeuLink.Components.States.OutputState import OutputState
-from PsyNeuLink.Components.States.State import *
+from PsyNeuLink.Components.States.OutputState import OutputState, PRIMARY_OUTPUT_STATE
+from PsyNeuLink.Components.States.ModulatorySignals.ModulatorySignal import *
 
 
 # class OutputStateLog(IntEnum):
@@ -137,55 +251,69 @@
 #     ALL = TIME_STAMP
 #     DEFAULTS = NONE
 
+# # Default control allocation mode values:
+# class DefaultControlAllocationMode(Enum):
+#     GUMBY_MODE = 0.0
+#     BADGER_MODE = 1.0
+#     TEST_MODE = 240
+# defaultControlAllocation = DefaultControlAllocationMode.BADGER_MODE.value
+DEFAULT_ALLOCATION_SAMPLES = np.arange(0.1, 1.01, 0.3)
 
-class ControlSignalError(Exception):
-    def __init__(self, error_value):
-        self.error_value = error_value
+CONTROL_SIGNAL_COST_OPTIONS = 'control_signal_cost_options'
+class ControlSignalCostOptions(IntEnum):
+    NONE               = 0
+    INTENSITY_COST     = 1 << 1
+    ADJUSTMENT_COST    = 1 << 2
+    DURATION_COST      = 1 << 3
+    ALL                = INTENSITY_COST | ADJUSTMENT_COST | DURATION_COST
+    DEFAULTS           = INTENSITY_COST
 
-PRIMARY_OUTPUT_STATE = 0
 
-class OutputStateError(Exception):
+class ControlSignalError(Exception):
     def __init__(self, error_value):
         self.error_value = error_value
 
+
     def __str__(self):
         return repr(self.error_value)
 
 
-class ControlSignal(OutputState):
+class ControlSignal(ModulatorySignal):
     """
-    OutputState(                                     \
-    owner,                                           \
-    function=LinearCombination(operation=SUM),       \
-    intensity_cost_function=Exponential,             \
-    adjustment_cost_function=Linear,                 \
-    duration_cost_function=Integrator,               \
-    cost_combination_function=Reduce(operation=SUM), \
-    allocation_samples=DEFAULT_ALLOCATION_SAMPLES,   \
-    params=None,                                     \
-    name=None,                                       \
-    prefs=None)
-
-    A subclass of OutputState that represents the output of an `EVCmechanism` provided to a `ControlProjection`.
+    ControlSignal(                                       \
+        owner,                                           \
+        function=LinearCombination(operation=SUM),       \
+        intensity_cost_function=Exponential,             \
+        adjustment_cost_function=Linear,                 \
+        duration_cost_function=Integrator,               \
+        cost_combination_function=Reduce(operation=SUM), \
+        allocation_samples=DEFAULT_ALLOCATION_SAMPLES,   \
+        modulation=ModulationParam.MULTIPLICATIVE        \
+        params=None,                                     \
+        name=None,                                       \
+        prefs=None)
+
+    A subclass of OutputState that represents the ControlSignal of a `ControlMechanism` provided to a 
+    `ControlProjection`.
 
     COMMENT:
 
         Description
         -----------
-            The OutputState class is a type in the State category of Component,
-            It is used primarily as the sender for MappingProjections
+            The ControlSignal class is a subtype of the OutputState type in the State category of Component,
+            It is used as the sender for ControlProjections
             Its FUNCTION updates its value:
                 note:  currently, this is the identity function, that simply maps variable to self.value
 
         Class attributes:
-            + componentType (str) = OUTPUT_STATES
+            + componentType (str) = CONTROL_SIGNAL
             + paramClassDefaults (dict)
                 + FUNCTION (LinearCombination)
                 + FUNCTION_PARAMS   (Operation.PRODUCT)
             + paramNames (dict)
 
         Class methods:
-            function (executes function specified in params[FUNCTION];  default: LinearCombination with Operation.SUM)
+            function (executes function specified in params[FUNCTION];  default: Linear)
 
         StateRegistry
         -------------
@@ -197,13 +325,13 @@ class ControlSignal(OutputState):
     Arguments
     ---------
 
-    owner : Mechanism
-        specifies the `EVCMechanism` to which to assign the ControlSignal.
+    owner : ControlMechanism
+        specifies the `ControlMechanism` to which to assign the ControlSignal.
 
-    function : Function or method : default LinearCombination(operation=SUM)
+    function : Function or method : default Linear
         specifies the function used to determine the `intensity` of the ControlSignal from its `allocation`.
 
-    intensity_cost_function : Optional[TransferFuntion] : default Exponential
+    intensity_cost_function : Optional[TransferFunction] : default Exponential
         specifies the function used to calculate the contribution of the ControlSignal's `intensity` to its `cost`.
 
     adjustment_cost_function : Optional[TransferFunction] : default Linear
@@ -219,7 +347,11 @@ class ControlSignal(OutputState):
 
     allocation_samples : list : default range(0.1, 1, 0.1)
         specifies the values used by `ControlSignal's `ControlSignal.owner` to determine its
-        `allocation_policy <EVCMechanism.EVCMechanism.allocation_policy>` (see `ControlSignal_Execution`).
+        `allocation_policy <ControlMechanism.allocation_policy>` (see `ControlSignal_Execution`).
+
+    COMMENT: [NEEDS DOCUMENTATION]
+    COMMENT
+    modulation : ModulationParam : default ModulationParam.MULTIPLICATIVE
 
     params : Optional[Dict[param keyword, param value]]
         a `parameter dictionary <ParameterState_Specifying_Parameters>` that can be used to specify the parameters for
@@ -240,8 +372,8 @@ class ControlSignal(OutputState):
     Attributes
     ----------
 
-    owner : EVCMechanism
-        the `EVCMechanism` to which the ControlSignal belongs.
+    owner : ControlMechanism
+        the `ControlMechanism` to which the ControlSignal belongs.
 
     allocation : float : default: defaultControlAllocation
         value used as `variable <ControlSignal.variable>` for the ControlSignal's `function <ControlSignal.function>`
@@ -252,7 +384,7 @@ class ControlSignal(OutputState):
 
     allocation_samples : list : DEFAULT_SAMPLE_VALUES
         set of values to sample by the ControlSignal's `owner <ControlSignal.owner>` to determine its
-        `allocation_policy <EVCMechanism.EVCMechanism.allocation_policy>`.
+        `allocation_policy <ControlMechanism.allocation_policy>`.
 
     variable : number, list or np.ndarray
         same as `allocation`;  used by `function <ControlSignal.function>` to compute the ControlSignal's `intensity`.
@@ -302,12 +434,16 @@ class ControlSignal(OutputState):
     cost : float
         combined result of all cost functions that are enabled.
 
-    sendsToProjections : [List[ControlProjection]]
+    modulation : ModulationParam
+        specifies the way in which the output of the ControlSignal is used to modulate the value of the parameter
+        it's `ControlProjection` controls.
+
+    efferents : [List[ControlProjection]]
         a list with one item -- the `ControlProjection` assigned to the ControlSignal.
 
     name : str : default <State subclass>-<index>
         name of the outputState.
-        Specified in the `name` argument of the constructor for the outputState.  If not is specified, a default is
+        Specified in the **name** argument of the constructor for the outputState.  If not is specified, a default is
         assigned by the StateRegistry of the mechanism to which the outputState belongs
         (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
 
@@ -319,7 +455,7 @@ class ControlSignal(OutputState):
 
     prefs : PreferenceSet or specification dict : State.classPreferences
         the `PreferenceSet` for the outputState.
-        Specified in the `prefs` argument of the constructor for the projection;  if it is not specified, a default is
+        Specified in the **prefs** argument of the constructor for the projection;  if it is not specified, a default is
         assigned using `classPreferences` defined in __init__.py
         (see :doc:`PreferenceSet <LINK>` for details).
 
@@ -339,37 +475,35 @@ class ControlSignal(OutputState):
 
     paramClassDefaults = State_Base.paramClassDefaults.copy()
     paramClassDefaults.update({
-        PROJECTION_TYPE: MAPPING_PROJECTION,
-        CONTROL_SIGNAL_COST_OPTIONS:ControlSignalCostOptions.DEFAULTS,
+        PROJECTION_TYPE: CONTROL_PROJECTION,
+        CONTROLLED_PARAM:None,
+        CONTROL_SIGNAL_COST_OPTIONS:ControlSignalCostOptions.DEFAULTS
     })
     #endregion
 
 
-    tc.typecheck
+    @tc.typecheck
     def __init__(self,
-                 owner,
-                 reference_value,
+                 owner=None,
+                 reference_value=None,
                  variable=None,
                  index=PRIMARY_OUTPUT_STATE,
                  calculate=Linear,
                  function=LinearCombination(operation=SUM),
                  intensity_cost_function:(is_function_type)=Exponential,
                  adjustment_cost_function:tc.optional(is_function_type)=Linear,
-                 duration_cost_function:tc.optional(is_function_type)=Integrator,
+                 duration_cost_function:tc.optional(is_function_type)=SimpleIntegrator,
                  cost_combination_function:tc.optional(is_function_type)=Reduce(operation=SUM),
                  allocation_samples=DEFAULT_ALLOCATION_SAMPLES,
+                 modulation:tc.optional(_is_modulation_param)=None,
                  params=None,
                  name=None,
                  prefs:is_pref_set=None,
                  context=None):
 
         # Note index and calculate are not used by ControlSignal, but included here for consistency with OutputState
-        if params:
-            try:
-                if params[ALLOCATION_SAMPLES] is not None:
-                    allocation_samples =  params[ALLOCATION_SAMPLES]
-            except KeyError:
-                pass
+        if params and ALLOCATION_SAMPLES in params and params[ALLOCATION_SAMPLES] is not None:
+            allocation_samples =  params[ALLOCATION_SAMPLES]
 
         # Assign args to params and functionParams dicts (kwConstants must == arg names)
         params = self._assign_args_to_param_dicts(function=function,
@@ -380,17 +514,16 @@ def __init__(self,
                                                   allocation_samples=allocation_samples,
                                                   params=params)
 
-        self.reference_value = reference_value
-
         # FIX: 5/26/16
         # IMPLEMENTATION NOTE:
         # Consider adding self to owner.outputStates here (and removing from ControlProjection._instantiate_sender)
         #  (test for it, and create if necessary, as per outputStates in ControlProjection._instantiate_sender),
 
         # Validate sender (as variable) and params, and assign to variable and paramsInstanceDefaults
-        super().__init__(owner,
-                         reference_value,
+        super().__init__(owner=owner,
+                         reference_value=reference_value,
                          variable=variable,
+                         modulation=modulation,
                          index=index,
                          calculate=calculate,
                          params=params,
@@ -398,35 +531,8 @@ def __init__(self,
                          prefs=prefs,
                          context=self)
 
-
-    def _validate_variable(self, variable, context=None):
-        """Insure variable is compatible with output component of owner.function relevant to this state
-
-        Validate self.variable against component of owner's value (output of owner's function)
-             that corresponds to this outputState (since that is what is used as the input to OutputState);
-             this should have been provided as reference_value in the call to OutputState__init__()
-
-        Note:
-        * This method is called only if the parameterValidationPref is True
-
-        :param variable: (anything but a dict) - variable to be validated:
-        :param context: (str)
-        :return none:
-        """
-        super(OutputState,self)._validate_variable(variable, context)
-
-        self.variableClassDefault = self.reference_value
-
-        # Insure that self.variable is compatible with (relevant item of) output value of owner's function
-        if not iscompatible(self.variable, self.reference_value):
-            raise OutputStateError("Value ({0}) of outputState for {1} is not compatible with "
-                                           "the output ({2}) of its function".
-                                           format(self.value,
-                                                  self.owner.name,
-                                                  self.reference_value))
-
     def _validate_params(self, request_set, target_set=None, context=None):
-        """Validate allocation_samples and controlSignal cost functions
+        """Validate allocation_samples and control_signal cost functions
 
         Checks if:
         - cost functions are all appropriate
@@ -511,7 +617,7 @@ def _validate_params(self, request_set, target_set=None, context=None):
                                              "a list or 1D np.array of numbers".
                                          format(allocation_samples, self.name))
 
-        # # If allocation_policy has been assigned, set self.value to it so it reflects the number of  controlSignals;
+        # # If allocation_policy has been assigned, set self.value to it so it reflects the number of  control_signals;
         # #    this is necessary, since function is not fully executed during initialization (in _instantiate_function)
         # #    it returns default_allocation policy which has only a singel item,
         # #    however validation of indices for outputStates requires that proper number of items be in self.value
@@ -561,7 +667,7 @@ def _instantiate_attributes_before_function(self, context=None):
 
             self.paramsCurrent[cost_function_name] = cost_function
 
-        self.controlSignalCostOptions = self.paramsCurrent[CONTROL_SIGNAL_COST_OPTIONS]
+        self.control_signal_cost_options = self.paramsCurrent[CONTROL_SIGNAL_COST_OPTIONS]
 
         # Assign instance attributes
         self.allocation_samples = self.paramsCurrent[ALLOCATION_SAMPLES]
@@ -573,11 +679,7 @@ def _instantiate_attributes_before_function(self, context=None):
         self.intensity = self.allocation
 
         # Default cost params
-        # # MODIFIED 1/23/17 OLD:
-        # self.intensity_cost = self.intensityCostFunction(self.intensity)
-        # MODIFIED 1/23/17 NEW:
         self.intensity_cost = self.intensity_cost_function(self.intensity)
-        # MODIFIED 1/23/17 END
         self.adjustment_cost = 0
         self.duration_cost = 0
         self.last_duration_cost = self.duration_cost
@@ -610,7 +712,7 @@ def update(self, params=None, time_scale=TimeScale.TRIAL, context=None):
         Use self.function to assign intensity
             - if ignoreIntensityFunction is set (for efficiency, if the execute method it is the identity function):
                 ignore self.function
-                pass allocation (input to controlSignal) along as its output
+                pass allocation (input to control_signal) along as its output
         Update cost
         Assign intensity to value of ControlSignal (done in setter property for value)
 
@@ -658,17 +760,17 @@ def update(self, params=None, time_scale=TimeScale.TRIAL, context=None):
         # compute cost(s)
         new_cost = intensity_cost = adjustment_cost = duration_cost = 0
 
-        if self.controlSignalCostOptions & ControlSignalCostOptions.INTENSITY_COST:
+        if self.control_signal_cost_options & ControlSignalCostOptions.INTENSITY_COST:
             intensity_cost = self.intensity_cost = self.intensity_cost_function(self.intensity)
             if self.prefs.verbosePref:
                 print("++ Used intensity cost")
 
-        if self.controlSignalCostOptions & ControlSignalCostOptions.ADJUSTMENT_COST:
+        if self.control_signal_cost_options & ControlSignalCostOptions.ADJUSTMENT_COST:
             adjustment_cost = self.adjustment_cost = self.adjustment_cost_function(intensity_change)
             if self.prefs.verbosePref:
                 print("++ Used adjustment cost")
 
-        if self.controlSignalCostOptions & ControlSignalCostOptions.DURATION_COST:
+        if self.control_signal_cost_options & ControlSignalCostOptions.DURATION_COST:
             duration_cost = self.duration_cost = self.duration_cost_function([self.last_duration_cost, new_cost])
             if self.prefs.verbosePref:
                 print("++ Used duration cost")
@@ -690,10 +792,10 @@ def update(self, params=None, time_scale=TimeScale.TRIAL, context=None):
                 cost_change_string = "+" + str(cost_change)
             print("Cost: {0} [{1}])".format(self.cost, cost_change_string))
 
-        #region Record controlSignal values in owner mechanism's log
+        #region Record control_signal values in owner mechanism's log
         # Notes:
-        # * Log controlSignals for ALL states of a given mechanism in the mechanism's log
-        # * Log controlSignals for EACH state in a separate entry of the mechanism's log
+        # * Log control_signals for ALL states of a given mechanism in the mechanism's log
+        # * Log control_signals for EACH state in a separate entry of the mechanism's log
 
         # Get receiver mechanism and state
         controller = self.owner
@@ -798,27 +900,27 @@ def toggle_cost_function(self, cost_function_name, assignment=ON):
             if not self.paramsCurrent[cost_function_name]:
                 raise ControlSignalError("Unable to toggle {} ON as function assignment is \'None\'".
                                          format(cost_function_name))
-            self.controlSignalCostOptions |= cost_option
+            self.control_signal_cost_options |= cost_option
         else:
-            self.controlSignalCostOptions &= ~cost_option
+            self.control_signal_cost_options &= ~cost_option
 
     # def set_intensity_cost(self, assignment=ON):
     #     if assignment:
-    #         self.controlSignalCostOptions |= ControlSignalCostOptions.INTENSITY_COST
+    #         self.control_signal_cost_options |= ControlSignalCostOptions.INTENSITY_COST
     #     else:
-    #         self.controlSignalCostOptions &= ~ControlSignalCostOptions.INTENSITY_COST
+    #         self.control_signal_cost_options &= ~ControlSignalCostOptions.INTENSITY_COST
     #
     # def set_adjustment_cost(self, assignment=ON):
     #     if assignment:
-    #         self.controlSignalCostOptions |= ControlSignalCostOptions.ADJUSTMENT_COST
+    #         self.control_signal_cost_options |= ControlSignalCostOptions.ADJUSTMENT_COST
     #     else:
-    #         self.controlSignalCostOptions &= ~ControlSignalCostOptions.ADJUSTMENT_COST
+    #         self.control_signal_cost_options &= ~ControlSignalCostOptions.ADJUSTMENT_COST
     #
     # def set_duration_cost(self, assignment=ON):
     #     if assignment:
-    #         self.controlSignalCostOptions |= ControlSignalCostOptions.DURATION_COST
+    #         self.control_signal_cost_options |= ControlSignalCostOptions.DURATION_COST
     #     else:
-    #         self.controlSignalCostOptions &= ~ControlSignalCostOptions.DURATION_COST
+    #         self.control_signal_cost_options &= ~ControlSignalCostOptions.DURATION_COST
     #
     def get_costs(self):
         """Return three-element list with the values of ``intensity_cost``, ``adjustment_cost`` and ``duration_cost``
@@ -838,4 +940,3 @@ def value(self):
     @value.setter
     def value(self, assignment):
         self._value = assignment
-
diff --git a/PsyNeuLink/Components/States/ModulatorySignals/GatingSignal.py b/PsyNeuLink/Components/States/ModulatorySignals/GatingSignal.py
new file mode 100644
index 00000000000..017e00ef72a
--- /dev/null
+++ b/PsyNeuLink/Components/States/ModulatorySignals/GatingSignal.py
@@ -0,0 +1,651 @@
+# Princeton University licenses this file to You under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.  You may obtain a copy of the License at:
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed
+# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and limitations under the License.
+
+
+# ******************************************  GatingSignal *****************************************************
+
+"""
+Overview
+--------
+
+A GatingSignal is a type of `ModulatorySignal` that is specialized for use with a `GatingMechanism` and a
+`GatingProjection`, to modify the `value <State.value> of the InputState(s) and/or OutputState(s) of one or more
+`Mechanisms <Mechanism>`. A GatingSignal receives a value specified by the the `function <GatingMechanism.function>`
+of the GatingMechanism to which it belongs, and assigns that to one or more `GatingProjections <GatingProjection>`,
+each of which projects to one or more InputState(s) and/or OutputState(s) to be gated, and is used to modulate the
+`value <State.value>` of those states.
+
+
+.. _GatingSignal_Creation:
+
+Creating a GatingSignal
+-----------------------
+
+A GatingSignal is created automatically whenever an `InputState` or `OutputState` of a `Mechanism` are
+`specified for gating <GatingMechanism_Gating_Signals>`.  GatingSignals can also be specified in the 
+**gating_signals** argument of the constructor for a `GatingMechanism`, using any of the formats described 
+`below <GatingSignal_Specification>`.  Although a GatingSignal can be created directly using its constructor 
+(or any of the other ways for `creating an outputState <OutputStates_Creation>`),  this is neither necessary 
+nor advisable, as a GatingSignal has dedicated components and requirements for configuration that must be met 
+for it to function properly.
+
+.. _GatingSignal_Specification:
+
+Specifying GatingSignals
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+When a GatingSignal is specified in context (e.g., the **gating_signals** argument of the constructor for a
+`GatingMechanism`, the specification can take any of the following forms:
+
+  * an *InputState* or *OutputState* of a Mechanism;
+  |
+  * a *Mechanism*, in which case its `primary inputState <Mechanism_InputStates>` will be used; 
+  |
+  * a *tuple*, with the *name* of the state as the 1st item, and the mechanism to which it belongs as the 2nd;  
+    note that this is a convenience format, which is simpler to use than a specification dictionary (see below), 
+    but precludes specification of any parameters <GatingSignal_Structure>` for the GatingSignal.
+  |
+  * a *specification dictionary*, that can take either of the following two forms:
+    * for a single state, the dictionary must have the following two entries: 
+        * *NAME*:str - a string that is the name of the state to be gated;
+        * *MECHANISM*:Mechanism - the Mechanism to which the state belongs;
+      in this case, the GatingSignal will named by appending "_GatingSignal" to the name of the state.
+    * for multiple states, the dictionary must have the following entry:
+        * <str>:list - where the string used for the key specifies the name to be used for the GatingSignal,
+          and the value is a list, each item of which is a `specification of a state <LINK>` to be gated by 
+          the GatingSignal (and that will receive a `GatingProjection` from it).
+    In either case, the dictionary can also contain a *MODULATION* entry, the value of which must be a `Modulation` 
+    value that determines how that particular GatingSignal will modulate the value of all of the states that it gates.
+
+.. _GatingSignal_Structure:
+
+Structure
+---------
+
+A GatingSignal is owned by a `GatingMechanism`, and associated with one or more `GatingProjections <GatingProjection>`, 
+each of which projects to the InputState or OutputState that it gates.  
+
+.. _GatingSignal_Modulation:
+
+Modulation
+~~~~~~~~~~
+
+Each GatingSignal has a `modulation <GatingSignal.modulation>` attribute that determines how the GatingProjection 
+is used by the state to which it projects to modify its value (see `ModulatorySignal_Modulation`
+for an explanation of how this attribute is specified and used to modulate the value of a state).  The default value 
+is set to the value of the `modulation <GatingMechanism.modulation>` attribute of the GatingMechanism to which the 
+GatingSignal belongs;  this the is same for all of the GatingSignals belonging to that GatingMechanism.  However, the
+`modulation <GatingSignal.modulation>` can be specified individually for a GatingSignal using a specification 
+dictionary where the GatingSignal is specified, as described `above <GatingSignal_Specification>`. The 
+`modulation <GatingSignal.modulation>` value of a GatingSignal is used by all of the 
+`GatingProjections <GatingProjection>` that project from that GatingSignal.
+
+.. _ControlSignal_Execution:
+
+Execution
+---------
+
+A GatingSignal cannot be executed directly.  It is executed whenever the `GatingMechanism` to which it belongs is
+executed.  When this occurs, the GatingMechanism provides the GatingSignal with a value that is used by its 
+`GatingProjection(s) <GatingProjection>` to modulate the :keyword:`value` of the states to which they project. Those 
+states use the value of the GatingProjection they receive to modify a parameter of their function.  How the modulation
+is executed is determined by the GatingSignal's `modulation` attribute
+(see `ModulatoryProjections_Modulation_Operation).
+
+.. note::
+   The change in the value of InputStates and OutputStates in response to the execution of a GatingMechanism are not 
+   applied until the mechanism(s) to which those states belong are next executed; see :ref:`Lazy Evaluation <LINK>` 
+   for an explanation of "lazy" updating).
+
+.. _GatingSignal_Examples:
+
+Examples
+~~~~~~~~
+
+*Gate an InputState and OutputState*.  The following example configures a `GatingMechanism` to gate the
+`primary InputState <Mechanism_InputState>` of one mechanism, and the `primary outputState <OutputState_Primary>`
+of another::
+
+    my_mechanism_A = TransferMechanism()
+    my_mechanism_B = TransferMechanism()
+    my_gating_mechanism = GatingMechanism(gating_signals=[my_mechanism_A, my_mechanism_B.output_state)
+
+Note that, in the **gating_signals** arg, the first item is reference to the mechanism_A rather than one of its
+states.  This is all that is necessary, the default for a `GatingSignal` is to modulate the
+`primary InputState <Mechanism_InputStates>` of a Mechanism.  The second item explicitly specifies the State to be
+gated (since it is not a default).  This will generate two GatingSignals, each of which will multiplicatively modulate
+the value of the InputState to which it projects.  This is because, by default, the
+`modulation <GatingSignal.modulation>` attribute of a GatingSignal is the *MULTIPLICATIVE_PARAM* for the
+`function <State.function>` to which it projects.  For an InputState, this is (also by default) the
+`slope <Linear.slope>` parameter of a `Linear` function.  Thus, the value of the GatingSignal is assigned to the
+slope, which multiplies the input to the State to determine its `value <State.value>`.
+
+*Modulate the InputStates of several Mechanisms*.  The following example creates a `GatingMechanism` that modulates the
+`InputState` of all the layers in a 3-layered feedforward neural network.  Ordinarily, gating modulates the
+*MULTIPLICATIVE_PARAM* of an InputState's `function <InputState.function>`.  In the example, this is changed so that
+it adds the `value <GatingSignal.value>` of the `GatingSignal` to the `value <InputState.value>` of each InputState::
+
+    My_Input_Layer = TransferMechanism(size=3)
+
+    My_Hidden_Layer = TransferMechanism(size=5)
+
+    My_Output_Layer = TransferMechanism(size=2)
+
+    My_Gating_Mechanism = GatingMechanism(gating_signals=[
+                                            {'GATE_ALL': [My_Input_Layer, My_Hidden_Layer, My_Output_Layer]},
+                                          modulation=ModulationParam.ADDITIVE)
+
+Note that, again, the gating_signals are listed as mechanisms, since their primary InputStates are to be gated.
+Since they are all listed in a single entry of a `state specification dictionary <LINK>`, they will all be gated by a
+single GatingSignal named *GATE_ALL*, that will send a `GatingProjections <GatingProjection>` to the InputState of
+each of the Mechanisms listed (the next example below shows how InputStates can be differentially gated by a
+`GatingMechanism`). Finally, note that the `ModulationParam` specified for the `GatingMechanism` (and therefore the
+default for its `GatingSignals <GatingSignal>) pertains to the `function <InputState.function>` of each `InputState`.
+By default that is a `Linear` function, the *ADDITIVE_PARAM* of which is its `intercept <Linear.intercept>` parameter.
+Therefore, in the example above, each time the InputStates are updated, the value of the GatingSignal will be assigned
+as the `intercept` of each InputState's `function <InputState.function>`, thus adding that amount to the input of the
+State in determining its `value <InputStat.value>`.
+
+*Gate InputStates differentially*.  In the example above, all of the InputStates were gated using a single
+GatingSignal.
+
+    My_Gating_Mechanism = GatingMechanism(gating_signals=[{NAME:'GATING_SIGNAL_A':
+                                                           GATE:My_Input_Layer
+                                                           MODULATION:ModulationParam.ADDITIVE},
+                                                           {NAME:'GATING_SIGNAL_B':
+                                                           GATE:My_Hidden_Layer, My_Output_Layer}])
+
+Here, two GatingSignals are specified as `state specification dictionaries <LINK>`, each of which contains
+an entry for the name of the GatingSignal, and a *GATE* entry that specifies the States to be gated
+(in this case, again exploiting the fact that the default is to modulate the primary InputState of a Mechanism).
+The first dict also contains a *MODULATION* entry that specifies the value of the
+`modulation <GatingSignal.modulation>` attribute for the GatingSignal.  The second one does not, so the default will
+be used (which, for a GatingSignal, is ModulationParam.MULTIPLICATIVE).  Thus, the InputState of ``My_Input_Layer``
+will be additively modulated, while the InputState of ``My_Hidden_Layer`` will be multiplicatively modulated by their
+GatingSignals, respectively.
+
+Class Reference
+---------------
+
+"""
+
+from PsyNeuLink.Components.Functions.Function import _is_modulation_param
+from PsyNeuLink.Components.States.InputState import InputState
+from PsyNeuLink.Components.States.OutputState import OutputState, PRIMARY_OUTPUT_STATE
+from PsyNeuLink.Components.States.ModulatorySignals.ModulatorySignal import *
+
+
+class GatingSignalError(Exception):
+    def __init__(self, error_value):
+        self.error_value = error_value
+
+    def __str__(self):
+        return repr(self.error_value)
+
+gating_signal_keywords = {GATED_STATE}
+gating_signal_keywords.update(modulatory_signal_keywords)
+
+
+class GatingSignal(ModulatorySignal):
+    """
+    GatingSignal(                                   \
+        owner,                                      \
+        function=LinearCombination(operation=SUM),  \
+        modulation=ModulationParam.MULTIPLICATIVE   \
+        params=None,                                \
+        name=None,                                  \
+        prefs=None)
+
+    A subclass of OutputState that represents the value of a GatingSignal provided to a `GatingProjection`.
+
+    COMMENT:
+
+        Description
+        -----------
+            The GatingSignal class is a subtype of the OutputState class in the State category of Component,
+            It is used primarily as the sender for GatingProjections
+            Its FUNCTION updates its value:
+                note:  currently, this is the identity function, that simply maps variable to self.value
+
+        Class attributes:
+            + componentType (str) = GATING_SIGNAL
+            + paramClassDefaults (dict)
+                + FUNCTION (LinearCombination)
+                + FUNCTION_PARAMS (Modulation.MULTIPLY)
+            + paramNames (dict)
+
+        Class methods:
+            function (executes function specified in params[FUNCTION];  default: Linear
+
+        StateRegistry
+        -------------
+            All OutputStates are registered in StateRegistry, which maintains an entry for the subclass,
+              a count for all instances of it, and a dictionary of those instances
+    COMMENT
+
+
+    Arguments
+    ---------
+
+    owner : GatingMechanism
+        specifies the `GatingMechanism` to which to assign the GatingSignal.
+
+    function : Function or method : default Linear
+        specifies the function used to determine the value of the GatingSignal from the value of its 
+        `owner <GatingMechanism.owner>`.
+
+    COMMENT: [NEEDS DOCUMENTATION]
+    COMMENT
+    modulation : ModulationParam : default ModulationParam.MULTIPLICATIVE 
+
+    params : Optional[Dict[param keyword, param value]]
+        a `parameter dictionary <ParameterState_Specifying_Parameters>` that can be used to specify the parameters for
+        the ControlSignal and/or a custom function and its parameters. Values specified for parameters in the dictionary
+        override any assigned to those parameters in arguments of the constructor.
+
+    name : str : default OutputState-<index>
+        a string used for the name of the outputState.
+        If not is specified, a default is assigned by the StateRegistry of the mechanism to which the outputState
+        belongs (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
+
+    prefs : Optional[PreferenceSet or specification dict : State.classPreferences]
+        the `PreferenceSet` for the outputState.
+        If it is not specified, a default is assigned using `classPreferences` defined in __init__.py
+        (see :doc:`PreferenceSet <LINK>` for details).
+
+
+    Attributes
+    ----------
+
+    owner : GatingMechanism
+        the `GatingMechanism` to which the GatingSignal belongs.
+
+    variable : number, list or np.ndarray
+        used by `function <GatingSignal.function>` to compute the GatingSignal's `value <GatingSignal.value>`.
+
+    function : TransferFunction :  default Linear(slope=1, intercept=0)
+        provides the GatingSignal's `value <GatingMechanism.value>`; the default is an identity function that
+        passes the input to the GatingMechanism as value for the GatingSignal. 
+
+    value : number, list or np.ndarray
+        result of `function <GatingSignal.function>`.
+    
+    modulation : ModulationParam
+        determines how the output of the GatingSignal is used to modulate the value of the state(s)
+        to which its GatingProjection(s) project(s).
+
+    efferents : [List[GatingProjection]]
+        a list of the `GatingProjections <GatingProjection>` assigned to the GatingSignal.
+
+    name : str : default <State subclass>-<index>
+        name of the outputState.
+        Specified in the **name** argument of the constructor for the outputState.  If not is specified, a default is
+        assigned by the StateRegistry of the mechanism to which the outputState belongs
+        (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
+
+        .. note::
+            Unlike other PsyNeuLink components, state names are "scoped" within a mechanism, meaning that states with
+            the same name are permitted in different mechanisms.  However, they are *not* permitted in the same
+            mechanism: states within a mechanism with the same base name are appended an index in the order of their
+            creation.
+
+    prefs : PreferenceSet or specification dict : State.classPreferences
+        the `PreferenceSet` for the outputState.
+        Specified in the **prefs** argument of the constructor for the projection;  if it is not specified, a default is
+        assigned using `classPreferences` defined in __init__.py
+        (see :doc:`PreferenceSet <LINK>` for details).
+
+    """
+
+    #region CLASS ATTRIBUTES
+
+    componentType = OUTPUT_STATES
+    paramsType = OUTPUT_STATE_PARAMS
+
+    classPreferenceLevel = PreferenceLevel.TYPE
+    # Any preferences specified below will override those specified in TypeDefaultPreferences
+    # Note: only need to specify setting;  level will be assigned to TYPE automatically
+    # classPreferences = {
+    #     kwPreferenceSetName: 'OutputStateCustomClassPreferences',
+    #     kp<pref>: <setting>...}
+
+    paramClassDefaults = State_Base.paramClassDefaults.copy()
+    paramClassDefaults.update({
+        PROJECTION_TYPE: GATING_PROJECTION,
+        GATED_STATE:None,
+    })
+    #endregion
+
+    @tc.typecheck
+    def __init__(self,
+                 owner=None,
+                 reference_value=None,
+                 variable=None,
+                 index=PRIMARY_OUTPUT_STATE,
+                 calculate=Linear,
+                 function=LinearCombination(operation=SUM),
+                 modulation:tc.optional(_is_modulation_param)=None,
+                 params=None,
+                 name=None,
+                 prefs:is_pref_set=None,
+                 context=None):
+
+        # Note: index and calculate are not used by GatingSignal;
+        #       they are included here for consistency with OutputState and possible use by subclasses.
+
+        # Assign args to params and functionParams dicts (kwConstants must == arg names)
+        params = self._assign_args_to_param_dicts(function=function,
+                                                  params=params)
+
+        # FIX: 5/26/16
+        # IMPLEMENTATION NOTE:
+        # Consider adding self to owner.outputStates here (and removing from GatingProjection._instantiate_sender)
+        #  (test for it, and create if necessary, as per outputStates in GatingProjection._instantiate_sender),
+
+        # Validate sender (as variable) and params, and assign to variable and paramsInstanceDefaults
+        super().__init__(owner=owner,
+                         reference_value=reference_value,
+                         variable=variable,
+                         modulation=modulation,
+                         index=index,
+                         calculate=calculate,
+                         params=params,
+                         name=name,
+                         prefs=prefs,
+                         context=self)
+
+    # def _instantiate_function(self, context=None):
+    #     super()._instantiate_function(context=context)
+    #     self.function_object.FunctionOutputTypeConversion = True
+    #     self.function_object.functionOutputType = FunctionOutputType.RAW_NUMBER
+    #     TEST = True
+
+    def _execute(self, function_params, context):
+        return float(super()._execute(function_params=function_params, context=context))
+
+
+def _parse_gating_signal_spec(owner, state_spec):
+    """Take specifications for one or more states to be gated, and return GatingSignal specification dictionary
+
+    state_spec can take any of the following forms:
+        - an existing GatingSignal
+        - an existing InputState or OutputState for a Mechanisms in self.system
+        - a list of state specifications (see below)
+        - a dictionary that contains either a:
+            - single state specification:
+                NAME:str - contains the name of an InputState or OutputState belonging to MECHANISM
+                MECHANISM:Mechanism - contains a reference to a Mechanism in self.system that owns NAME'd state
+                <PARAM_KEYWORD>:<GatingSignal param value>
+            - multiple state specification:
+                NAME:str - used as name of GatingSignal
+                STATES:List[tuple, dict] - each item must be state specification tuple or dict
+                <PARAM_KEYWORD>:<GatingSignal param value>
+    
+    Each state specification must be a:
+        - (str, Mechanism) tuple
+        - {NAME:str, MECHANISM:Mechanism} dict
+        where:
+            str is the name of an InputState or OutputState of the Mechanism,
+            Mechanism is a reference to an existing that belongs to self.system 
+    
+    Checks for duplicate state specifications within state_spec or with any existing GatingSignal of the owner
+        (i.e., states that will receive more than one GatingProjection from the owner)
+        
+    If state_spec is already a GatingSignal, it is returned (in the GATING_SIGNAL entry) along with its parsed elements 
+    
+    Returns dictionary with the following entries:
+        NAME:str - name of either the gated state (if there is only one) or the GatingSignal
+        STATES:list - list of states to be gated
+        GATING_SIGNAL:GatingSignal or None
+        PARAMS:dict - params dict if any were included in the state_spec
+    """
+    
+    from PsyNeuLink.Components.States.ModulatorySignals.GatingSignal import GatingSignal
+    from PsyNeuLink.Components.Projections.Projection import _validate_receiver
+    from PsyNeuLink.Components.Projections.ModulatoryProjections.GatingProjection import GatingProjection
+
+    GATING_SIGNAL_SUFFIX = '_' + GatingSignal.__name__
+    DEFAULT_GATING_SIGNAL_NAME = 'Default'+ GATING_SIGNAL_SUFFIX
+
+    gating_signal = None
+    states = []
+    params = {}
+    state_name = None
+    mech = None
+
+    # def _get_default_gating_signal_name():
+    #     """Return default name for gating signal
+    #     Default_GatingSignal if it is the first,
+    #     Default_GatingSignal-2 for second
+    #     Default_GatingSignal-3 for third, etc.
+    #     """
+    #
+    #     # FIX: USE REGISTRY HERE (AS FOR OUTPUTSTATE REGISTRY ON MECHANISM)
+    #     if owner.gating_signals:
+    #         # # Get the number of existing gating_signals with the default name
+    #         # index = len([gs for gs in owner.gating_signals if ('Default'+ GATING_SIGNAL_SUFFIX) in gs.name])
+    #     else:
+    #         index = ''
+    #     if index:
+    #         index = repr(index+1)
+    #     return 'Default'+GATING_SIGNAL_SUFFIX+index
+
+    # Specification is for a GatingSignal - return as is
+    if isinstance(state_spec, GatingSignal):
+        gating_signal = state_spec
+        gating_signal_name = gating_signal.name
+        states = []
+        for proj in gating_signal.efferents:
+            _validate_receiver(owner, proj, Mechanism, GATING_SIGNAL)
+            states.append(proj.receiver.owner)
+        if not states:
+            raise GatingSignalError("Attempt to assign an existing {} to {} that has no GatingProjections".
+                                       format(GATING_SIGNAL, owner.name))
+
+    # For all other specs:
+    #    - if it is a single spec (state name and mech):
+    #        validate that the mech is in self.system, has a state of that name, and then return the state
+    #    - if it is a list:
+    #        iterate through list, calling _parse_gating_signal_spec recursively, to build up the list of states
+
+    # Specification is for an existing GatingProjection
+    #    so check if it is to a state of a mechanism in self.system
+    elif isinstance(state_spec, GatingProjection):
+        _validate_receiver(owner, state_spec, Mechanism, GATING_SIGNAL)
+        state_name = state_spec.receiver.name
+        gating_signal_name = state_name + GATING_SIGNAL_SUFFIX
+        mech = state_spec.reciever.owner
+
+    # Specification is for an existing InputState or OutputState,
+    #    so check that it's owner belongs to self.system
+    elif isinstance(state_spec, (InputState, OutputState)):
+        # if not state_spec.owner.system in owner.system.mechanisms:
+        # # IMPLEMENTATION NOTE: REINSTATE WHEN ASSIGNMENT OF GatingMechanism TO SYSTEM IS RESOLVED (IN COMPOSITION??)
+        # if not (set(state_spec.owner.systems) & set(owner.systems)):
+        #     raise GatingSignalError("The State specified in the {} arg for {} ({}) "
+        #                                 "belongs to a mechanism that is not in the same system ({})".
+        #                                 format(GATING_SIGNALS, owner.name,
+        #                                        state_spec.name,
+        #                                        state_spec.owner.systems))
+        state_name = state_spec.name
+        gating_signal_name = state_name + GATING_SIGNAL_SUFFIX
+        mech = state_spec.owner
+
+    # Specification is for a Mechanism,
+    #    so check that it belongs to the same system as self
+    #    and use primary InputState as the default
+    elif isinstance(state_spec, Mechanism):
+        # if state_spec.system and not state_spec.system in owner.system.mechanisms:
+        # # IMPLEMENTATION NOTE: REINSTATE WHEN ASSIGNMENT OF GatingMechanism TO SYSTEM IS RESOLVED (IN COMPOSITION??)
+        # if state_spec.systems and not (set(state_spec.systems) & set(owner.systems)):
+        #     raise GatingSignalError("The Mechanism specified in the {} arg for {} ({}) "
+        #                                 "does not belong to the same system ({})".
+        #                                 format(GATING_SIGNALS, owner.name,
+        #                                        state_spec.name,
+        #                                        state_spec.owner.systems))
+        mech = state_spec
+        state_spec = state_spec.input_states[0]
+        state_name = state_spec.name
+        gating_signal_name = state_name + GATING_SIGNAL_SUFFIX
+
+    elif isinstance(state_spec, tuple):
+        state_name = state_spec[0]
+        mech = state_spec[1]
+        gating_signal_name = state_name + GATING_SIGNAL_SUFFIX
+        # Check that 1st item is a str (presumably the name of one of the mechanism's states)
+        if not isinstance(state_name, str):
+            raise GatingSignalError("1st item of specification tuple for the state to be gated by {} of {} ({})"
+                                       "must be a string that is the name of the state".
+                                       format(GATING_SIGNAL, owner.name, state_name))
+        # Check that 2nd item is a mechanism
+        if not isinstance(mech, Mechanism):
+            raise GatingSignalError("2nd item of specification tuple for the state to be gated by {} of {} ({})"
+                                       "must be a Mechanism that is the mech to which the state {} belongs".
+                                       format(GATING_SIGNAL, owner.name, mech, state_name))
+
+    # Specification is a list, presumably of one or more states specs
+    elif isinstance(state_spec, list):
+        # Validate each item in the list (which should be a state state_spec), and
+        #    - add the state(s) returned to state list
+        #    - assign state_name as None,
+        #        since there is no single name that can be used as the name for the GatingSignal
+        gating_signal_name = DEFAULT_GATING_SIGNAL_NAME
+        for spec in state_spec:
+            spec_dict = _parse_gating_signal_spec(owner, spec)
+            states.extend(spec_dict[STATES])
+
+    # Specification is a dict that could be for a single state state_spec or a list of ones
+    elif isinstance(state_spec, dict):
+
+        # FIX: IS THIS NECESSARY? (GIVEN THE FUNCTIONALITY UNDER 'ELSE':  USE KEY AS NAME AND VALUE AS LIST OF STATES)
+        # If it has a STATES entry, it must be for a list
+        if STATES in state_spec:
+            # Validate that the STATES entry has a list
+            state_specs = state_spec[STATES]
+            if not isinstance(state_specs, list):
+                raise GatingSignalError("The {} entry of the dict in the {} arg for {} must be "
+                                           "a list of state specifications".
+                                            format(STATES, GATING_SIGNALS, owner.name))
+            # Validate each item in the list (which should be a state state_spec), and
+            #    - add the state(s) returned to state list
+            #    - assign state_name to the NAME entry
+            #        (which will be used as the name for the GatingSignal in _instantiate_gating_signal)
+            for spec in state_specs:
+                spec_dict = _parse_gating_signal_spec(owner, spec)
+                states.extend(spec_dict[STATES])
+            if NAME in state_spec:
+                state_name = state_spec[NAME]
+                gating_signal_name = state_name
+            else:
+                gating_signal_name = DEFAULT_GATING_SIGNAL_NAME
+
+        # If it doesn't have a STATES entry
+        else:
+            # If there is a non-keyword key, treat as the name to be used for the GatingSignal,
+            #    and the value a state spec or list of ones
+            state_name = next((key for key in state_spec if not key in gating_signal_keywords), None)
+            key_as_name = explicit_name = None
+            if state_name:
+                key_as_name = state_name
+                spec_dict = _parse_gating_signal_spec(owner, state_spec[key_as_name])
+                states = spec_dict[STATES]
+                # If there *IS* a NAME entry, then use that (i.e., override key as the name)
+                if NAME in state_spec:
+                    explicit_name = state_spec[NAME]
+                gating_signal_name = explicit_name or key_as_name
+            # Otherwise, it must be for a single state state_spec,
+            #    which means it must have a NAME and a MECHANISM entry:
+            else:
+                if not NAME in state_spec:
+                    raise GatingSignalError("Specification dict for the state to be gated by {} of {} must have a "
+                                               "NAME entry that is the name of the state".
+                                               format(GATING_SIGNAL, owner.name))
+                state_name = state_spec[NAME]
+                gating_signal_name = state_name + GATING_SIGNAL_SUFFIX
+
+                # GatingSignal projects to a single state (named in NAME entry)
+                if not MECHANISM in state_spec:
+                    raise GatingSignalError("Specification dict for state to be gated by {} of {} ({}) must have a "
+                                               "MECHANISM entry specifying the mechanism to which the state belongs".
+                                               format(GATING_SIGNAL, owner.name, state_name))
+                mech = state_spec[MECHANISM]
+
+        # Check that all of the other entries in the dict are for valid GatingSignal params
+        #    - skip any entries specifying gating signal (i.e., non-keyword keys being used as the GatingSignal name
+        #    - place others in params
+        for param_entry in [entry for entry in state_spec if not entry in {gating_signal_name, key_as_name, MECHANISM}]:
+            if not param_entry in gating_signal_keywords:
+                raise GatingSignalError("Entry in specification dictionary for {} arg of {} ({}) "
+                                           "is not a valid {} parameter".
+                                           format(GATING_SIGNAL, owner.name, param_entry,
+                                                  GatingSignal.__name__))
+            params[param_entry] = state_spec[param_entry]
+
+    else:
+        # raise GatingSignalError("PROGRAM ERROR: unrecognized GatingSignal specification for {} ({})".
+        #                             format(self.name, state_spec))
+        raise GatingSignalError("Specification of {} for {} ({}) is not a valid {} specification".
+                                    format(GATING_SIGNAL, owner.name, state_spec, GATING_SIGNAL))
+        # raise GatingSignalError("Specification of {} for {} ({}) must be an InputState or OutputState, "
+        #                            "a tuple specifying a name for one and a mechanism to which it belongs ,"
+        #                            "a list of state specifications, "
+        #                            "a {} specification dict with one or more state specifications and "
+        #                            "entries for {} parameters, or an existing GatingSignal".
+        #                             format(GATING_SIGNAL, owner.name, state_spec, GATING_SIGNAL, GATING_SIGNAL))
+
+    # If a states list has not already been constructed, do so here
+    if not states:
+        # Check that specified state is an InputState or OutputState of the Mechanism
+        if state_name in mech.input_states:
+            state_type = INPUT_STATE
+            state = mech.input_states[state_name]
+        elif state_name in mech.output_states:
+            state_type = OUTPUT_STATE
+            state = mech.output_states[state_name]
+        else:
+            raise GatingSignalError("{} (in specification of {}  {}) is not an "
+                                       "InputState or OutputState of {}".
+                                        format(state_name, GATING_SIGNAL, owner.name, mech))
+        # Check that the Mechanism is in GatingMechanism's system
+        # if not owner.system and not mech in owner.system.mechanisms:
+        # # IMPLEMENTATION NOTE: REINSTATE WHEN ASSIGNMENT OF GatingMechanism TO SYSTEM IS RESOLVED (IN COMPOSITION??)
+        # if not (set(owner.systems) & set(mech.systems)):
+        #     raise GatingSignalError("Specification in {} arg for {} ({} {} of {}) "
+        #                                 "must be for a Mechanism in {}".
+        #                                 format(GATING_SIGNALS,
+        #                                        owner.name,
+        #                                        state_name,
+        #                                        state_type,
+        #                                        mech.name,
+        #                                        owner.systems))
+        states = [state]
+
+    # Check for any duplicate states in specification for this GatingSignal or existing ones for the owner
+    all_gated_states = []
+    # Get gated states from any already instantiated GatingSignals in gating_signals arg
+    if owner.gating_signals:
+        #                                   _gating_signal_arg     already instantiated GatingSignal
+        for gating_signal in [gs for gs in owner.gating_signals if isinstance(gs, GatingSignal)]:
+            #                  gated state
+            all_gated_states.extend([proj.receiver for proj in gating_signal.efferents])
+    # Add states for current GatingSignal
+    all_gated_states.extend(states)
+    # Check for duplicates
+    if len(all_gated_states) != len(set(all_gated_states)):
+        for test_state in all_gated_states:
+            if next((test_state == state  for state in all_gated_states), None):
+                raise GatingSignalError("{} of {} receives more than one GatingProjection from the {}s in {}".
+                                        format(test_state.name, test_state.owner.name,
+                                               GatingSignal.__name__, owner.name))
+        raise GatingSignalError("PROGRAM ERROR: duplicate state detected in {} specifications for {} ({})"
+                                   "but could not find the offending state".
+                                   format(GATING_SIGNAL, owner.name, gating_signal_name))
+
+    return {NAME: gating_signal_name,
+            STATES: states,
+            PARAMS: params,
+            GATING_SIGNAL: gating_signal}
\ No newline at end of file
diff --git a/PsyNeuLink/Components/States/ModulatorySignals/LearningSignal.py b/PsyNeuLink/Components/States/ModulatorySignals/LearningSignal.py
new file mode 100644
index 00000000000..21886cb5840
--- /dev/null
+++ b/PsyNeuLink/Components/States/ModulatorySignals/LearningSignal.py
@@ -0,0 +1,330 @@
+# Princeton University licenses this file to You under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.  You may obtain a copy of the License at:
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed
+# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and limitations under the License.
+
+
+# ******************************************  LearningSignal *****************************************************
+
+"""
+Overview
+--------
+
+A LearningSignal is a type of `ModulatorySignal`, that is specialized for use with a `LearningMechanism` and a
+`LearningProjection`, to modify the parameter of a `MappingProjection`.  A LearningSignal receives an error signal
+from the `LearningMechanism` to which it belongs, and uses that to compute a `learning_signal` that is assigned as the
+`value <LearningProjection.value>` of its `LearningProjection`. The LearningProjection conveys its value to a
+`ParameterState` of the projection being learned, which in turns uses that to modify the corresponding parameter.
+By default, the projection is a `MappingProjection`, the parameter is its 'matrix <MappingProjection.matrix>`
+parameter, and the `learning_signal` is a matrix of weight changes that are added to MappingProjection's
+`matrix <MappingProjection.matrix>`.
+
+.. _LearningSignal_Creation:
+
+Creating a LearningSignal
+------------------------
+
+A LearningSignal is created automatically whenever a `MappingProjection` is 
+`specified for learning <LearningMechanism_Creation>` and the projection belongs to the same system as the 
+`LearningMechanism`.  LearningSignals can also be specified in the **learning_signals**
+argument of the constructor for a `LearningMechanism`.  Although a LearningSignal can be created directly using its 
+constructor (or any of the other ways for `creating an `OutputState <OutputStates_Creation>`), this is neither
+necessary nor advisable, as a LearningSignal has dedicated components and requirements for configuration that must be 
+met for it to function properly.
+
+.. _LearningSignal_Specification:
+
+Specifying LearningSignals
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+When a LearningSignal is specified in context (e.g., the **learning_signals** argument of the constructor for a
+`LearningMechanism`, the specification can take any of the following forms:
+
+  * a *ParameterState* of the Projection to which the parameter belongs;
+  |
+  * a *Projection*, which must be either a `LearningProjection`, or a `MappingProjection` to which the 
+    LearingSignal should send a `LearningProjection`.  In both cases, it is assumed that the LearningProjection 
+    projects to the *MATRIX* ParameterState of a `MappingProjection`. 
+  |
+  * a *tuple*, with the *name* of the parameter as its 1st item. and the *projection* to which it belongs as the 2nd;
+    note that this is a convenience format, which is simpler to use than a specification dictionary (see below), 
+    but precludes specification of any `parameters <LearningSignal_Structure>` for the LearningSignal.
+  |
+  * a *specification dictionary*, that must contain at least the following two entries:
+    * *NAME*:str - a string that is the name of the parameter to be controlled;
+    * *PROJECTION*:Projection - the Projection to which the parameter belongs; 
+      (note: the Projection itself should be specified even if the parameter belongs to its function).
+    The dictionary can also contain entries for any other LearningSignal attributes to be specified
+    (e.g., a LEARNING_RATE entry; see `below <ControlSignal_Structure>` for a description of LearningSignal attributes).
+
+.. _LearningSignal_Structure:
+
+Structure
+---------
+
+A LearningSignal is owned by an `LearningMechanism`, and associated with one or more 
+`LearningProjections <LearningProjection>` that project(s) to the `parameterStates <ParameterState>` associated with 
+the parameter(s) to be learned.  A LearningSignal has the following primary attributes:
+
+.. _LearningSignal_Modulation:
+
+* `modulation <LearningSignal.modulation>` : determines how the LearningSignal's `value <LearningSignal.value>` is
+  used by the ParameterState(s) to which it projects to modify their value (see `ModulatorySignal_Modulation` for an
+  explanation of how the modulation is specified and used to modulate the value of a parameter). The default value
+  is set to the value of the `modulation <LearningMechanism.modulation>` attribute of the LearningMechanism to which 
+  the LearningSignal belongs;  this the is same for all of the LearningSignals belonging to that LearningMechanism.  
+  However, the `modulation <LearningSignal.modulation>` can be specified individually for a LearningSignal using a 
+  specification dictionary where the LearningSignal is specified, as described `above <LearningaSignal_Specification>`.
+  The `modulation <LearningSignal.modulation>` value of a LearningSignal is used by all of the 
+  `LearningProjections <LearningProjection>` that project from that LearningSignal.
+    
+.. _LearningSignal_Learning_Rate:
+
+* `learning_rate <LearningSignal.learning_rate>`:
+   COMMENT:
+      FROM LearningMechanism DOCSTRING:  ADD DESCRIPTION OF HOW IT AFFECTS ParameterState's function
+   COMMENT
+   the learning rate for the LearningMechanism (used to specify the :keyword:`learning_rate` parameter for its
+   `function <LearningMechanism.function>`.  Specifiying this (or the learning_rate parameter of the
+   `function <LearningMechanism.function>` directly) supercedes any specification of a learning_rate for
+   any `process <Process.Process_Base.learning_rate>` and/or `system <System.System_Base.learning_rate>` to which
+   the LearningMechanism belongs.  The default is `None`, in which case the LearingMechanism (and its
+   `function <LearningMechanism.function>`) inherit the specification of the `learning_rate
+   <Process.Process_Base.learning_rate>` for the process in which the LearningMechanism is being executed.
+   If that is `None`, then it inherits it from the system in which it is being executed.  If that is also `None`,
+   then it uses the default value assigned by its `function <LearningMechanism.function>`.
+
+.. _LearningSignal_Function:
+
+* `function <LearningSignal.function>`: converts the `learning_signal <LearningMechanism.learning_signal>` it receives
+  from the LearningMechanism to which it belongs to its `value <LearningSignal.value>`. By default this is an identity 
+  function (:keyword:`Linear(slope=1, intercept=0))`), that simply uses the `learning_signal` as its 
+  `value <LearningSignal.value>`.  However, :keyword:`function` can be assigned another `TransferFunction`, or any 
+  other function that takes a 2d array or matrix and returns a similar value.
+
+  .. note:: The `index <OutputState.OutputState.index>` and `calculate <OutputState.OutputState.calculate>`
+            attributes of a LearningSignal are automatically assigned and should not be modified.
+
+
+.. _LearningSignal_Execution:
+
+Execution
+---------
+
+A LearningSignal cannot be executed directly.  It is executed whenever the `LearningMechanism` to which it belongs is
+executed.  When this occurs, the LearningMechanism provides the LearningSignal with an `learning_signal`, that is used 
+by its `function <LearningSignal.function>` to compute its `value <LearningSignal.value>` for that round of execution.  The `intensity` 
+That is used by its associated `ControlProjection` to set the :keyword:`value` of the `parameterState <ParameterState>` to which it
+to which it projects. The paramemterState uses that value, in turn, to modify the value of the parameter of the 
+projection or its function being learned.
+
+.. note::
+   The changes in a parameter in response to the execution of a LearningMechanism are not applied until the mechanism
+   with the parameter being learned is next executed; see :ref:`Lazy Evaluation <LINK>` for an explanation of
+   "lazy" updating).
+
+Class Reference
+---------------
+
+"""
+
+# import Components
+from PsyNeuLink.Components.Functions.Function import _is_modulation_param
+from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.LearningMechanisms.LearningMechanism import *
+from PsyNeuLink.Components.States.OutputState import OutputState, PRIMARY_OUTPUT_STATE
+from PsyNeuLink.Components.States.ModulatorySignals.ModulatorySignal import *
+
+
+class LearningSignalError(Exception):
+    def __init__(self, error_value):
+        self.error_value = error_value
+
+
+    def __str__(self):
+        return repr(self.error_value)
+
+
+class LearningSignal(ModulatorySignal):
+    """
+    LearningSignal(                                      \
+        owner,                                           \
+        function=LinearCombination(operation=SUM),       \
+        modulation=ModulationParam.MULTIPLICATIVE        \
+        learning_rate=None                               \
+        params=None,                                     \
+        name=None,                                       \
+        prefs=None)
+
+    A subclass of OutputState that represents the ControlSignal of a `ControlMechanism` provided to a 
+    `ControlProjection`.
+
+    COMMENT:
+
+        Description
+        -----------
+            The ControlSignal class is a subtype of the OutputState type in the State category of Component,
+            It is used as the sender for ControlProjections
+            Its FUNCTION updates its value:
+                note:  currently, this is the identity function, that simply maps variable to self.value
+
+        Class attributes:
+            + componentType (str) = CONTROL_SIGNAL
+            + paramClassDefaults (dict)
+                + FUNCTION (LinearCombination)
+                + FUNCTION_PARAMS   (Operation.PRODUCT)
+            + paramNames (dict)
+
+        Class methods:
+            function (executes function specified in params[FUNCTION];  default: Linear)
+
+        StateRegistry
+        -------------
+            All OutputStates are registered in StateRegistry, which maintains an entry for the subclass,
+              a count for all instances of it, and a dictionary of those instances
+    COMMENT
+
+
+    Arguments
+    ---------
+
+    owner : ControlMechanism
+        specifies the `ControlMechanism` to which to assign the ControlSignal.
+
+    function : Function or method : default Linear
+        specifies the function used to determine the `intensity` of the ControlSignal from its `allocation`.
+    
+    learning_rate : float or None : default None
+        specifies the learning_rate for the LearningSignal's `LearningProjections <LearningProjection>`
+        (see `learning_rate <LearningSignal.learning_rate>` for details).  If it is not specified, the
+        `learning_rate <LearningMechanism.learning_rate>` of the `LearningMechanism` to which the LearningSignal
+        belongs will be used.
+
+    COMMENT: [NEEDS DOCUMENTATION]
+    COMMENT
+    modulation : ModulationParam : default ModulationParam.MULTIPLICATIVE
+
+    params : Optional[Dict[param keyword, param value]]
+        a `parameter dictionary <ParameterState_Specifying_Parameters>` that can be used to specify the parameters for
+        the ControlSignal and/or a custom function and its parameters. Values specified for parameters in the dictionary
+        override any assigned to those parameters in arguments of the constructor.
+
+    name : str : default OutputState-<index>
+        a string used for the name of the outputState.
+        If not is specified, a default is assigned by the StateRegistry of the mechanism to which the outputState
+        belongs (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
+
+    prefs : Optional[PreferenceSet or specification dict : State.classPreferences]
+        the `PreferenceSet` for the outputState.
+        If it is not specified, a default is assigned using `classPreferences` defined in __init__.py
+        (see :doc:`PreferenceSet <LINK>` for details).
+
+
+    Attributes
+    ----------
+
+    owner : ControlMechanism
+        the `ControlMechanism` to which the ControlSignal belongs.
+
+    variable : number, list or np.ndarray
+        same as `allocation`;  used by `function <ControlSignal.function>` to compute the ControlSignal's `intensity`.
+
+    function : TransferFunction :  default Linear(slope=1, intercept=0)
+        converts `allocation` into the ControlSignal's `intensity`.  The default is the identity function, which
+        assigns the ControlSignal's `allocation` as its `intensity`.
+
+    value : number, list or np.ndarray
+        result of `function <ControlSignal.function>`; same as `intensity`.
+
+    learning_rate : float : None
+        determines the learning rate for the LearningSignal.  It is used to specify the 
+        `learning_rate <LearningProjection.learning_rate>` parameter for the LearningProjection(s) that 
+        project from the LearningSignal (see description of `learning_rate <LearningProjection_Learning_Rate>` 
+        for additional details).
+
+    modulation : ModulationParam
+        specifies the way in which the output of the ControlSignal is used to modulate the value of the parameter
+        it's `ControlProjection` controls.
+
+    efferents : [List[ControlProjection]]
+        a list with one item -- the `ControlProjection` assigned to the ControlSignal.
+
+    name : str : default <State subclass>-<index>
+        name of the outputState.
+        Specified in the **name** argument of the constructor for the outputState.  If not is specified, a default is
+        assigned by the StateRegistry of the mechanism to which the outputState belongs
+        (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
+
+        .. note::
+            Unlike other PsyNeuLink components, state names are "scoped" within a mechanism, meaning that states with
+            the same name are permitted in different mechanisms.  However, they are *not* permitted in the same
+            mechanism: states within a mechanism with the same base name are appended an index in the order of their
+            creation.
+
+    prefs : PreferenceSet or specification dict : State.classPreferences
+        the `PreferenceSet` for the outputState.
+        Specified in the **prefs** argument of the constructor for the projection;  if it is not specified, a default is
+        assigned using `classPreferences` defined in __init__.py
+        (see :doc:`PreferenceSet <LINK>` for details).
+
+    """
+
+    #region CLASS ATTRIBUTES
+
+    componentType = OUTPUT_STATES
+    paramsType = OUTPUT_STATE_PARAMS
+
+    classPreferenceLevel = PreferenceLevel.TYPE
+    # Any preferences specified below will override those specified in TypeDefaultPreferences
+    # Note: only need to specify setting;  level will be assigned to TYPE automatically
+    # classPreferences = {
+    #     kwPreferenceSetName: 'OutputStateCustomClassPreferences',
+    #     kp<pref>: <setting>...}
+
+    paramClassDefaults = State_Base.paramClassDefaults.copy()
+    paramClassDefaults.update({
+        PROJECTION_TYPE: LEARNING_PROJECTION,
+        LEARNED_PARAM:None
+    })
+    #endregion
+
+
+    @tc.typecheck
+    def __init__(self,
+                 owner=None,
+                 reference_value=None,
+                 variable=None,
+                 index=PRIMARY_OUTPUT_STATE,
+                 calculate=Linear,
+                 function=LinearCombination(operation=SUM),
+                 learning_rate: tc.optional(parameter_spec) = None,
+                 modulation:tc.optional(_is_modulation_param)=None,
+                 params=None,
+                 name=None,
+                 prefs:is_pref_set=None,
+                 context=None):
+
+        # Assign args to params and functionParams dicts (kwConstants must == arg names)
+        params = self._assign_args_to_param_dicts(function=function,
+                                                  learning_rate=learning_rate,
+                                                  params=params)
+
+        # FIX: 5/26/16
+        # IMPLEMENTATION NOTE:
+        # Consider adding self to owner.outputStates here (and removing from ControlProjection._instantiate_sender)
+        #  (test for it, and create if necessary, as per outputStates in ControlProjection._instantiate_sender),
+
+        # Validate sender (as variable) and params, and assign to variable and paramsInstanceDefaults
+        super().__init__(owner=owner,
+                         reference_value=reference_value,
+                         variable=variable,
+                         modulation=modulation,
+                         index=index,
+                         calculate=calculate,
+                         params=params,
+                         name=name,
+                         prefs=prefs,
+                         context=self)
+
+        self.learning_rate = self.learning_rate or owner.learning_rate
\ No newline at end of file
diff --git a/PsyNeuLink/Components/States/ModulatorySignals/ModulatorySignal.py b/PsyNeuLink/Components/States/ModulatorySignals/ModulatorySignal.py
new file mode 100644
index 00000000000..20b0d679598
--- /dev/null
+++ b/PsyNeuLink/Components/States/ModulatorySignals/ModulatorySignal.py
@@ -0,0 +1,333 @@
+# Princeton University licenses this file to You under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.  You may obtain a copy of the License at:
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed
+# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and limitations under the License.
+
+
+# ********************************************  ModulatorySignal *******************************************************
+
+# ***ADD mod_afferents TO STATE doscstring (IF NOT ALREADY THERE)
+# ***REFERENCE THIS DOCSTRING IN AdaptiveMechanism DOSCTRING, AND ITS SUBCLASSSES
+
+"""
+Overview
+--------
+A ModulatorySignal is a subclass of `OutputState` that belongs to an `AdaptiveMechanism`, and is used to
+`modulate <ModulatorySignal_Modulation>` the `value <State.value>` of one or more `States <State>` by of way one or
+more `ModulatoryProjections <ModulatoryProjection>`.  ModulatorySignals modulate the value of a state by modifying a
+parameter of the State's `function <State.function>`.  There are three types of ModulatySignals, each of which is
+associated with a particular type of `AdaptiveMechanism` and `ModulatoryProjection`, and modifies the value of a
+diffent type of State, as described below (and shown in the `figure <ModulatorySignal_Anatomy_Figure>`):
+
+* `LearningSignal`
+    This takes the `value <LearningSignal.value>` assigned to it by the `LearningMechanism` to which it belongs,
+    and uses it to modulate the parameter of a `PathwayProjection` -- usually the `matrix <MappingProjection.matrix>`
+    parameter of a `MappingProjection`.
+..
+* `ControlSignal`
+    This takes the `value <ControlSignal.value>` assigned to it by the `ControlMechanism` to which it belongs,
+    and uses it to modulate the parameter of a `Mechanism` or its `function <Mechanism.function>`.
+..
+* `GatingSignal`
+    This takes the `value <GatingSignal.value>` assigned to it by the `GatingMechanism` to which it belongs,
+    and uses it to modulate the value of the `InputState` or `OutputState` of a `Mechanism`.
+
+.. _ModulatorySignal_Creation:
+
+Creating a ModulatorySignal
+---------------------------
+
+A ModulatorySignal is a base class, and cannot be instantiated directly.  However, the three types of ModulatorySignals
+listed above can be created directly, by calling the constructor for the desired type.  More commonly, however,
+ModulatorySignals are created automatically by the `AdaptiveMechanism` to which they belong, or by specifying them
+in the constructor for the `AdaptiveMechanism` to which it belongs (the details of which are described in the
+documentation for each type of ModulatorySignal).
+
+.. _ModulatorySignal_Structure:
+
+Structure
+---------
+
+A ModulatorySignal is associated with one or more `ModulatoryProjections <ModulatoryProjection>` of the
+corresponding type, that project to the State(s), the value(s) of which it modulates.  THe ModulatoryProjections
+received by a `State` are listed in its `mod_afferents` attribute. The method by which a ModulatorySignal
+modulates a State's `value <State.value>` is determined by the ModulatorySignal's
+`modulation <ModulatorySignal.modulation>` attribute, as described below.
+
+.. _ModulatorySignal_Modulation:
+
+Modulation
+~~~~~~~~~~
+
+A ModulatorySignal modulates the value of a `State` either by modifying a parameter of the State's `function
+<State.function>` (which determines the State's `value <State.value>`), or by assigning a value to the State directly.
+The `function <State.function>` of every state designates one of its parameters as its *MULTIPLICATIVE_PARAM* and
+another as its *MULTIPLICATIVE_PARAM*.  The `modulation <ModulatorySignal.modulation>` attribute of a ModulatorySignal
+determines which of these two parameters should be assigned its value, or which of two other actions to take when the
+State updates its `value <State.value>`.  It is specified using a value of `ModulationParam <Function.ModulationParam>`.
+The default for `ControlSignals <ControlSignal>` and `GatingSignals <GatingSignal>` is `ModulationParam.MULTIPLICATIVE`,
+which multiplies the State`s `variable <State.variable>` by the `value <ModulatorySignal>` of the ModulatorySignal
+before passing it to the State's `function <State.function>`.  The default for `LearningSignals <LearningSignal>` is
+`ModulationParam.ADDITIVE`, which adds the `value <LearningSignal.value>` of the LearningSignal (i.e., the weight
+changes computed by the `LearningMechanism`) to the State's `variable <State.variable>` (i.e., the current weight
+`matrix <MappingProjection.matrix>` for the `MappingProjection` being learned).  The
+`modulation <ModulatorySignal.modulation>` attribute can be specified in the **modulation** arg of
+the ModulatorySignal's constructor, or in a *MODULATION* entry of a `state specification dictionary <LINK>` used to
+create the ModulatorySignal. If it is not specified when a ModulatorySignal is created, it is assigned the value of
+the `modulation <AdaptiveMechanism_Base.modulation>` attribute for the `AdaptiveMechanism` to which it belongs.
+
+.. note::
+   `OVERRIDE <ModulatorySignal_Modulation>` can be specified for **only one** ModulatoryProjection to a State;
+   specifying it for more than one causes an error.
+
+
+.. _ModulatorySignal_Anatomy_Figure:
+
+**Anatomy of Modulation**
+
+.. figure:: _static/Modulation_fig.pdf
+   :alt: Modulation
+   :scale: 150 %
+
+   **Three types of Modulatory Components and the States they modulate**.
+   The table below lists the default ModulatoryParam for each type of ModulatorySignal, and the default Function
+   and modulated parameter of its recipient State.  The figure below shows a detailed view of how
+   ModulatorySignals modulate the parameters of a State's function.
+
+   +--------------------+-----------------------+--------------------------------------+------------------------------+
+   |     Modulatory     |Default ModulationParam|                                      | Default Function (mod param) |
+   |     Component      | for ModulatorySignal  |          Recipient State             |    for Recipient State       |
+   +====================+=======================+======================================+==============================+
+   | **Control** (blue) |   *MULTIPLICATIVE*    |     `Mechanism` `ParameterState`     |       `Linear` (`slope`)     |
+   +--------------------+-----------------------+--------------------------------------+------------------------------+
+   | **Gating** (brown) |   *MULTIPLICATIVE*    |`Mechanism` `InputState`/`OutputState`|       `Linear` (`slope`)     |
+   +--------------------+-----------------------+--------------------------------------+------------------------------+
+   |**Learning** (green)|     *ADDITIVE*        | `MappingProjection` `ParameterState` | `Accumulator` (`increment`)  |
+   +--------------------+-----------------------+--------------------------------------+------------------------------+
+
+**Detailed View of Modulation**
+
+.. figure:: _static/Modulation_Detail_fig.pdf
+   :alt: Modulation_Detail
+   :scale: 150 %
+
+   How a ModulatorySignal signal influences the value of a state is determined by its
+   `modulation <ModulatorySignal.modulation>` attribute, which is specified as a value of `ModulationParam`:
+   *ADDITIVE* and *MULTIPLICATIVE* specify that the `value <ModulatorySignal.value>` of the ModulatorySignal
+   be assigned to the correspondingly designated parameter of the State's function;  *OVERRIDE* specifies
+   that ModulatorySignal's `value <ModulatorySignal.value>` by assigned directly as the State's
+   `value <State.value>`, in effect ignoring its `variable <State.variable>` and `function <State.function>`.
+
+
+.. _ModulatorySignal_Execution:
+
+Execution
+---------
+
+ModulatorySignals cannot be executed.  They are updated when the `AdaptiveMechanism` to which they belong is executed.
+When a ModulatorySignal is updated, it calculates its value, which is then made available to the
+`ModulatoryProjections <ModulatoryProjection>` listed in its `efferents <ModulatorySignal.efferents>` attribute.
+When those projections execute, they convey the ModulatorySignal's value to the function of the `State` to which they
+project.  The State's `function <State.function>` then uses that value for the parameter designated by the
+`modulation <ModulatorySignal.modulation>` attribute of the ModulatorySignal when the State is updated.  For example,
+consider a `ControlSignal` that modulates the `bias <Logistic.bias>` parameter of a `Logistic` Function used by a
+`TransferMechanism`; and assume that the `ParameterState` for the bias parameter (to which the ControlSignal projects)
+uses a `Linear` function to update its value (which is the default for a ParameterState).  If the
+`modulation  <ModulatorySignal.modulation>` attribute of the `ControlSignal` is `ModulationParam.MULTIPLICATIVE`,
+then it will be assigned to the `slope <Linear>` parameter of the ParameterState's `function <ParameterState.function>`.
+Accordingly, when the ParameterState is updated it will multiply the bias parameter's value by the value of the
+ControlSignal to determine the value of the bias parameter.  The result will used as the value of the bias for the
+Logistic Function when the TransferMechanism is executed (see `State_Execution` for additional details).
+
+.. note::
+   The change in the value of a `State` in response to a ModulatorySignal does not occur until the Mechanism to
+   which the state belongs is next executed; see :ref:`Lazy Evaluation <LINK>` for an explanation of "lazy" updating).
+
+.. _ModulatorySignal_Class_Reference:
+
+Class Reference
+---------------
+"""
+
+from PsyNeuLink.Components.States.State import *
+from PsyNeuLink.Components.States.OutputState import OutputState
+
+
+class ModulatorySignalError(Exception):
+    def __init__(self, error_value):
+        self.error_value = error_value
+
+    def __str__(self):
+        return repr(self.error_value)
+
+modulatory_signal_keywords = {MECHANISM, MODULATION}
+modulatory_signal_keywords.update(component_keywords)
+
+
+class ModulatorySignal(OutputState):
+    """
+    ModulatorySignal(                               \
+        owner,                                      \
+        function=LinearCombination(operation=SUM),  \
+        modulation=ModulationParam.MULTIPLICATIVE   \
+        params=None,                                \
+        name=None,                                  \
+        prefs=None)
+
+    A subclass of OutputState that represents the value of a ModulatorySignal provided to a `GatingProjection`.
+
+    COMMENT:
+
+        Description
+        -----------
+            The ModulatorySignal class is a subtype of the OutputState class in the State category of Component,
+            It is used primarily as the sender for GatingProjections
+            Its FUNCTION updates its value:
+                note:  currently, this is the identity function, that simply maps variable to self.value
+
+        Class attributes:
+            + componentType (str) = GATING_SIGNAL
+            + paramClassDefaults (dict)
+                + FUNCTION (LinearCombination)
+                + FUNCTION_PARAMS (Modulation.MULTIPLY)
+            + paramNames (dict)
+
+        Class methods:
+            function (executes function specified in params[FUNCTION];  default: Linear
+
+        StateRegistry
+        -------------
+            All OutputStates are registered in StateRegistry, which maintains an entry for the subclass,
+              a count for all instances of it, and a dictionary of those instances
+    COMMENT
+
+
+    Arguments
+    ---------
+
+    owner : GatingMechanism
+        specifies the `GatingMechanism` to which to assign the ModulatorySignal.
+
+    function : Function or method : default Linear
+        specifies the function used to determine the value of the ModulatorySignal from the value of its
+        `owner <GatingMechanism.owner>`.
+
+    COMMENT:
+        [NEEDS DOCUMENTATION]
+    COMMENT
+
+    modulation : ModulationParam : default ModulationParam.MULTIPLICATIVE
+        specifies the type of modulation the ModulatorySignal uses to determine the value of the State(s) it modulates.
+
+    params : Optional[Dict[param keyword, param value]]
+        a `parameter dictionary <ParameterState_Specifying_Parameters>` that can be used to specify the parameters for
+        the ControlSignal and/or a custom function and its parameters. Values specified for parameters in the dictionary
+        override any assigned to those parameters in arguments of the constructor.
+
+    name : str : default OutputState-<index>
+        a string used for the name of the outputState.
+        If not is specified, a default is assigned by the StateRegistry of the mechanism to which the outputState
+        belongs (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
+
+    prefs : Optional[PreferenceSet or specification dict : State.classPreferences]
+        the `PreferenceSet` for the outputState.
+        If it is not specified, a default is assigned using `classPreferences` defined in __init__.py
+        (see :doc:`PreferenceSet <LINK>` for details).
+
+
+    Attributes
+    ----------
+
+    owner : AdaptiveMechanism
+        the `AdaptiveMechanism` to which the ModulatorySignal belongs.
+
+    variable : number, list or np.ndarray
+        value assigned by the ModulatorySignal's `owner <ModulatorySignal.owner>`, and used by the ModulatorySignal's
+        `function <ModulatorySignal.function>` to compute its `value <ModulatorySignal.value>`.
+
+    function : TransferFunction :  default Linear(slope=1, intercept=0)
+        provides the ModulatorySignal's `value <ModulatorySignal.value>`; the default is an identity function that
+        assigns `variable <ModulatorySignal.variable` as ModulatorySignal's `value <ModulatorySignal.value>`.
+
+    value : number, list or np.ndarray
+        result of `function <ModulatorySignal.function>`, and is the value used to determine the `value <State.value>`
+        of the State(s) being modulated.
+    
+    modulation : ModulationParam
+        determines how the output of the ModulatorySignal is used to modulate the value of the state(s) being modulated.
+
+    efferents : [List[GatingProjection]]
+        a list of the `ModulatoryProjections <ModulatoryProjection>` assigned to the ModulatorySignal.
+
+    name : str : default <ModulatorySignal>-<index>
+        name of the ModulatorySignal.
+        Specified in the **name** argument of the constructor for the ModulatorySignal.  If not is specified, a default
+        is assigned by the StateRegistry of the mechanism to which the ModulatorySignal belongs
+        (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
+
+        .. note::
+            Unlike other PsyNeuLink components, State names are "scoped" within a mechanism, meaning that States with
+            the same name are permitted in different Mechanisms.  However, they are *not* permitted in the same
+            Mechanism: States within a Mechanism with the same base name are appended an index in the order of their
+            creation.
+
+    prefs : PreferenceSet or specification dict : State.classPreferences
+        the `PreferenceSet` for the ModulatorySignal.
+        Specified in the **prefs** argument of the constructor for the ModulatorySignal;
+        if it is not specified, a default is assigned using `classPreferences` defined in __init__.py
+        (see :doc:`PreferenceSet <LINK>` for details).
+
+    """
+
+    componentType = MODULATORY_SIGNAL
+    # paramsType = OUTPUT_STATE_PARAMS
+
+    classPreferenceLevel = PreferenceLevel.TYPE
+    # Any preferences specified below will override those specified in TypeDefaultPreferences
+    # Note: only need to specify setting;  level will be assigned to TYPE automatically
+    # classPreferences = {
+    #     kwPreferenceSetName: 'OutputStateCustomClassPreferences',
+    #     kp<pref>: <setting>...}
+
+    paramClassDefaults = State_Base.paramClassDefaults.copy()
+
+    def __init__(self,
+                 owner,
+                 reference_value,
+                 variable,
+                 modulation,
+                 index,
+                 calculate,
+                 params,
+                 name,
+                 prefs,
+                 context):
+
+        # Assign args to params and functionParams dicts (kwConstants must == arg names)
+        params = self._assign_args_to_param_dicts(params=params,
+                                                  modulation=modulation)
+
+        # If owner or reference_value has not been assigned, defer init to State.instantiate_projection_to_state()
+        if owner is None or reference_value is None:
+            # Store args for deferred initialization
+            self.init_args = locals().copy()
+            self.init_args['context'] = self
+            self.init_args['name'] = name
+
+            # Flag for deferred initialization
+            self.value = DEFERRED_INITIALIZATION
+            return
+
+        super().__init__(owner,
+                         reference_value,
+                         variable=variable,
+                         index=index,
+                         calculate=calculate,
+                         params=params,
+                         name=name,
+                         prefs=prefs,
+                         context=context)
+
+        self._modulation = self.modulation or owner.modulation
\ No newline at end of file
diff --git a/PsyNeuLink/Components/States/ModulatorySignals/__init__.py b/PsyNeuLink/Components/States/ModulatorySignals/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/PsyNeuLink/Components/States/OutputState.py b/PsyNeuLink/Components/States/OutputState.py
index 79b5206b03d..8b61e578594 100644
--- a/PsyNeuLink/Components/States/OutputState.py
+++ b/PsyNeuLink/Components/States/OutputState.py
@@ -15,26 +15,30 @@
 
 OutputState(s) represent the result(s) of executing a mechanism.  This may be the result(s) of its
 `function <OutputState.function>` and/or values derived from that result.  The full set of results are stored in the
-mechanism's `value <Mechanism.Mechanism_Base.value>` attribute.  OutputStates are used to represent individual items
-of the mechanism's `value <Mechanism.Mechanism_Base.value>`, and/or useful quantities derived from them.  For example,
-the `function <TransferMechanism.TransferMechanism.function>` of a `TransferMechanism` generates a single result (the
-transformed value of its input);  however, a TransferMechanism has outputStates that represent not only that
-result, but also its mean and variance (if it is an array).  In contrast, the `function <DDM.DDM.function>` of a
-`DDM` mechanism generates several results (such as decision accuracy and response time), each of which is assigned as
-the `value <OutputState.value>` of a different outputState.  The outputState(s) of a mechanism can serve as the input to
-other  mechanisms (by way of `projections <Projections>`), or as the output of a process and/or system.  The
-outputState's `sendsToProjections <OutputState.sendsToProjections>` attribute lists all of its outgoing projections.
+mechanism's `output_value <Mechanism.Mechanism_Base.output_value>` attribute.  OutputStates are used to represent 
+individual items of the mechanism's `value <Mechanism.Mechanism_Base.value>`, and/or useful quantities derived from 
+them.  For example, the `function <TransferMechanism.TransferMechanism.function>` of a `TransferMechanism` generates 
+a single result (the transformed value of its input);  however, a TransferMechanism can also be assigned outputStates 
+that represent its mean, variance or other derived values.  In contrast, the `function <DDM.DDM.function>` 
+of a `DDM` mechanism generates several results (such as decision accuracy and response time), each of which can be 
+assigned as the `value <OutputState.value>` of a different outputState.  The outputState(s) of a mechanism can serve 
+as the input to other  mechanisms (by way of `projections <Projections>`), or as the output of a process and/or 
+system.  The outputState's `efferents <OutputState.efferents>` attribute lists all of its outgoing 
+projections.
 
 .. _OutputStates_Creation:
 
 Creating an OutputState
 -----------------------
 
-An outputState can be created by calling its constructor. However, in general this is not necessary as a mechanism
-usually automatically creates the outputState(s) it needs when it is created.  For example, if the mechanism is
-created within the `pathway` of a `process <Process>`, an outputState will be created and assigned as the
+An outputState can be created by calling its constructor. However, in general this is not necessary, as a mechanism
+automatically creates a default outputState if none is explicitly specified, that contains the primary result of its
+`function <Mechanism_Base.function>`.  For example, if the mechanism is created within the `pathway` of a 
+`process <Process>`, an outputState will be created and assigned as the
 `sender <MappingProjection.MappingProjection.sender>` of a `MappingProjection` to the next mechanism in the pathway,
-or to the process's `output <Process_Input_And_Ouput>` if the mechanism is a `TERMINAL` mechanism for that process.
+or to the process's `output <Process_Input_And_Output>` if the mechanism is a `TERMINAL` mechanism for that process.
+Process_Input_And_Ouput.  Other configurations can also easily be specified using a mechanism's 
+`output_states <Mechanism_Base.output_states>` attribute (see `OutputState_Specification` below).
 
 An outputState must be owned by a mechanism. Therefore, if the outputState is created explicitly, the mechanism to
 which it belongs must be specified in the **owner** argument of its constructor; if the outputState is specified
@@ -43,48 +47,54 @@
 
 .. _OutputState_Primary:
 
-Every mechanism has at least one outputState, referred to as its *primary outputState*, that is automatically created
-and assigned to the mechanism's `outputState <Mechanism.Mechanism_Base.outputState>` attribute (note the singular),
-and also as the first entry in the OrderedDictionary of the mechanism's
-`outputStates <Mechanism.Mechanism_Base.outputStates>` attribute (note the plural).  The `value <OutputState.value>` of
-the primary outputState is assigned as the first (and often only) item of the mechanism's
-`value <Mechanism.Mechanism_Base.value>`, which is the result of the mechanism`s
-`function <Mechanism.Mechanism_Base.function>`.  In addition to the primary outputState, many mechanisms also assign
-an outputState for each additional item of their `value <Mechanism.Mechanism_Base.value>`, and some assign
-additional outputStates that calculate values derived from one or more of those items.
+Primary OutputState
+~~~~~~~~~~~~~~~~~~~
+
+Every mechanism has at least one outputState, referred to as its *primary outputState*.  If outputStates are not
+`explicitly specified <OutputState_Specification>` for a mechanism, a primary outputState is automatically created
+and assigned to its `outputState <Mechanism.Mechanism_Base.outputState>` attribute (note the singular),
+and also to the first entry of the mechanism's `outputStates <Mechanism.Mechanism_Base.outputStates>` attribute 
+(note the plural).  The `value <OutputState.value>` of the primary outputState is assigned as the first (and often 
+only) item of the mechanism's `output_value <Mechanism.Mechanism_Base.output_value>`, which is the result of the 
+mechanism`s `function <Mechanism.Mechanism_Base.function>`.
 
 .. _OutputState_Specification:
 
-If one or more custom outputStates need to be added when a mechanism is created, or added to an existing
-mechanism, they can be specified in an entry of the mechanism's
-`parameter dictionary <ParameterState_Specifying_Parameters>`, using the key OUTPUT_STATES.  For a single
-outputState, the value of the entry can be any of the specifications in the the list below.  To create multiple
-outputStates, the value can be either: a list, each item of which can be any of the specifications below; or
-it can be an OrderedDict in which the key for each entry is a string  specifying the name for the outputState to be
-created, and its value is one of the specifications below.  Each outputState to be created can be specified using any
-of the following formats:
+OutputState Specification
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The primary outputState of a mechanism can be supplemented or replaced using the **output_states** argument of a 
+mechanism's constructor <Mechanism_OutputStates>`.  If any are specified there, *all*  of the outputStates desired 
+must be specified there;  that is, explicitly specifying *any* outputStates in an **output_states** argument 
+replaces any defaults.  Therefore, if the default outputState -- that usually contains the result of the mechanism's
+function -- is to be retained, it too must be specified along with any additional ones desired.  OutputStates are 
+specified in a list, either in the **output_states** argument of a `mechanism's constructor <Mechanism_Creation>`, or
+by direct assignment to its `output_states <OutputState.output_states>` attribute.  The specification of each 
+outputState in a list can be any of the following: 
 
     * A reference to an **existing outputState**.  Its `variable <OutputState.variable>` must match (in the
       number and type of its elements) the item of the owner mechanism's `value <Mechanism.Mechanism_Base.value>` to
       which the outputState is assigned (designated by its `index <OutputState_Index>` attribute).
     ..
-    * A reference to the **OutputState class** or a ** name string**.  This creates a default outputState that is
-      assigned the first item of the owner mechanism's `value <Mechanism.Mechanism_Base.value>` as its value.  If the
-      name of the OutputState class or its keyword (OUTPUTSTATE) are used, a default name is assigned to the
-      outputState;  if a string is used, it is assigned as the name of the outputState
-      (see :ref:`naming conventions <LINK>`).
+    COMMENT:
+    * A reference to an **existing mechanism**.    
     ..
-    * A **specification dictionary**.  This can include entries, each of which uses the name of an argument for
+    COMMENT
+    * A **specification dictionary**.  Each entry should use the name of an argument for
       an outputState parameter (used in the outputState constructor) as its key, and the value of that
       parameter as its value.  By default, the outputState is assigned to the first item of the owner mechanism's
-      `value <Mechanism.Mechanism_Base.value>`.  However, :keyword:`INDEX <OutputState_Index>` can be used to
-      specify the outputState's  `index <OutputState_Index>` attribute and assign it to different item of the
-      mechanism's `value <Mechanism.Mechanism_Base.value>`.
+      `value <Mechanism.Mechanism_Base.value>`.  However, an entry with :keyword:`INDEX <OutputState_Index>` as its
+      key can be used to assign it to different item of the mechanism's `value <Mechanism.Mechanism_Base.value>`.
+    ..
+    * A **string**.  This creates a default outputState using that string as its name, and is assigned as the
+      `primary outputState <OutputState_Primary>` for the mechanism.
     ..
     * A **value**.  This creates a default outputState using the specified value as the outputState's
       `variable <OutputState.value>`.  This must be compatible with (have the same number and type of elements as) the
       item of the owner mechanism's `value <Mechanism.Mechanism_Base.value>` to which the outputState will be assigned
-      (its primary outputState by default, or designated by its `index <OutputState.index>` attribute).
+      (its primary outputState by default, or designated by its `index <OutputState.index>` attribute).  A default 
+      name will be assigned based on the name of the mechanism (see :ref:`naming conventions <LINK>`).
+      
       COMMENT:
          AT PRESENT THIS IS NOT USEFUL;  HOWEVER, IN THE FUTURE (E.G., WHEN GATING PROJECTIONS TO OUTPUT STATES
          IS ADDED) IT MAY BE USEFUL FOR SPECIFYING A BASEVALUE (E.G., DEFAULT) FOR THE OUTPUTSTATE.
@@ -95,29 +105,82 @@
        type of elements) as the item of its owner mechanism's `value <Mechanism.Mechanism_Base.value>` to which it is
        assigned.
 
-COMMENT:
-    OutputStates can also be added by using the :py:func:`assign_output_state <OutputState.assign_output_state>` method.
-COMMENT
 
-.. _OutputState_Names:
 
-Assigning outputStates explicitly (i.e., including an OUTPUT_STATES entry in the mechanism's
-`parameter dictionary <ParameterState_Specifying_Parameters>`) adds them to any that are automatically generated for
-that mechanism.  If the name of an explicitly specified outputState is the same as one that was created automatically
-(or another one that was created explicitly), its name will be suffixed with a numerical index (incremented for each
-outputState with that name), and the outputState will be added to the list (that is, it will *not* replace ones that
-were already created).
+The list of outputStates for a mechanism can be accessed using its `output_states <Mechanism_Base.output_states>` 
+attribute, and new ones can be added to an existing mechanism by appending a list (containing specifications in the
+form listed above) to the output_states list. Assigning without appending will replace any existing outputStates.  
+If the name of an explicitly specified outputState is the same as one that was created automatically (or another one 
+that was created explicitly), its name will be suffixed with a numerical index (incremented for each outputState with 
+that name), and the outputState will be added to the list (that is, it will *not* replace ones that were already 
+created).
+
+The values of a mechanism's outputStats will be assigned as items in its `output_value <Mechanism.output_value>` 
+attribute, in the order in which they are assigned in the **output_states** argument of its constructor, and listed
+in its `output_states <Mechanism.output_states>` attribute.  Note that the `output_value <Mechanism.output_value>`
+of a mechanism is distinct from its `value <Mechanism_Base.value>` attribute, which contains the full and unmodified 
+results of its `function <Mechanism_Base.function>`.
+
+.. _OutputState_Standard:
+
+Standard OutputStates
+^^^^^^^^^^^^^^^^^^^^^  
+
+Most types of mechanisms have a `standard_output_states` class attribute, that contains a list of predefined 
+outputStates relevant to that type of mechanism (for example, the TransferMechanism class has outputStates for 
+calculating the mean, median, variance, and standard deviation of its result).  The names of these are listed as 
+attributes of a class with the name <ABBREVIATED_CLASS_NAME>_OUTPUT>.  For example, the TransferMechanism class 
+defines `TRANSFER_OUTPUT`, with attributes MEAN, MEDIAN, VARIANCE and STANDARD_DEV that are the names of 
+predefined outputStates in its `standard_output_states <TransferMechanism.standard_output_states>` attribute.
+These can be used in the list of outputStates specified for a TransferMechanism object, as in the example below:: 
+
+    my_mech = TransferMechanism[default_input_value=[0,0],
+                                function=Logistic(),
+                                output_states=[TRANSFER_OUTPUT.RESULT, 
+                                               TRANSFER_OUTPUT.MEAN,
+                                               TRANSFER_OUTPUT.VARIANCE]
+
+In this example, ``my_mech`` is configured with three outputStates;  the first will be named *RESULT* and will 
+represent logistic transform of the 2-element input vector;  the second will be named  *MEAN* and will represent mean 
+of the result (i.e., of its two elements); and the third will be named *VARIANCE* and contain the variance of the 
+result.
+
+.. _OutputState_Customization:
+
+OutputState Customization
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The default outputState uses the first (and usually only) item of the owner mechanism's 
+`value <Mechanism.Mechanism_Base.value>` as its value.  However, this can be modified in two ways, using the
+outputState's `index <OutputState.index>` and `calculate <OutputState.calculate>` attributes (see 
+`OutputState_Attributes` below). If the mechanism's `function <Mechanism.function>` returns a value with more than one 
+item (i.e., a list of lists, or a 2d np.array), then an outputState can be assigned to any of those items by specifying 
+its `index <OutputState.index>` attribute. An outputState can also be configured to transform
+the value of the item, by specifying a function for its `calculate <OutputState.calculate>` attribute; the result
+will then be assigned as the outputState's `value <OutputState.value>`.  An outputState's `index <OutputState.index>` 
+and `calculate <OutputState.calculate>` attributes can be assigned when the outputState is assigned to a mechanism, 
+by including *INDEX* and *CALCULATE* entries in a  `specification dictionary <OutputState_Specification>` for the 
+outputState, as in the following example::
+
+    my_mech = DDM(function=BogaczEtAl(),
+                  output_states=[ DDM.DECISION_VARIABLE,
+                                  DDM.PROB_UPPER_THRESHOLD,
+                                  { NAME: 'DECISION ENTROPY',
+                                    INDEX: 2},
+                                    CALCULATE: Entropy().function } ] )
 
-.. _OutputState_Index_and_Calculate:
+COMMENT:
+   ADD VERSION IN WHICH INDEX IS SPECIFICED USING DDM_standard_output_states
+COMMENT
 
-By default, an explicitly specified outputState will use the first item of the owner mechanism's
-`value <Mechanism.Mechanism_Base.value>`.  However, it can be assigned to a different item by specifying its
-`index <OutputState.index>` attribute.  The `variable <OutputState.variable>` of
-an outputState must match (in the number and type of its elements) the item of the mechanism's
-`value <Mechanism.Mechanism_Base.value>` to which it is assigned. An outputState can also be configured to transform
-the value of the item, by specifying a function for its `calculate <OutputState.calculate>` attribute;  the function
-must be able to take as input a value that is compatible with the item of the Mechahnism's
-`value <Mechanism.Mechanism_Base.value>` to which the outputState is assigned.
+In this example, ``my_mech`` is configured with three outputStates.  The first two are standard outputStates that 
+represent the decision variable of the DDM and the probability of it crossing of the upper (vs. lower) threshold.  the 
+third is a custom outputState, that computes the entropy of the probability of crossing the upper threshold.  It uses
+the `Entropy` Function for its `calculate <OutputState.calculate>` attribute, and INDEX is assigned ``2`` to reference  
+the third item of the DDM's `value <DDM.value>` attribute, which contains the probability of crossing the upper 
+threshold.  The three outputStates will be assigned to the `output_states <Mechanism.output_states>` attribute of 
+``my_mech``, and their values will be assigned as items in its `output_value <Mechanism.output_value>` attribute, 
+each in the order in which it is listed in the **output_states** argument of the constructor for ``my_mech``.  
 
 .. _OutputState_Structure:
 
@@ -126,9 +189,9 @@
 
 Every outputState is owned by a `mechanism <Mechanism>`. It can send one or more
 `MappingProjections <MappingProjection>` to other mechanisms.  If its owner is a `TERMINAL` mechanism of a process
-and/or system, then the outputState will also be treated as the output of that `process <Process_Input_And_Ouput>`
+and/or system, then the outputState will also be treated as the output of that `process <Process_Input_And_Output>`
 and/or of a system.  The projections that the outputState sends are listed in its
-`sendsToProjections <OutputState.sendsToProjections>` attribute.
+`efferents <OutputState.efferents>` attribute.
 
 Like all PsyNeuLink components, an outputState has the three following core attributes:
 
@@ -156,27 +219,35 @@
 
 .. _OutputState_Attributes:
 
-An outputState also has two additional attributes that determine its operation:
+Additional Attributes
+~~~~~~~~~~~~~~~~~~~~~
+
+An outputState also has two additional attributes that determine its operation, and can be used to further 
+`customize the outputState <OutputState_Customization>`:
 
 .. _OutputState_Index:
 
 * `index <OutputState.index>`: this determines the item of its owner mechanism's
   `value <Mechanism.Mechanism_Base.value>` to which it is assigned.  By default, this is set to 0, which assigns it to
-  the first item of the mechanism's `value <Mechanism.Mechanism_Base.value>`.
+  the first item of the mechanism's `value <Mechanism.Mechanism_Base.value>`.  Its value must be equal to or less than
+  one minus the number of outputStates in the mechanism's `output_states <Mechanism.output_states>` attribute.  In 
+  addition, the `variable <OutputState.variable>` of an outputState must match (in the number and type of its elements) 
+  the item of the mechanism's `value <Mechanism.Mechanism_Base.value>` to which the index refers.      
 
 .. _OutputState_Calculate:
 
-* `calculate <OutputState.calculate>`:  this specifies the function used to convert the item of the owner mechanism's
+* `calculate <OutputState.calculate>`:  this specifies a function used to convert the item of the owner mechanism's
   `value <Mechanism.Mechanism_Base.value>` (designated by the outputState's `index <OutputState.index>` attribute),
-  before assigning it as the outputState's `value <OutputState.vaue>`.  The result is combined with the result of the
-  outputState's `function <OutputState.function>` attribute (which aggregates the value of its projections), to
-  determine the outputState's `value <OutputState.value>` (though see `note <OutputState_Function_Note_1>`). The
-  default for `calculate  <OutputState.calculate>` is an identity function (`Linear` with **slope**\ =1 and
-  **intercept**\ =0), which simply assigns the specified item of the mechanism's
-  `value <Mechanism.Mechanism_Base.value>` unmodified as the `value <OutputState.value>` of the outputState. However,
-  `calculate  <OutputState.calculate>` can be assigned any function that can take as its input the designated item
-  of the owner mechanism's `value <Mechanism.Mechanism_Base.value>`, and the result of which can be combined with the
-  result of the outputState's `function <OutputState.function>`.
+  before assigning it as the outputState's `value <OutputState.vaue>`.  The result is used as the input to the
+  outputState's `function <OutputState.function>` attribute (which implements the effects of any 
+  `GatingProjections` to the outputState), the result of whch is assigned as the outputState's 
+  `value <OutputState.value>` (though see `note <OutputState_Function_Note_1>`). The 
+  `calculate  <OutputState.calculate>` attribute can be assigned any function that can take as its input the 
+  item of the owner mechanism's `value <Mechanism.Mechanism_Base.value>` designated by the outputState's
+  `index <OutputState.index>` attribute, and the result of which can be used as the variable for the outputState's 
+  `function <OutputState.function>`.  The default is an identity function (`Linear` with **slope**\\ =1 and
+  **intercept**\\ =0), that simply assigns the specified item of the mechanism's `value <Mechanism.Mechanism_Base.value>`
+  unmodified as the variable for outputState's `function <OutputState.function>`.  
 
 .. _OutputState_Execution:
 
@@ -191,7 +262,7 @@
 `value <Mechanism.Mechanism_Base.value>` as its input.  This is possibly modified by the result of the outputState's
 `function <OutputState.function>` (though see `note <OutputState_Function_Note_2>`).  The final result is assigned as
 the outputState's `value <OutputState.value>`, as well as to a corresponding item of the mechanism's
-`outputValue  <Mechanism.Mechanism_Base.outputValue>` attribute. It is also used as the input to any projections for
+`output_values  <Mechanism.Mechanism_Base.output_values>` attribute. It is also used as the input to any projections for
 which the outputState is the `sender <Projection.Projection.sender>`.
 
 .. _OutputState_Class_Reference:
@@ -215,6 +286,42 @@
 
 
 PRIMARY_OUTPUT_STATE = 0
+SEQUENTIAL = 'SEQUENTIAL'
+
+# Standard OutputStates
+# OUTPUT_RESULT = {NAME: RESULT}
+#
+# OUTPUT_MEAN = {NAME:MEAN,
+#                CALCULATE:lambda x: np.mean(x)}
+#
+# OUTPUT_MEDIAN = {NAME:MEDIAN,
+#                    CALCULATE:lambda x: np.median(x)}
+#
+# OUTPUT_STAND_DEVIATION = {NAME:STANDARD_DEV,
+#                           CALCULATE:lambda x: np.std(x)}
+#
+# OUTPUT_VARIANCE = {NAME:VARIANCE,
+#                    CALCULATE:lambda x: np.var(x)}
+
+
+# This is a convenience class that provides list of standard_output_state names in IDE
+class OUTPUTS():
+    RESULT=RESULT
+    MEAN=MEAN
+    MEDIAN=MEDIAN
+    STANDARD_DEV=STANDARD_DEV
+    VARIANCE=VARIANCE
+
+standard_output_states = [{NAME: RESULT},
+                          {NAME:MEAN,
+                           CALCULATE:lambda x: np.mean(x)},
+                          {NAME:MEDIAN,
+                           CALCULATE:lambda x: np.median(x)},
+                          {NAME:STANDARD_DEV,
+                           CALCULATE:lambda x: np.std(x)},
+                          {NAME:VARIANCE,
+                           CALCULATE:lambda x: np.var(x)}]
+
 
 class OutputStateError(Exception):
     def __init__(self, error_value):
@@ -228,10 +335,11 @@ class OutputState(State_Base):
     """
     OutputState(                               \
     owner,                                     \
-    value=None,                                \
+    reference_value,                           \
+    variable=None,                             \
+    function=LinearCombination(operation=SUM), \
     index=PRIMARY_OUTPUT_STATE,                \
     calculate=Linear,                          \
-    function=LinearCombination(operation=SUM), \
     params=None,                               \
     name=None,                                 \
     prefs=None)
@@ -275,12 +383,21 @@ class OutputState(State_Base):
     reference_value : number, list or np.ndarray
         a template that specifies the format of the item of the owner mechanism's
         `value <Mechanism.Mechanism_Base.value>` attribute to which the outputState will be assigned (specified by
-        the :keyword:`index` argument).  This must match (in number and type of elements) the outputState's
-        `variable <OutputState.variable>` argument.  It is used to insure the compatibility of the source of the
+        the **index** argument).  This must match (in number and type of elements) the outputState's
+        **variable** argument.  It is used to insure the compatibility of the source of the
         input for the outputState with its `variable <OutputState.variable>`.
 
-    value : number, list or np.ndarray
-        specifies the template for the outputState's `value <OutputState.value>`.
+    variable : number, list or np.ndarray
+        specifies the template for the outputState's `variable <OutputState.variable>`.
+
+    function : Function, function, or method : default LinearCombination(operation=SUM)
+        function used to aggregate the values of the projections received by the outputState.
+        It must produce a result that has the same format (number and type of elements) as the item of the mechanism's
+        `value <Mechanism.Mechanism_Base.value>` to which the outputState is assigned (specified by its
+        **index** argument).
+
+        .. note::
+             This is not used a present (see `note <OutputState_Function_Note_2>` for additonal details).
 
     index : int : default PRIMARY_OUTPUT_STATE
         specifies the item of the owner mechanism's `value <Mechanism.Mechanism_Base.value>` used as input for the
@@ -294,15 +411,6 @@ class OutputState(State_Base):
         has the same format (number and type of elements) as the item of the mechanism's
         `value <Mechanism.Mechanism_Base.value>`.
 
-    function : Function, function, or method : default LinearCombination(operation=SUM)
-        function used to aggregate the values of the projections received by the outputState.
-        It must produce a result that has the same format (number and type of elements) as the item of the mechanism's
-        `value <Mechanism.Mechanism_Base.value>` to which the outputState is assigned (specified by its
-        **index** argument).
-
-        .. note::
-             This is not used a present (see `note <OutputState_Function_Note_2>` for additonal details).
-
     params : Optional[Dict[param keyword, param value]]
         a `parameter dictionary <ParameterState_Specifying_Parameters>` that can be used to specify the parameters for
         the outputState, its function, and/or a custom function and its parameters. Values specified for parameters
@@ -325,10 +433,6 @@ class OutputState(State_Base):
     owner : Mechanism
         the mechanism to which the outputState belongs.
 
-    sendsToProjections : Optional[List[Projection]]
-        a list of the projections sent by the outputState (i.e., for which the outputState is a
-        `sender <Projection.Projection.sender>`).
-
     variable : number, list or np.ndarray
         assigned the item of the owner mechanism's `value <Mechanism.Mechanism_Base.value>` specified by the
         outputState's `index <OutputState.index>` attribute.
@@ -342,19 +446,19 @@ class OutputState(State_Base):
         the outputState's `index <OutputState.index>` attribute.  The result is combined with the result of the
         outputState's `function <OutputState.function>` ((though see `note below <OutputState_Function_Note_2>`)
         to determine both the `value <OutputState.value>` of the outputState, as well as the value of the
-        corresponding item of the owner mechanism's `outputValue <Mechanism.Mechanism_Base.outputValue>`.
+        corresponding item of the owner mechanism's `output_values <Mechanism.Mechanism_Base.output_values>`.
         The default (`Linear`) transfers the value unmodified.
 
     function : CombinationFunction : default LinearCombination(operation=SUM))
         performs an element-wise (Hadamard) aggregation  of the values of the projections received by the
         outputState.  The result is combined with the result of the function specified by
         `calculate <OutputState.calculate>`, and assigned as both the outputState's `value <OutputState.value>`
-        and the corresponding item of the owner's `outputValue <Mechanism.Mechanism_Base.outputValue>`.
+        and the corresponding item of the owner's `output_values <Mechanism.Mechanism_Base.output_values>`.
 
         .. _OutputState_Function_Note_2:
 
         .. note::
-           Currently PsyNeuLink does not currently support projections to outputStates.  Therefore, the
+           PsyNeuLink does not currently support projections to outputStates.  Therefore, the
            :keyword:`function` attribute is not used.  It is implemented strictly for consistency with other
            state classes, and for potential future use.
            COMMENT:
@@ -368,11 +472,15 @@ class OutputState(State_Base):
         assigned the result of `function <OutputState.function>`
         (though see note under `function <OutputState.function>) combined with the result of the function specified
         by `calculate <OutputState.calculate>`;  the same value is assigned to the corresponding item of the owner
-        mechanism's `outputValue <Mechanism.Mechanism_Base.outputValue>`.
+        mechanism's `output_values <Mechanism.Mechanism_Base.output_values>`.
+
+    efferents : Optional[List[Projection]]
+        a list of the projections sent by the outputState (i.e., for which the outputState is a
+        `sender <Projection.Projection.sender>`).
 
     name : str : default <State subclass>-<index>
         name of the outputState.
-        Specified in the `name` argument of the constructor for the outputState.  If not is specified, a default is
+        Specified in the **name** argument of the constructor for the outputState.  If not is specified, a default is
         assigned by the StateRegistry of the mechanism to which the outputState belongs
         (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
 
@@ -384,7 +492,7 @@ class OutputState(State_Base):
 
     prefs : PreferenceSet or specification dict : State.classPreferences
         the `PreferenceSet` for the outputState.
-        Specified in the `prefs` argument of the constructor for the projection;  if it is not specified, a default is
+        Specified in the **prefs** argument of the constructor for the projection;  if it is not specified, a default is
         assigned using `classPreferences` defined in __init__.py
         (see :doc:`PreferenceSet <LINK>` for details).
 
@@ -406,14 +514,14 @@ class OutputState(State_Base):
     paramClassDefaults.update({PROJECTION_TYPE: MAPPING_PROJECTION})
     #endregion
 
-    tc.typecheck
+    @tc.typecheck
     def __init__(self,
                  owner,
                  reference_value,
                  variable=None,
-                 index=PRIMARY_OUTPUT_STATE,
-                 calculate:function_type=Linear,
                  function=LinearCombination(operation=SUM),
+                 index=PRIMARY_OUTPUT_STATE,
+                 calculate:is_function_type=Linear,
                  params=None,
                  name=None,
                  prefs:is_pref_set=None,
@@ -440,7 +548,6 @@ def __init__(self,
                          prefs=prefs,
                          context=self)
 
-
     def _validate_variable(self, variable, context=None):
         """Insure variable is compatible with output component of owner.function relevant to this state
 
@@ -461,9 +568,9 @@ def _validate_variable(self, variable, context=None):
 
         # Insure that self.variable is compatible with (relevant item of) output value of owner's function
         if not iscompatible(self.variable, self.reference_value):
-            raise OutputStateError("Value ({0}) of outputState for {1} is not compatible with "
-                                           "the output ({2}) of its function".
-                                           format(self.value,
+            raise OutputStateError("Variable ({}) of outputState for {} is not compatible with "
+                                           "the output ({}) of its function".
+                                           format(self.variable,
                                                   self.owner.name,
                                                   self.reference_value))
 
@@ -483,8 +590,8 @@ def _validate_params(self, request_set, target_set=None, context=None):
                 self.owner.value[target_set[INDEX]]
             except IndexError:
                 raise OutputStateError("Value of {} argument for {} is greater than the number of items in "
-                                       "the outputValue ({}) for its owner mechanism ({})".
-                                       format(INDEX, self.name, self.owner.outputValue, self.owner.name))
+                                       "the output_values ({}) for its owner mechanism ({})".
+                                       format(INDEX, self.name, self.owner.output_values, self.owner.name))
 
         # IMPLEMENT: VALIDATE THAT CALCULATE FUNCTION ACCEPTS VALUE CONSISTENT WITH
         #            CORRESPONDING ITEM OF OWNER MECHANISM'S VALUE
@@ -516,24 +623,25 @@ def _instantiate_attributes_after_function(self, context=None):
         if isinstance(self.calculate, type):
             self.calculate = self.calculate().function
 
+    def _execute(self, function_params, context):
+        """Call self.function with owner's value as variable
+        """
 
-    def update(self, params=None, time_scale=TimeScale.TRIAL, context=None):
+        # IMPLEMENTATION NOTE: OutputStates don't current receive TransmissiveProjections,
+        #                      so there is no need to use their value (as do InputStates)
+        value = self.function(variable=self.owner.value[self.index],
+                                params=function_params,
+                                context=context)
 
-        super().update(params=params, time_scale=time_scale, context=context)
+        return type_match(self.calculate(self.owner.value[self.index]), type(value))
 
-        # FIX: FOR NOW, self.value IS ALWAYS None (SINCE OUTPUTSTATES DON'T GET PROJECTIONS, AND
-        # FIX:     AND State.update RETURNS None IF THERE ARE NO PROJECTIONS, SO IT ALWAYS USES CALCULATE (BELOW).
-        # FIX:     HOWEVER, NEED TO INTEGRATE self.value and self.function WITH calculate:
-        # IMPLEMENT: INCORPORATE paramModulationOperation HERE, AS PER PARAMETER STATE:
-        #            TO COMBINE self.value ASSIGNED IN CALL TO SUPER (FROM PROJECTIONS)
-        #            WITH calculate(self.owner.value[index]) PER BELOW
+    @property
+    def pathway_projections(self):
+        return self.efferents
 
-        # # MODIFIED 4/15/17 OLD:
-        # if not self.value:
-        #     self.value = type_match(self.calculate(self.owner.value[self.index]), type(self.owner.value[self.index]))
-        # # MODIFIED 4/15/17 NEW:
-        self.value = type_match(self.calculate(self.owner.value[self.index]), type(self.owner.value[self.index]))
-        # # MODIFIED 4/15/17 END
+    @pathway_projections.setter
+    def pathway_projections(self, assignment):
+        self.efferents = assignment
 
 
 
@@ -555,42 +663,25 @@ def _instantiate_output_states(owner, context=None):
     # MODIFIED 12/7/16 END
     """Call State._instantiate_state_list() to instantiate orderedDict of outputState(s)
 
-    Create OrderedDict of outputState(s) specified in paramsCurrent[INPUT_STATES]
-    If INPUT_STATES is not specified, use self.variable to create a default output state
+    Create ContentAddressableList of outputState(s) specified in paramsCurrent[OUTPUT_STATES]
+    If OUTPUT_STATES is not specified, use self.value to create a default output state
     When completed:
-        - self.outputStates contains an OrderedDict of one or more outputStates
-        - self.outputState contains first or only outputState in OrderedDict
-        - paramsCurrent[OUTPUT_STATES] contains the same OrderedDict (of one or more outputStates)
+        - self.outputStates contains a ContentAddressableList of one or more outputStates;
+        - self.output_state contains first or only outputState in list;
+        - paramsCurrent[OUTPUT_STATES] contains the same ContentAddressableList (of one or more outputStates)
         - each outputState corresponds to an item in the output of the owner's function
         - if there is only one outputState, it is assigned the full value
 
     (See State._instantiate_state_list() for additional details)
 
     IMPLEMENTATION NOTE:
-        default(s) for self.paramsCurrent[OUTPUT_STATES] (self.value) is assigned here
+        default(s) for self.paramsCurrent[OUTPUT_STATES] (self.value) are assigned here
         rather than in _validate_params, as it requires function to have been instantiated first
-
-    :param context:
-    :return:
     """
 
     constraint_value = []
 
-    # # MODIFIED 3/3/17 OLD:
-    # owner_value = np.atleast_2d(owner.value)
-
-    # MODIFIED 3/3/17 NEW:
-    # # IMPLEMENTATION NOTE:  ?? IS THIS REDUNDANT WITH SAME TEST IN Mechanism.execute ?
-    # owner_value = owner.value
-    # converted_to_2d = np.atleast_2d(owner.value)
-    # # If owner_value is a list of heterogenous elements, use as is
-    # if converted_to_2d.dtype == object:
-    #     owner_value = owner.value
-    # # Otherwise, use value converted to 2d np.array
-    # else:
-    #     owner_value = converted_to_2d
-
-    # MODIFIED 3/7/17 NEWER
+    # Get owner.value
     # IMPLEMENTATION NOTE:  ?? IS THIS REDUNDANT WITH SAME TEST IN Mechanism.execute ?  JUST USE RETURN VALUE??
     owner_value = owner.value
     # IMPLEMENTATION NOTE:  THIS IS HERE BECAUSE IF return_value IS A LIST, AND THE LENGTH OF ALL OF ITS
@@ -612,36 +703,177 @@ def _instantiate_output_states(owner, context=None):
         else:
             owner_value = converted_to_2d
 
-    # MODIFIED 3/3/17 END
-
+    # Get the value of each outputState
     # IMPLEMENTATION NOTE:
     # Should change the default behavior such that, if len(owner_value) == len owner.paramsCurrent[OUTPUT_STATES]
     #        (that is, there is the same number of items in owner_value as there are outputStates)
     #        then increment index so as to assign each item of owner_value to each outputState
-    if owner.paramsCurrent[OUTPUT_STATES]:
-        for output_state in owner.paramsCurrent[OUTPUT_STATES]:
+    # IMPLEMENTATION NOTE:  SHOULD BE REFACTORED TO USE _parse_state_spec TO PARSE ouput_states arg
+    if owner.output_states:
+        for i, output_state in enumerate(owner.output_states):
+
             # Default is PRIMARY_OUTPUT_STATE
             index = PRIMARY_OUTPUT_STATE
-            # If output_state is already an OutputState object, get its index attribute
+            output_state_value = owner_value[index]
+
+            # output_state is:
+
+            # OutputState object, so get its index attribute
             if isinstance(output_state, OutputState):
                 index = output_state.index
-            # If output_state is a specification dict, get its INDEX attribute if specified
+                output_state_value = owner_value[index]
+
+            # string, so check if it is the name of a standard_output_state and, if so, get its dict
+            elif isinstance(output_state, str) and hasattr(owner, STANDARD_OUTPUT_STATES):
+                # check if string matches the name entry of a dict in standard_output_states
+                item = next((item for item in owner.standard_output_states.names if output_state is item), None)
+                if item is not None:
+                    # assign dict to owner's output_state list
+                    owner.output_states[owner.output_states.index(output_state)] = \
+                                                            owner.standard_output_states.get_dict(output_state)
+                    output_state = item
+
+            # specification dict, so get its INDEX attribute if specified, and apply calculate function if specified
+            # if isinstance(output_state, dict):
             elif isinstance(output_state, dict):
                 try:
                     index = output_state[INDEX]
                 except KeyError:
                     pass
-            constraint_value.append(owner_value[index])
+                if CALCULATE in output_state:
+                    output_state_value = output_state[CALCULATE](owner_value[index])
+                else:
+                    output_state_value = owner_value[index]
+
+            # If output_state is none of the above, it should be a string
+            #    (being used as the name of a default OutputState)
+            else:
+                if not isinstance(output_state, str):
+                    raise OutputStateError("PROGRAM ERROR: unrecognized item ({}) in output_states specification for {}"
+                                           .format(output_state, owner.name))
+
+            constraint_value.append(output_state_value)
+
     else:
         constraint_value = owner_value
 
-    owner.outputStates = _instantiate_state_list(owner=owner,
-                                                state_list=owner.paramsCurrent[OUTPUT_STATES],
-                                                state_type=OutputState,
-                                                state_param_identifier=OUTPUT_STATES,
-                                                constraint_value=constraint_value,
-                                                constraint_value_name="output",
-                                                context=context)
-    # Assign self.outputState to first outputState in dict
-    owner.outputState = list(owner.outputStates.values())[0]
+    state_list = _instantiate_state_list(owner=owner,
+                                         state_list=owner.output_states,
+                                         state_type=OutputState,
+                                         state_param_identifier=OUTPUT_STATES,
+                                         constraint_value=constraint_value,
+                                         constraint_value_name="output",
+                                         context=context)
+
+    # FIX: This is a hack to avoid recursive calls to assign_params, in which output_states never gets assigned
+    # FIX: Hack to prevent recursion in calls to setter and assign_params
+    if 'COMMAND_LINE' in context:
+        owner.output_states = state_list
+    else:
+        owner._output_states = state_list
+
+
+class StandardOutputStates():
+    """Collection of OutputState specification dictionaries for standard_output_states of a class
+    
+    data attribute contains dictionary of OutputState specification dictionaries
+    indices contains a list of the default index for each OutputState specified
+    names containes a list of the default name of each OutputState
+
+    **indices** arg of constructore specifies how to assign the INDEX entry for each dict listed in output_state_dicts;
+        the arg can be:
+            * PRIMARY_OUTPUT_STATES keyword - assigns the INDEX for the owner's primary outputState to all indices;
+            * SEQUENTIAL keyword - assigns sequentially incremented int to each INDEX entry
+           * a list of ints - assigns each int to the corresponding entry in output_state_dicts
+        if it is not specified, assigns `None` to each INDEX entry.
+
+    `get_dict` method takes a name as its arg and returns a copy of the designated OutputState specification dictionary
+
+    """
+
+    @tc.typecheck
+    def __init__(self,
+                 owner:Component,
+                 output_state_dicts:list,
+                 indices:tc.optional(tc.any(int, str, list))=None):
+
+        # Validate that all items in output_state_dicts are dicts
+        for item in output_state_dicts:
+            if not isinstance(item, dict):
+                raise OutputStateError("All items of {} for {} must be dicts (but {} is not)".
+                                     format(self.__class__.__name__, owner.componentName, item))
+        self.data = output_state_dicts.copy()
+
+        # Assign indices
+
+        # List was provided, so check that:
+        # - it has the appropriate number of items
+        # - they are all ints
+        # and then assign each int to the INDEX entry in the corresponding dict in output_state_dicts
+        # outputState
+        if isinstance(indices, list):
+            if len(indices) != len(output_state_dicts):
+                raise OutputStateError("Length of the list of indices provided to {} for {} ({}) "
+                                       "must equal the number of outputStates dicts provided ({})"
+                                       "length".format(self.__class__.__name__,
+                                                       owner.name,
+                                                       len(indices),
+                                                       len(output_state_dicts)))
+
+            if not all(isinstance(item, int) for item in indices):
+                raise OutputStateError("All the items in the list of indices provided to {} for {} ({}) must be ints".
+                                       format(self.__class__.__name__, self.name, owner.name, index))
+
+            for index, state_dict in zip(indices, self.data):
+                state_dict[INDEX] = index
+
+        # Assign indices sequentially based on order of items in output_state_dicts arg
+        elif indices is SEQUENTIAL:
+            for index, state_dict in enumerate(self.data):
+                state_dict[INDEX] = index
+
+        # Assign PRIMARY_OUTPUT_STATE as INDEX for all outputStates in output_state_dicts
+        elif indices is PRIMARY_OUTPUT_STATE:
+            for state_dict in self.data:
+                state_dict[INDEX] = PRIMARY_OUTPUT_STATE
+
+        # No indices specification, so assign None to INDEX for all outputStates in output_state_dicts
+        else:
+            for state_dict in self.data:
+                state_dict[INDEX] = None
+
+
+        # Add names of each outputState as property of the owner's class that returns its name string
+        for state in self.data:
+            setattr(owner.__class__, state[NAME], make_readonly_property(state[NAME]))
+
+        # Add <NAME_INDEX> of each outputState as property of the owner's class, that returns its index
+        for state in self.data:
+            setattr(owner.__class__, state[NAME]+'_INDEX', make_readonly_property(state[INDEX]))
+
+    @tc.typecheck
+    def get_dict(self, name:str):
+        return self.data[self.names.index(name)].copy()
+    
+    @property
+    def names(self):
+        return [item[NAME] for item in self.data]
+
+    @property
+    def indices(self):
+        return [item[INDEX] for item in self.data]
+
+
+def make_readonly_property(val):
+    """Return property that provides read-only access to its value
+    """
+
+    def getter(self):
+        return val
+
+    def setter(self, val):
+        raise UtilitiesError("{} is read-only property of {}".format(val, self.__class__.__name__))
 
+    # Create the property
+    prop = property(getter).setter(setter)
+    return prop
diff --git a/PsyNeuLink/Components/States/ParameterState.py b/PsyNeuLink/Components/States/ParameterState.py
index ecceaac09e2..adf82120a2b 100644
--- a/PsyNeuLink/Components/States/ParameterState.py
+++ b/PsyNeuLink/Components/States/ParameterState.py
@@ -14,12 +14,12 @@
 --------
 
 A parameterState belongs to either a `mechanism <Mechanism>` or a `MappingProjection`, and is used to represent and
-possibly modify the value of a parameter of its owner or it owner's function.  It can receive one or more
-`ControlProjections <ControlProjection>` and/or `LearningProjections <LearningProjection>` that modify that
-parameter.   The projections received by a parameterState are listed in its
-`receivesFromProjections <ParameterState.receivesFromProjections>` attribute.
-Its `function <ParameterState.function>` combines the values of these inputs, and uses the result to modify the value
-of the parameter for which it is responsible.
+possibly modify the value of the parameter used by its owner or owner's `function <Component.function>`.  It can
+receive one or more `ControlProjections <ControlProjection>` and/or `LearningProjections <LearningProjection>` that
+modify the value of the parameter. The projections received by a parameterState are listed in its
+`path_afferents <ParameterState.path_afferents>` attribute. Its `function <ParameterState.function>` combines the
+values of those projections, and uses the result to modify the value of the parameter that is used by the
+owner or its `function <Component.function>`.
 
 
 .. _ParameterState_Creation:
@@ -27,13 +27,12 @@
 Creating a ParameterState
 -------------------------
 
-A parameterState can be created by calling its constructor, but in general this is not necessary or advisable as
-parameterStates are created automatically when the mechanism or projection to which they belong is created.  The
-`owner <ParamaterState.owner>` of a parameterState must be a `mechanism <Mechanism>` or `MappingProjection`.  If the
-`owner <ParamaterState.owner>` is not explicitly specified, and can't be determined by context, the parameterState
-will be assigned to the :ref:`DefaultProcessingMechanism`.  One parameterState is created for each configurable
-parameter of its owner, as well as for each parameter that has been specified for that component's :keyword:`function`.
-Each parameterState is created using the specification of the parameter for which it is responsible, as described below.
+A ParameterState can be created by calling its constructor, but in general this is not necessary or advisable as
+ParameterStates are created automatically when the Mechanism or Projection to which they belong is created.  The
+`owner <ParamaterState.owner>` of a ParameterState must be a `Mechanism <Mechanism>` or `MappingProjection`.  One
+ParameterState is created for each configurable parameter of its owner, as well as for each configurable parameter
+of the owner's `function <Component.function>`.  Each ParameterState is created using the value specified for the
+corresponding parameter, as described below.
 
 .. _ParameterState_Specifying_Parameters:
 
@@ -42,125 +41,114 @@
 
 Parameters can be specified in one of several places:
 
-    * In the **argument for the parameter** of the constructor for the `component <Component>` to which the parameter 
+    * In the **argument for the parameter** of the constructor for the `Component` to which the parameter
       belongs (see :ref:`Component_Specifying_Functions_and_Parameters` for additional details).
     ..
-    * In a **parameter specification dictionary** assigned to the **params** argument in the constructor for the 
-      component to which the parameter belongs. The entry for each parameter must use the name of the parameter
+    * In a *parameter specification dictionary* assigned to the **params** argument in the constructor for the
+      Component to which the parameter belongs. The entry for each parameter must use the name of the parameter
       (or a corresponding keyword) as its key, and the parameter's specification as its value (see 
-      `examples <ParameterState_Specification_Examples>` below). Parameters for a component's :keyword:`function` 
-      must be specified in an entry with the key FUNCTION_PARAMS, the value of which is a parameter dictionary 
-      containing an entry for each of the function's parameters to be specified.  When a value is assigned to a 
-      parameter in a parameter dictionary, it overrides any value assigned to the argument for the parameter in the 
-      component's constructor.
+      `examples <ParameterState_Specification_Examples>` below). Parameters for a Component's
+      `function <Component.function>` can be specified in an entry with the key *FUNCTION_PARAMS*,
+      a value that is itself a parameter specification dictionary containing an entry for each of the
+      function's parameters to be specified.  When a value is assigned to a parameter in a specification dictionary,
+      it overrides any value assigned to the argument for the parameter in the Component's constructor.
     ..
-    * By direct assignment to the corresponding attribute of the component to which the parameter belongs.  The
-      attribute always has the same name as the parameter and can be referenced using standard python attribute
-      notation (e.g., myComponent.paramter_name).      
+    * By direct assignment to the Component's attribute for the parameter (see below).
     ..
-    * In the `assign_params` method for the component.
+    * In the `assign_params` method for the Component.
     ..
-    * When the component is executed, in the **runtime_params** argument of a call to component's
-      `execute <Mechanism.Mechanism_Base.execute>`
-      COMMENT:
-          or `run <Mechanism.Mechanism_Base.run>` methods
-      COMMENT
-      method (only for a mechanism), or in a tuple with the mechanism where it is specified as part of the
-      `pathway` for a process (see :ref:`Runtime Specification <ParameterState_Runtime_Parameters>` below).
-
-The value specified for the parameter (either explicitly or by default) is assigned as the parameterState's
-`baseValue <ParameterState.baseValue>`, and any projections assigned to it are added to its
-`receiveFromProjections <ParameterState.receivesFromProjections>` attribute. When the parameterState's owner is
-executed, the parameterState's `baseValue <ParameterState.baseValue>` is combined with the value of the projections
-it receives to determine the value of the parameter for which the parameterState is responsible
-(see `ParameterState_Execution` for details).
-
-The specification of a parameter can take any of the following forms:
-
-    * A **value**.  This must be a valid value for the parameter.  it creates a default parameterState,
-      assigns the parameter's default value as the parameterState's `baseValue <ParameterState.baseValue>`,
-      and assigns the parameter's name as the name of the parameterState.
+    * When the Component is executed, in the **runtime_params** argument of a call to component's
+      `execute <Mechanism.Mechanism_Base.execute>` method.
+
+The value specified for a parameter (either explicitly or by default) is assigned to an attribute of the
+Component or its `function <Mechanism.function>` to which the parameter belongs.  The attribute has the same
+name as the parameter, and can be referenced using standard python attribute ("dot") notation;  for example,
+the value of a parameter named *param* is assigned to an attribute named ``param`` that can be referenced as
+``my_component.param``).
+
+When the Component is executed, it updates the ParameterState by calling the ParameterState's
+`function <ParameterState.function>` with the attribute's value for the parameter.  The result is
+assigned as the ParameterState's `value <ParameterState.value>`, which is used by the owner
+of the ParameterState as the value of the correspondign parameter of its own `function <Component.function>`.
+
+.. note::
+   It is important to note the distinction between the `function <ParameterState.function>` of a ParameterState,
+   and the `function <Component.function>` of its owner.**  The former is used to determine the value of a parameter
+   used by the latter (see `figure <ModulatorySignals_Figure>`, and `State_Execution` for additional details).
+
+The specification of the initial value of a parameter can take any of the following forms:
+
+    * A **value**.  This must be a valid value for the parameter.  it creates a default ParameterState,
+      assigns the parameter's default value as the ParameterState's `value <ParameterState.value>`,
+      and assigns the parameter's name as the name of the ParameterState.
     ..
-    * A reference to an existing **parameterState** object.
-      COMMENT:
-      It's name must be the name of a parameter of the
-      owner's ``function``, and its
-      COMMENT
-      Its value must be a valid one for the parameter.
+    * A reference to an existing **ParameterState** object.  It's name must be the name of a parameter of the
+      owner or its `function <Component.function>`, and its value must be a valid one for the parameter.
 
       .. note::
           This capability is provided for generality and potential
           future use, but its current use is not advised.
     ..
-    * A `projection specification <Projection_In_Context_Specification>`.  This creates a default parameterState,
-      assigns the parameter's default value as the parameterState's `baseValue <ParameterState.baseValue>`,
-      and assigns the parameter's name as the name of the parameterState.  It also creates and/or assigns the
-      specified projection, and assigns the parameterState as the projection's
-      `receiver <Projection.Projection.receiver>`.  The projection must be a `ControlProjection` or
-      `LearningProjection`, and its value must be a valid one for the parameter.
+    * A Modulatory specification.  This can be an existing `ControlSignal` or `ControlProjection` object, a
+      `LearningSignal` or `LearningProjection` object, a constructor or the class name for any of these, or the
+      keywords *CONTROL* or *LEARNING*.  Any of these create a default ParameterState, assign the parameter's default
+      value as the ParameterState's `value <ParameterState.value>`, and assign the parameter's name as the name of the
+      ParameterState.  They also create and/or assign the corresponding ModulatorySignal and ModulatoryProjection,
+      and assign the ParameterState as the ModulatoryProjection's `receiver <Projection.Projection.receiver>`.
+      If the ModulatorySignal and/or ModulatoryProjection already exist, their value(s) must be valid one(s) for the
+      parameter.  Note that only Control and Learning Modulatory components can be assigned to a ParameterState
+      (Gating components cannot -- they can only be assgined to InputStates or OutputStates).
     ..
-    * A `ParamValueProjection` or 2-item (value, projection specification) **tuple**.  This creates a default
+    * A 2-item (value, Modulatory specification) **tuple**.  This creates a default
       parameterState, uses the value (1st) item of the tuple as parameterState's
-      `baseValue <ParameterState.baseValue>`, and assigns the parameter's name as the name of the parameterState.
+      `base_value <ParameterState.base_value>`, and assigns the parameter's name as the name of the parameterState.
       The projection (2nd) item of the tuple is used to create and/or assign the specified projection, that is assigned
       the parameterState as its `receiver <Projection.Projection.receiver>`.  The projection must be a
       `ControlProjection` or `LearningProjection`, and its value must be a valid one for the parameter.
 
       .. note::
-          Currently, the :keyword:`function` of a component, although it can be specified a parameter value,
-          cannot be assigned
-          COMMENT:
-          a ControlProjection, LearningProjection, or a runtime specification.
-          COMMENT
-          a ControlProjection or a LearningProjection. This may change in the future.
-
-The **default value** assigned to a parameterState is the default value of the argument for the parameter in the
-constructor for the parameter's owner.  If the value of a parameter is specified as `None`, `NotImplemented`,
-or any other non-numeric value that is not one of those listed above, then no parameter state is created and the
-parameter cannot be modified by a `ControlProjection`, 'LearningProjection', or 
-`runtime specification <ParameterState_Runtime_Parameters>`.
-
-COMMENT:
-    - No parameterState is created for parameters that are:
-       assigned a non-numeric value (including None, NotImplemented, False or True)
-          unless it is:
-              a tuple (could be one specifying ControlProjection, LearningProjection or ModulationOperation)
-              a dict with an entry with the key FUNCTION_PARAMS and a value that is a dict (otherwise exclude)
-       a function
-           IMPLEMENTATION NOTE: FUNCTION_RUNTIME_PARAM_NOT_SUPPORTED
-           (this is because paramInstanceDefaults[FUNCTION] could be a class rather than an bound method;
-           i.e., not yet instantiated;  could be rectified by assignment in _instantiate_function)
-
-    - self.variable must be compatible with self.value (enforced in _validate_variable)
-        note: although it may receive multiple projections, the output of each must conform to self.variable,
-              as they will be combined to produce a single value that must be compatible with self.variable
-COMMENT
+          Currently, the `function <Component.function>` of a Component, although it can be specified as a
+          parameter value, cannot be assigned a `ModulatorySignal` or modified in the **runtime_params** argument of
+          a call to a Mechanism's `execute <Mechanism.execute>` method. This may change in the future.
+
+The **default value** assigned to a ParameterState is the default value of the argument for the corresponding parameter
+in the constructor for the parameter's owner.  If the value of a parameter is specified as `None`, `NotImplemented`,
+or any other non-numeric value that is not one of those listed above, then no ParameterState is created and the
+parameter cannot be modified by a `ModulatorySignal` or in the **runtime_params** argument of a call to a
+Mechanism's `execute <Mechanism.execute>` method.
 
 
 .. _ParameterState_Specification_Examples:
 
-**Examples**
+Examples
+~~~~~~~~
 
-In the following example, a mechanism is created with a function that has four parameters,
-each of which is specified using a different format::
+In the following example, a mechanism is created by specifying two of its parameters, as well as its
+`function <Component.function>` and two of that function's parameters, each using a different specification format::
 
-    my_mechanism = SomeMechanism(function=SomeFunction(param_a=1.0,
-                                                       param_b=(0.5, ControlProjection),
-                                                       param_c=(36, ControlProjection(function=Logistic),
-                                                       param_d=ControlProjection)))
+    my_mechanism = RecurrentTransferMechanism(size=5
+                                              noise=ControlSignal),
+                                              function=Logistic(gain=(0.5, ControlSignal),
+                                                                bias=(1.0, ControlSignal(
+                                                                              modulation=ModulationParam.ADDITIVE))))
 
-The first parameter of the mechanism's function (``param_a``) is assigned a value directly; the second (``param_b``) is
-assigned a value and a ControlProjection; the third (``param_c``) is assigned a value and a
-`ControlProjection with a specified function  <ControlProjection_Structure>`; and the fourth (``param_d``) is
-assigned just a `ControlProjection` (the default value for the parameter will be used).
+The first argument of the constructor for the Mechanism specifies its `size <Component.size>` parameter by
+directly assigning a value to it.  The second specifies the `noise <RecurrentTransferMechanism.noise>` parameter
+by assigning a default `ControlSignal`;  this will use the default value of the
+`noise <RecurrentTransferMechanism.noise>` attribute.  The **function** argument is specified using the constructor for
+a `Logistic` function, that specifies two of its parameters.  The `gain <Logistic.gain>` parameter
+is specified using a tuple, the first item of which is the value to be assigned, and the second that specifies
+a default `ControlSignal`.  The `bias <Logistic.bias>` parameter is also specified using a tuple,
+in this case with a constructor for the ControlSignal that specifies its `modulation <ControlSignal.modulation>`
+parameter.
 
 In the following example, a `MappingProjection` is created, and its
 `matrix <MappingProjection.MappingProjection.matrix>` parameter is assigned a random weight matrix (using a
-`matrix keyword <Matrix_Keywords>`) and `LearningProjection`::
+`matrix keyword <Matrix_Keywords>`) and `LearningSignal`::
 
     my_mapping_projection = MappingProjection(sender=my_input_mechanism,
                                               receiver=my_output_mechanism,
-                                              matrix=(RANDOM_CONNECTIVITY_MATRIX, LearningProjection))
+                                              matrix=(RANDOM_CONNECTIVITY_MATRIX, LearningSignal))
 
 .. note::
    the `matrix <MappingProjection.MappingProjection.matrix>` parameter belongs to the MappingProjection's
@@ -168,201 +156,82 @@
    its arguments are available in the constructor for the projection (see
    `Component_Specifying_Functions_and_Parameters` for a more detailed explanation).
 
-COMMENT:
-    ADD EXAMPLE USING A PARAMS DICT, INCLUDING FUNCTION_PARAMS, AND assign_params
-COMMENT
+The example below shows how to specify the parameters in the first example using a parameter specification dictionary::
+
+    my_mechanism = RecurrentTransferMechanism(
+                              size=5
+                              params={SIZE:5,
+                                      'size':ControlSignal,
+                                      FUNCTION:Logistic,
+                                      FUNCTION_PARAMS:{GAIN:(0.5, ControlSignal),
+                                                       BIAS:(1.0, ControlSignal(modulation=ModulationParam.ADDITIVE))))
+
+There are several things to note here.  First, the parameter specification dictionary must be assigned to the
+**params** argument of the constructor.  Second, both methods for specifying a parameter -- directly in an argument
+for the parameter, or in an entry of a parameter specification dictionary -- can be used within the same constructor.
+If a particular parameter is specified in both ways (as is the case for **size** in the example), the value in the
+parameter specification dictionary takes priority (i.e., it is the value that will be assigned to the parameter).  If
+the parameter is specified in a parameter specification dictionary, the key for the parameter must be a string that is
+the same as the name of parameter (i.e., identical to how it appears as an arg in the constructor; as is shown
+for **size** in the example), or using a keyword that resolves to such a string (as shown for *NOISE* in the
+example).  Finally, the keyword *FUNCTION_PARAMS* can be used in a parameter specification dictionary to specify
+parameters of the Component's `function <Component.function>`, as shown for the **gain** and **bias** parameters of
+the Logistic function in the example.
 
 .. _ParameterState_Structure:
 
 Structure
 ---------
 
-Every parameterState is owned by a `mechanism <Mechanism>` or `MappingProjection`. It can receive one or more
-`ControlProjections <ControlProjection>` or `LearningProjections <LearningProjection>`.  However, the format (the
-number and type of its elements) of each must match the value of the parameter for which the parameterState is
-responsible.  When the parameterState is updated (i.e., the owner is executed) the values of its projections are
-combined (using the  parameterState's `function <ParameterState.function>`) and the result is used to modify the
-parameter for which the parameterState is responsible (see `Execution <ParameterState_Execution>` below).  The
-projections received by a parameterState are listed in its `receiveFromProjections
-<ParameterState.receivesFromProjections>` attribute. Like all PsyNeuLink components, it has the three following core
-attributes:
-
-* `variable <ParameterState.variable>`:  this serves as a template for the `value <Projection.Projection.value>` of
-  each projection that the parameterState receives.  It must match the format (the number and type of elements) of the
-  parameter for which the parameterState is responsible. Any projections the parameterState receives must, it turn,
-  match the format of :keyword:`variable`.
-
-* `function <ParameterState.function>`:  this performs an elementwise (Hadamard) aggregation  of the values of the
-  projections received by the parameterState.  The default function is `LinearCombination` that multiplies the
-  values. A custom function can be specified (e.g., to perform a Hadamard sum, or to handle non-numeric values in
-  some way), so long as it generates a result that is compatible with the `value <ParameterState.value>` of the
-  parameterState.
-
-* `value <ParameterState.value>`:  this is the value assigned to the parameter for which the parameterState is
-  responsible.  It is the `baseValue <ParameterState.baseValue>` of the parameterState, modified by
-  aggregated value of the projections received by the parameterState returned by the
-  `function <ParameterState.function>.
-
-In addition, a parameterState has two other attributes that are used to determine the value it assigns to the
-parameter for which it is responsible (as shown in the `figure <ParameterState_Figure>` below):
-
-.. ParameterState_BaseValue:
-
-* `baseValue <ParameterState.baseValue>`:  this is the default value of the parameter for which the
-  parameterState is responsible.  It is combined with the result of the parameterState's
-  `function <ParameterState.function>` to determine the value of the parameter for which the parameterState is
-  responsible.
-
-.. ParameterState_Parameter_Modulation_Operation:
-
-* `parameterModulationOperation <ParameterState.parameterModulationOperation>`: this determines how the
-  result of the parameterState's `function <ParameterState.function>` (the aggregated values of the projections it
-  receives) is combined with its `baseValue <ParameterState.baseValue>` to generate the value of the parameter
-  for which it is responsible.  This must be a value of `ModulationOperation`.  It can be specified in either
-  the **parameter_modulation_operation** argument of the parameterState's constructor, or in a 
-  PARAMETER_MODULATION_OPERATION entry of a `parameter dictionary <ParameterState_Specifying_Parameters>` in 
-  either the **params** argument of the parameterState's constructor or within a PARAMETER_STATE_PARAMS 
-  dictionary in a `runtime specification <ParameterState_Runtime_Parameters>`. The default is value is
-  `ModulationOperation.PRODUCT`, which multiples the parameterState's `baseValue <ParameterState.baseValue>` by the 
-  aggregated value of the result of the parameterState's `function <ParameterState.function>` to determine the value 
-  of the parameter.
-
-All of the user-modifiable parameters of a component are listed in its `user_params <Component.user_params>` attribute, 
-which is a read-only dictionary with an entry for each parameter.  The parameters of a component can be 
-modified individually by assigning a value to the corresponding attribute, or in groups using the component's 
-`assign_params <Component.assign_params>` method.  The parameters for a component's `function <Component.function>` 
+Every ParameterState is owned by a `Mechanism` or `MappingProjection`. It can receive one or more
+`ControlProjections <ControlProjection>` or `LearningProjections <LearningProjection>`, that are listed in its
+`mod_afferents <ParameterState.mod_afferents>` attribute.  However, the `value <ModulatoryProjection.value>` of each
+must be compatible with (i.e., have the number and type of elements as) the value of the parameter for which the
+ParameterState is responsible.  A ParameterState cannot receive `PathwayProjections <PathwayProjection>` or
+`GatingProjections <GatingProjection>`.  When the ParameterState is updated (i.e., its owner is executed), it uses
+the values of its ControlProjections and LearningProjections to determine whether and how to modify its parameter's
+attribute value, which is then assigned as the ParameterState's `value <ParameterState.value>`
+(see `ParameterState_Execution` for addition details). ParameterStates have the following core attributes:
+
+* `variable <ParameterState.variable>`:  the parameter's attribute value -- that is, the value assigned to the
+  attribute of the ParameterState's owner for the parameter;  it can be thought of as the parameter's "base" value.
+  It is used by `function <ParameterState.function>` to determine the *ParameterState's* `value <ParameterState.value>`.
+  It must match the format (the number and type of elements) of the parameter's attribute value.
+
+* `mod_afferents <ParameterState.mod_afferents>`: lists the `ModulatoryProjections <ModulationProjection>` that
+  project to the ParameterState.  These specify either the value of the ParameterState's
+  `function <ParameterState.funtion>`, the `value <ParameterState.value>` of the ParameterState itself
+  (see `ModulatorySignals_Modulation).
+
+* `function <ParameterState.function>`:  takes `variable <Parameter.variable>` as its input, and the values
+  specified for the function's parameters by any `ModulatoryProjections` (listed in `mod_afferents
+  <ParameterState.mod_afferents>`, and assigns its result to the ParameterState's `value <ParameterState.value>`.
+
+* `value <ParameterState.value>`: the result of `function <ParameterState.function>`; used by the ParameterState's
+  owner as the value of the parameter of its `function <Component.function>` for which the the ParameterState is
+  responsble when the owner executes.
+
+All of the user-modifiable parameters of a Component are listed in its `user_params <Component.user_params>` attribute,
+which is a read-only dictionary with an entry for each parameter.  The parameters of a Component can be
+modified individually by assigning a value to the corresponding attribute, or in groups using the Component's
+`assign_params <Component.assign_params>` method.  The parameters for a Component's `function <Component.function>`
 are listed in its `function_params <Component.function_params>` attribute, which is a read-only dictionary with an 
-entry for each of its function's parameter.  The parameters of a component's function can be modified by
-assigning a value to the corresponding attribute of the component's `function_object <Component.function_object>` 
-attribute (e.g., myMechanism.function_object.my_parameter), or in a FUNCTION_PARAMS dict in `assign_params`.  
-
-.. _ParameterState_Figure:
-
-The figure below shows how the specifications for a parameter are combined by its parameterState to determine the
-parameter's value.
-
-    **How a ParameterState Determines the Value of a Parameter**
-
-    .. figure:: _static/ParameterState_fig_without_runtime_params.pdf
-       :alt: ParameterState
-       :scale: 75 %
-
-       ..
-
-       +--------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------+
-       | Component    | Impact of ParameterState on Parameter Value                                                                                                                  |
-       +==============+==============================================================================================================================================================+
-       | A (brown)    | `baseValue <ParameterState.baseValue>` (default value of the parameter)                                                                                      |
-       +--------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------+
-       | B (blue)     | parameterState's `function <ParameterState.function>` combines `value <Projection.Projection.value>` of projections                                          |
-       +--------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------+
-       | C (green)    | parameterState's `parameterModulationOperation <ParameterState.parameterModulationOperation>` combines projections and `baseValue <ParameterState.baseValue>`|
-       +--------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------+
-
-       In example, the values for the parameters (shown in brown) -- ``param_x`` for the mechanism, and ``param_y``
-       for its ``function`` -- specify the :py:data:`baseValue <ParameterState.baseValue>` of the paramterState for
-       each parameter (labeled "A" in the figure).  These are the values that will be used for
-       those parameters absent any other influences. However, ``param_y`` is assigned a ControlProjection, so its value
-       will also be determined by the value of the ControlProjection to its parameterState;  that will be combined
-       with its `baseValue <ParameterState.baseValue>` using the
-       `parameterModulationOperation <ParameterState.parameterModulationOperation>`
-       specified for the mechanism (``ModulationOperation.SUM``, shown in green, and labeled "C" in the figure). If
-       there had been more than one ControlProjection specified, their values would have been combined using the
-       parameterState's `function <ParameterState.function>` (lableled "B" in the figure), before combining the result
-       with the
-       baseValue.
-
+entry for each of its function's parameter.  The parameters of a Component's function can be modified by
+assigning a value to the corresponding attribute of the Component's `function_object <Component.function_object>`
+attribute (e.g., myMechanism.function_object.my_parameter), or in FUNCTION_PARAMS dict in a
+parameter specification dictionary assigned to the **params** arg of a Component's constructor or its
+`assign_params <Component.assign_params>` method.
 
 .. _ParameterState_Execution:
 
 Execution
 ---------
 
-A parameterState cannot be executed directly.  It is executed when the mechanism to which it belongs is executed.
-When this occurs, the parameterState executes any `ControlProjections` and/or `LearningProjections` it receives, and
-calls its `function <ParameterState.function>` to aggregate their values.  It then combines the result with the
-parameterState's `baseValue <ParameterState.baseValue>` using its
-`parameterModulationOperation <ParameterState.parameterModulationOperation>` attribute, combines the result with any 
-`runtime specification <ParameterState_Runtime_Parameters>` for the parameter using the `ModulationOperation` 
-specified for runtime parameters, and finally assigns the result as the `value <ParameterState.value>` of the 
-parameterState.  This is used as the value of the parameter for which the parameterState is responsible.
-
-.. _ParameterState_Runtime_Parameters:
-
-Runtime Specification of Parameters
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. note::
-   This is an advanced feature, that is generally not required for most applications.
-
-In general, it should not be necessary to modify parameters programmatically each time a process or system is
-executed or run; ordinarily, this should be done using `control projections <ControlProjection>` and/or
-`learning projections <LearningProjection>`.  However, if necessary, it is possible to modify parameters
-"on-the-fly" in two ways:  by specifying runtime parameters for a mechanism as part of a tuple where it is
-specified in the `pathway <Process.Process_Base.pathway>` of a process, or in the
-`execute <Mechanism.Mechanism_Base.execute>`
-COMMENT:
-    or :py:meth:`run <Mechanism.Mechanism_Base.run>` methods
-COMMENT
-method for a mechanism, process or system (see `Mechanism_Runtime_Parameters`).  By default, runtime assignment of
-a parameter value is *one-time*:  that is, it applies only for the round of execution in which it is specified,
-and the parameter's value returns to the default for the instance of the component to which it belongs after
-execution.  The `runtimeParamsStickyAssignmentPref` can be used to specify persistent assignment of a runtime value,
-however in general it is better to modify a parameter's value permantently by assigning the value directly its 
-corresponding attribute, or using the `assign_params` method of its component.
-
-COMMENT:
-    IS THE MECHANISM TUPLE SPECIFICATION ONE TIME OR EACH TIME? <- BUG IN merge_dictionary()
-    IS THE RUN AND EXECUTE SPECIFICATION ONE TRIAL OR ALL TRIALS IN THAT RUN?
-COMMENT
-
-.. note::
-   At this time, runtime specification can be used only  for the parameters of a mechanism or of its ``function``.
-   Since the function itself is not currently assigned a parameterState, it cannot be modified at runtime;  nor is
-   there currently a method for runtime specification for the parameters of a MappingProjection.  These may be
-   supported in the future.
- 
-.. _ParameterState_Runtime_Figure:
-
-COMMENT:
-   XXXXX MAKE SURE ROLE OF ParamModulationOperation FOR runtime params IS EXPLAINED THERE (OR EXPLAIN HERE)
-   XXXX DOCUMENT THAT MOD OP CAN BE SPECIFIED IN A TUPLE WITH PARAM VALUE (INSTEAD OF PROJECTION) AS PER FIGURE?
-COMMENT
-
-The figure below shows how runtime paramter specification combines the others ways to specify a parameter's value:
-
-    .. figure:: _static/ParameterState_fig_with_runtime_params.pdf
-       :alt: ParameterState
-       :scale: 75 %
-
-       ..
-
-       +--------------+--------------------------------------------------------------------+
-       | Component    | Impact of ParameterState on Parameter Value (including at runtime) |
-       +==============+====================================================================+
-       | A (brown)    | ``baseValue`` (default value of parameter``)                       |
-       +--------------+--------------------------------------------------------------------+
-       | B (blue)     | parameterState's ``function`` combines ``value`` of  projections   |
-       +--------------+--------------------------------------------------------------------+
-       | C (green)    | runtime parameter influences projection-modulated ``baseValue``    |
-       +--------------+--------------------------------------------------------------------+
-       | D (violet)   | runtime specification of parameter value                           |
-       +--------------+--------------------------------------------------------------------+
-       | E (red)      | combined projection values modulate ``baseValue``                  |
-       +--------------+--------------------------------------------------------------------+
-       
-       * 1st example:  param_x is given a runtime value (violet) but no runtime ModulationOperation;
-         param_y is given a runtime value (violet) and also a runtime ModulationOperation (red);
-        the parameterState's parameterModulationOperation is set to MULTIPLY (green).
-       ..
-       * 2nd example:  param_x is given a runtime value (violet) and also a runtime ModulationOperation (red);
-         param_y is given a runtime value (violet) but no runtime ModulationOperation;
-         the parameterState's parameterModulationOperation is set to SUM (green)
-
-       COMMENT:
-           NOTES: CAPS FOR PARAM SPECIFICATION IN DICTS -> KEYWORDS
-                  AUGMENT FIGURE TO SHOW PARAM SPECIFICATIONS FOR BOTH THE OBJECT AND ITS FUNCTION
-       COMMENT
+A ParameterState cannot be executed directly.  It is executed when the Mechanism to which it belongs is executed.
+When this occurs, the ParameterState executes any `ModulatoryProjections` it receives, the values of which
+modulate parameters of the ParameterState's `function <ParameterState.function>`.  The ParameterState then calls
+its `function <ParameterState.function>` and the result is used as the value of the parameter of its owner's function
+when that executes.
 
 .. _ParameterState_Class_Reference:
 
@@ -371,9 +240,10 @@
 
 """
 
+from PsyNeuLink.Components.Functions.Function import *
 from PsyNeuLink.Components.States.State import *
 from PsyNeuLink.Components.States.State import _instantiate_state
-from PsyNeuLink.Components.Functions.Function import *
+
 
 class ParameterStateError(Exception):
     def __init__(self, error_value):
@@ -388,9 +258,9 @@ class ParameterState(State_Base):
     ParameterState(                                              \
     owner,                                                       \
     reference_value=None                                         \
-    value=None,                                                  \
     function=LinearCombination(operation=PRODUCT),               \
-    parameter_modulation_operation=ModulationOperation.MULTIPLY, \
+    variable=None,                                               \
+    parameter_modulation_operation=Modulation.MULTIPLY,          \
     params=None,                                                 \
     name=None,                                                   \
     prefs=None)
@@ -411,16 +281,14 @@ class ParameterState(State_Base):
             + classPreferences
             + classPreferenceLevel (PreferenceLevel.Type)
             + paramClassDefaults (dict)
-                + FUNCTION (LinearCombination)
-                + FUNCTION_PARAMS  (Operation.PRODUCT)
+                + FUNCTION (Linear)
                 + PROJECTION_TYPE (CONTROL_PROJECTION)
-                + PARAMETER_MODULATION_OPERATION   (ModulationOperation.MULTIPLY)
             + paramNames (dict)
 
         Class methods
         -------------
             _instantiate_function: insures that function is ARITHMETIC) (default: Operation.PRODUCT)
-            update_state: updates self.value from projections, baseValue and runtime in PARAMETER_STATE_PARAMS
+            update_state: updates self.value from projections, base_value and runtime in PARAMETER_STATE_PARAMS
 
         StateRegistry
         -------------
@@ -429,32 +297,25 @@ class ParameterState(State_Base):
 
     COMMENT
 
-
     Arguments
     ---------
 
-    owner : Mechanism or Projection
-        the `mechanism <Mechanism>` or `projection <Projection>` to which to which the parameterState belongs; it must
-        be specified or determinable from the context in which the parameterState is created. The owner of a
-        parameterState for the parameter of a :keyword:`function` should be specified as the mechanism or projection
+    owner : Mechanism or MappingProjection
+        the `Mechanism` or `MappingProjection` to which to which the ParameterState belongs; it must be specified or
+        determinable from the context in which the ParameterState is created. The owner of a ParameterState for the
+        parameter of a `function <Component.function>` should be specified as the Mechanism or Projection
         to which the function belongs.
 
     reference_value : number, list or np.ndarray
-        specifies the default value of the parameter for which the parameterState is responsible.
+        specifies the default value of the parameter for which the ParameterState is responsible.
 
-    value : number, list or np.ndarray
-        specifies the template for the parametersState's `variable <ParameterState.variable>` (since a parameterState's
-        `variable <ParameterState.variable>` and `value <ParameterState.value>` attributes must have the same format
-        (number and type of elements).
+    variable : number, list or np.ndarray
+        specifies the parameter's initial value and attribute value — that is, the value of the attribute of the
+        ParameterState's owner or its `function <Component.function>` assigned to the parameter.
 
     function : Function or method : default LinearCombination(operation=SUM)
-        specifies the function used to aggregate the values of the projections received by the parameterState.
-        It must produce a result that has the same format (number and type of elements) as its input.
-
-    parameter_modulation_operation : ModulationOperation : default ModulationOperation.MULTIPLY
-        specifies the operation by which the values of the projections received by the parameterState are used
-        to modify its `baseValue <ParameterState.baseValue>` before assigning it as the value of the parameter for
-        which the parameterState is responsible.
+        specifies the function used to convert the parameter's attribute value (same as the ParameterState's
+        `variable <ParameterState.variable>`) to the ParameterState's `value <ParameterState.value>`.
 
     params : Optional[Dict[param keyword, param value]]
         a `parameter dictionary <ParameterState_Specifying_Parameters>` that can be used to specify the parameters for
@@ -475,44 +336,39 @@ class ParameterState(State_Base):
     Attributes
     ----------
 
-    owner : Mechanism
-        the mechanism to which the parameterState belongs.
-
-    receivesFromProjections : Optional[List[Projection]]
-        a list of the projections received by the parameterState (i.e., for which it is a
-        `receiver <Projection.Projection.receiver>`); generally these are `ControlProjection(s) <ControlProjection>`
-        and/or `LearningProjection(s) <LearningProjection>`.
+    owner : Mechanism or MappingProjection
+        the `Mechanism` or `MappingProjection` to which the parameterState belongs.
 
     variable : number, list or np.ndarray
-        the template for the `value <Projection.Projection.value>` of each projection that the parameterState receives,
-        each of which must match the format (number and types of elements) of the parameterState's :keyword:`variable`.
-
-    function : CombinationFunction : default LinearCombination(operation=PRODUCT))
-        performs an element-wise (Hadamard) aggregation  of the `value <Projecction.Projection.value>` of each
-        projection received by the parameterState.
-
-    baseValue : number, list or np.ndarray
-        the default value for the parameterState.  It is combined with the aggregated value of any projections it
-        receives using its `parameterModulationOperation <ParameterState.parameterModulationOperation>`
-        and then assigned to `value <ParameterState.value>`.
-
-    parameterModulationOperation : ModulationOperation : default ModulationOperation.PRODUCT
-        the arithmetic operation used to combine the aggregated value of any projections is receives
-        (the result of the parameterState's `function <ParameterState.function>`) with its
-        `baseValue <ParameterState.baseValue>`, the result of which is assigned to `value <ParameterState.value>`.
+        the parameter's attribute value — that is, the value of the attribute of the
+        ParameterState's owner or its `function <Component.function>` assigned to the parameter.
+
+    mod_afferents : Optional[List[Projection]]
+        a list of the `ModulatoryProjection <ModulatoryProjection>` that project to the ParameterState (i.e.,
+        for which it is a `receiver <Projection.Projection.receiver>`); these can be
+        `ControlProjection(s) <ControlProjection>` and/or `LearningProjection(s) <LearningProjection>`,
+        but not `GatingProjection <GatingProjection>`.  The `value <ModulatoryProjection.value>` of each
+        must match the format (number and types of elements) of the ParameterState's
+        `variable <ParameterState.variable>`.
+
+    function : Function : default Linear
+        converts the parameter's attribute value (same as the ParameterState's `variable <ParameterState.variable>`)
+        to the ParameterState's `value <ParameterState.value>`, under the influence of any
+        `ModulatoryProjections <ModulatoryProjection>` received by the ParameterState (and listed in its
+        `mod_afferents <ParameterState.mod_afferents>` attribute.  The result is assigned as the ParameterState's
+        `value <ParameterState>`.
 
     value : number, list or np.ndarray
-        the aggregated value of the projections received by the ParameterState, combined with the
-        `baseValue <ParameterState.baseValue>` using its
-        `parameterModulationOperation <ParameterState.parameterModulationOperation>`
-        COMMENT:
-        as well as any runtime specification
-        COMMENT
-        .  This is the value assigned to the parameter for which the parameterState is responsible.
+        the result returned by the ParameterState's `function <ParameterState.function>`, and used by the
+        ParameterState's owner or its `function <Component.function>` as the value of the parameter for which the
+        ParmeterState is responsible.  Note that this is not necessarily the same as the parameter's attribute value
+        (that is, the value of the owner's attribute for the parameter), since the ParameterState's
+        `function <ParameterState.function>` may modify the latter under the influence of its
+        `mod_afferents <ParameterState.mod_afferents>`.
 
     name : str : default <State subclass>-<index>
         the name of the inputState.
-        Specified in the `name` argument of the constructor for the outputState.  If not is specified, a default is
+        Specified in the **name** argument of the constructor for the outputState.  If not is specified, a default is
         assigned by the StateRegistry of the mechanism to which the outputState belongs
         (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
 
@@ -524,7 +380,7 @@ class ParameterState(State_Base):
 
     prefs : PreferenceSet or specification dict : State.classPreferences
         the `PreferenceSet` for the inputState.
-        Specified in the `prefs` argument of the constructor for the projection;  if it is not specified, a default is
+        Specified in the **prefs** argument of the constructor for the projection;  if it is not specified, a default is
         assigned using `classPreferences` defined in __init__.py
         (see :doc:`PreferenceSet <LINK>` for details).
 
@@ -552,17 +408,20 @@ def __init__(self,
                  owner,
                  reference_value=None,
                  variable=None,
-                 function=LinearCombination(operation=PRODUCT),
-                 parameter_modulation_operation=ModulationOperation.MULTIPLY,
+                 function=Linear(),
                  params=None,
                  name=None,
                  prefs:is_pref_set=None,
                  context=None):
 
+        # FIX: UPDATED TO INCLUDE LEARNING [CHANGE THIS TO INTEGRATOR FUNCTION??]
+        # # Reassign default for MATRIX param of MappingProjection
+        # if isinstance(owner, MappingProjection) and name is MATRIX:
+        #     function = LinearCombination(operation=SUM)
+
         # Assign args to params and functionParams dicts (kwConstants must == arg names)
         params = self._assign_args_to_param_dicts(function=function,
-                                                 parameter_modulation_operation=parameter_modulation_operation,
-                                                 params=params)
+                                                  params=params)
 
         self.reference_value = reference_value
 
@@ -575,26 +434,20 @@ def __init__(self,
                                              prefs=prefs,
                                              context=self)
 
-        self.parameterModulationOperation = self.paramsCurrent[PARAMETER_MODULATION_OPERATION]
-
     def _validate_params(self, request_set, target_set=None, context=None):
-        """Insure that parameterState (as identified by its name) is for a valid parameter for owner
+        """Insure that parameterState (as identified by its name) is for a valid parameter of the owner
 
         Parameter can be either owner's, or owner's function_object
         """
 
-        # # MODIFIED 11/29/16 OLD:
-        # if not self.name in self.owner.function_params.keys():
-        # MODIFIED 11/29/16 NEW:
         # If the parameter is not in either the owner's user_params dict or its function_params dict, throw exception
         if not self.name in self.owner.user_params.keys() and not self.name in self.owner.function_params.keys():
-        # MODIFIED 11/29/16 END
             raise ParameterStateError("Name of requested parameterState ({}) does not refer to a valid parameter "
                                       "of the component ({}) or it function ({})".
                                       format(self.name,
                                              # self.owner.function_object.__class__.__name__,
                                              self.owner.name,
-                                             self.owner.function_object.componentName))
+                                             self.owner.function.componentName))
 
         super()._validate_params(request_set=request_set, target_set=target_set, context=context)
 
@@ -609,21 +462,8 @@ def _instantiate_function(self, context=None):
         :param context:
         :return:
         """
-
-        # If parameterState is for a matrix of a MappingProjection,
-        #     its parameter_modulation_operation should be SUM (rather than PRODUCT)
-        #         so that weight changes (e.g., from a learningSignals) are added rather than multiplied
-        if self.name == MATRIX:
-            # IMPLEMENT / TEST: 10/20/16 THIS SHOULD BE ABLE TO REPLACE SPECIFICATION IN LEARNING PROJECTION
-            self.params[PARAMETER_MODULATION_OPERATION] = ModulationOperation.ADD
-
         super()._instantiate_function(context=context)
 
-        # Insure that function is LinearCombination
-        if not isinstance(self.function.__self__, (LinearCombination)):
-            raise StateError("Function {0} for {1} of {2} must be of LinearCombination type".
-                                 format(self.function.__self__.componentName, FUNCTION, self.name))
-
         # # Insure that output of function (self.value) is compatible with relevant parameter's reference_value
         if not iscompatible(self.value, self.reference_value):
             raise ParameterStateError("Value ({0}) of the {1} parameterState for the {2} mechanism is not compatible "
@@ -633,119 +473,46 @@ def _instantiate_function(self, context=None):
                                                   self.owner.name,
                                                   self.reference_value))
 
+    def _execute(self, function_params, context):
+        """Call self.function with current parameter value as the variable
 
-    def update(self, params=None, time_scale=TimeScale.TRIAL, context=None):
-        """Parse params for parameterState params and XXX ***
-
-# DOCUMENTATION:  MORE HERE:
-        - get ParameterStateParams
-        - pass params to super, which aggregates inputs from projections
-        - combine input from projections (processed in super) with baseValue using paramModulationOperation
-        - combine result with value specified at runtime in PARAMETER_STATE_PARAMS
-        - assign result to self.value
-
-        :param params:
-        :param time_scale:
-        :param context:
-        :return:
+        Get backingfield ("base") value of param of function of Mechanism to which the ParameterState belongs. 
+        Update its value in call to state's function.
         """
 
-        super().update(params=params,
-                       time_scale=time_scale,
-                       context=context)
-
-        # FIX: REWRITE AS IF FOR EFFICIENCY (SINCE MOST COMMONLY PARAMETER_MODULATION_OPERATION *WON'T* BE SPECIFIED
-        #region COMBINE PROJECTIONS INPUT WITH BASE PARAM VALUE
+        # Most commonly, ParameterState is for the parameter of a function
         try:
-            # Check whether ModulationOperation for projections has been specified at runtime
-            # Note: this is distinct from ModulationOperation for runtime parameter (handled below)
-            self.parameterModulationOperation = self.stateParams[PARAMETER_MODULATION_OPERATION]
-        except (KeyError, TypeError):
-            # If not, try to get from params (possibly passed from projection to ParameterState)
-            try:
-                self.parameterModulationOperation = params[PARAMETER_MODULATION_OPERATION]
-            except (KeyError, TypeError):
-                pass
-            # If not, ignore (leave self.parameterModulationOperation assigned to previous value)
-            pass
+            param_value = getattr(self.owner.function_object, '_'+ self.name)
+            # param_value = self.owner.function_object.params[self.name]
 
-        # If self.value has not been set, assign to baseValue
-        if self.value is None:
-            if not context:
-                context = kwAssign + ' Base Value'
-            else:
-                context = context + kwAssign + ' Base Value'
-            self.value = self.baseValue
+       # Otherwise, should be for an attribute of the ParameterState's owner:
+        except AttributeError:
+            # param_value = self.owner.params[self.name]
+            param_value = getattr(self.owner, '_'+ self.name)
 
-        # Otherwise, combine param's value with baseValue using modulatonOperation
-        else:
-            if not context:
-                context = kwAssign + ' Modulated Value'
-            else:
-                context = context + kwAssign + ' Modulated Value'
-            self.value = self.parameterModulationOperation(self.baseValue, self.value)
-        #endregion
-
-        # FIX: STRIP VALUES OUT OF ARRAY OR LIST OF THAT IS WHAT PARAMETER REQUIRES (USE TYPE-MATCH?)
-        # FIX: DEHACK TEST FOR MATRIX
-        # FIX: MOVE TO PROPERTY
-        # # MODIFIED 2/21/17 NEW: FOR EVC BUT BREAKS LEARNING
-        # # If this parameterState is for a parameter of its owner's function, then assign the value there as well
-        # if self.name in self.owner.function_params and not 'matrix' in self.name:
-        #     param_type = type(getattr(self.owner.function.__self__, self.name))
-        #     # # MODIFIED 4/20/17 OLD:
-        #     # self.owner.function.__self__.paramsCurrent[self.name] = type_match(self.value, param_type)
-        #     # MODIFIED 4/20/17 NEW:
-        #     param_back_field_name = '_' + self.name
-        #     self.owner.function.__self__.paramsCurrent[param_back_field_name] = type_match(self.value, param_type)
-        #     # MODIFIED 4/20/17 END
-
-        #region APPLY RUNTIME PARAM VALUES
-        # If there are not any runtime params, or runtimeParamModulationPref is disabled, return
-        if (not self.stateParams or self.prefs.runtimeParamModulationPref is ModulationOperation.DISABLED):
-            return
+        value = self.function(variable=param_value,
+                              params=function_params,
+                              context=context)
 
-        # Assign class-level pref as default operation
-        default_operation = self.prefs.runtimeParamModulationPref
+        # TEST PRINT
+        # TEST DEBUG MULTILAYER
+        # if MATRIX == self.name:
+        #     print("\n{}\n@@@ WEIGHT CHANGES FOR {} TRIAL {}:\n{}".
+        #           format(self.__class__.__name__.upper(), self.owner.name, CentralClock.trial, value))
 
-        # If there is a runtime param specified, could be a (parameter value, ModulationOperation) tuple
-        try:
-            value, operation = self.stateParams[self.name]
-
-        except KeyError:
-            # No runtime param for this param state
-            return
-
-        except TypeError:
-            # If single ("exposed") value, use default_operation (class-level runtimeParamModulationPref)
-            self.value = default_operation(self.stateParams[self.name], self.value)
-        else:
-            # If tuple, use param-specific ModulationOperation as operation
-            self.value = operation(value, self.value)
-
-        # # MODIFIED 4/20/17 REMOVED (SEEMS NOT TO BE NEEDED)
-        # # MODIFIED 2/21/17 NEW: FOR EVC, BUT BREAKS LEARNING
-        # # If this parameterState is for a parameter of its owner's function, then assign the value there as well
-        # if self.name in self.owner.function_params and not 'matrix' in self.name:
-        # # if self.name in self.owner.function_params:
-        # #     setattr(self.owner.function.__self__, self.name, self.value)
-        #     param_type = type(getattr(self.owner.function.__self__, self.name))
-        #     setattr(self.owner.function.__self__, self.name, type_match(self.value, param_type))
-        TEST = True
-
-        #endregion
+        return value
 
     @property
-    def value(self):
-        return self._value
+    def pathway_projections(self):
+        raise ParameterStateError("PROGRAM ERROR: Attempt to access path_projection for {};"
+                                  "it is a {} which does not have {}s".
+                                  format(self.name, PARAMETER_STATE, TRANSMISSIVE_PROJECTION))
 
-    @value.setter
-    def value(self, assignment):
-        self._value = assignment
-        # # MODIFIED 2/21/17 NEW:
-        # # If this parameterState is for a parameter of its owner's function, then assign the value there as well
-        # if self.name in self.owner.function_params:
-        #     setattr(self.owner.function.__self__, self.name, self.value)
+    @pathway_projections.setter
+    def pathway_projections(self, value):
+        raise ParameterStateError("PROGRAM ERROR: Attempt to assign path_projection to {};"
+                                  "it is a {} which cannot accept {}s".
+                                  format(self.name, PARAMETER_STATE, TRANSMISSIVE_PROJECTION))
 
 
 def _instantiate_parameter_states(owner, context=None):
@@ -757,10 +524,10 @@ def _instantiate_parameter_states(owner, context=None):
 
     """
 
-    # TBI / IMPLEMENT: use specs to implement paramterStates below
+    # TBI / IMPLEMENT: use specs to implement parameterStates below
+
+    owner._parameter_states = ContentAddressableList(ParameterState, name=owner.name+'.parameter_states')
 
-    owner.parameterStates = {}
-    #
     # Check that parameterStates for owner have not been explicitly suppressed (by assigning to None)
     try:
         no_parameter_states = not owner.params[PARAMETER_STATES]
@@ -778,16 +545,12 @@ def _instantiate_parameter_states(owner, context=None):
 
     # Instantiate parameterState for each param in user_params (including all params in function_params dict),
     #     using its value as the state_spec
-    # MODIFIED 4/1/17 OLD:
-    # for param_name, param_value in owner.user_params.items():
-    # MODIFIED 4/1/17 NEW:
     # IMPLEMENTATION NOTE:  Use user_params_for_instantiation since user_params may have been overwritten
     #                       when defaults were assigned to paramsCurrent in Component.__init__,
     #                       (since that will assign values to the properties of each param;
     #                       and that, in turn, will overwrite their current values with the defaults from paramsCurrent)
     for param_name, param_value in owner.user_params_for_instantiation.items():
         _instantiate_parameter_state(owner, param_name, param_value, context=context)
-    # MODIFIED 4/1/17 END
 
 
 def _instantiate_parameter_state(owner, param_name, param_value, context):
@@ -795,15 +558,19 @@ def _instantiate_parameter_state(owner, param_name, param_value, context):
 
     Include ones in owner.user_params[FUNCTION_PARAMS] (nested iteration through that dict)
     Exclude if it is a:
-        parameterState that already exists (e.g., in case of call from Component.assign_params)
+        parameterState that already exists (e.g., in case of a call from Component.assign_params)
         non-numeric value (including None, NotImplemented, False or True)
             unless it is:
-                a tuple (could be on specifying ControlProjection, LearningProjection or ModulationOperation)
+                a tuple (could be on specifying ControlProjection, LearningProjection or Modulation)
                 a dict with the name FUNCTION_PARAMS (otherwise exclude)
         function or method
             IMPLEMENTATION NOTE: FUNCTION_RUNTIME_PARAM_NOT_SUPPORTED
             (this is because paramInstanceDefaults[FUNCTION] could be a class rather than an bound method;
             i.e., not yet instantiated;  could be rectified by assignment in _instantiate_function)
+    # FIX: UPDATE WITH MODULATION_MODS
+    # FIX:    CHANGE TO Integrator FUnction ONCE LearningProjection MODULATES ParameterState Function:
+    If param_name is FUNCTION_PARAMS and param is a matrix (presumably for a MappingProjection) 
+        modify ParameterState's function to be LinearCombination (rather Linear which is the default)
     """
 
 
@@ -820,23 +587,25 @@ def _instantiate_parameter_state(owner, param_name, param_value, context):
     # Only allow a FUNCTION_PARAMS dict
     elif isinstance(param_value, ReadOnlyOrderedDict) and param_name is FUNCTION_PARAMS:
         pass
+    # FIX: UPDATE WITH MODULATION_MODS
+    # WHAT ABOUT GatingProjection??
     # Allow ControlProjection, LearningProjection
     elif isinstance(param_value, Projection):
-        from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection
-        from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection
+        from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
+        from PsyNeuLink.Components.Projections.ModulatoryProjections.LearningProjection import LearningProjection
         if isinstance(param_value, (ControlProjection, LearningProjection)):
             pass
         else:
             return
     # Allow Projection class
     elif inspect.isclass(param_value) and issubclass(param_value, Projection):
-        from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection
-        from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection
+        from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
+        from PsyNeuLink.Components.Projections.ModulatoryProjections.LearningProjection import LearningProjection
         if issubclass(param_value, (ControlProjection, LearningProjection)):
             pass
         else:
             return
-    # Allow tuples (could be spec that includes a projection or ModulationOperation)
+    # Allow tuples (could be spec that includes a projection or Modulation)
     elif isinstance(param_value, tuple):
         # # MODIFIED 4/18/17 NEW:
         # # FIX: EXTRACT VALUE HERE (AS IN Component.__init__?? [4/18/17]
@@ -853,12 +622,15 @@ def _instantiate_parameter_state(owner, param_name, param_value, context):
     else:
         return
 
+    # Assign parameterStates to component for parameters of its function (function_params), except for ones that are:
+    #    - another component
+    #    - a function or method
+    #    - have a value of None (see IMPLEMENTATION_NOTE below)
+    #    - they have the same name as another parameter of the component (raise exception for this)
     if param_name is FUNCTION_PARAMS:
         for function_param_name in param_value.keys():
             function_param_value = param_value[function_param_name]
-            # Assignment of ParameterState for Component objects, function or method are not currently supported
-            if isinstance(function_param_value, (function_type, method_type, Component)):
-                continue
+
             # IMPLEMENTATION NOTE:
             # The following is necessary since, if ANY parameters of a function are specified, entries are made
             #    in the FUNCTION_PARAMS dict of its owner for ALL of the function's params;  however, their values
@@ -871,6 +643,10 @@ def _instantiate_parameter_state(owner, param_name, param_value, context):
             if function_param_value is None:
                 continue
 
+            if not _is_legal_param_value(owner, function_param_value):
+                continue
+
+            # Raise exception if the function parameter's name is the same as one that already exists for its owner
             if function_param_name in owner.user_params:
                 if inspect.isclass(owner.function):
                     function_name = owner.function.__name__
@@ -880,19 +656,26 @@ def _instantiate_parameter_state(owner, param_name, param_value, context):
                                           "with the same name as a parameter of the component itself".
                                           format(function_name, owner.name, function_param_name))
 
+            # Use function_param_value as constraint
+            # IMPLEMENTATION NOTE:  need to copy, since _instantiate_state() calls _parse_state_value()
+            #                       for constraints before state_spec, which moves items to subdictionaries,
+            #                       which would make them inaccessible to the subsequent parse of state_spec
+            from copy import deepcopy
+            constraint_value = deepcopy(function_param_value)
+
+            # Assign parameterState for function_param to the component
             state = _instantiate_state(owner=owner,
                                       state_type=ParameterState,
                                       state_name=function_param_name,
                                       state_spec=function_param_value,
                                       state_params=None,
-                                      constraint_value=function_param_value,
+                                      constraint_value=constraint_value,
                                       constraint_value_name=function_param_name,
                                       context=context)
             if state:
-                owner.parameterStates[function_param_name] = state
-            continue
+                owner._parameter_states[function_param_name] = state
 
-    else:
+    elif _is_legal_param_value(owner, param_value):
         state = _instantiate_state(owner=owner,
                                   state_type=ParameterState,
                                   state_name=param_name,
@@ -902,5 +685,49 @@ def _instantiate_parameter_state(owner, param_name, param_value, context):
                                   constraint_value_name=param_name,
                                   context=context)
         if state:
-            owner.parameterStates[param_name] = state
+            owner._parameter_states[param_name] = state
+
+
+def _is_legal_param_value(owner, value):
+
+    # LEGAL PARAMETER VALUES:
+
+    # lists, arrays numeric values or tuple
+    if is_value_spec(value) or isinstance(value, tuple):
+        return True
+
+    if isinstance(value, dict) and VALUE in value:
+        return True
+
+    # keyword that resolves to one of the above
+    if get_param_value_for_keyword(owner, value) is not None:
+        return True
+
+    # Assignment of ParameterState for Component objects, function or method are not currently supported
+    if isinstance(value, (function_type, method_type, Component)):
+        return False
+
+
+def _get_parameter_state(sender_owner, sender_type, param_name, component):
+    """Return parameterState for named parameter of a mechanism requested by owner
+    """
+
+    # Validate that component is a Mechanism or Projection
+    if not isinstance(component, (Mechanism, Projection)):
+        raise ParameterStateError("Request for {} of a component ({}) that is not a {} or {}".
+                                  format(PARAMETER_STATE, component, MECHANISM, PROJECTION))
+
+    try:
+        return component._parameter_states[param_name]
+    except KeyError:
+        # Check that param (named by str) is an attribute of the mechanism
+        if not (hasattr(component, param_name) or hasattr(component.function_object, param_name)):
+            raise ParameterStateError("{} (in specification of {}  {}) is not an attribute "
+                                        "of {} or its function"
+                                        .format(param_name, sender_type, sender_owner.name, component))
+        # Check that the mechanism has a parameterState for the param
+        if not param_name in component._parameter_states.names:
+            raise ParameterStateError("There is no ParameterState for the parameter ({}) of {} "
+                                        "specified in {} for {}".
+                                        format(param_name, component.name, sender_type, sender_owner.name))
 
diff --git a/PsyNeuLink/Components/States/State.py b/PsyNeuLink/Components/States/State.py
index c3c8691c9ea..b4228373147 100644
--- a/PsyNeuLink/Components/States/State.py
+++ b/PsyNeuLink/Components/States/State.py
@@ -13,25 +13,37 @@
 Overview
 --------
 
-A State provides an interface to a `projection <Projection>`, and represents the `value <Projection>`
-associated with that projection.  There are three types of states, all of which are used by
-`mechanisms <Mechanism>` and one of which is used by `MappingProjections <MappingProjection>`, as summarized below:
-
-* **InputState**:
-     used by a mechanism to represent its input, and as the `receiver` of any incoming
-     `MappingProjections <MappingProjection>`.
-
-* **ParameterState**:
-    * used by a mechanism to represent the value of one of its parameters, or a parameter of its :keyword:`function`,
-      possibly regulated by a `ControlProjection`;
+A State provides an interface to one or more `projections <Projection>`, and receives the `value(s) <Projection>`
+provide by them.  The value of a state can be modulated by a `ModulatoryProjection`. There are three primary types of
+states, all of which are used by `Mechanisms <Mechanism>`, one of which is used by
+`MappingProjections <MappingProjection>`, and all of which are subject to modulation by
+`ModulatorySignals <ModulatorySignal>`, as summarized below:
+
+* `InputState`:
+    used by a mechanism to receive input from `MappingProjections <MappingProjection>`;
+    its value can be modulated by a `GatingSignal`.
+
+* `ParameterState`:
+    * used by a mechanism to represent the value of one of its parameters, or a parameter of its
+      `function <Mechanism.function>`, that can be modulated by a `ControlSignal`;
     * used by a `MappingProjection` to represent the value of its `matrix <MappingProjection.MappingProjection.matrix>`
-    parameter, possibly regulated by a `LearningProjection`.
-
-* **OutputState**:
-    * used by a mechanism to represent its output, and as the `sender <Projection>` of any outgoing projection(s):
-      * `MappingProjection` for a `ProcessingMechanism <ProcessingMechanism>`;
-      * `ControlProjection` for a `ControlMechanism <ControlMechanism>`;
-      * `LearningProjection` for a `MonitoringMechanism <MonitoringMechanism>`.
+      parameter, that can be modulated by a `LearningSignal`.
+
+* `OutputState`:
+    used by a mechanism to send its value to any efferent projections.  For
+    `ProcessingMechanisms <ProcessingMechanism>` these are `PathwayProjections <PathwayProjection>`, most commonly
+    `MappingProjection <MappingProjection>`.  For `ModulatoryMechanisms <ModulatoryMechanism>`, these
+    `ModulatoryProjectios <ModulatoryProjection>` as described below. The `value <OutputState.value> of an
+    OutputState can be modulated by a `GatingSignal`.
+
+* `ModulatorySignal`:
+    used by an `AdaptiveMechanism` to modulate the value of the primary types of states listed above.
+    There are three types of ModulatorySignals:
+    * `LearningSignal`, used by a `LearningMechanism` to modulate the *MATRIX* ParameterState of a `MappingProjection`;
+    * `ControlSignal`, used by a `ControlMechanism` to modulate the `ParameterState` of a `Mechanism`;
+    * `GatingSignal`, used by a `GatingMechanism` to modulate the `InputState` or `OutputState` of a `Mechanism`.
+    Modulation is discussed further `below <State_Modulation>`, and described in detail under
+    `ModulatorySignals <ModulatorySignal_Modulation>`.
 
 .. _State_Creation:
 
@@ -39,42 +51,114 @@
 ----------------
 
 States can be created using the constructor for one of the subclasses.  However, in general, they are created
-automatically by the objects to which they belong, and/or through specification in context (e.g., when
-`specifying the parameters <ParameterState_Specifying_Parameters>` of a mechanism or its function to be controlled,
-or of a `MappingProjections to be learned <MappingProjection_Tuple_Specification>).
+automatically by the objects to which they belong, or by specifying the state in the constructor for the object
+to which it belongs.  For example, `InputStates <InputState>` and `OutputStates <OutputStates>` can be specified,
+in the **input_states** and **output_states** arguments, respectively, of the constructor for a `Mechanism`;
+and a `ParameterState` can be specified in the argument of the constructor for a function of a Mechanism or Projection,
+where its parameters are specified.  A state can be specified in those cases in any of the following forms:
+
+    * an existing **State** object;
+    ..
+    * the name of a **state subclass** (`InputState`, `ParmeterState`, or `OutputState` - a default state of the
+      corresponding type will be created, using a default value for the state that is determined by the context
+      in which it is specified.
+    ..
+    * a **value**.  This creates a default State using the specified value as its default `value <State.value>`.
+    ..
+    * a **state specification dictionary**; every state specification can contain the following *KEY*:<value>
+      entries, in addition to those specific to a particular state subtype (see subtype documentation):
+      ..
+      * *NAME*:<str> - the string is used as the name of the state;
+      ..
+      * *VALUE*:<value> - the value is used as the default value of the state;
+      COMMENT:
+          ..
+          * *PROJECTIONS*:<List> - the list must contain specifications for one or more
+            `projections <Projection_In_Context_Specification> to or from the state,
+            depending the type of state and the context in which it is specified;
+      COMMENT
+      ..
+      * *str*:<List> - the key is used as the name of the state, and the list must contain specifications for
+        one or more `projections <Projection_In_Context_Specification>` to or from the state,
+        depending on the type of state and the context in which it is specified;
+        ..
+    * a **2-item tuple** - the first item must be a value, used as the default value for the state,
+      and the second item must be a specification for a `projection <Projection_In_Context_Specification>`
+      to or from the state, depending on the type of state and the context in which it is specified;
+
+COMMENT:
+*** EXAMPLES HERE
+COMMENT
+
+.. _State_Structure:
 
 Structure
 ---------
 
-Every state is owned by either a `mechanism <Mechanism>` or a `projection <Projection>`. Like all PsyNeuLink
-components, a state has the three following core attributes:
+Every State is owned by either a `Mechanism <Mechanism>` or a `Projection <Projection>`. Like all PsyNeuLink
+components, a State has the three following core attributes:
 
-    * `variable <State.variable>`:  for an `inputState <InputState>` and `parameterState <ParameterState>`,
+    * `variable <State.variable>`:  for an `InputState` and `ParameterState`,
       the value of this is determined by the  value(s) of the projection(s) that it receives (and that are listed in
-      its `receivesFromProjections <State.receivesFromProjections>` attribute).  For an `outputState <OutputState>`,
-      it is the item of the owner mechanism's :keyword:`value` to which the outputState is assigned (specified by the
-      outputStates `index <OutputState_Index>` attribute.
+      its `path_afferents <State.path_afferents>` attribute).  For an `OutputState`, it is the item of the owner
+      mechanism's `value <Mechanism.value>` to which the OutputState is assigned (specified by the OutputState's
+      `index <OutputState_Index>` attribute.
     ..
-    * `function <State.function>`:  for an `inputState <InputState>` and `parameterState <ParameterState>`, this
-      aggregates the values of the projections that the state receives (the default is `LinearCombination` that sums
-      the values).  At present, the `function <OutputState.OutputState.function>` of an outputState is not
-      actively used;  it simply returns the item of the owner mechanism's :keyword:`value` to which the outputState is
-      assigned.
+    * `function <State.function>`:  for an `InputState` this aggregates the values of the projections that the state
+      receives (the default is `LinearCombination` that sums the values), under the potential influence of a
+      `GatingSignal`;  for a `ParameterState`, it determines the value of the associated parameter, under the
+      potential influence of a `ControlSignal` (for a `Mechanism`) or a `LearningSignal` (for a `MappingProjection`);
+      for an OutputState, it conveys the result  of the Mechanism's function to its
+      `output_values <Mechanism.output_values> attribute, under the potential influence of a `GatingSignal`.
+      See  `ModulatorySignals <ModulatorySignal_Structure>` and the `AdaptiveMechanism <AdaptiveMechanism>` associated
+      with each type for a description of how they can be used to modulate the `function <State.function> of a State.
     ..
-    * `value <State.value>`:  for an `inputState <InputState>` and `parameterState <ParameterState>`, this is the
-      aggregated value of the projections it receives.  For an `outputState <OutputState>`, this is th item of the
-      owner mechanism's :keyword:`value` to which the outputState is assigned, possibly modified by a function
-      assigned to its `calculate <OutputState_Calculate>` attribute.
+    * `value <State.value>`:  for an `InputState` this is the aggregated value of the `PathwayProjections` it
+      receives;  for a `ParameterState`, this represents the value of the parameter that will be used by the
+      State's owner or its `function <Component.function>`; for an `OutputState`, it is the item of the  owner
+      Mechanism's `value <Mechanisms.value>` to which the OutputState is assigned, possibly modified by its
+      `calculate <OutputState_Calculate>` attribute and/or a `GatingSignal`, and used as the
+      `value <Projection.value>` of the projections listed in its `efferents <OutputState.path_efferents>` attribute.
+
+.. _State_Modulation:
+
+Modulation
+~~~~~~~~~~
+
+Every type of State has a `mod_afferents <State.mod_afferents>` attribute, that lists the
+`ModulatoryProjections <ModulatoryProjection>` it receives.  Each ModulatoryProjection comes from a `ModulatorySignal`
+that specifies how it should modulate the State's `value <State.value>` when the State is updated (see
+`ModulatorySignal_Modulation` and `ModulatorySignal_Anatomy_Figure).  In most cases, a ModulatorySignal uses the
+State's `function <State.function>` to modulate its `value <State.value>`.  The function of every State assigns one
+of its parameters as its *MULTIPLICATIVE_PARAM* and another as its *MULTIPLICATIVE_PARAM*. The
+`modulation <ModulatorySigal.modulation>` attribute of a ModulatorySignal determines which of these to modify when
+the State uses it `function <State.function>` to calculate its `value  <State.value>`.  However, the ModulatorySignal
+can also be configured to override the State's `value <State.value>` (i.e., assign it directly), or to disable
+modulation, using one of the values of 'ModulationParm` for its `modulation <ModulatorySignal.modulation>` attribute
+(see `ModulatorySignal_Modulation` for a more detailed discussion).
+
+.. _State_Execution:
 
 Execution
 ---------
 
-A state cannot be executed directly.  It is executed when the component to which it belongs is executed.
-When this occurs, each inputState and parameterState belonging to the component executes any projections it receives,
-calls its :keyword:`function` to aggregate their values, and then assigns this as that state's :keyword:`value` --
-this conforms to a "lazy evaluation" protocol (see :ref:`Lazy Evaluation <LINK>` for a more detailed discussion).
-The :keyword:`value` of an outputState is assigned after the :keyword:`function` of the mechanism that owns it has
-been called (see `Mechanism OutputStates <Mechanism_OutputStates>`).
+States cannot be executed.  They are updated when the component to which they belong is executed.  InputStates and
+ParameterStates belonging to a Mechanism are updated before the Mechanism's function is called.  OutputStates
+are updated after the Mechanism's function is called.  When a State is updated, it executes any Projections that
+project to it (listed in its `all_afferents <State.all_afferents>` attribute.  It uses the values it receives from any
+`PathWayProjections` (listed in its `path_afferents` attribute) as the variable for its `function <State.function>`,
+It then executes all of the ModulatoryProjections it receives.  Different ModulatorySignals may call for different
+forms of modulation (see `ModulatorySignal_Modulation`).  Accordingly, it separately sums the values specified
+by any ModulatorySignals for the *MULTIPLICATIVE_PARAM* of its `function <State.function>`, and similarly for the
+*ADDITIVE_PARAM*.  It then applies the summed value for each to the corresponding parameter of its
+`function <State.function>`.  If any of the ModulatorySignals specifies *OVERRIDE*, then the value of that
+ModulatorySignal is used as the State's `value <State.value>`. Finally, the State calls its
+`function <State.function>` to determine its `value <State.value>`.
+
+.. note::
+   The change in the value of a `State` does not occur until the Mechanism to which the state belongs is next
+   executed; This conforms to a "lazy evaluation" protocol (see :ref:`Lazy Evaluation <LINK>` for an explanation
+   of "lazy" updating).
 
 .. _State_Class_Reference:
 
@@ -83,13 +167,31 @@
 
 """
 
-#
-from collections import OrderedDict
-
-import numpy as np
-
+import inspect
+import copy
+import collections
 from PsyNeuLink.Components.Functions.Function import *
-from PsyNeuLink.Globals.Registry import  register_category
+from PsyNeuLink.Components.Functions.Function import _get_modulated_param
+from PsyNeuLink.Components.Projections.Projection import projection_keywords, _is_projection_spec
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
+
+state_keywords = component_keywords.copy()
+state_keywords.update({STATE_VALUE,
+                       STATE_PARAMS,
+                       STATE_PROJECTIONS,
+                       MODULATORY_PROJECTIONS,
+                       PROJECTION_TYPE,
+                       LEARNING_PROJECTION_PARAMS,
+                       LEARNING_SIGNAL_SPECS,
+                       CONTROL_PROJECTION_PARAMS,
+                       CONTROL_SIGNAL_SPECS,
+                       GATING_PROJECTION_PARAMS
+                       })
+
+def _is_state_type (spec):
+    if issubclass(spec, State):
+        return True
+    return False
 
 # Note:  This is created only for assignment of default projection types for each state subclass (see .__init__.py)
 #        Individual stateRegistries (used for naming) are created for each mechanism
@@ -126,19 +228,18 @@ def __str__(self):
 #             # from Components.Defaults import DefaultState
 #             return DefaultState(name, params)
 
+
+
 # DOCUMENT:  INSTANTATION CREATES AN ATTIRBUTE ON THE OWNER MECHANISM WITH THE STATE'S NAME + kwValueSuffix
 #            THAT IS UPDATED BY THE STATE'S value setter METHOD (USED BY LOGGING OF MECHANISM ENTRIES)
 class State_Base(State):
     """
-    State_Base(  \
-    owner        \
-    value=None,  \
-    params=None, \
-    name=None,   \
+    State_Base(        \
+    owner,             \
+    params=None,       \
+    name=None,         \
     prefs=None)
 
-
-
     Abstract class for State.
 
     .. note::
@@ -150,8 +251,8 @@ class State_Base(State):
         Description
         -----------
             Represents and updates the state of an input, output or parameter of a mechanism
-                - receives inputs from projections (self.receivesFromProjections, STATE_PROJECTIONS)
-                - inputStates and parameterStates: combines inputs from all projections (mapping, control or learning)
+                - receives inputs from projections (self.path_afferents, STATE_PROJECTIONS)
+                - input_states and parameterStates: combines inputs from all projections (mapping, control or learning)
                     and uses this as variable of function to update the value attribute
                 - outputStates: represent values of output of function
             Value attribute:
@@ -180,7 +281,7 @@ class State_Base(State):
         Class attributes
         ----------------
             + componentCategory = kwStateFunctionCategory
-            + className = kwState
+            + className = STATE
             + suffix
             + classPreference (PreferenceSet): StatePreferenceSet, instantiated in __init__()
             + classPreferenceLevel (PreferenceLevel): PreferenceLevel.CATEGORY
@@ -244,21 +345,34 @@ class State_Base(State):
     owner : Mechanism or Projection
         object to which the state belongs.
 
-    baseValue : number, list or np.ndarray
+    base_value : number, list or np.ndarray
         value with which the state was initialized.
+    
+    all_afferents : Optional[List[Projection]]
+        list of all Projections received by the State (i.e., for which it is a `receiver <Projection.receiver>`.
+
+    path_afferents : Optional[List[Projection]]
+        list all `PathwayProjections <PathwayProjection>` received by the State.
+        (note:  only `InputStates <InputState> have path_efferents;  the list is empty for other types of States).
 
-    receivesFromProjections : Optional[List[Projection]]
-        list of projections for which the state is a :keyword:`receiver`.
+    mod_afferents : Optional[List[GatingProjection]]
+        list of all `ModulatoryProjections <ModulatoryProjection>` received by the State.
 
-    sendsToProjections : Optional[List[Projection]]
-        list of projections for which the state is a :keyword:`sender`.
+    efferents : Optional[List[Projection]]
+        list of outoging Projections from the State (i.e., for which is a `sender <Projection.sender>`
+        (note:  only `OutputStates <OutputState> have efferents;  the list is empty for other types of States).
+
+    function : TransferFunction : default determined by type
+        used to determine the state's own value from the value of the projection(s) it receives;  the parameters that 
+        the TrasnferFunction identifies as ADDITIVE and MULTIPLICATIVE are subject to modulation by a 
+        `ModualtoryProjection <ModulatoryProjection_Structure>`. 
 
     value : number, list or np.ndarray
         current value of the state (updated by `update <State.update>` method).
 
     name : str : default <State subclass>-<index>
         the name of the state.
-        Specified in the `name` argument of the constructor for the state;  if not is specified,
+        Specified in the **name** argument of the constructor for the state;  if not is specified,
         a default is assigned by StateRegistry based on the states's subclass
         (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
 
@@ -270,7 +384,7 @@ class State_Base(State):
 
     prefs : PreferenceSet or specification dict : State.classPreferences
         the `PreferenceSet` for the state.
-        Specified in the `prefs` argument of the constructor for the projection;  if it is not specified, a default is
+        Specified in the **prefs** argument of the constructor for the projection;  if it is not specified, a default is
         assigned using `classPreferences` defined in __init__.py
         (see :doc:`PreferenceSet <LINK>` for details).
 
@@ -278,9 +392,8 @@ class State_Base(State):
 
     #region CLASS ATTRIBUTES
 
-    kpState = "State"
     componentCategory = kwStateComponentCategory
-    className = kwState
+    className = STATE
     suffix = " " + className
     paramsType = None
 
@@ -294,13 +407,13 @@ class State_Base(State):
     requiredParamClassDefaultTypes.update({FUNCTION_PARAMS : [dict],
                                            PROJECTION_TYPE: [str, Projection]})   # Default projection type
     paramClassDefaults = Component.paramClassDefaults.copy()
-    paramClassDefaults.update({STATE_PROJECTIONS: []})
+    paramClassDefaults.update({STATE_PROJECTIONS:[],
+                               MODULATORY_PROJECTIONS:[]})
     paramNames = paramClassDefaults.keys()
 
-    #endregion
-
+    @tc.typecheck
     def __init__(self,
-                 owner,
+                 owner:tc.any(Mechanism, Projection),
                  variable=None,
                  params=None,
                  name=None,
@@ -330,7 +443,8 @@ def __init__(self,
             - prefs (dict): dictionary containing system preferences (default: Prefs.DEFAULTS)
             - context (str)
             - **kargs (dict): dictionary of arguments using the following keywords for each of the above kargs:
-                + kwStateValue = value
+                # + STATE_VALUE = value
+                + VARIABLE = variable
                 + STATE_PARAMS = params
                 + kwStateName = name
                 + kwStatePrefs = prefs
@@ -341,7 +455,11 @@ def __init__(self,
         """
         if kargs:
             try:
-                variable = kargs[kwStateValue]
+                # # MODIFIED 5/10/17 OLD:
+                # variable = kargs[STATE_VALUE]
+                # MODIFIED 5/10/17 NEW:
+                variable = kargs[VARIABLE]
+                # MODIFIED 5/10/17 END
             except (KeyError, NameError):
                 pass
             try:
@@ -361,21 +479,29 @@ def __init__(self,
             except (KeyError, NameError):
                 pass
 
+        # Enforce that only called from subclass
         if not isinstance(context, State_Base):
             raise StateError("Direct call to abstract class State() is not allowed; "
                                       "use state() or one of the following subclasses: {0}".
                                       format(", ".join("{!s}".format(key) for (key) in StateRegistry.keys())))
 
-        # FROM MECHANISM:
-        # Note: pass name of mechanism (to override assignment of componentName in super.__init__)
+        # Enforce that subclass must implement and _execute method
+        if not hasattr(self, '_execute'):
+            raise StateError("{}, as a subclass of {}, must implement an _execute() method".
+                             format(self.__class__.__name__, STATE))
 
-        # VALIDATE owner
-        if isinstance(owner, (Mechanism, Projection)):
-            self.owner = owner
-        else:
-            raise StateError("\'owner\' argument ({0}) for {1} must be a mechanism or projection".
-                                      format(owner, name))
+        # Assign args to params and functionParams dicts (kwConstants must == arg names)
+        params = self._assign_args_to_param_dicts(params=params)
 
+        # # VALIDATE owner
+        # if isinstance(owner, (Mechanism, Projection)):
+        #     self.owner = owner
+        # else:
+        #     raise StateError("\'owner\' argument ({0}) for {1} must be a mechanism or projection".
+        #                               format(owner, name))
+        self.owner = owner
+
+        # Register state with StateRegistry of owner (mechanism to which the state is being assigned)
         register_category(entry=self,
                           base_class=State_Base,
                           name=name,
@@ -383,8 +509,16 @@ def __init__(self,
                           # sub_group_attr='owner',
                           context=context)
 
-        self.receivesFromProjections = []
-        self.sendsToProjections = []
+        self.path_afferents = []
+        self.mod_afferents = []
+        self.efferents = []
+        self._stateful = False
+
+        self._path_proj_values = []
+        # Create dict with entries for each ModualationParam and initialize - used in update() to coo
+        self._mod_proj_values = {}
+        for attrib, value in get_class_attributes(ModulationParam):
+            self._mod_proj_values[getattr(ModulationParam,attrib)] = []
 
         # VALIDATE VARIABLE, PARAM_SPECS, AND INSTANTIATE self.function
         super(State_Base, self).__init__(variable_default=variable,
@@ -405,27 +539,15 @@ def __init__(self,
             if projections:
                 self._instantiate_projections_to_state(projections=projections, context=context)
 
-# # FIX LOG: EITHER GET RID OF THIS NOW THAT @property HAS BEEN IMPLEMENTED, OR AT LEAST INTEGRATE WITH IT
-#         # add state to KVO observer dict
-#         self.observers = {self.kpState: []}
-#
-# # FIX: WHY IS THIS COMMENTED OUT?  IS IT HANDLED BY SUBCLASSES??
-    # def register_category(self):
-    #     register_mechanism_state_subclass(self)
-
     def _validate_variable(self, variable, context=None):
         """Validate variable and assign validated values to self.variable
 
-        Sets self.baseValue = self.value = self.variable = variable
+        Sets self.base_value = self.value = self.variable = variable
         Insures that it is a number of list or tuple of numbers
 
         This overrides the class method, to perform more detailed type checking
         See explanation in class method.
         Note:  this method (or the class version) is called only if the parameter_validation attribute is True
-
-        :param variable: (anything but a dict) - variable to be validated:
-        :param context: (str)
-        :return none:
         """
 
         super(State,self)._validate_variable(variable, context)
@@ -435,7 +557,9 @@ def _validate_variable(self, variable, context=None):
         else:
             context = context + kwAssign + ' Base Value'
 
-        self.baseValue = self.variable
+        # # MODIFIED 6/1/17 OLD:
+        # self.base_value = self.variable
+        # MODIFIED 6/1/17 END
 
     def _validate_params(self, request_set, target_set=None, context=None):
         """validate projection specification(s)
@@ -450,27 +574,19 @@ def _validate_params(self, request_set, target_set=None, context=None):
                     + PROJECTION_PARAMS:<dict> - must be dict of params for PROJECTION_TYPE
             # IMPLEMENTATION NOTE: TBI - When learning projection is implemented
             # + FUNCTION_PARAMS:  <dict>, every entry of which must be one of the following:
-            #     ParameterState, projection, ParamValueProjection tuple or value
-
-        :param request_set:
-        :param target_set:
-        :param context:
-        :return:
+            #     ParameterState, projection, 2-item tuple or value
         """
 
-        # Check params[STATE_PROJECTIONS] before calling validate_param:
-        # - if projection specification is an object or class reference, needs to be wrapped in a list
-        try:
-            projections = target_set[STATE_PROJECTIONS]
-        except KeyError:
-            # If no projections, ignore (none will be created)
-            projections = None
-        else:
-            # If specification is not a list, wrap it in one:
+        if STATE_PROJECTIONS in target_set:
+            # if projection specification is an object or class reference, needs to be wrapped in a list
             # - to be consistent with paramClassDefaults
             # - for consistency of treatment below
+            projections = target_set[STATE_PROJECTIONS]
             if not isinstance(projections, list):
                 projections = [projections]
+        else:
+            # If no projections, ignore (none will be created)
+            projections = None
 
         super(State, self)._validate_params(request_set, target_set, context=context)
 
@@ -498,33 +614,33 @@ def _instantiate_function(self, context=None):
         This constraint reflects the role of State functions:
             they simply update the value of the State;
             accordingly, their variable and value must be compatible
-
-        :param context:
-        :return:
         """
 
-        is_matrix = False
+        var_is_matrix = False
         # If variable is a matrix (e.g., for the MATRIX parameterState of a MappingProjection),
-        #     it needs to be embedded in a list so that it is properly handled in by LinearCombination
+        #     it needs to be embedded in a list so that it is properly handled by LinearCombination
         #     (i.e., solo matrix is returned intact, rather than treated as arrays to be combined);
         # Notes:
         #     * this is not a problem when LinearCombination is called in state.update(), since that puts
         #         projection values in a list before calling LinearCombination to combine them
         #     * it is removed from the list below, after calling _instantiate_function
+        # FIX: UPDATE WITH MODULATION_MODS REMOVE THE FOLLOWING COMMENT:
         #     * no change is made to PARAMETER_MODULATION_FUNCTION here (matrices may be multiplied or added)
-        #         (that is handled by the indivudal state subclasses (e.g., ADD is enforced for MATRIX parameterState)
-        if (isinstance(self.variable, np.matrix) or
-                (isinstance(self.variable, np.ndarray) and self.variable.ndim >= 2)):
+        #         (that is handled by the individual state subclasses (e.g., ADD is enforced for MATRIX parameterState)
+        if ((inspect.isclass(self.function) and issubclass(self.function, LinearCombination) or
+                 isinstance(self.function, LinearCombination)) and
+                (isinstance(self.variable, np.matrix) or
+                (isinstance(self.variable, np.ndarray) and self.variable.ndim >= 2))):
             self.variable = [self.variable]
-            is_matrix = True
+            var_is_matrix = True
 
         super()._instantiate_function(context=context)
 
         # If it is a matrix, remove from list in which it was embedded after instantiating and evaluating function
-        if is_matrix:
+        if var_is_matrix:
             self.variable = self.variable[0]
 
-        # Insure that output of function (self.value) is compatible with (same format as) its input (self.variable)
+        # Insure that output of the function (self.value) is compatible with (same format as) its input (self.variable)
         #     (this enforces constraint that State functions should only combine values from multiple projections,
         #     but not transform them in any other way;  so the format of its value should be the same as its variable).
         if not iscompatible(self.variable, self.value):
@@ -540,15 +656,21 @@ def _instantiate_function(self, context=None):
                                              self.variable))
 
     def _instantiate_projections_to_state(self, projections, context=None):
-        """Instantiate projections to a state and assign them to self.receivesFromProjections
+        """Instantiate projections to a state and assign them to self.path_afferents
 
-        For each projection spec in STATE_PROJECTIONS, check that it is one or a list of any of the following:
+        For each spec in projections arg, check that it is one or a list of any of the following:
         + Projection class (or keyword string constant for one):
             implements default projection for projection class
         + Projection object:
             checks that receiver is self
             checks that projection function output is compatible with self.value
-        + specification dict:
+        + State object or State class
+            check that it is compatible with (i.e., a legitimate sender for) projection
+            if it is class, instantiate default
+            assign as sender of the projection
+        + Mechanism object:
+            check that it is compatible with (i.e., a legitimate sender for) projection
+        + specification dict (usually from STATE_PROJECTIONS entry of params dict):
             checks that projection function output is compatible with self.value
             implements projection
             dict must contain:
@@ -556,14 +678,21 @@ def _instantiate_projections_to_state(self, projections, context=None):
                 + PROJECTION_PARAMS:<dict> - must be dict of params for PROJECTION_TYPE
         If any of the conditions above fail:
             a default projection is instantiated using self.paramsCurrent[PROJECTION_TYPE]
-        Each projection in the list is added to self.receivesFromProjections
+        For each projection:
+            if it is a MappingProjection, it is added to self.path_afferents
+            if it is a LearningProjection, ControlProjection, or GatingProjection, it is added to self.mod_afferents
         If kwMStateProjections is absent or empty, no projections are created
-
-        :param context: (str)
-        :return state: (State)
         """
 
         from PsyNeuLink.Components.Projections.Projection import Projection_Base
+        from PsyNeuLink.Components.Projections.PathwayProjections.PathwayProjection \
+            import PathwayProjection_Base
+        from PsyNeuLink.Components.Projections.ModulatoryProjections.ModulatoryProjection \
+            import ModulatoryProjection_Base
+        from PsyNeuLink.Components.Projections.ModulatoryProjections.LearningProjection import LearningProjection
+        from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
+        from PsyNeuLink.Components.Projections.ModulatoryProjections.GatingProjection import GatingProjection
+
         # If specification is not a list, wrap it in one for consistency of treatment below
         # (since specification can be a list, so easier to treat any as a list)
         projection_list = projections
@@ -576,14 +705,10 @@ def _instantiate_projections_to_state(self, projections, context=None):
         default_string = ""
         kwDefault = "default "
 
-        # MODIFIED 12/1/16 OLD:
-        # default_projection_type = self.paramsCurrent[PROJECTION_TYPE]
-        # MODIFIED 12/1/16 NEW:
         default_projection_type = self.paramClassDefaults[PROJECTION_TYPE]
-        # MODIFIED 12/1/16 END
 
         # Instantiate each projection specification in the projection_list, and
-        # - insure it is in self.receivesFromProjections
+        # - insure it is in self.path_afferents
         # - insure the output of its function is compatible with self.value
         for projection_spec in projection_list:
 
@@ -594,32 +719,36 @@ def _instantiate_projections_to_state(self, projections, context=None):
                 item_suffix_string = ""
 
 # FIX: FROM HERE TO BOTTOM OF METHOD SHOULD ALL BE HANDLED IN __init__() FOR PROJECTION_SPEC
+# FIX: OR A _parse_projection_spec METHOD
             projection_object = None # flags whether projection object has been instantiated; doesn't store object
             projection_type = None   # stores type of projection to instantiate
+            sender = None
             projection_params = {}
 
-            # INSTANTIATE PROJECTION_SPEC
+            # PARSE AND INSTANTIATE PROJECTION_SPEC --------------------------------------------------------------------
+
             # If projection_spec is a Projection object:
             # - call _check_projection_receiver() to check that receiver is self; if not, it:
             #     returns object with receiver reassigned to self if chosen by user
             #     else, returns new (default) PROJECTION_TYPE object with self as receiver
-            #     note: in that case, projection will be in self.receivesFromProjections list
+            #     note: in that case, projection will be in self.path_afferents list
             if isinstance(projection_spec, Projection_Base):
                 if projection_spec.value is DEFERRED_INITIALIZATION:
-                    from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection
-                    if isinstance(projection_spec, LearningProjection):
-                        # Assign projection to parameterState
-                        self.receivesFromProjections.append(projection_spec)
-                        projection_spec.init_args[kwReceiver] = self
+                    if isinstance(projection_spec, ModulatoryProjection_Base):
+                        # Assign projection to mod_afferents
+                        self.mod_afferents.append(projection_spec)
+                        projection_spec.init_args[RECEIVER] = self
                         # Skip any further initialization for now
-                        #   (remainder will occur as part of deferred init for LearningProjection)
+                        #   (remainder will occur as part of deferred init for
+                        #    ControlProjection or GatingProjection)
                         continue
-                    # Complete init for other projections (e.g., ControlProjection)
+
+                    # Complete init for other (presumably Mapping) projections
                     else:
                         # Assume init was deferred because receiver could not be determined previously
                         #  (e.g., specified in function arg for receiver object, or as standalone projection in script)
                         # Assign receiver to init_args and call _deferred_init for projection
-                        projection_spec.init_args[kwReceiver] = self
+                        projection_spec.init_args[RECEIVER] = self
                         projection_spec.init_args['name'] = self.owner.name+' '+self.name+' '+projection_spec.className
                         # FIX: REINSTATE:
                         # projection_spec.init_args['context'] = context
@@ -638,20 +767,45 @@ def _instantiate_projections_to_state(self, projections, context=None):
                     default_string = kwDefault
 # FIX:  REPLACE DEFAULT NAME (RETURNED AS DEFAULT) PROJECTION_SPEC NAME WITH State'S NAME, LEAVING INDEXED SUFFIX INTACT
 
+            # If projection_spec is a State or State class
+            # - check that it is appropriate for the type of projection
+            # - create default instance if it is a class (it will use deferred_init since owner is not yet known)
+            # - Assign to sender (for assignment as projection's sender below)
+            # - default projection itself will be created below
+            elif (isinstance(projection_spec, State) or
+                          inspect.isclass(projection_spec) and issubclass(projection_spec, State)):
+                # If it is State, get its type (for check below)
+                if isinstance(projection_spec, State):
+                    state_type = type(projection_spec)
+                # If it is State class, instantiate default
+                else:
+                    projection_spec = projection_spec()
+                # Check appropriateness of State
+                _check_projection_sender_compatiability(self, default_projection_type, state_type)
+                # Assign State as projections's sender (for use below)
+                sender = projection_spec
+
+            # If projection_spec is a Mechanism:
+            # - check compatibility with projection's type
+            # - default projection itself will be created below
+            elif isinstance(projection_spec, Mechanism):
+                _check_projection_sender_compatiability(self, default_projection_type, type(projection_spec))
+
             # If projection_spec is a dict:
             # - get projection_type
             # - get projection_params
             # Note: this gets projection_type but does NOT not instantiate projection; so,
-            #       projection is NOT yet in self.receivesFromProjections list
+            #       projection is NOT yet in self.path_afferents list
             elif isinstance(projection_spec, dict):
                 # Get projection type from specification dict
                 try:
                     projection_type = projection_spec[PROJECTION_TYPE]
+                    _check_projection_sender_compatiability(self, default_projection_type, projection_type)
                 except KeyError:
                     projection_type = default_projection_type
                     default_string = kwDefault
                     if self.prefs.verbosePref:
-                        print("{0}{1} not specified in {2} params{3}; default {4} will be assigned".
+                        warnings.warn("{0}{1} not specified in {2} params{3}; default {4} will be assigned".
                               format(item_prefix_string,
                                      PROJECTION_TYPE,
                                      STATE_PROJECTIONS,
@@ -661,8 +815,8 @@ def _instantiate_projections_to_state(self, projections, context=None):
                     # IMPLEMENTATION NOTE:  can add more informative reporting here about reason for failure
                     projection_type, error_str = self._parse_projection_ref(projection_spec=projection_type,
                                                                            context=self)
-                    if error_str:
-                        print("{0}{1} {2}; default {4} will be assigned".
+                    if error_str and self.prefs.verbosePref:
+                        warnings.warn("{0}{1} {2}; default {4} will be assigned".
                               format(item_prefix_string,
                                      PROJECTION_TYPE,
                                      error_str,
@@ -675,7 +829,7 @@ def _instantiate_projections_to_state(self, projections, context=None):
                     projection_params = projection_spec[PROJECTION_PARAMS]
                 except KeyError:
                     if self.prefs.verbosePref:
-                        print("{0}{1} not specified in {2} params{3}; default {4} will be assigned".
+                        warnings.warn("{0}{1} not specified in {2} params{3}; default {4} will be assigned".
                               format(item_prefix_string,
                                      PROJECTION_PARAMS,
                                      STATE_PROJECTIONS, state_name_string,
@@ -684,11 +838,11 @@ def _instantiate_projections_to_state(self, projections, context=None):
 
             # Check if projection_spec is class ref or keyword string constant for one
             # Note: this gets projection_type but does NOT instantiate the projection (that happens below),
-            #       so projection is NOT yet in self.receivesFromProjections list
+            #       so projection is NOT yet in self.path_afferents list
             else:
                 projection_type, err_str = self._parse_projection_ref(projection_spec=projection_spec,context=self)
-                if err_str:
-                    print("{0}{1} {2}; default {4} will be assigned".
+                if err_str and self.verbosePref:
+                    warnings.warn("{0}{1} {2}; default {4} will be assigned".
                           format(item_prefix_string,
                                  PROJECTION_TYPE,
                                  err_str,
@@ -697,13 +851,13 @@ def _instantiate_projections_to_state(self, projections, context=None):
                                  default_projection_type.__class__.__name__))
 
             # If neither projection_object nor projection_type have been assigned, assign default type
-            # Note: this gets projection_type but does NOT not instantiate projection; so,
-            #       projection is NOT yet in self.receivesFromProjections list
+            # Note: this gets projection_type but does NOT instantiate projection; so,
+            #       projection is NOT yet in self.path_afferents list
             if not projection_object and not projection_type:
                     projection_type = default_projection_type
                     default_string = kwDefault
                     if self.prefs.verbosePref:
-                        print("{0}{1} is not a Projection object or specification for one{2}; "
+                        warnings.warn("{0}{1} is not a Projection object or specification for one{2}; "
                               "default {3} will be assigned".
                               format(item_prefix_string,
                                      projection_spec.name,
@@ -711,17 +865,29 @@ def _instantiate_projections_to_state(self, projections, context=None):
                                      default_projection_type.__class__.__name__))
 
             # If projection_object has not been assigned, instantiate projection_type
-            # Note: this automatically assigns projection to self.receivesFromProjections and
-            #       to it's sender's sendsToProjections list:
-            #           when a projection is instantiated, it assigns itself to:
-            #               its receiver's .receivesFromProjections attribute (in Projection._instantiate_receiver)
-            #               its sender's .sendsToProjections list attribute (in Projection._instantiate_sender)
+            # Note: this automatically assigns projection to self.path_afferents and to it's sender's efferents list;
+            #       when a projection is instantiated, it assigns itself to:
+            #           its receiver's .path_afferents attribute (in Projection._instantiate_receiver)
+            #           its sender's .efferents attribute (in Projection._instantiate_sender)
             if not projection_object:
-                projection_spec = projection_type(receiver=self,
-                                                  name=self.owner.name+' '+self.name+' '+projection_type.className,
-                                                  # name=self.owner.name + ' '+projection_type.className,
-                                                  params=projection_params,
-                                                  context=context)
+                kwargs = {RECEIVER:self,
+                          NAME:self.owner.name+' '+self.name+' '+projection_type.className,
+                          PARAMS:projection_params,
+                          CONTEXT:context}
+                # If the projection_spec was a State (see above) and assigned as the sender, assign to SENDER arg
+                if sender:
+                    kwargs.update({SENDER:sender})
+                # If the projection was specified with a keyword or attribute value
+                #     then move it to the relevant entry of the params dict for the projection
+                # If projection_spec was in the form of a matrix keyword, move it to a matrix entry in the params dict
+                if issubclass(projection_type, PathwayProjection_Base) and projection_spec in MATRIX_KEYWORD_SET:
+                    kwargs.update({MATRIX:projection_spec})
+                # If projection_spec was in the form of a ModulationParam value,
+                #    move it to a MODULATION entry in the params dict
+                elif (issubclass(projection_type, ModulatoryProjection_Base) and
+                          isinstance(projection_spec, ModulationParam)):
+                    kwargs[PARAMS].update({MODULATION:projection_spec})
+                projection_spec = projection_type(**kwargs)
 
             # Check that output of projection's function (projection_spec.value is compatible with
             #    variable of the State to which it projects;  if it is not, raise exception:
@@ -732,28 +898,51 @@ def _instantiate_projections_to_state(self, projections, context=None):
             # Initialization of projection is deferred
             if projection_spec.value is DEFERRED_INITIALIZATION:
                 # Assign instantiated "stub" so it is found on deferred initialization pass (see Process)
-                self.receivesFromProjections.append(projection_spec)
+                if isinstance(projection_spec, ModulatoryProjection_Base):
+                    self.mod_afferents.append(projection_spec)
+                else:
+                    self.path_afferents.append(projection_spec)
                 continue
 
-            # Projection specification is valid, so assign projection to State's receivesFromProjections list
-            elif iscompatible(self.variable, projection_spec.value):
-                # This is needed to avoid duplicates, since instantiation of projection (e.g., of ControlProjection)
-                #    may have already called this method and assigned projection to self.receivesFromProjections list
-                if not projection_spec in self.receivesFromProjections:
-                    self.receivesFromProjections.append(projection_spec)
+            # Projection was instantiated, so:
+            #    - validate value
+            #    - assign to State's path_afferents or mod_afferents list
+            # If it is a ModualatoryProjection:
+            #    - check that projection's value is compatible with value of the function param being modulated
+            #    - assign projection to mod_afferents
+            if isinstance(projection_spec, ModulatoryProjection_Base):
+                function_param_value = _get_modulated_param(self, projection_spec).function_param_val
+                # Match the projection's value with the value of the function parameter
+                mod_proj_spec_value = type_match(projection_spec.value, type(function_param_value))
+                # If the match was successful (i.e., they are compatible), assign the projection to mod_afferents
+                if function_param_value is None or iscompatible(function_param_value, mod_proj_spec_value):
+                    # Avoid duplicates, since instantiation of projection (e.g, by Mechanism)
+                    #    may have already called this method and assigned projection to self.mod_afferents
+                    if not projection_spec in self.mod_afferents:
+                        self.mod_afferents.append(projection_spec)
+                    continue
+            # Otherwise:
+            #    - check that projection's value is compatible with the state's variable
+            #    - assign projection to path_afferents
+            else:
+                if iscompatible(self.variable, projection_spec.value):
+                    # This is needed to avoid duplicates, since instantiation of projection (e.g., of ControlProjection)
+                    #    may have already called this method and assigned projection to self.path_afferents list
+                    if not projection_spec in self.path_afferents:
+                        self.path_afferents.append(projection_spec)
+                    continue
 
             # Projection specification is not valid
-            else:
-                raise StateError("{}Output of function for {}{} ( ({})) is not compatible with value of {} ({})".
-                                 format(item_prefix_string,
-                                        default_string,
-                                        projection_spec.name,
-                                        projection_spec.value,
-                                        item_suffix_string,
-                                        self.value))
+            raise StateError("{}Output of function for {}{} ( ({})) is not compatible with value of {} ({})".
+                             format(item_prefix_string,
+                                    default_string,
+                                    projection_spec.name,
+                                    projection_spec.value,
+                                    item_suffix_string,
+                                    self.value))
 
     def _instantiate_projection_from_state(self, projection_spec, receiver, context=None):
-        """Instantiate projection from a state and assign it to self.sendsToProjections
+        """Instantiate outgoing projection from a state and assign it to self.efferents
 
         Check that projection_spec is one of the following:
         + Projection class (or keyword string constant for one):
@@ -769,11 +958,8 @@ def _instantiate_projection_from_state(self, projection_spec, receiver, context=
                 + PROJECTION_PARAMS:<dict> - must be dict of params for PROJECTION_TYPE
         If any of the conditions above fail:
             a default projection is instantiated using self.paramsCurrent[PROJECTION_TYPE]
-        Projection is added to self.sendsToProjections
+        Projection is added to self.efferents
         If kwMStateProjections is absent or empty, no projections are created
-
-        :param context: (str)
-        :return state: (State)
         """
 
         from PsyNeuLink.Components.Projections.Projection import Projection_Base
@@ -791,7 +977,7 @@ def _instantiate_projection_from_state(self, projection_spec, receiver, context=
         # MODIFIED 12/1/16 END
 
         # Instantiate projection specification and
-        # - insure it is in self.sendsToProjections
+        # - insure it is in self.efferents
         # - insure self.value is compatible with the projection's function variable
 
 # FIX: FROM HERE TO BOTTOM OF METHOD SHOULD ALL BE HANDLED IN __init__() FOR PROJECTION_SPEC
@@ -812,7 +998,7 @@ def _instantiate_projection_from_state(self, projection_spec, receiver, context=
         # - call _check_projection_sender() to check that sender is self; if not, it:
         #     returns object with sender reassigned to self if chosen by user
         #     else, returns new (default) PROJECTION_TYPE object with self as sender
-        #     note: in that case, projection will be in self.sendsToProjections list
+        #     note: in that case, projection will be in self.efferents list
         if isinstance(projection_spec, Projection_Base):
             projection_object, default_class_name = self._check_projection_sender(projection_spec=projection_spec,
                                                                                  receiver=receiver,
@@ -832,7 +1018,7 @@ def _instantiate_projection_from_state(self, projection_spec, receiver, context=
         # - get projection_type
         # - get projection_params
         # Note: this gets projection_type but does NOT not instantiate projection; so,
-        #       projection is NOT yet in self.sendsToProjections list
+        #       projection is NOT yet in self.efferents list
         elif isinstance(projection_spec, dict):
             # Get projection type from specification dict
             try:
@@ -874,7 +1060,7 @@ def _instantiate_projection_from_state(self, projection_spec, receiver, context=
 
         # Check if projection_spec is class ref or keyword string constant for one
         # Note: this gets projection_type but does NOT instantiate the projection,
-        #       so projection is NOT yet in self.sendsToProjections list
+        #       so projection is NOT yet in self.efferents list
         else:
             projection_type, err_str = self._parse_projection_ref(projection_spec=projection_spec,context=self)
             if err_str:
@@ -888,7 +1074,7 @@ def _instantiate_projection_from_state(self, projection_spec, receiver, context=
 
         # If neither projection_object nor projection_type have been assigned, assign default type
         # Note: this gets projection_type but does NOT not instantiate projection; so,
-        #       projection is NOT yet in self.receivesFromProjections list
+        #       projection is NOT yet in self.path_afferents list
         if not projection_object and not projection_type:
                 projection_type = default_projection_type
                 default_string = kwDefault
@@ -901,11 +1087,11 @@ def _instantiate_projection_from_state(self, projection_spec, receiver, context=
                                  default_projection_type.__class__.__name__))
 
         # If projection_object has not been assigned, instantiate projection_type
-        # Note: this automatically assigns projection to self.sendsToProjections and
-        #       to it's receiver's receivesFromProjections list:
+        # Note: this automatically assigns projection to self.efferents and
+        #       to it's receiver's afferents list:
         #           when a projection is instantiated, it assigns itself to:
-        #               its receiver's .receivesFromProjections attribute (in Projection._instantiate_receiver)
-        #               its sender's .sendsToProjections list attribute (in Projection._instantiate_sender)
+        #               its receiver's .path_afferents attribute (in Projection._instantiate_receiver)
+        #               its sender's .efferents list attribute (in Projection._instantiate_sender)
         if not projection_object:
             projection_spec = projection_type(sender=self,
                                               receiver=receiver,
@@ -930,12 +1116,12 @@ def _instantiate_projection_from_state(self, projection_spec, receiver, context=
                          projection_spec.name
                          ))
 
-        # If projection is valid, assign to State's sendsToProjections list
+        # If projection is valid, assign to State's efferents list
         else:
             # This is needed to avoid duplicates, since instantiation of projection may have already called this method
-            #    and assigned projection to self.sendsToProjections list
-            if not projection_spec in self.sendsToProjections:
-                self.sendsToProjections.append(projection_spec)
+            #    and assigned projection to self.efferents list
+            if not projection_spec in self.efferents:
+                self.efferents.append(projection_spec)
 
     def _check_projection_receiver(self, projection_spec, messages=None, context=None):
         """Check whether Projection object references State as receiver and, if not, return default Projection object
@@ -974,7 +1160,7 @@ def _check_projection_receiver(self, projection_spec, messages=None, context=Non
             if reassign == 'r':
                 projection_spec.receiver = self
                 # IMPLEMENTATION NOTE: allow the following, since it is being carried out by State itself
-                self.receivesFromProjections.append(projection_spec)
+                self.path_afferents.append(projection_spec)
                 if self.prefs.verbosePref:
                     print("{0} reassigned to {1}".format(projection_spec.name, messages[name]))
                 return (projection_spec, None)
@@ -1028,7 +1214,7 @@ def _check_projection_sender(self, projection_spec, receiver, messages=None, con
             if reassign == 'r':
                 projection_spec.sender = self
                 # IMPLEMENTATION NOTE: allow the following, since it is being carried out by State itself
-                self.sendsToProjections.append(projection_spec)
+                self.efferents.append(projection_spec)
                 if self.prefs.verbosePref:
                     print("{0} reassigned to {1}".format(projection_spec.name, messages[name]))
                 return (projection_spec, None)
@@ -1085,52 +1271,16 @@ def _parse_projection_ref(self,
                 return (projection_spec, None)
             # projection_spec was class but not Projection
             else:
-                return (None, "not a Projection subclass")
-
-# # FIX: NO LONGER USED;  SUPERCEDED BY value setter METHOD ABOVE.  INCOROPRATE VALIDATION THERE??
-#     def add_observer_for_keypath(self, object, keypath):
-#         self.observers[keypath].append(object)
-#
-# # IMPLEMENTATION NOTE:  USE DECORATOR TO MAKE SURE THIS IS CALLED WHENEVER state.value IS ASSIGNED
-#     def set_value(self, new_value):
-#         """Validate value, assign it, and update any observers
-#
-#         Uses valueClassDefault as the template for validating new_value
-#         :param new_value:
-#         :return:
-#         """
-#
-#         # Validate new_value
-#         if self.prefs.paramValidationPref:
-#             if not isinstance(new_value, self.variableInstanceDefault):
-#                 raise StateError("Value {0} of {1} must be of type {2}".
-#                                      format(new_value, self.name, self.variableInstanceDefault))
-#             # Check that each element is a number
-#             for element in new_value:
-#                 if not isinstance(element, numbers.Number):
-#                     raise StateError("Item {0} ({1}) in value of {2} is not a number".
-#                                          format(new_value.index(element), element, self.name))
-#
-#         old_value = self.value
-#
-#         # Set value
-#         self.value = new_value
-#
-#         # Update observers
-#         if self.observers:
-#         # if len(self.observers[self.kpState]):
-#             for observer in self.observers[self.kpState]:
-#                 observer.observe_value_at_keypath(self.kpState, old_value, new_value)
-#
+                return (None, "not a Projection subclass")#
 
 
     def update(self, params=None, time_scale=TimeScale.TRIAL, context=None):
-        """Update each projection, combine them, and assign result to value
+        """Update each projection, combine them, and assign return result
 
-        Call update for each projection in self.receivesFromProjections (passing specified params)
+        Call update for each projection in self.path_afferents (passing specified params)
         Note: only update LearningSignals if context == LEARNING; otherwise, just get their value
         Call self.function (default: LinearCombination function) to combine their values
-        Assign result to self.value
+        Returns combined values of
 
     Arguments:
     - context (str)
@@ -1140,144 +1290,176 @@ def update(self, params=None, time_scale=TimeScale.TRIAL, context=None):
 
     """
 
-        #region GET STATE-SPECIFIC PARAM_SPECS
+        # SET UP -------------------------------------------------------------------------------------------------------
+        # Get state-specific param_specs
         try:
             # Get State params
             self.stateParams = params[self.paramsType]
         except (KeyError, TypeError):
-            self.stateParams = None
+            self.stateParams = {}
         except (AttributeError):
             raise StateError("PROGRAM ERROR: paramsType not specified for {}".format(self.name))
         #endregion
 
-        #region FLAG FORMAT OF INPUT
+        # Flag format of input
         if isinstance(self.value, numbers.Number):
             # Treat as single real value
             value_is_number = True
         else:
             # Treat as vector (list or np.array)
             value_is_number = False
-        #endregion
 
-        #region AGGREGATE INPUT FROM PROJECTION_SPECS
+        # AGGREGATE INPUT FROM PROJECTIONS -----------------------------------------------------------------------------
 
-        #region Get type-specific params from PROJECTION_PARAMS
+        # Get type-specific params from PROJECTION_PARAMS
         mapping_params = merge_param_dicts(self.stateParams, MAPPING_PROJECTION_PARAMS, PROJECTION_PARAMS)
-        control_projection_params = merge_param_dicts(self.stateParams, CONTROL_PROJECTION_PARAMS, PROJECTION_PARAMS)
         learning_projection_params = merge_param_dicts(self.stateParams, LEARNING_PROJECTION_PARAMS, PROJECTION_PARAMS)
+        control_projection_params = merge_param_dicts(self.stateParams, CONTROL_PROJECTION_PARAMS, PROJECTION_PARAMS)
+        gating_projection_params = merge_param_dicts(self.stateParams, GATING_PROJECTION_PARAMS, PROJECTION_PARAMS)
         #endregion
 
-        #region For each projection: get its params, pass them to it, and get the projection's value
-        projection_value_list = []
+        #For each projection: get its params, pass them to it, get the projection's value, and append to relevant list
+        self._path_proj_values = []
+        for value in self._mod_proj_values:
+            self._mod_proj_values[value] = []
+
+        from PsyNeuLink.Components.Process import ProcessInputState
+        from PsyNeuLink.Components.Projections.PathwayProjections.PathwayProjection \
+            import PathwayProjection_Base
+        from PsyNeuLink.Components.Projections.ModulatoryProjections.ModulatoryProjection \
+            import ModulatoryProjection_Base
+        from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
+        from PsyNeuLink.Components.Projections.ModulatoryProjections.LearningProjection import LearningProjection
+        from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
+        from PsyNeuLink.Components.Projections.ModulatoryProjections.GatingProjection import GatingProjection
+
+        # If owner is a Mechanism, get its execution_id
+        if isinstance(self.owner, (Mechanism, Process)):
+            self_id = self.owner._execution_id
+        # If owner is a MappingProjection, get it's sender's execution_id
+        elif isinstance(self.owner, MappingProjection):
+            self_id = self.owner.sender.owner._execution_id
+        else:
+            raise StateError("PROGRAM ERROR: Object ({}) of type {} has a {}, but this is only allowed for "
+                             "Mechanisms and MappingProjections".
+                             format(self.owner.name, self.owner.__class__.__name__, self.__class__.__name__,))
 
-        for projection in self.receivesFromProjections:
 
-            from PsyNeuLink.Components.Process import ProcessInputState
-            from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
-            sender = projection.sender
+        modulatory_override = False
 
-            # MODIFIED 2/19/17 NEW:
-            # Only update if sender has also executed in this round (i.e., has matching execution_id)
-            if isinstance(self.owner, (Mechanism, Process)):
-                if sender.owner._execution_id != self.owner._execution_id:
-                    continue
-            elif isinstance(self.owner, MappingProjection):
-                if sender.owner._execution_id != self.owner.sender.owner._execution_id:
-                    continue
+        # Get values of all Projections
+        for projection in self.all_afferents:
+
+            # Only update if sender has also executed in this round
+            #     (i.e., has same execution_id as owner)
+            # Get sender's execution id
+            if hasattr(projection, 'sender'):
+                sender = projection.sender
             else:
-                raise StateError("PROGRAM ERROR: Object ({}) of type {} has a {}, but this is only allowed for "
-                                 "Mechanisms and MappingProjections".
-                                 format(self.owner.name, self.owner.__class__.__name__, self.__class__.__name__,))
+                if self.verbosePref:
+                    warnings.warn("{} to {} {} of {} ignored [has no sender]".format(projection.__class__.__name__,
+                                                                                     self.name,
+                                                                                     self.__class__.__name__,
+                                                                                     self.owner.name))
+                continue
 
-            # MODIFIED 2/19/17 END
+            sender_id = sender.owner._execution_id
+            if isinstance(sender.owner, Mechanism):
+                if (not sender.owner.ignore_execution_id) and (sender_id != self_id):
+                    continue
+            else:
+                if sender_id != self_id:
+                    continue
 
-            # FIX: FOR EACH PROJECTION TO INPUT_STATE, CHECK IF SENDER IS FROM PROCESS INPUT OR TARGET INPUT
-            # FIX: IF SO, ONLY INCLUDE IF THEY BELONG TO CURRENT PROCESS;
+            # Only accept projections from a Process to which the owner Mechanism belongs
             if isinstance(sender, ProcessInputState):
                 if not sender.owner in self.owner.processes.keys():
                     continue
 
-            from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
-            from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection
-            from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection
-
             # Merge with relevant projection type-specific params
             if isinstance(projection, MappingProjection):
                 projection_params = merge_param_dicts(self.stateParams, projection.name, mapping_params, )
-            elif isinstance(projection, ControlProjection):
-                projection_params = merge_param_dicts(self.stateParams, projection.name, control_projection_params)
             elif isinstance(projection, LearningProjection):
                 projection_params = merge_param_dicts(self.stateParams, projection.name, learning_projection_params)
+            elif isinstance(projection, ControlProjection):
+                projection_params = merge_param_dicts(self.stateParams, projection.name, control_projection_params)
+            elif isinstance(projection, GatingProjection):
+                projection_params = merge_param_dicts(self.stateParams, projection.name, gating_projection_params)
             if not projection_params:
                 projection_params = None
 
-            # MODIFIED 2/21/17 OLD:
-            # Update LearningSignals only if context == LEARNING;  otherwise, just get current value
+            # FIX: UPDATE FOR LEARNING
+            # Update LearningSignals only if context == LEARNING;  otherwise, assign zero for projection_value
             # Note: done here rather than in its own method in order to exploit parsing of params above
-            if isinstance(projection, LearningProjection):
-                if LEARNING in context:
-                    projection_value = projection.execute(time_scale=time_scale,
-                                                          params=projection_params,
-                                                          context=context)
-                else:
-                    projection_value = projection.value
-
+            if isinstance(projection, LearningProjection) and not LEARNING in context:
+                # projection_value = projection.value
+                projection_value = projection.value * 0.0
             else:
-                # Update all non-LearningProjections and get value
                 projection_value = projection.execute(params=projection_params,
                                                       time_scale=time_scale,
                                                       context=context)
-            # # MODIFIED 2/21/17 NEW:
-            # # projection_value = projection.execute(params=projection_params,
-            # #                                       time_scale=time_scale,
-            # #                                       context=context)
-            # if isinstance(projection, LearningProjection):
-            #     projection_value = projection.value
-            # else:
-            #     # Update all non-LearningProjections and get value
-            #     projection_value = projection.execute(params=projection_params,
-            #                                           time_scale=time_scale,
-            #                                           context=context)
-            # MODIFIED 2/21/17 END
 
             # If this is initialization run and projection initialization has been deferred, pass
             if INITIALIZING in context and projection_value is DEFERRED_INITIALIZATION:
                 continue
-            # Add projection_value to list (for aggregation below)
-            projection_value_list.append(projection_value)
-        #endregion
-
-        #region Aggregate projection values
-
-        # If there were projections:
-        if projection_value_list:
-
-            try:
-                # pass only function params
-                function_params = self.stateParams[FUNCTION_PARAMS]
-            except (KeyError, TypeError):
-                function_params = None
 
-            # Combine projection values
-            combined_values = self.function(variable=projection_value_list,
-                                            params=function_params,
-                                            context=context)
-
-            # If self.value is a number, convert combined_values back to number
-            if value_is_number and combined_values:
-                combined_values = combined_values[0]
-
-        # There were no projections
-        else:
-            # mark combined_values as none, so that (after being assigned to self.value)
-            #    it is ignored in execute method (i.e., not combined with baseValue)
-            combined_values = None
-        #endregion
+            if isinstance(projection, PathwayProjection_Base):
+                # Add projection_value to list of TransmissiveProjection values (for aggregation below)
+                self._path_proj_values.append(projection_value)
+
+            # If it is a ModulatoryProjection, add its value to the list in the dict entry for the relevant mod_param
+            elif isinstance(projection, ModulatoryProjection_Base):
+                # Get the meta_param to be modulated from modulation attribute of the  projection's ModulatorySignal
+                #    and get the function parameter to be modulated to type_match the projection value below
+                mod_meta_param, mod_param_name, mod_param_value = _get_modulated_param(self, projection)
+                # If meta_param is DISABLE, ignore the ModulatoryProjection
+                if mod_meta_param is Modulation.DISABLE:
+                    continue
+                if mod_meta_param is Modulation.OVERRIDE:
+                    # If paramValidationPref is set, allow all projections to be processed
+                    #    to be sure there are no other conflicting OVERRIDES assigned
+                    if self.owner.paramValidationPref:
+                        if modulatory_override:
+                            raise StateError("Illegal assignment of {} to more than one {} ({} and {})".
+                                             format(MODULATION_OVERRIDE, MODULATORY_SIGNAL,
+                                                    projection.name, modulatory_override[2]))
+                        modulatory_override = (MODULATION_OVERRIDE, projection_value, projection)
+                        continue
+                    # Otherwise, for efficiency, assign OVERRIDE value to State here and return
+                    else:
+                        self.value = type_match(projection_value, type(self.value))
+                        return
+                else:
+                    mod_value = type_match(projection_value, type(mod_param_value))
+                self._mod_proj_values[mod_meta_param].append(mod_value)
+
+        # Handle ModulatoryProjection OVERRIDE
+        #    if there is one and it wasn't been handled above (i.e., if paramValidation is set)
+        if modulatory_override:
+            self.value = type_match(modulatory_override[1], type(self.value))
+            return
+
+        # AGGREGATE ModulatoryProjection VALUES  -----------------------------------------------------------------------
+
+        # For each modulated parameter of the state's function, 
+        #    combine any values received from the relevant projections into a single modulation value
+        #    and assign that to the relevant entry in the params dict for the state's function.
+        for mod_param, value_list in self._mod_proj_values.items():
+            if value_list:
+                aggregated_mod_val = mod_param.reduce(value_list)
+                function_param = self.function_object.params[mod_param.attrib_name]
+                if not FUNCTION_PARAMS in self.stateParams:
+                    self.stateParams[FUNCTION_PARAMS] = {function_param: aggregated_mod_val}
+                else:
+                    self.stateParams[FUNCTION_PARAMS].update({function_param: aggregated_mod_val})
 
-        #region ASSIGN STATE VALUE
-        context = context + kwAggregate + ' Projection Inputs'
-        self.value = combined_values
-        #endregion
+        # CALL STATE'S function TO GET ITS VALUE  ----------------------------------------------------------------------
+        try:
+            # pass only function params (which implement the effects of any ModulatoryProjections)
+            function_params = self.stateParams[FUNCTION_PARAMS]
+        except (KeyError, TypeError):
+            function_params = None
+        self.value = self._execute(function_params=function_params, context=context)
 
     def execute(self, input=None, time_scale=None, params=None, context=None):
         return self.function(variable=input, params=params, time_scale=time_scale, context=context)
@@ -1323,14 +1505,6 @@ def value(self, assignment):
             self.owner.log.entries[self.name] = LogEntry(CurrentTime(), context, assignment)
             # self.owner.log.entries[self.name] = LogEntry(CentralClock, context, assignment)
 
-    @property
-    def baseValue(self):
-        return self._baseValue
-
-    @baseValue.setter
-    def baseValue(self, value):
-        self._baseValue = value
-
     @property
     def projections(self):
         return self._projections
@@ -1339,15 +1513,10 @@ def projections(self):
     def projections(self, assignment):
         self._projections = assignment
 
-    # @property
-    # def receivesFromProjections(self):
-    #     return self._receivesFromProjections
-    #
-    # @receivesFromProjections.setter
-    # def receivesFromProjections(self, assignment):
-    #     self._receivesFromProjections = assignment
+    @property
+    def all_afferents(self):
+        return self.path_afferents + self.mod_afferents
 
-# **************************************************************************************
 
 def _instantiate_state_list(owner,
                            state_list,              # list of State specs, (state_spec, params) tuples, or None
@@ -1356,13 +1525,18 @@ def _instantiate_state_list(owner,
                            constraint_value,       # value(s) used as default for state and to check compatibility
                            constraint_value_name,  # name of constraint_value type (e.g. variable, output...)
                            context=None):
-    """Instantiate and return an OrderedDictionary of States specified in state_list
+    """Instantiate and return a ContentAddressableList of states specified in state_list
 
     Arguments:
     - state_type (class): State class to be instantiated
     - state_list (list): List of State specifications (generally from owner.paramsCurrent[kw<State>]),
-                         (state_spec, params_dict) tuple(s), or None
-                         if None, instantiate a default using constraint_value as state_spec
+                             each itme of which must be a:
+                                 string (used as name)
+                                 value (used as constraint value)
+                                 # ??CORRECT: (state_spec, params_dict) tuple  
+                                     SHOULDN'T IT BE: (state_spec, projection) tuple?
+                                 dict (key=name, value=constraint_value or param dict) 
+                         if None, instantiate a single default state using constraint_value as state_spec
     - state_param_identifier (str): kw used to identify set of states in params;  must be one of:
         - INPUT_STATE
         - OUTPUT_STATE
@@ -1377,37 +1551,29 @@ def _instantiate_state_list(owner,
 
     If state_list is None:
         - instantiate a default State using constraint_value,
-        - place as the single entry in the OrderedDict
+        - place as the single entry of the list returned.
     Otherwise, if state_list is:
         - a single value:
             instantiate it (if necessary) and place as the single entry in an OrderedDict
         - a list:
-            instantiate each item (if necessary) and place in an OrderedDict
-        - an OrderedDict:
-            instantiate each entry value (if necessary)
-    In each case, generate an OrderedDict with one or more entries, assigning:
-        the key for each entry the name of the outputState if provided,
-            otherwise, use kwMechanism<state_type>States-n (incrementing n for each additional entry)
-        the state value for each entry to the corresponding item of the mechanism's state_type state's value
-        the dict to both self.<state_type>States and paramsCurrent[kwMechanism<state_type>States]
-        self.<state_type>State to self.<state_type>States[0] (the first entry of the dict)
+            instantiate each item (if necessary) and place in a ContentAddressableList
+    In each case, generate a ContentAddressableList with one or more entries, assigning:
+        # the key for each entry the name of the outputState if provided,
+        #     otherwise, use MECHANISM<state_type>States-n (incrementing n for each additional entry)
+        # the state value for each entry to the corresponding item of the mechanism's state_type state's value
+        # the dict to both self.<state_type>States and paramsCurrent[MECHANISM<state_type>States]
+        # self.<state_type>State to self.<state_type>States[0] (the first entry of the dict)
     Notes:
         * if there is only one state, but the value of the mechanism's state_type has more than one item:
             assign it to the sole state, which is assumed to have a multi-item value
         * if there is more than one state:
             the number of states must match length of mechanisms state_type value or an exception is raised
-
-    :param state_type:
-    :param state_param_identifier:
-    :param constraint_value:
-    :param constraint_value_name:
-    :param context:
-    :return:
     """
 
     state_entries = state_list
+    # FIX: INSTANTIATE DICT SPEC BELOW FOR ENTRIES IN state_list
 
-    # If kwMechanism<*>States is None, instantiate a default state_type using constraint_value
+    # If no states were passed in, instantiate a default state_type using constraint_value
     if not state_entries:
         # assign constraint_value as single item in a list, to be used as state_spec below
         state_entries = constraint_value
@@ -1420,8 +1586,8 @@ def _instantiate_state_list(owner,
                                          constraint_value_name,
                                          constraint_value))
 
-    # kwMechanism<*>States should now be either a list (possibly constructed in _validate_params) or an OrderedDict:
-    if isinstance(state_entries, (list, OrderedDict, np.ndarray)):
+    # States should be either in a list, or possibly an np.array (from constraint_value assignment above):
+    if isinstance(state_entries, (ContentAddressableList, list, np.ndarray)):
 
         # VALIDATE THAT NUMBER OF STATES IS COMPATIBLE WITH NUMBER OF CONSTRAINT VALUES
         num_states = len(state_entries)
@@ -1465,18 +1631,19 @@ def _instantiate_state_list(owner,
         # Iterate through list or state_dict:
         # - instantiate each item or entry as state_type State
         # - get name, and use as key to assign as entry in self.<*>states
-        states = OrderedDict()
+        states = ContentAddressableList(component_type=State_Base,
+                                        name=owner.name+' ContentAddressableList of ' + state_param_identifier)
 
         # Instantiate state for entry in list or dict
         # Note: if state_entries is a list, state_spec is the item, and key is its index in the list
-        for key, state_spec in state_entries if isinstance(state_entries, dict) else enumerate(state_entries):
+        for index, state_spec in state_entries if isinstance(state_entries, dict) else enumerate(state_entries):
             state_name = ""
 
-            # State_entries is already an OrderedDict, so use:
-            # - entry key as state's name
+            # State_entries is a dict, so use:
+            # - entry index as state's name
             # - entry value as state_spec
-            if isinstance(key, str):
-                state_name = key
+            if isinstance(index, str):
+                state_name = index
                 state_constraint_value = constraint_value
                 # Note: state_spec has already been assigned to entry value by enumeration above
                 # MODIFIED 12/11/16 NEW:
@@ -1503,7 +1670,7 @@ def _instantiate_state_list(owner,
 
                 # If state_spec is a string, then use:
                 # - string as the name for a default state
-                # - key (index in list) to get corresponding value from constraint_value as state_spec
+                # - index (index in list) to get corresponding value from constraint_value as state_spec
                 # - assign same item of constraint_value as the constraint
                 if isinstance(state_spec, str):
                     # Use state_spec as state_name if it has not yet been used
@@ -1512,9 +1679,23 @@ def _instantiate_state_list(owner,
                     # Add index suffix to name if it is already been used
                     # Note: avoid any chance of duplicate names (will cause current state to overwrite previous one)
                     else:
-                        state_name = state_spec + '_' + str(key)
-                    state_spec = constraint_value[key]
-                    state_constraint_value = constraint_value[key]
+                        state_name = state_spec + '_' + str(index)
+                    state_spec = constraint_value[index]
+                    state_constraint_value = constraint_value[index]
+
+                # FIX: 5/21/17  ADD, AND DEAL WITH state_spec AND state_constraint
+                # elif isinstance(state_spec, dict):
+                #     # If state_spec has NAME entry
+                #     if NAME in state_spec:
+                #         # If it has been used, add suffix to it
+                #         if state_name is state_spec[NAME]:
+                #             state_name = state_spec[NAME] + '_' + str(key)
+                #         # Otherwise, use it
+                #         else:
+                #             state_name = state_spec[NAME]
+                #     state_spec = ??
+                #     state_constraint_value = ??
+
 
                 # If state_spec is NOT a string, then:
                 # - use default name (which is incremented for each instance in register_categories)
@@ -1527,39 +1708,37 @@ def _instantiate_state_list(owner,
                         state_name = 'Default_' + state_param_identifier[:-1]
                     # Add incremented index suffix for each state name
                     else:
-                        state_name = 'Default_' + state_param_identifier[:-1] + "-" + str(key+1)
-                    # MODIFIED 12/11/16 NEW:
+                        state_name = 'Default_' + state_param_identifier[:-1] + "-" + str(index+1)
                     # If it is an "exposed" number, make it a 1d np.array
                     if isinstance(state_spec, numbers.Number):
                         state_spec = np.atleast_1d(state_spec)
-                    # MODIFIED 12/11/16 END
 
-                    state_constraint_value = constraint_value[key]
+                    state_constraint_value = constraint_value[index]
 
             state = _instantiate_state(owner=owner,
-                                                state_type=state_type,
-                                                state_name=state_name,
-                                                state_spec=state_spec,
-                                                state_params=state_params,
-                                                constraint_value=state_constraint_value,
-                                                constraint_value_name=constraint_value_name,
-                                                context=context)
-
-            # Get name of state, and use as key to assign to states OrderedDict
+                                       state_type=state_type,
+                                       state_name=state_name,
+                                       state_spec=state_spec,
+                                       state_params=state_params,
+                                       constraint_value=state_constraint_value,
+                                       constraint_value_name=constraint_value_name,
+                                       context=context)
+
+            # Get name of state, and use as index to assign to states ContentAddressableList
             states[state.name] = state
         return states
 
     else:
-        # This shouldn't happen, as kwMechanism<*>States was validated to be one of the above in _validate_params
-        raise StateError("PROGRAM ERROR: {0} for is not a recognized {1} specification for {2}; "
+        # This shouldn't happen, as MECHANISM<*>States was validated to be one of the above in _validate_params
+        raise StateError("PROGRAM ERROR: {} for {} is not a recognized \'{}\' specification for {}; "
                          "it should have been converted to a list in Mechanism._validate_params)".
-                         format(state_entries, state_param_identifier, owner.__class__.__name__))
+                         format(state_entries, owner.name, state_param_identifier, owner.__class__.__name__))
 
 
-def _instantiate_state(owner,                   # Object to which state will belong
+def _instantiate_state(owner,                  # Object to which state will belong
                       state_type,              # State subclass
-                      state_name,              # Name used to refer to subclass in prompts
-                      state_spec,              # State subclass, object, spec dict or value
+                      state_name,              # Name for state (also used to refer to subclass in prompts)
+                      state_spec,              # State subclass, object, spec dict, tuple, projection, value or str
                       state_params,            # params for state
                       constraint_value,        # Value used to check compatibility
                       constraint_value_name,   # Name of constraint_value's type (e.g. variable, output...)
@@ -1575,6 +1754,12 @@ def _instantiate_state(owner,                   # Object to which state will bel
     + State object:
         check owner is owner (if not, user is given options in _check_state_ownership)
         check compatibility of value with constraint_value
+    + 2-item tuple: (only allowed for ParameterState spec)
+        assign first item to state_spec
+            if it is a string:
+                test if it is a keyword and get its value by calling keyword method of owner's execute method
+                otherwise, return None (suppress assignment of parameterState)
+        assign second item to STATE_PARAMS{STATE_PROJECTIONS:<projection>}
     + Projection object:
         assign constraint_value to value
         assign projection to STATE_PARAMS{STATE_PROJECTIONS:<projection>}
@@ -1582,130 +1767,58 @@ def _instantiate_state(owner,                   # Object to which state will bel
         assign constraint_value to value
         assign projection class spec to STATE_PARAMS{STATE_PROJECTIONS:<projection>}
     + specification dict for State (see XXX for context):
-        check compatibility of kwStateValue with constraint_value
-    + ParamValueProjection tuple: (only allowed for ParameterState spec)
-        assign ParamValueProjection.value to state_spec
-            if it is a string:
-                test if it is a keyword and get its value by calling keyword method of owner's execute method
-                otherwise, return None (suppress assignment of parameterState)
-        assign ParamValueProjection.projection to STATE_PARAMS{STATE_PROJECTIONS:<projection>}
-    + 2-item tuple: (only allowed for ParameterState spec)
-        assign first item to state_spec
-            if it is a string:
-                test if it is a keyword and get its value by calling keyword method of owner's execute method
-                otherwise, return None (suppress assignment of parameterState)
-        assign second item to STATE_PARAMS{STATE_PROJECTIONS:<projection>}
+        check compatibility of STATE_VALUE with constraint_value
     + value:
-        if it is a string:
-            test if it is a keyword and get its value by calling keyword method of owner's execute method
-            otherwise, return None (suppress assignment of parameterState)
-        check compatibility with constraint_value
+        implement default using the value
+    + str:
+        test if it is a keyword and get its value by calling keyword method of owner's execute method
+        # otherwise, return None (suppress assignment of parameterState)
+        otherwise, implement default using the string as its name
+    Check compatibility with constraint_value
     If any of the conditions above fail:
         a default State of specified type is instantiated using constraint_value as value
 
-    If state_params is specified, include with instantiation of state
-
-    :param context: (str)
-    :return state: (State)
+    If state_params is specified, include as params arg with instantiation of state
+    
+    Returns a state or None
     """
 
     # IMPLEMENTATION NOTE: CONSIDER MOVING MUCH IF NOT ALL OF THIS TO State.__init__()
 
-    #region VALIDATE ARGS
+    # FIX: IF VARIABLE IS IN state_params EXTRACT IT AND ASSIGN IT TO constraint_value 5/9/17
+
+    # VALIDATE ARGS
     if not inspect.isclass(state_type) or not issubclass(state_type, State):
-        raise StateError("state_type arg ({0}) to _instantiate_state "
-                             "must be a State subclass".format(state_type))
+        raise StateError("PROGRAM ERROR: state_type arg ({}) for _instantiate_state must be a State subclass".
+                         format(state_type))
     if not isinstance(state_name, str):
-        raise StateError("state_name arg ({0}) to _instantiate_state must be a string".
+        raise StateError("PROGRAM ERROR: state_name arg ({}) for _instantiate_state must be a string".
                              format(state_name))
     if not isinstance(constraint_value_name, str):
-        raise StateError("constraint_value_name arg ({0}) to _instantiate_state must be a string".
+        raise StateError("PROGRAM ERROR: constraint_value_name arg ({}) for _instantiate_state must be a string".
                              format(constraint_value_name))
-    #endregion
-
-    # Assume state is specified as a value, so set state_variable to it; if otherwise, will be overridden below
-    state_variable = state_spec
-
-    if state_params is None:
-        state_params = {}
-
-    # Used locally to report type of specification for State
-    #  if value is not compatible with constraint_value
-    spec_type = None
-
-    #region CHECK FORMAT OF constraint_value AND CONVERT TO SIMPLE VALUE
-
-    # Projection class, object, or keyword: set to paramClassDefaults (of owner or owner's function)
-    # FIX: ADD TEST FOR STR AND IN projection_keywords HERE
-    if (isinstance(constraint_value, (str, Projection)) or
-            (inspect.isclass(constraint_value) and
-                 issubclass(constraint_value, (Projection)))):
-        from PsyNeuLink.Components.Projections.Projection import projection_keywords
-        from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection
-        from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection
-        # Disallow if it is not ControlProjection or a LearningProjection
-        if (constraint_value in projection_keywords or
-                    isinstance(constraint_value, (ControlProjection, LearningProjection)) or
-                    # isinstance(constraint_value, Projection) or
-                    (inspect.isclass(constraint_value) and
-                issubclass(constraint_value, (ControlProjection, LearningProjection)))
-                ):
-            try:
-                constraint_value = owner.paramClassDefaults[state_name]
-            # If parameter is not for owner itself, try owner's function
-            except KeyError:
-                constraint_value = owner.user_params[FUNCTION].paramClassDefaults[state_name]
-
-    # State class: set to variableClassDefault
-    elif inspect.isclass(constraint_value) and issubclass(constraint_value, State):
-            constraint_value = state_spec.variableClassDefault
-
-    # State object: set to its value (i.e., output of its function)
-    elif isinstance(constraint_value, state_type):
-        constraint_value = constraint_value.value
-
-    # Specification dict; presumably it is for a State
-    elif isinstance(constraint_value, dict):
-        constraint_value = constraint_value[kwStateValue]
-
-    # ParamValueProjection tuple, set to ParamValueProjection.value:
-    elif isinstance(constraint_value, ParamValueProjection):
-        constraint_value = constraint_value.value
-
-    # FIX: IS THIS OK?  OR DOES IT TRAP PROJECTION KEYWORD?
-    # keyword; try to resolve to a value, otherwise return None to suppress instantiation of state
-    if isinstance(constraint_value, str):
-        constraint_value = get_param_value_for_keyword(owner, constraint_value)
-        if constraint_value is None:
-            return None
-
-    # function; try to resolve to a value, otherwise return None to suppress instantiation of state
-    if isinstance(constraint_value, function_type):
-        constraint_value = get_param_value_for_function(owner, constraint_value)
-        if constraint_value is None:
-            return None
-
-    # Otherwise, assumed to be a value
-
-    # # MODIFIED 6/14/16: QQQ - WHY DOESN'T THIS WORK HERE?? (DONE BELOW, JUST BEFORE CALLING state = state_type(<>)
-    # # CONVERT constraint_value TO NP ARRAY AS ACTUAL STATE VALUES WILL BE SO CONVERTED (WHERE ??)
-    # #  Convert constraint_value to np.array as actual state value is converted
-    # constraint_value = convert_to_np_array(constraint_value,1)
-    # # MODIFIED END
-
-    #endregion
-
-    #region CHECK COMPATIBILITY OF state_spec WITH constraint_value
-
-    #region State subclass
-    # If state_spec is a subclass:
-    # - instantiate default using constraint_value as value
-    if inspect.isclass(state_spec) and issubclass(state_spec, state_type):
-        state_variable = constraint_value
-    #endregion
 
-    #region State object
-    # If state_spec is a State object:
+    state_params = state_params or {}
+
+    # PARSE constraint_value
+    constraint_dict = _parse_state_spec(owner=owner,
+                                        state_type=state_type,
+                                        state_spec=constraint_value,
+                                        value=None,
+                                        params=None)
+    constraint_value = constraint_dict[VARIABLE]
+    # constraint_value = constraint_dict[VALUE]
+
+    # PARSE state_spec using constraint_value as default for value
+    state_spec = _parse_state_spec(owner=owner,
+                                   state_type=state_type,
+                                   state_spec=state_spec,
+                                   # state_spec=state_spec,
+                                   name=state_name,
+                                   params=state_params,
+                                   value=constraint_value)
+
+    # state_spec is State object
     # - check that its value attribute matches the constraint_value
     # - check that its owner = owner
     # - if either fails, assign default
@@ -1730,194 +1843,26 @@ def _instantiate_state(owner,                   # Object to which state will bel
                                      state_spec.value,
                                      constraint_value,
                                      state_type.__name__))
-            state = constraint_value
-            spec_type = state_name
-    #endregion
-
-    #region Specification dict
-    # If state_spec is a specification dict:
-    # - check that kwStateValue matches constraint_value and assign to state_variable
-    # - add/assign kwState params to state_params
-    if isinstance(state_spec, dict):
-        try:
-            state_variable =  state_spec[kwStateValue]
-        except KeyError:
-            state_variable = constraint_value
-            if owner.prefs.verbosePref:
-                print("{0} missing from inputState specification dict for {1};  default ({2}) will be used".
-                                     format(kwStateValue, owner.name, constraint_value))
-        if not iscompatible(state_variable, constraint_value):
+            # state = constraint_value
             state_variable = constraint_value
-            spec_type = kwStateValue
-
-        # Add state_spec[STATE_PARAMS] to state_params
-        try:
-            state_params.update(state_spec[STATE_PARAMS])
-        # state_spec[STATE_PARAMS] was not specified
-        except KeyError:
-            pass
-        # MODIFIED 12/7/16 OLD:
-        # try:
-        #     state_name = state_spec[NAME]
-        #     del state_spec[NAME]
-        # except KeyError:
-        #     pass
-        # MODIFIED 12/7/16 NEW:
-        # Add state_spec[STATE_PARAMS] to state_params
-        for spec in state_spec:
-            if spec is NAME:
-                # Assign state name
-                state_name = state_spec[spec]
-                # Do not include in params dict for state
-                #  (IMPLEMENTATION NOTE: but don't delete, as apparently still yoked to paramClassDefaults)
-                continue
-            state_params[spec] = state_spec[spec]
-        # MODIFIED 12/7/16 END
 
-    #endregion
+    # Otherwise, state_spec should now be a state specification dict
+    state_variable = state_spec[VARIABLE]
+    state_value = state_spec[VALUE]
 
-    # IMPLEMENTATION NOTE:  CONSOLIDATE ALL THE PROJECTION-RELATED STUFF BELOW:
-
-    # IMPLEMENTATION NOTE:  CONSIDER DEPRECATING THIS
-    #region ParamValueProjection
-    # If state_type is ParameterState and state_spec is a ParamValueProjection tuple:
-    # - check that ParamValueProjection.value matches constraint_value and assign to state_variable
-    # - assign ParamValueProjection.projection to STATE_PARAMS:{STATE_PROJECTIONS:<projection>}
-    # Note: validity of projection specification or compatibility of projection's variable or function output
-    #       with state value is handled in State._instantiate_projections_to_state
-    if isinstance(state_spec, ParamValueProjection):
-        from PsyNeuLink.Components.States.ParameterState import ParameterState
-        if not issubclass(state_type, ParameterState):
-            raise StateError("ParamValueProjection ({0}) not permitted as specification for {1} (in {2})".
-                                 format(state_spec, state_type.__name__, owner.name))
-        state_variable =  state_spec.value
-        # If it is a string, try to resolve as keyword
-        if isinstance(state_variable, str):
-            state_variable = get_param_value_for_keyword(owner, state_variable)
-            if not state_variable:
-                return None
-        if not iscompatible(state_variable, constraint_value):
-            state_variable = constraint_value
-            spec_type = 'ParamValueProjection'
-        state_params.update({STATE_PROJECTIONS:[state_spec.projection]})
-    #endregion
-
-    # FIX: MOVE THIS TO METHOD THAT CAN ALSO BE CALLED BY Function._instantiate_function()
-    PARAM_SPEC = 0
-    PROJECTION_SPEC = 1
-    #region
-    # 2-item tuple (param_value, projection_spec) [convenience notation for projection to parameterState]:
-    # If state_type is ParameterState, and state_spec is a tuple with two items, the second of which is a
-    #    projection specification (MAPPING_PROJECTION, CONTROL_PROJECTION, LEARNING_PROJECTION, CONTROL or LEARNING,
-    #    or class ref to one of those), allow it (though should use ParamValueProjection)
-    # - check that first item matches constraint_value and assign to state_variable
-    # - assign second item as projection to STATE_PARAMS:{STATE_PROJECTIONS:<projection>}
-    # Note: validity of projection specification or compatibility of projection's variable or function output
-    #       with state value is handled in State._instantiate_projections_to_state
-    # IMPLEMENTATION NOTE:
-    #    - need to do some checking on state_spec[PROJECTION_SPEC] to see if it is a projection
-    #      since it could just be a numeric tuple used for the variable of a state;
-    #      could check string against ProjectionRegistry (as done in _parse_projection_ref in State)
-    if (isinstance(state_spec, tuple) and len(state_spec) is 2 and
-            (state_spec[PROJECTION_SPEC] in {MAPPING_PROJECTION,
-                                             CONTROL_PROJECTION,
-                                             LEARNING_PROJECTION,
-                                             CONTROL,
-                                             LEARNING} or
-                 isinstance(state_spec[PROJECTION_SPEC], Projection) or
-                 (inspect.isclass(state_spec[PROJECTION_SPEC]) and issubclass(state_spec[PROJECTION_SPEC], Projection)))
-        ):
-        from PsyNeuLink.Components.States.ParameterState import ParameterState
-        if not issubclass(state_type, ParameterState):
-            raise StateError("Tuple with projection spec ({0}) not permitted as specification "
-                                      "for {1} (in {2})".format(state_spec, state_type.__name__, owner.name))
-        state_variable =  state_spec[PARAM_SPEC]
-        projection_to_state = state_spec[PROJECTION_SPEC]
-        # If it is a string, assume it is a keyword and try to resolve to value
-        if isinstance(state_variable, str):
-            # Evaluate keyword to get template for state_variable
-            state_variable = get_param_value_for_keyword(owner, state_variable)
-            if state_variable is None:
-                return None
-        # If it is a function, call to resolve to value
-        if isinstance(state_variable, function_type):
-            state_variable = get_param_value_for_function(owner, state_variable)
-            if state_variable is None:
-                return None
-
-        constraint_value = state_variable
-        # if not iscompatible(state_variable, constraint_value):
-        #     state_variable = constraint_value
-        #     spec_type = 'ParamValueProjection'
-        state_params.update({STATE_PROJECTIONS:[projection_to_state]})
-    #endregion
-
-    #region Keyword String
-    if isinstance(state_spec, str):
-        if state_spec in projection_keywords:
-            state_spec = state_variable
-            state_variable = constraint_value
-        else:
-            state_spec = get_param_value_for_keyword(owner, state_spec)
-            if state_spec is None:
-                return None
-    #endregion
-
-    #region Function
-    if isinstance(state_spec, function_type):
-        state_spec = get_param_value_for_function(owner, state_spec)
-        if state_spec is None:
-            return None
-    #endregion
-
-    # Projection
-    # If state_spec is a Projection object or Projection class
-    # - assign constraint_value to state_variable
-    # - assign ParamValueProjection.projection to STATE_PARAMS:{STATE_PROJECTIONS:[<projection>]}
-    # Note: validity of projection specification or compatibility of projection's variable or function output
-    #       with state value is handled in State._instantiate_projections_to_state
-    try:
-        # Projection class
-        issubclass(state_spec, Projection)
-        state_variable =  constraint_value
-        state_params.update({STATE_PROJECTIONS:[state_spec]})
-    except TypeError:
-        # Projection object
-        if isinstance(state_spec, (Projection, str)):
-            state_variable =  constraint_value
-            state_params.update({STATE_PROJECTIONS:[state_spec]})
-
-    # FIX:  WHEN THERE ARE MULTIPLE STATES, LENGTH OF constraint_value GROWS AND MISMATCHES state_variable
-    # IMPLEMENT:  NEED TO CHECK FOR ITEM OF constraint_value AND CHECK COMPATIBLITY AGAINST THAT
-    #         # Do one last check for compatibility of value with constraint_value (in case state_spec was a value)
-    if not iscompatible(state_variable, constraint_value):
-        # FIX:  IMPLEMENT TEST OF constraint_index HERE 5/26/16
-        # pass
+    # Check that it's variable is compatible with constraint_value, and if not, assign the latter as default variable
+    if constraint_value is not None and not iscompatible(state_variable, constraint_value):
+        if owner.prefs.verbosePref:
+            print("{} is not compatible with constraint value ({}) specified for {} of {};  latter will be used".
+                  format(VARIABLE, constraint_value, state_type, owner.name))
         state_variable = constraint_value
-        spec_type = state_name
-    #endregion
+    # else:
+    #     constraint_value = state_variable
 
-    #region WARN IF DEFAULT (constraint_value) HAS BEEN ASSIGNED
-    # spec_type has been assigned, so iscompatible() failed above and constraint value was assigned
-    if spec_type:
-        if owner.prefs.verbosePref:
-            print("Value ({0}) of {1} (type: {2}) is not compatible with {3} ({4}) of {6};"
-                  " default {4} will be created using {5}".
-                  format(state_variable,
-                         state_name,
-                         spec_type,
-                         constraint_value_name,
-                         constraint_value.__class__.__name__,
-                         constraint_value,
-                         owner.__class__.__name__))
-    #endregion
-    #endregion
-
-    #region INSTANTIATE STATE:
-    # Instantiate new State
+    # INSTANTIATE STATE:
     # Note: this will be either a default State instantiated using constraint_value as its value
     #       or one determined by a specification dict, depending on which of the following obtained above:
-    # - state_spec was a ParamValueProjection tuple
+    # - state_spec was a 2-item tuple
     # - state_spec was a specification dict
     # - state_spec was a value
     # - value of specified State was incompatible with constraint_value
@@ -1926,15 +1871,15 @@ def _instantiate_state(owner,                   # Object to which state will bel
     # - setting prefs=NotImplemented causes TypeDefaultPreferences to be assigned (from ComponentPreferenceSet)
     # - alternative would be prefs=owner.prefs, causing state to inherit the prefs of its owner;
 
-    #  Convert constraint_value to np.array to match state_variable (which, as output of function, will be np.array)
+    #  Convert constraint_value to np.array to match state_variable (which, as output of function, will be an np.array)
     constraint_value = convert_to_np_array(constraint_value,1)
 
     # Implement default State
     state = state_type(owner=owner,
                        reference_value=constraint_value,
                        variable=state_variable,
-                       name=state_name,
-                       params=state_params,
+                       name=state_spec[NAME],
+                       params=state_spec[PARAMS],
                        prefs=None,
                        context=context)
 
@@ -1947,8 +1892,6 @@ def _instantiate_state(owner,                   # Object to which state will bel
 #                NOT SURE WHAT THE PURPOSE IS
 #     setattr(owner, state.name+'.value', state.value)
 
-    #endregion
-
     return state
 
 def _check_parameter_state_value(owner, param_name, value):
@@ -2018,3 +1961,288 @@ def _check_state_ownership(owner, param_name, mechanism_state):
         mechanism_state.owner = owner
     return mechanism_state
 
+
+def _check_projection_sender_compatiability(owner, projection_type, sender_type):
+    from PsyNeuLink.Components.States.OutputState import OutputState
+    from PsyNeuLink.Components.States.ModulatorySignals.LearningSignal import LearningSignal
+    from PsyNeuLink.Components.States.ModulatorySignals.ControlSignal import ControlSignal
+    from PsyNeuLink.Components.States.ModulatorySignals.GatingSignal import GatingSignal
+    from PsyNeuLink.Components.Projections.ModulatoryProjections.LearningProjection import LearningProjection
+    from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
+    from PsyNeuLink.Components.Projections.ModulatoryProjections.GatingProjection import GatingProjection
+
+    if issubclass(projection_type, MappingProjection) and issubclass(sender_type, OutputState):
+        return
+    if issubclass(projection_type, LearningProjection) and issubclass(sender_type, LearningSignal):
+        return
+    if issubclass(projection_type, ControlProjection) and issubclass(sender_type, ControlSignal):
+        return
+    if issubclass(projection_type, GatingProjection) and issubclass(sender_type, GatingSignal):
+        return
+    else:
+        raise StateError("Illegal specification of a {} for {} of {}".
+                         format(sender_type, owner.__class__.__name__, owner.owner.name))
+
+
+# FIX 5/23/17:  UPDATE TO ACCOMODATE (param, ControlSignal) TUPLE
+@tc.typecheck
+def _parse_state_spec(owner,
+                      state_type:_is_state_type,
+                      state_spec,
+                      name:tc.optional(str)=None,
+                      variable=None,
+                      value=None,
+                      # projections:tc.any(list, bool)=[],
+                      # modulatory_projections:tc.any(list,bool)=[],
+                      params=None,
+                      force_dict=False):
+
+    """Return either state object or state specification dict for state_spec
+    
+    If state_spec is or resolves to a state object:
+        if force_dict is False:  return state object 
+        if force_dict is True: parse into state specification_dictionary 
+            (replacing any components with their id to avoid problems with deepcopy)   
+    Otherwise, return state specification dictionary using arguments provided as defaults
+    Warn if variable is assigned the default value, and verbosePref is set on owner.
+    **value** arg should generally be a constraint for the value of the state;  
+        if state_spec is a Projection, and method is being called from:
+            InputState, value should be the projection's value; 
+            ParameterState, value should be the projection's value; 
+            OutputState, value should be the projection's variable
+    Any entries with keys other than XXX are moved to entries of the dict in the PARAMS entry
+    """
+
+    from PsyNeuLink.Components.Projections.Projection import projection_keywords
+
+    # # IMPLEMENTATION NOTE:  ONLY CALLED IF force_dict=True;  CAN AVOID BY NEVER SETTING THAT OPTION TO True
+    # #                       STILL NEEDS WORK: SEEMS TO SET PARAMS AND SO CAUSES CALL TO assign_params TO BAD EFFECT
+    # # Get convert state object into state specification dictionary,
+    # #     replacing any set, dict or Component with its id to avoid problems with deepcopy
+    # @tc.typecheck
+    # def _state_dict(state:State):
+    #     @tc.typecheck
+    #     # Checks if item is Component and returns its id if it is
+    #     def filter_params(item):
+    #         if isinstance(item, (set, dict, Component)):
+    #             item = id(item)
+    #         return item
+    #     if hasattr(state, 'params') and state.params:
+    #         for param in state.params:
+    #             if isinstance(param, collections.Iterable) and not isinstance(param, str):
+    #                 for index, item in param if isinstance(param, dict) else enumerate(param):
+    #                     state.params[param][index] = filter_params(item)
+    #             else:
+    #                 state.params[param] = filter_params(state.params[param])
+    #     return dict(**{NAME:state.name,
+    #                   VARIABLE:state.variable,
+    #                   VALUE:state.value,
+    #                   # PARAMS:{STATE_PROJECTIONS:state.pathway_projections}})
+    #                   PARAMS:state.params})
+
+    # Validate that state_type is a State class
+    if not inspect.isclass(state_type) or not issubclass(state_type, State):
+        raise StateError("\'state_type\' arg ({}) must be a sublcass of {}".format(state_type,
+                                                                                   State.__name__))
+    state_type_name = state_type.__name__
+
+    # State object:
+    # - check that it is of the specified type and, if so:
+    #     - if force_dict is False, return the primary state object
+    #     - if force_dict is True, get state's attributes and return their values in a state specification dictionary
+
+    if isinstance(state_spec, State):
+        if isinstance(state_spec, state_type):
+            # if force_dict:
+            #     return _state_dict(state_spec)
+            # else:
+            #     return state_spec
+            return state_spec
+        else:
+            raise StateError("PROGRAM ERROR: state_spec specified as class ({}) that does not match "
+                             "class of state being instantiated ({})".format(state_spec, state_type_name))
+
+    # Mechanism object:
+    # - call owner to get primary state of specified type;
+    # - if force_dict is False, return the primary state object
+    # - if force_dict is True, get primary state's attributes and return their values in a state specification dict
+    if isinstance(state_spec, Mechanism):
+        primary_state = owner._get_primary_state(state_type)
+        # if force_dict:
+        #     return _state_dict(primary_state)
+        # else:
+        #     return primary_state
+        return primary_state
+
+    # # Avoid modifying any objects passed in via state_spec
+    # state_spec = copy.deepcopy(state_spec)
+
+    # params = params or {}
+
+    if params:
+        # If variable is specified in state_params, use that
+        if VARIABLE in params and params[VARIABLE] is not None:
+            variable = params[VARIABLE]
+
+    # Create default dict for return
+    state_dict = {NAME: name,
+                  VARIABLE: variable,
+                  VALUE: value,
+                  PARAMS: params}
+
+    # State class
+    if inspect.isclass(state_spec) and issubclass(state_spec, State):
+        if state_spec is state_type:
+            state_dict[VARIABLE] = state_spec.variableClassDefault
+        else:
+            raise StateError("PROGRAM ERROR: state_spec specified as class ({}) that does not match "
+                             "class of state being instantiated ({})".format(state_spec, state_type_name))
+
+    # # State object [PARSED INTO DICT HERE]
+    # elif isinstance(state_spec, State):
+    #     if state_spec is state_type:
+    #         name = state_spec.name
+    #         # variable = state_spec.value
+    #         # variable = state_spec.variableClassDefault
+    #         variable = state_spec.variable
+    #         value = state_spec.value
+    #         modulatory_projections =  state_spec.mod_projections
+    #         params = state_spec.user_params.copy()
+    #     else:
+    #         raise StateError("PROGRAM ERROR: state_spec specified as class ({}) that does not match "
+    #                          "class of state being instantiated ({})".format(state_spec, state_type))
+
+    # Specification dict
+    # - move any entries other than for standard args into dict in params entry
+    elif isinstance(state_spec, dict):
+        # Dict has a single entry in which the key is not a recognized keyword,
+        #    so assume it is of the form {<STATE_NAME>:<STATE SPECIFICATION DICT>}:
+        #    assign STATE_NAME as name, and return parsed SPECIFICATION_DICT
+        if len(state_spec) == 1 and list(state_spec.keys())[0] not in (state_keywords | STANDARD_ARGS):
+            name, state_spec = list(state_spec.items())[0]
+            state_dict = _parse_state_spec(owner=owner,
+                                           state_type=state_type,
+                                           state_spec=state_spec,
+                                           name=name,
+                                           variable=variable,
+                                           value=value,
+                                           # projections=projections,
+                                           # modulatory_projections=modulatory_projections,
+                                           # params=params)
+                                           )
+            if state_dict[PARAMS]:
+                params = params or {}
+                # params.update(state_dict[PARAMS])
+                # Use name specified as key in state_spec (overrides one in SPEFICATION_DICT if specified):
+                state_dict[PARAMS].update(params)
+
+        else:
+            # Warn if VARIABLE was not in dict
+            if not VARIABLE in state_spec and owner.prefs.verbosePref:
+                print("{} missing from specification dict for {} of {};  default ({}) will be used".
+                      format(VARIABLE, state_type, owner.name, state_spec))
+            # Move all params-relevant entries from state_spec into params
+            for spec in [param_spec for param_spec in state_spec.copy()
+                         if not param_spec in STANDARD_ARGS]:
+                params = params or {}
+                params[spec] = state_spec[spec]
+                # MODIFIED 6/5/17 OLD: [REINSTATED, BUT CAUSING TROUBLE IN STROOP TEST SCRIPT]
+                del state_spec[spec]
+                # MODIFIED 6/5/17 END
+            state_dict.update(state_spec)
+            # state_dict = state_spec
+            if params:
+                if state_dict[PARAMS] is None:
+                    state_dict[PARAMS] = {}
+                state_dict[PARAMS].update(params)
+
+    # # 2-item tuple (spec, projection)
+    # 2-item tuple (spec, Component)
+    elif isinstance(state_spec, tuple):
+        if len(state_spec) != 2:
+            raise StateError("Tuple provided as state_spec for {} of {} ({}) must have exactly two items".
+                             format(state_type_name, owner.name, state_spec))
+        # IMPLEMENTATION NOTE: Mechanism allowed in tuple to accomodate specification of param for ControlSignal
+        if not (_is_projection_spec(state_spec[1]) or isinstance(state_spec[1], (Mechanism, State))):
+            raise StateError("2nd item of tuple in state_spec for {} of {} ({}) must be a specification "
+                             "for a mechanism, state, or projection".
+                             format(state_type_name, owner.__class__.__name__, state_spec[1]))
+        # Put projection spec from second item of tuple in params
+        params = params or {}
+        # FIX 5/23/17: NEED TO HANDLE NON-MODULATORY PROJECTION SPECS
+        params.update({STATE_PROJECTIONS:[state_spec[1]]})
+
+        # Parse state_spec in first item of tuple (without params)
+        state_dict = _parse_state_spec(owner=owner,
+                                       state_type=state_type,
+                                       state_spec=state_spec[0],
+                                       name=name,
+                                       variable=variable,
+                                       value=value,
+                                       # projections=projections,
+                                       params={})
+        # Add params (with projection spec) to any params specified in first item of tuple
+        if state_dict[PARAMS] is None:
+            state_dict[PARAMS] = {}
+        state_dict[PARAMS].update(params)
+
+    # Projection class, object, or keyword:
+    #     set variable to value and assign projection spec to STATE_PROJECTIONS entry in params
+    # IMPLEMENTATION NOTE:  It is the caller's responsibility to assign the value arg
+    #                           appropriately for the state being requested, for:
+    #                               InputState, projection's value;
+    #                               ParameterState, projection's (= parameter's) value;
+    #                               OutputState, projection's variable .
+    # Don't allow matrix keywords -- force them to be converted from a string into a value (below)
+    elif _is_projection_spec(state_spec, include_matrix_keywords=False):
+        # state_spec = state_variable
+        state_dict[VARIABLE] =  value
+        if state_dict[PARAMS] is None:
+            state_dict[PARAMS] = {}
+        state_dict[PARAMS].update({STATE_PROJECTIONS:[state_spec]})
+
+    # string (keyword or name specification)
+    elif isinstance(state_spec, str):
+        # Check if it is a keyword
+        spec = get_param_value_for_keyword(owner, state_spec)
+        # A value was returned, so use value of keyword as variable
+        if spec is not None:
+            state_dict[VARIABLE] = spec
+            if owner.prefs.verbosePref:
+                print("{} not specified for {} of {};  default ({}) will be used".
+                      format(VARIABLE, state_type, owner.name, value))
+        # It is not a keyword, so treat string as the name for the state
+        else:
+            state_dict[NAME] = state_spec
+
+    # function; try to resolve to a value, otherwise return None to suppress instantiation of state
+    elif isinstance(state_spec, function_type):
+        state_dict[VALUE] = get_param_value_for_function(owner, state_spec)
+        if state_dict[VALUE] is None:
+            # return None
+            raise StateError("PROGRAM ERROR: state_spec for {} of {} is a function ({}), "
+                             "but it failed to return a value".format(state_type_name, owner.name, state_spec))
+
+    # value, so use as variable of input_state
+    elif is_value_spec(state_spec):
+        state_dict[VARIABLE] = state_spec
+        state_dict[VALUE] = state_spec
+
+    elif state_spec is None:
+        # pass
+        raise StateError("PROGRAM ERROR: state_spec for {} of {} is None".format(state_type_name, owner.name))
+
+    else:
+        if name and hasattr(owner, name):
+            owner_name = owner.name
+        else:
+            owner_name = owner.__class__.__name__
+        raise StateError("PROGRAM ERROR: state_spec for {} of {} is an unrecognized specification ({})".
+                         format(state_type_name, owner.name, state_spec))
+
+
+    # If variable is none, use value:
+    if state_dict[VARIABLE] is None:
+        state_dict[VARIABLE] = state_dict[VALUE]
+
+    return state_dict
diff --git a/PsyNeuLink/Components/System.py b/PsyNeuLink/Components/System.py
index bc86b9032ef..b5aa5e19ff0 100644
--- a/PsyNeuLink/Components/System.py
+++ b/PsyNeuLink/Components/System.py
@@ -41,7 +41,7 @@
     and assign the result as their output.
 
 * `AdpativeMechanism`
-    These are used to adjust the operation of other components.  There are two types:  
+    These are used to adjust the operation of other components.  There are two types:
     `LearningMechanisms <LearningMechanism>` that adjust projections, and `ControlMechanisms <ControlMechanism>`
     that adjust the parameters of other mechanisms and/or their functions.
 
@@ -50,19 +50,19 @@
 Creating a System
 -----------------
 
-Systems are created by calling :py:func:`system`.  If no arguments are provided, a system with a single process 
-containing a single :ref:`default mechanism <LINK>` will be created.  More generally, a system is 
+Systems are created by calling :py:func:`system`.  If no arguments are provided, a system with a single process
+containing a single :ref:`default mechanism <LINK>` will be created.  More generally, a system is
 created from one or more `processes <Process>` that are specified in the **processes** argument of its constructor.
-Whenever a system is created, a `ControlMechanism <ControlMechanism>` is created for it and assigned as its 
-`controller`.  The controller can be specified by assigning an existing ControlMechanism to the **controller** 
-argument of the system's constructor, or specifying a class of ControlMechanism;  if none is specified, 
-a `DefaultControlMechanism` is created.  
+Whenever a system is created, a `ControlMechanism <ControlMechanism>` is created for it and assigned as its
+`controller`.  The controller can be specified by assigning an existing ControlMechanism to the **controller**
+argument of the system's constructor, or specifying a class of ControlMechanism;  if none is specified,
+a `DefaultControlMechanism` is created.
 
 .. note::
-   At present, only `processes <Process>` can be assigned to a system; `mechanisms <Mechanism>` cannot be assigned 
-   directly to a system.  They must be assigned to the `pathway <Process_Pathway>` of a process, and then that process 
-   must be 
-   included 
+   At present, only `processes <Process>` can be assigned to a system; `mechanisms <Mechanism>` cannot be assigned
+   directly to a system.  They must be assigned to the `pathway <Process_Pathway>` of a process, and then that process
+   must be
+   included
    in the **processes** argument of the constructor for the system.
 
 
@@ -76,16 +76,16 @@
 Graph
 ~~~~~
 
-When a system is created, a graph is constructed that describes the projections (edges) among its mechanisms (nodes).  
-The graph is assigned to the system's `graph` attribute.  A system's graph can be displayed using its `show_graph` 
-method.  The graph is stored as a dictionary of dependencies, that can be passed to graph theoretical tools for 
-analysis.  A system can have recurrent processing pathways, such as feedback loops, in which case the system will 
-have a cyclic graph.  PsyNeuLink also uses the graph of a system to determine the order in which its mechanisms are 
-executed.  In order to do so in an orderly manner, however, the graph must be acyclic.  So, for execution, 
-PsyNeuLink constructs an `executionGraph` from the system's `graph`. If the  system is acyclic, these are the same.  
-If the system is cyclic, then the `executionGraph` is a subset of the `graph` in which the dependencies (edges) 
-associated with projections that close a loop have been removed. Note that this only impacts the order of execution;  
-the projections themselves remain in effect, and will be fully functional during the execution of the affected 
+When a system is created, a graph is constructed that describes the projections (edges) among its mechanisms (nodes).
+The graph is assigned to the system's `graph` attribute.  A system's graph can be displayed using its `show_graph`
+method.  The graph is stored as a dictionary of dependencies, that can be passed to graph theoretical tools for
+analysis.  A system can have recurrent processing pathways, such as feedback loops, in which case the system will
+have a cyclic graph.  PsyNeuLink also uses the graph of a system to determine the order in which its mechanisms are
+executed.  In order to do so in an orderly manner, however, the graph must be acyclic.  So, for execution,
+PsyNeuLink constructs an `executionGraph` from the system's `graph`. If the  system is acyclic, these are the same.
+If the system is cyclic, then the `executionGraph` is a subset of the `graph` in which the dependencies (edges)
+associated with projections that close a loop have been removed. Note that this only impacts the order of execution;
+the projections themselves remain in effect, and will be fully functional during the execution of the affected
 mechanisms (see `System_Execution` below for a more detailed description).
 
 .. _System_Mechanisms:
@@ -206,15 +206,15 @@
 Learning
 ~~~~~~~~
 The system will execute learning if it is specified for any process in the system.  The system's `learning` attribute
-indicates whether learning is enabled for the system. Learning is executed for any components (individual projections 
+indicates whether learning is enabled for the system. Learning is executed for any components (individual projections
 or processes) for which it is specified after all processing mechanisms in the
 system have been executed, but before the controller is executed (see below).  The learning components of a system
 can be displayed using the system's `show_graph` method with its **show_learning** argument assigned :keyword:`True`.
-The stimuli used for learning (both inputs and targets) can be specified in either of two formats, 
-sequence or mechanism, that are described in the :doc:`Run` module; see `Run_Inputs` and `Run_Targets`).  Both 
-formats require that an input be provided for each `ORIGIN` mechanism of the system (listed in its `originMechanisms 
-<System_Base.originMechanisms>` attribute).  If the targets are specified in sequence or mechanism format, 
-one target must be provided for each `TARGET` mechanism (listed in its 
+The stimuli used for learning (both inputs and targets) can be specified in either of two formats,
+sequence or mechanism, that are described in the :doc:`Run` module; see `Run_Inputs` and `Run_Targets`).  Both
+formats require that an input be provided for each `ORIGIN` mechanism of the system (listed in its `originMechanisms
+<System_Base.originMechanisms>` attribute).  If the targets are specified in sequence or mechanism format,
+one target must be provided for each `TARGET` mechanism (listed in its
 `targetMechanisms <System_Base.targetMechanisms>` attribute).  Targets can also be specified in a
 `function format <Run_Targets_Function_Format>`, which generates a target for each execution of the mechanism.
 
@@ -227,13 +227,13 @@
 
 Control
 ~~~~~~~
-Every system is associated with a single `controller`.  The controller uses an `ObjectiveMechanism` to monitor  
-the outputState(s) of one or more mechanisms in the system (listed in its `monitored_output_states` attribute), 
+Every system is associated with a single `controller`.  The controller uses an `ObjectiveMechanism` to monitor
+the outputState(s) of one or more mechanisms in the system (listed in its `monitored_output_states` attribute),
 and uses that information to set the value of parameters for those or other mechanisms in the system, or their functions
 (see :ref:`ControlMechanism_Monitored_OutputStates` for a description of how to specify which outputStates are
 monitored, and :ref:`ControlProjection_Creation` for specifying parameters to be controlled).  The control components
-of a system can be displayed using the system's `show_graph` method with its **show_control** argument assigned 
-:keyword:``True`. The controller is executed after all other mechanisms in the system are executed, and sets the 
+of a system can be displayed using the system's `show_graph` method with its **show_control** argument assigned
+:keyword:``True`. The controller is executed after all other mechanisms in the system are executed, and sets the
 values of any parameters that it controls, which then take effect in the next round of execution.
 
 COMMENT:
@@ -258,25 +258,31 @@
 
 import math
 import re
-from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.LearningMechanisms.LearningMechanism import LearningMechanism
+import logging
+
 from collections import OrderedDict
-from collections import UserList, Iterable
+
 from toposort import *
 
 from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.ControlMechanism import ControlMechanism_Base
-from PsyNeuLink.Components.Mechanisms.Mechanism import MechanismList, MechanismTuple,\
-                                                       OBJECT_ITEM, PARAMS_ITEM, PHASE_ITEM
+from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.LearningMechanisms.LearningMechanism import LearningMechanism
+from PsyNeuLink.Components.Mechanisms.Mechanism import MechanismList
+    # PARAMS_ITEM, PHASE_ITEM
 from PsyNeuLink.Components.Mechanisms.Mechanism import MonitoredOutputStatesOption
-from PsyNeuLink.Components.Process import ProcessInputState, ProcessList, ProcessTuple
-from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection, _is_learning_spec
+from PsyNeuLink.Components.Process import ProcessList, ProcessTuple
 from PsyNeuLink.Components.ShellClasses import *
 from PsyNeuLink.Globals.Registry import register_category
+from PsyNeuLink.Globals.TimeScale import TimeScale
+from PsyNeuLink.scheduling.Scheduler import Scheduler
+
+logger = logging.getLogger(__name__)
 
 # ProcessRegistry ------------------------------------------------------------------------------------------------------
 
 defaultInstanceCount = 0 # Number of default instances (used to index name)
 
 # inspect() keywords
+SCHEDULER = 'scheduler'
 PROCESSES = 'processes'
 MECHANISMS = 'mechanisms'
 ORIGIN_MECHANISMS = 'origin_mechanisms'
@@ -316,17 +322,18 @@ def __str__(self):
 
 from PsyNeuLink.Components.Process import process
 from PsyNeuLink.Components import SystemDefaultControlMechanism
-from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ObjectiveMechanism import ObjectiveMechanism
-
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ObjectiveMechanisms.ObjectiveMechanism import ObjectiveMechanism
 
 # System factory method:
 @tc.typecheck
 def system(default_input_value=None,
            processes:list=[],
+           scheduler=None,
            initial_values:dict={},
            controller=SystemDefaultControlMechanism,
            enable_controller:bool=False,
            monitor_for_control:list=[MonitoredOutputStatesOption.PRIMARY_OUTPUT_STATES],
+           control_signals:tc.optional(list)=None,
            # learning:tc.optional(_is_learning_spec)=None,
            learning_rate:tc.optional(parameter_spec)=None,
            targets:tc.optional(tc.any(list, np.ndarray))=None,
@@ -340,8 +347,9 @@ def system(default_input_value=None,
     processes=None,                           \
     initial_values=None,                      \
     controller=SystemDefaultControlMechanism, \
-    enable_controller=:keyword:`False`,       \
-    monitor_for_control=`None`,               \
+    enable_controller=:keyword:False,         \
+    monitor_for_control=None,                 \
+    control_signals=None,                     \
     learning_rate=None,                       \
     targets=None,                             \
     params=None,                              \
@@ -356,7 +364,8 @@ def system(default_input_value=None,
         initial_values=None,                      \
         controller=SystemDefaultControlMechanism, \
         enable_controller=:keyword:`False`,       \
-        monitor_for_control=`None`,               \
+        monitor_for_control=None,                 \
+        control_signals=None,                     \
         learning=None,                            \
         targets=None                              \
         params=None,                              \
@@ -376,7 +385,7 @@ def system(default_input_value=None,
     ---------
 
     default_input_value : list or ndarray of values : default default input for `ORIGIN` mechanism of each Process
-        the input to the system if none is provided in a call to the `execute <System_Base.exeucte> or
+        the input to the system if none is provided in a call to the `execute <System_Base.execute> or
         `run <System_Base.run> methods. Should contain one item corresponding to the input of each `ORIGIN` mechanism
         in the system.
         COMMENT:
@@ -394,7 +403,7 @@ def system(default_input_value=None,
         list or 1d np.array that must be compatible with the format of the first item of the mechanism's value
         (i.e., mechanism.value[0]).
 
-    controller : ControlMechanism : default DefaultController
+    controller : ControlMechanism : default SystemDefaultControlMechanism
         specifies the `ControlMechanism` used to monitor the value of the outputState(s) for mechanisms specified in
         `monitor_for_control`, and that specify the value of `ControlProjections` in the system.
 
@@ -451,9 +460,11 @@ def system(default_input_value=None,
     return System_Base(default_input_value=default_input_value,
                        processes=processes,
                        controller=controller,
+                       scheduler=scheduler,
                        initial_values=initial_values,
                        enable_controller=enable_controller,
                        monitor_for_control=monitor_for_control,
+                       control_signals=control_signals,
                        # learning=learning,
                        learning_rate=learning_rate,
                        targets=targets,
@@ -471,7 +482,8 @@ class System_Base(System):
     initial_values=None,                      \
     controller=SystemDefaultControlMechanism, \
     enable_controller=:keyword:`False`,       \
-    monitor_for_control=`None`,               \
+    monitor_for_control=None,                 \
+    control_signals=None,                     \
     learning_rate=None,                       \
     targets=None,                             \
     params=None,                              \
@@ -516,7 +528,7 @@ class System_Base(System):
         + classPreferenceLevel (PreferenceLevel): PreferenceLevel.CATEGORY
         + variableClassDefault = inputValueSystemDefault                     # Used as default input value to Process)
         + paramClassDefaults = {PROCESSES: [Mechanism_Base.defaultMechanism],
-                                CONTROLLER: DefaultController,
+                                CONTROLLER: SystemDefaultControlMechanism,
                                 TIME_SCALE: TimeScale.TRIAL}
        Class methods
        -------------
@@ -557,7 +569,7 @@ class System_Base(System):
             Derived from self.input and self.processes.
             Used to construct :py:data:`executionGraph <System_Base.executionGraph>` and execute the System
 
-    controller : ControlMechanism : default DefaultController
+    controller : ControlMechanism : default SystemDefaultControlMechanism
         the ControlMechanism used to monitor the value of the outputState(s) for mechanisms specified in
         ``monitor_for_control`` argument, and specify the value of ControlProjections in the system.
 
@@ -582,8 +594,8 @@ class System_Base(System):
     graph : OrderedDict
         contains a graph of all of the mechanisms in the system.
         Each entry specifies a set of <Receiver>: {sender, sender...} dependencies.
-        The key of each entry is a receiver mech_tuple, and
-        the value is a set of mech_tuples that send projections to that receiver.
+        The key of each entry is a receiver object_item, and
+        the value is a set of mechs that send projections to that receiver.
         If a key (receiver) has no dependents, its value is an empty set.
 
     executionGraph : OrderedDict
@@ -609,50 +621,50 @@ class System_Base(System):
         The key of each entry is a `Mechanism` object, and the value of each entry is a list of `processes <Process>`.
 
         .. Note: the following attributes use lists of tuples (mechanism, runtime_param, phaseSpec) and MechanismList
-              xxx_mech_tuples are lists of tuples defined in the Process pathways;
+              xxx_mechs are lists of tuples defined in the Process pathways;
                   tuples are used because runtime_params and phaseSpec are attributes that need
                   to be able to be specified differently for the same mechanism in different contexts
                   and thus are not easily managed as mechanism attributes
               xxxMechanismLists point to MechanismList objects that provide access to information
-                  about the mechanism <type> listed in mech_tuples (i.e., the mechanisms, names, etc.)
+                  about the mechanism <type> listed in mechs (i.e., the mechanisms, names, etc.)
 
-        .. _all_mech_tuples : list of (mechanism, runtime_param, phaseSpec) tuples
+        .. _all_mechs : list of (mechanism, runtime_param, phaseSpec) tuples
             Tuples for all mechanisms in the system (serve as keys in self.graph).
 
         .. _allMechanisms : MechanismList
-            Contains all mechanisms in the system (based on _all_mech_tuples).
+            Contains all mechanisms in the system (based on _all_mechs).
 
-        .. _origin_mech_tuples : list of (mechanism, runtime_param, phaseSpec) tuples
+        .. _origin_mechs : list of (mechanism, runtime_param, phaseSpec) tuples
             Tuples for all ORIGIN mechanisms in the system.
 
-        .. _terminal_mech_tuples : list of (mechanism, runtime_param, phaseSpec) tuples
+        .. _terminal_mechs : list of (mechanism, runtime_param, phaseSpec) tuples
             Tuples for all TERMINAL mechanisms in the system.
 
-        .. _monitoring_mech_tuples : list of (mechanism, runtime_param, phaseSpec) tuples
+        .. _monitoring_mechs : list of (mechanism, runtime_param, phaseSpec) tuples
             Tuples for all MonitoringMechanisms in the system (used for learning).
 
-        .. _target_mech_tuples : list of (mechanism, runtime_param, phaseSpec) tuples
+        .. _target_mechs : list of (mechanism, runtime_param, phaseSpec) tuples
             Tuples for all TARGET `ObjectiveMechanisms <ObjectiveMechanism>`  in the system that are a `TERMINAL`
             for at least on process to which it belongs and that process has learning enabled --  the criteria for
             being a target used in learning.
 
-        .. _learning_mech_tuples : list of (mechanism, runtime_param, phaseSpec) tuples
+        .. _learning_mechs : list of (mechanism, runtime_param, phaseSpec) tuples
             Tuples for all LearningMechanisms in the system (used for learning).
 
-        .. _control_mech_tuple : list of a single (mechanism, runtime_param, phaseSpec) tuple
+        .. _control_object_item : list of a single (mechanism, runtime_param, phaseSpec) tuple
             Tuple for the controller in the system.
 
     originMechanisms : MechanismList
         contains all `ORIGIN` mechanisms in the system (i.e., that don't receive projections from any other
         mechanisms.
 
-        .. based on _origin_mech_tuples
+        .. based on _origin_mechs
            system.input contains the input to each `ORIGIN` mechanism
 
     terminalMechanisms : MechanismList
         contains all `TERMINAL` mechanisms in the system (i.e., that don't project to any other ProcessingMechanisms).
 
-        .. based on _terminal_mech_tuples
+        .. based on _terminal_mechs
            system.ouput contains the output of each TERMINAL mechanism
 
     recurrentInitMechanisms : MechanismList
@@ -662,13 +674,13 @@ class System_Base(System):
     monitoringMechanisms : MechanismList)
         contains all `MONITORING` mechanisms in the system (used for learning).
         COMMENT:
-            based on _monitoring_mech_tuples)
+            based on _monitoring_mechs)
         COMMENT
 
     targetMechanisms : MechanismList)
         contains all `TARGET` mechanisms in the system (used for learning.
         COMMENT:
-            based on _target_mech_tuples)
+            based on _target_mechs)
         COMMENT
 
     targetInputStates : List[SystemInputState]
@@ -680,11 +692,11 @@ class System_Base(System):
         contains `controller` of the system
         COMMENT:
             ??and any other `ControlMechanisms <ControlMechanism>` in the system
-            (based on _control_mech_tuples).
+            (based on _control_mechs).
         COMMENT
 
     value : 3D ndarray
-        contains an array of 2D arrays, each of which is the `outputValue `of a `TERMINAL` mechanism in the system.
+        contains an array of 2D arrays, each of which is the `output_values `of a `TERMINAL` mechanism in the system.
 
         .. _phaseSpecMax : int
             Maximum phase specified for any mechanism in system.  Determines the phase of the last (set of)
@@ -708,14 +720,14 @@ class System_Base(System):
 
     name : str : default System-<index>
         the name of the system;
-        Specified in the `name` argument of the constructor for the system;
+        Specified in the **name** argument of the constructor for the system;
         if not is specified, a default is assigned by SystemRegistry
         (see :doc:`Registry <LINK>` for conventions used in naming, including for default and duplicate names).
 
 
     prefs : PreferenceSet or specification dict : System.classPreferences
         the `PreferenceSet` for system.
-        Specified in the `prefs` argument of the constructor for the system;  if it is not specified, a default is
+        Specified in the **prefs** argument of the constructor for the system;  if it is not specified, a default is
         assigned using `classPreferences` defined in __init__.py
         (see :ref:`PreferenceSet <LINK>` for details).
 
@@ -751,6 +763,7 @@ class System_Base(System):
                                'learning': False
                                })
 
+    # FIX 5/23/17: ADD control_signals ARGUMENT HERE (AND DOCUMENT IT ABOVE)
     @tc.typecheck
     def __init__(self,
                  default_input_value=None,
@@ -759,11 +772,13 @@ def __init__(self,
                  controller=SystemDefaultControlMechanism,
                  enable_controller=False,
                  monitor_for_control=None,
+                 control_signals=None,
                  # learning=None,
                  learning_rate=None,
                  targets=None,
                  params=None,
                  name=None,
+                 scheduler=None,
                  prefs:is_pref_set=None,
                  context=None):
 
@@ -776,11 +791,16 @@ def __init__(self,
                                                   controller=controller,
                                                   enable_controller=enable_controller,
                                                   monitor_for_control=monitor_for_control,
+                                                  control_signals=control_signals,
                                                   learning_rate=learning_rate,
                                                   targets=targets,
                                                   params=params)
 
         self.function = self.execute
+        self.scheduler_processing = scheduler
+        self.scheduler_learning = None
+        self.termination_processing = None
+        self.termination_learning = None
 
         register_category(entry=self,
                           base_class=System_Base,
@@ -802,30 +822,34 @@ def __init__(self,
 
         # Get/assign controller
 
-        # Controller is DefaultControlMechanism
-        from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.DefaultControlMechanism import DefaultControlMechanism
-        if self.paramsCurrent[CONTROLLER] is DefaultControlMechanism:
-            # Get DefaultController from MechanismRegistry
-            from PsyNeuLink.Components.Mechanisms.Mechanism import MechanismRegistry
-            self.controller = list(MechanismRegistry[DEFAULT_CONTROL_MECHANISM].instanceDict.values())[0]
-        # Controller is not DefaultControlMechanism
+        # Existing controller has been assigned
+        if isinstance(self.controller, ControlMechanism_Base):
+            if self.controller.system is None:
+                self.controller.system = self
+            elif not self.controller.system is self:
+                raise SystemError("The controller assigned to {} ({}) already belongs to another system ({})".
+                                  format(self.name, self.controller.name, self.controller.system.name))
+
+        # Instantiate controller from class specification
         else:
-            # Instantiate specified controller
-            # MODIFIED 11/6/16 OLD:
-            self.controller = self.paramsCurrent[CONTROLLER](params={SYSTEM: self})
-            # # MODIFIED 11/6/16 NEW:
-            # self.controller = self.paramsCurrent[CONTROLLER](system=self)
-            # MODIFIED 11/6/16 END
+            self.controller = self.controller(system=self,
+                                              monitor_for_control=monitor_for_control,
+                                              control_signals=control_signals)
 
         # Check whether controller has input, and if not then disable
-        try:
-            has_input_states = bool(self.controller.inputStates)
-        except:
-            has_input_states = False
+        # # MODIFIED 5/10/17 OLD:
+        # try:
+        #     has_input_states = bool(self.controller.input_states)
+        # except:
+        #     has_input_states = False
+        # MODIFIED 5/10/17 NEW:
+        has_input_states = isinstance(self.controller.input_states, ContentAddressableList)
+        # MODIFIED 5/10/17 END
+
         if not has_input_states:
             # If controller was enabled (and verbose is set), warn that it has been disabled
             if self.enable_controller and self.prefs.verbosePref:
-                print("{} for {} has no inputStates, so controller will be disabled".
+                print("{} for {} has no input_states, so controller will be disabled".
                       format(self.controller.name, self.name))
             self.enable_controller = False
 
@@ -920,7 +944,7 @@ def _instantiate_function(self, context=None):
             super(System_Base, self)._instantiate_function(context=context)
         # Otherwise, just set System output info to the corresponding info for the last mechanism(s) in self.processes
         else:
-            self.value = self.processes[-1].outputState.value
+            self.value = self.processes[-1].output_state.value
 
     def _instantiate_processes(self, input=None, context=None):
 # FIX: ALLOW Projections (??ProjectionTiming TUPLES) TO BE INTERPOSED BETWEEN MECHANISMS IN PATHWAY
@@ -935,7 +959,7 @@ def _instantiate_processes(self, input=None, context=None):
         Note: specification of input for system takes precedence over specification for processes
 
         # ??STILL THE CASE, OR MOVED TO _instantiate_graph:
-        Iterate through Process._mech_tuples for each Process;  for each sequential pair:
+        Iterate through Process._mechs for each Process;  for each sequential pair:
             - create set entry:  <receiving Mechanism>: {<sending Mechanism>}
             - add each pair as an entry in self.executionGraph
         """
@@ -944,8 +968,8 @@ def _instantiate_processes(self, input=None, context=None):
         # self.variable = []
         # MODIFIED 2/8/17 END
         self.mechanismsDict = {}
-        self._all_mech_tuples = []
-        self._allMechanisms = MechanismList(self, self._all_mech_tuples)
+        self._all_mechs = []
+        self._allMechanisms = MechanismList(self, self._all_mechs)
 
         # Get list of processes specified in arg to init, possibly appended by EVCMechanism (with prediction processes)
         processes_spec = self.processes
@@ -1073,54 +1097,32 @@ def _instantiate_processes(self, input=None, context=None):
             # Get max of Process phaseSpecs
             self._phaseSpecMax = int(max(math.floor(process._phaseSpecMax), self._phaseSpecMax))
 
-            # Iterate through mechanism tuples in Process' mech_tuples
-            #     to construct self._all_mech_tuples and mechanismsDict
-            # FIX: ??REPLACE WITH:  for sender_mech_tuple in Process._mech_tuples
-            for sender_mech_tuple in process._mech_tuples:
+            # Iterate through mechanism tuples in Process' mechs
+            #     to construct self._all_mechs and mechanismsDict
+            # FIX: ??REPLACE WITH:  for sender_object_item in Process._mechs
+            for sender_object_item in process._mechs:
 
-                sender_mech = sender_mech_tuple.mechanism
+                sender_mech = sender_object_item
 
                 # THIS IS NOW DONE IN _instantiate_graph
                 # # Add system to the Mechanism's list of systems of which it is member
-                # if not self in sender_mech_tuple[MECHANISM].systems:
+                # if not self in sender_object_item[MECHANISM].systems:
                 #     sender_mech.systems[self] = INTERNAL
 
-                # Assign sender mechanism entry in self.mechanismsDict, with mech_tuple as key and its Process as value
+                # Assign sender mechanism entry in self.mechanismsDict, with object_item as key and its Process as value
                 #     (this is used by Process._instantiate_pathway() to determine if Process is part of System)
                 # If the sender is already in the System's mechanisms dict
-                if sender_mech_tuple.mechanism in self.mechanismsDict:
-                    existing_mech_tuple = self._allMechanisms._get_tuple_for_mech(sender_mech)
-                    if not sender_mech_tuple is existing_mech_tuple:
-                        # Contents of tuple are the same, so use the tuple in _allMechanisms
-                        if (sender_mech_tuple.phase == existing_mech_tuple.phase and
-                                    sender_mech_tuple.params == existing_mech_tuple.params):
-                            pass
-                        # Contents of tuple are different, so raise exception
-                        else:
-                            if sender_mech_tuple.phase != existing_mech_tuple.phase:
-                                offending_tuple_field = 'phase'
-                                offending_value = PHASE_ITEM
-                            else:
-                                offending_tuple_field = 'process_input'
-                                offending_value = PARAMS_ITEM
-                            raise SystemError("The same mechanism in different processes must have the same parameters:"
-                                              "the {} ({}) for {} in {} does not match the value({}) in {}".
-                                              format(offending_tuple_field,
-                                                     sender_mech_tuple.mechanism,
-                                                     sender_mech_tuple[offending_value],
-                                                     process,
-                                                     existing_mech_tuple[offending_value],
-                                                     self.mechanismsDict[sender_mech_tuple.mechanism]
-                                                     ))
+                if sender_object_item in self.mechanismsDict:
+                    # existing_object_item = self._allMechanisms._get_tuple_for_mech(sender_mech)
                     # Add to entry's list
                     self.mechanismsDict[sender_mech].append(process)
                 else:
                     # Add new entry
                     self.mechanismsDict[sender_mech] = [process]
-                if not sender_mech_tuple in self._all_mech_tuples:
-                    self._all_mech_tuples.append(sender_mech_tuple)
+                if not sender_object_item in self._all_mechs:
+                    self._all_mechs.append(sender_object_item)
 
-            process._allMechanisms = MechanismList(process, tuples_list=process._mech_tuples)
+            process._allMechanisms = MechanismList(process, components_list=process._mechs)
 
         # # MODIFIED 2/8/17 OLD: [SEE ABOVE]
         # self.variable = convert_to_np_array(self.variable, 2)
@@ -1142,7 +1144,7 @@ def _instantiate_graph(self, context=None):
                 containing mechanisms to be executed at the same time
 
         graph contains a dictionary of dependency sets for all mechanisms in the system:
-            reciever_mech_tuple : {sender_mech_tuple, sender_mech_tuple...}
+            reciever_object_item : {sender_object_item, sender_object_item...}
         executionGraph contains an acyclic subset of graph used to determine sequence of mechanism execution;
 
         They are constructed as follows:
@@ -1188,8 +1190,8 @@ def build_dependency_sets_by_traversing_projections(sender_mech):
                 return
 
             # Delete any projections to mechanism from processes or mechanisms in processes not in current system
-            for input_state in sender_mech.inputStates.values():
-                for projection in input_state.receivesFromProjections:
+            for input_state in sender_mech.input_states:
+                for projection in input_state.all_afferents:
                     sender = projection.sender.owner
                     system_processes = self.processes
                     if isinstance(sender, Process):
@@ -1199,8 +1201,8 @@ def build_dependency_sets_by_traversing_projections(sender_mech):
                         del projection
 
             # If sender_mech has no projections left, raise exception
-            if not any(any(projection for projection in input_state.receivesFromProjections)
-                       for input_state in sender_mech.inputStates.values()):
+            if not any(any(projection for projection in input_state.all_afferents)
+                       for input_state in sender_mech.input_states):
                 raise SystemError("{} only receives projections from other processes or mechanisms not"
                                   " in the current system ({})".format(sender_mech.name, self.name))
 
@@ -1226,8 +1228,8 @@ def build_dependency_sets_by_traversing_projections(sender_mech):
                                              projection.receiver.owner._role in (LEARNING, CONTROL)) or
                                 # itself!
                                  projection.receiver.owner is sender_mech
-                            for projection in output_state.sendsToProjections)
-                        for output_state in sender_mech.outputStates.values())):
+                            for projection in output_state.efferents)
+                        for output_state in sender_mech.output_states)):
                 try:
                     if sender_mech.systems[self] is ORIGIN:
                         sender_mech.systems[self] = SINGLETON
@@ -1237,21 +1239,21 @@ def build_dependency_sets_by_traversing_projections(sender_mech):
                     sender_mech.systems[self] = TERMINAL
                 return
 
-            for outputState in sender_mech.outputStates.values():
+            for output_state in sender_mech.output_states:
 
-                for projection in outputState.sendsToProjections:
+                for projection in output_state.efferents:
                     receiver = projection.receiver.owner
-                    receiver_tuple = self._allMechanisms._get_tuple_for_mech(receiver)
+                    # receiver_tuple = self._allMechanisms._get_tuple_for_mech(receiver)
 
                     # If receiver is not in system's list of mechanisms, must belong to a process that has
                     #    not been included in the system, so ignore it
-                    if not receiver_tuple or is_monitoring_mech(receiver):
+                    if not receiver or is_monitoring_mech(receiver):
                         continue
 
                     try:
-                        self.graph[receiver_tuple].add(self._allMechanisms._get_tuple_for_mech(sender_mech))
+                        self.graph[receiver].add(sender_mech)
                     except KeyError:
-                        self.graph[receiver_tuple] = {self._allMechanisms._get_tuple_for_mech(sender_mech)}
+                        self.graph[receiver] = {sender_mech}
 
                     # Use toposort to test whether the added dependency produced a cycle (feedback loop)
                     # Do not include dependency (or receiver on sender) in executionGraph for this projection
@@ -1267,23 +1269,23 @@ def build_dependency_sets_by_traversing_projections(sender_mech):
                     #     and would introduce a cycle irrespective of the tuple in which it appears in the graph
                     # FIX: MODIFY THIS TO (GO BACK TO) USING if receiver_tuple in self.executionGraph
                     # FIX  BUT CHECK THAT THEY ARE IN DIFFERENT PHASES
-                    if receiver in self.execution_graph_mechs:
+                    if receiver in self.executionGraph:
                         # Try assigning receiver as dependent of current mechanism and test toposort
                         try:
                             # If receiver_tuple already has dependencies in its set, add sender_mech to set
-                            if self.executionGraph[receiver_tuple]:
-                                self.executionGraph[receiver_tuple].\
-                                    add(self._allMechanisms._get_tuple_for_mech(sender_mech))
-                            # If receiver_tuple set is empty, assign sender_mech to set
+                            if self.executionGraph[receiver]:
+                                self.executionGraph[receiver].\
+                                    add(sender_mech)
+                            # If receiver set is empty, assign sender_mech to set
                             else:
-                                self.executionGraph[receiver_tuple] = \
-                                    {self._allMechanisms._get_tuple_for_mech(sender_mech)}
+                                self.executionGraph[receiver] = \
+                                    {sender_mech}
                             # Use toposort to test whether the added dependency produced a cycle (feedback loop)
                             list(toposort(self.executionGraph))
                         # If making receiver dependent on sender produced a cycle (feedback loop), remove from graph
                         except ValueError:
-                            self.executionGraph[receiver_tuple].\
-                                remove(self._allMechanisms._get_tuple_for_mech(sender_mech))
+                            self.executionGraph[receiver].\
+                                remove(sender_mech)
                             # Assign sender_mech INITIALIZE_CYCLE as system status if not ORIGIN or not yet assigned
                             if not sender_mech.systems or not (sender_mech.systems[self] in {ORIGIN, SINGLETON}):
                                 sender_mech.systems[self] = INITIALIZE_CYCLE
@@ -1296,11 +1298,11 @@ def build_dependency_sets_by_traversing_projections(sender_mech):
                         try:
                             # FIX: THIS WILL ADD SENDER_MECH IF RECEIVER IS IN GRAPH BUT = set()
                             # FIX: DOES THAT SCREW UP ORIGINS?
-                            self.executionGraph[receiver_tuple].\
-                                add(self._allMechanisms._get_tuple_for_mech(sender_mech))
+                            self.executionGraph[receiver].\
+                                add(sender_mech)
                         except KeyError:
-                            self.executionGraph[receiver_tuple] = \
-                                {self._allMechanisms._get_tuple_for_mech(sender_mech)}
+                            self.executionGraph[receiver] = \
+                                {sender_mech}
 
                     if not sender_mech.systems:
                         sender_mech.systems[self] = INTERNAL
@@ -1347,13 +1349,13 @@ def build_dependency_sets_by_traversing_projections(sender_mech):
                                 for proc in projection.sender.owner.processes
                                 if isinstance(projection.sender.owner,Mechanism))
                         # For all the projections to each inputState
-                        for projection in input_state.receivesFromProjections)
-                    # For all inputStates for the first_mech
-                    for input_state in first_mech.inputStates.values()):
+                        for projection in input_state.path_afferents)
+                    # For all input_states for the first_mech
+                    for input_state in first_mech.input_states):
                 # Assign its set value as empty, marking it as a "leaf" in the graph
-                mech_tuple = self._allMechanisms._get_tuple_for_mech(first_mech)
-                self.graph[mech_tuple] = set()
-                self.executionGraph[mech_tuple] = set()
+                object_item = first_mech
+                self.graph[object_item] = set()
+                self.executionGraph[object_item] = set()
                 first_mech.systems[self] = ORIGIN
 
             build_dependency_sets_by_traversing_projections(first_mech)
@@ -1363,7 +1365,7 @@ def build_dependency_sets_by_traversing_projections(sender_mech):
         # # LABELS ANY MECH AS A TARGET THAT PROJECTS TO AN ObjectiveMechanism WITH LEARNING AS ITS role
         # for mech in self.mechanisms:
         #     for output_state in mech.outputStates.values():
-        #         for projection in output_state.sendsToProjections:
+        #         for projection in output_state.efferents:
         #             receiver = projection.receiver.owner
         #             if isinstance(receiver, ObjectiveMechanism) and receiver.role == LEARNING:
         #                 mech.systems[self] = TERMINAL
@@ -1375,8 +1377,8 @@ def build_dependency_sets_by_traversing_projections(sender_mech):
         # Print graph
         if self.verbosePref:
             warnings.warn("In the system graph for \'{}\':".format(self.name))
-            for receiver_mech_tuple, dep_set in self.executionGraph.items():
-                mech = receiver_mech_tuple.mechanism
+            for receiver_object_item, dep_set in self.executionGraph.items():
+                mech = receiver_object_item
                 if not dep_set:
                     print("\t\'{}\' is an {} mechanism".
                           format(mech.name, mech.systems[self]))
@@ -1387,51 +1389,51 @@ def build_dependency_sets_by_traversing_projections(sender_mech):
                     elif status in {INTERNAL, INITIALIZE_CYCLE}:
                         status = 'an ' + status
                     print("\t\'{}\' is {} mechanism that receives projections from:".format(mech.name, status))
-                    for sender_mech_tuple in dep_set:
-                        print("\t\t\'{}\'".format(sender_mech_tuple.mechanism.name))
+                    for sender_object_item in dep_set:
+                        print("\t\t\'{}\'".format(sender_object_item.name))
 
         # For each mechanism (represented by its tuple) in the graph, add entry to relevant list(s)
         # Note: ignore mechanisms belonging to controllerProcesses (e.g., instantiated by EVCMechanism)
         #       as they are for internal use only;
         #       this also ignored learning-related mechanisms (they are handled below)
-        self._origin_mech_tuples = []
-        self._terminal_mech_tuples = []
-        self.recurrent_init_mech_tuples = []
-        self._control_mech_tuple = []
+        self._origin_mechs = []
+        self._terminal_mechs = []
+        self.recurrent_init_mechs = []
+        self._control_object_item = []
 
-        for mech_tuple in self.executionGraph:
+        for object_item in self.executionGraph:
 
-            mech = mech_tuple.mechanism
+            mech = object_item
 
             if mech.systems[self] in {ORIGIN, SINGLETON}:
                 for process, status in mech.processes.items():
                     if process._isControllerProcess:
                         continue
-                    self._origin_mech_tuples.append(mech_tuple)
+                    self._origin_mechs.append(object_item)
                     break
 
-            if mech_tuple.mechanism.systems[self] in {TERMINAL, SINGLETON}:
+            if object_item.systems[self] in {TERMINAL, SINGLETON}:
                 for process, status in mech.processes.items():
                     if process._isControllerProcess:
                         continue
-                    self._terminal_mech_tuples.append(mech_tuple)
+                    self._terminal_mechs.append(object_item)
                     break
 
-            if mech_tuple.mechanism.systems[self] in {INITIALIZE_CYCLE}:
+            if object_item.systems[self] in {INITIALIZE_CYCLE}:
                 for process, status in mech.processes.items():
                     if process._isControllerProcess:
                         continue
-                    self.recurrent_init_mech_tuples.append(mech_tuple)
+                    self.recurrent_init_mechs.append(object_item)
                     break
 
-            if isinstance(mech_tuple.mechanism, ControlMechanism_Base):
-                if not mech_tuple.mechanism in self._control_mech_tuple:
-                    self._control_mech_tuple.append(mech_tuple)
+            if isinstance(object_item, ControlMechanism_Base):
+                if not object_item in self._control_object_item:
+                    self._control_object_item.append(object_item)
 
-        self.originMechanisms = MechanismList(self, self._origin_mech_tuples)
-        self.terminalMechanisms = MechanismList(self, self._terminal_mech_tuples)
-        self.recurrentInitMechanisms = MechanismList(self, self.recurrent_init_mech_tuples)
-        self.controlMechanism = MechanismList(self, self._control_mech_tuple) # Used for inspection and in case there
+        self.originMechanisms = MechanismList(self, self._origin_mechs)
+        self.terminalMechanisms = MechanismList(self, self._terminal_mechs)
+        self.recurrentInitMechanisms = MechanismList(self, self.recurrent_init_mechs)
+        self.controlMechanism = MechanismList(self, self._control_object_item) # Used for inspection and in case there
                                                                               # are multiple controllers in the future
 
         try:
@@ -1449,7 +1451,7 @@ def build_dependency_sets_by_traversing_projections(sender_mech):
         # self.executionList = toposort_flatten(self.executionGraph, sort=False)
         # MODIFIED 10/31/16 NEW:
         temp = toposort_flatten(self.executionGraph, sort=False)
-        self.executionList = self._toposort_with_ordered_mech_tuples(self.executionGraph)
+        self.executionList = self._toposort_with_ordered_mechs(self.executionGraph)
         # MODIFIED 10/31/16 END
 
         # MODIFIED 2/8/17 NEW:
@@ -1457,7 +1459,7 @@ def build_dependency_sets_by_traversing_projections(sender_mech):
         self.variable = []
         for mech in self.originMechanisms:
             orig_mech_input = []
-            for input_state in mech.inputStates.values():
+            for input_state in mech.input_states:
                 orig_mech_input.extend(input_state.value)
             self.variable.append(orig_mech_input)
         self.variable = convert_to_np_array(self.variable, 2)
@@ -1472,7 +1474,7 @@ def build_dependency_sets_by_traversing_projections(sender_mech):
         # FIX: ONLY CHECK ONES THAT RECEIVE PROJECTIONS
         if self.initial_values is not None:
             for mech, value in self.initial_values.items():
-                if not mech in self.execution_graph_mechs:
+                if not mech in self.executionGraph:
                     raise SystemError("{} (entry in initial_values arg) is not a Mechanism in \'{}\'".
                                       format(mech.name, self.name))
                 mech._update_value
@@ -1491,28 +1493,28 @@ def _instantiate_stimulus_inputs(self, context=None):
 
             # Skip if ORIGIN mechanism already has a projection from a SystemInputState in current system
             # (this avoids duplication from multiple passes through _instantiate_graph)
-            if any(self is projection.sender.owner for projection in origin_mech.inputState.receivesFromProjections):
+            if any(self is projection.sender.owner for projection in origin_mech.input_state.path_afferents):
                 continue
 
             # Check, for each ORIGIN mechanism, that the length of the corresponding item of self.variable matches the
             # length of the ORIGIN inputState's variable attribute
-            if len(self.variable[i]) != len(origin_mech.inputState.variable):
+            if len(self.variable[i]) != len(origin_mech.input_state.variable):
                 raise SystemError("Length of input {} ({}) does not match the length of the input ({}) for the "
                                   "corresponding ORIGIN mechanism ()".
                                    format(i,
                                           len(self.variable[i]),
-                                          len(origin_mech.inputState.variable),
+                                          len(origin_mech.input_state.variable),
                                           origin_mech.name))
 
             stimulus_input_state = SystemInputState(owner=self,
-                                                        variable=origin_mech.inputState.variable,
+                                                        variable=origin_mech.input_state.variable,
                                                         prefs=self.prefs,
                                                         name="System Input {}".format(i))
             self.stimulusInputStates.append(stimulus_input_state)
             self.inputs.append(stimulus_input_state.value)
 
             # Add MappingProjection from stimulus_input_state to ORIGIN mechainsm's inputState
-            from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
+            from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
             MappingProjection(sender=stimulus_input_state,
                     receiver=origin_mech,
                     name=self.name+' Input Projection to '+origin_mech.name)
@@ -1529,7 +1531,7 @@ def build_dependency_sets_by_traversing_projections(sender_mech, process):
 
             # MappingProjections are legal recipients of learning projections (hence the call)
             #  but do not send any projections, so no need to consider further
-            from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
+            from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
             if isinstance(sender_mech, MappingProjection):
                 return
 
@@ -1540,9 +1542,6 @@ def build_dependency_sets_by_traversing_projections(sender_mech, process):
                                   "must be a LearningMechanism or an ObjectiveMechanism".
                                   format(sender_mech))
 
-
-            # MODIFIED 3/12/17 NEW:
-
             # MANAGE TARGET ObjectiveMechanism FOR INTERNAL or TERMINAL CONVERGENCE of PATHWAYS
 
             # If sender_mech is an ObjectiveMechanism, and:
@@ -1582,17 +1581,17 @@ def build_dependency_sets_by_traversing_projections(sender_mech, process):
                                      not receiver_mech is sender_mech)
                                 # receivers of senders to sender_mech
                                 for receiver_mech in [proj.receiver.owner for proj in
-                                                      mech.outputState.sendsToProjections])
+                                                      mech.output_state.efferents])
                         # senders to sender_mech
                         for mech in [proj.sender.owner
-                                     for proj in sender_mech.inputStates[SAMPLE].receivesFromProjections]):
+                                     for proj in sender_mech.input_states[SAMPLE].path_afferents]):
 
                     # Get the ProcessingMechanism that projected to sender_mech
-                    error_source_mech = sender_mech.inputStates[SAMPLE].receivesFromProjections[0].sender.owner
+                    error_source_mech = sender_mech.input_states[SAMPLE].path_afferents[0].sender.owner
 
                     # Get the other ObjectiveMechanism to which the error_source projects (in addition to sender_mech)
                     other_obj_mech = next((projection.receiver.owner for projection in
-                                           error_source_mech.outputState.sendsToProjections if
+                                           error_source_mech.output_state.efferents if
                                            isinstance(projection.receiver.owner, ObjectiveMechanism)), None)
                     sender_mech = other_obj_mech
 
@@ -1600,11 +1599,11 @@ def build_dependency_sets_by_traversing_projections(sender_mech, process):
                 # None of the mechanisms that project to it are a TERMINAL mechanism
                 elif not all(all(projection.sender.owner.processes[proc] is TERMINAL
                                  for proc in projection.sender.owner.processes)
-                             for projection in sender_mech.inputStates[SAMPLE].receivesFromProjections):
+                             for projection in sender_mech.input_states[SAMPLE].path_afferents):
 
                     # Get the LearningMechanism to which the sender_mech projected
                     try:
-                        learning_mech = sender_mech.outputState.sendsToProjections[0].receiver.owner
+                        learning_mech = sender_mech.output_state.efferents[0].receiver.owner
                         if not isinstance(learning_mech, LearningMechanism):
                             raise AttributeError
                     except AttributeError:
@@ -1615,11 +1614,11 @@ def build_dependency_sets_by_traversing_projections(sender_mech, process):
                         import ACTIVATION_INPUT, ERROR_SIGNAL
 
                     # Get the ProcessingMechanism that projected to sender_mech
-                    error_source_mech = sender_mech.inputStates[SAMPLE].receivesFromProjections[0].sender.owner
+                    error_source_mech = sender_mech.input_states[SAMPLE].path_afferents[0].sender.owner
 
                     # Get the other LearningMechanism to which the error_source projects (in addition to sender_mech)
                     error_signal_mech = next((projection.receiver.owner for projection in
-                                              error_source_mech.outputState.sendsToProjections if
+                                              error_source_mech.output_state.efferents if
                                               projection.receiver.name is ACTIVATION_INPUT), None)
 
 
@@ -1627,7 +1626,7 @@ def build_dependency_sets_by_traversing_projections(sender_mech, process):
                     #    from any other ObjectiveMechanism or LearningMechanism in the system;
                     # If it does, get the first one found
                     error_signal_projection = next ((projection for projection
-                                                     in learning_mech.inputStates[ERROR_SIGNAL].receivesFromProjections
+                                                     in learning_mech.input_states[ERROR_SIGNAL].path_afferents
                                                      if (isinstance(projection.sender.owner,(ObjectiveMechanism,
                                                                                             LearningMechanism)) and
                                                      not projection.sender.owner is sender_mech and
@@ -1661,8 +1660,8 @@ def build_dependency_sets_by_traversing_projections(sender_mech, process):
                     # and reassign learning_mech.function_object.error_matrix
                     #     (to the one for the projection to which error_signal_mech projects)
                     else:
-                        mp = MappingProjection(sender=error_signal_mech.outputStates[ERROR_SIGNAL],
-                                               receiver=learning_mech.inputStates[ERROR_SIGNAL],
+                        mp = MappingProjection(sender=error_signal_mech.output_states[ERROR_SIGNAL],
+                                               receiver=learning_mech.input_states[ERROR_SIGNAL],
                                                matrix=IDENTITY_MATRIX)
                         if mp is None:
                             raise SystemError("Could not instantiate a MappingProjection "
@@ -1671,17 +1670,19 @@ def build_dependency_sets_by_traversing_projections(sender_mech, process):
 
                         # Reassign error_matrix to one for the projection to which the error_signal_mech projects
                         learning_mech.function_object.error_matrix = \
-                            error_signal_mech.outputStates['learning_signal'].sendsToProjections[0].receiver
-                        if 'error_matrix' in learning_mech.parameterStates:
-                            del learning_mech.parameterStates['error_matrix']
+                            error_signal_mech._output_states['matrix_LearningSignal'].efferents[0].receiver
+                        # Delete error_matrix parameterState for error_matrix
+                        #    (since its value, which was the IDENTITY_MATRIX, is now itself ParameterState,
+                        #     and Components are not allowed  as the value of a ParameterState
+                        #     -- see ParameterState._instantiate_parameter_state())
+                        if 'error_matrix' in learning_mech._parameter_states:
+                            del learning_mech._parameter_states['error_matrix']
 
                         sender_mech = error_signal_mech
-            # MODIFIED 3/12/17 END
-
 
             # Delete any projections to mechanism from processes or mechanisms in processes not in current system
-            for input_state in sender_mech.inputStates.values():
-                for projection in input_state.receivesFromProjections:
+            for input_state in sender_mech.input_states:
+                for projection in input_state.all_afferents:
                     sender = projection.sender.owner
                     system_processes = self.processes
                     if isinstance(sender, Process):
@@ -1691,14 +1692,14 @@ def build_dependency_sets_by_traversing_projections(sender_mech, process):
                         del projection
 
             # If sender_mech has no projections left, raise exception
-            if not any(any(projection for projection in input_state.receivesFromProjections)
-                       for input_state in sender_mech.inputStates.values()):
+            if not any(any(projection for projection in input_state.path_afferents)
+                       for input_state in sender_mech.input_states):
                 raise SystemError("{} only receives projections from other processes or mechanisms not"
                                   " in the current system ({})".format(sender_mech.name, self.name))
 
-            for outputState in sender_mech.outputStates.values():
+            for output_state in sender_mech.output_states:
 
-                for projection in outputState.sendsToProjections:
+                for projection in output_state.efferents:
                     receiver = projection.receiver.owner
                     try:
                         self.learningGraph[receiver].add(sender_mech)
@@ -1762,15 +1763,15 @@ def build_dependency_sets_by_traversing_projections(sender_mech, process):
 
         # FIX: USE TOPOSORT TO FIND, OR AT LEAST CONFIRM, TARGET MECHANISMS, WHICH SHOULD EQUAL COMPARATOR MECHANISMS
         self.learningExecutionList = toposort_flatten(self.learningExecutionGraph, sort=False)
-        # self.learningExecutionList = self._toposort_with_ordered_mech_tuples(self.learningExecutionGraph)
+        # self.learningExecutionList = self._toposort_with_ordered_mechs(self.learningExecutionGraph)
 
         # Construct monitoringMechanisms and targetMechanisms MechanismLists
 
         # MODIFIED 3/12/17 NEW: [MOVED FROM _instantiate_graph]
-        self._monitoring_mech_tuples = []
-        self._target_mech_tuples = []
+        self._monitoring_mechs = []
+        self._target_mechs = []
 
-        from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
+        from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
         for item in self.learningExecutionList:
             if isinstance(item, MappingProjection):
                 continue
@@ -1782,13 +1783,13 @@ def build_dependency_sets_by_traversing_projections(sender_mech, process):
                         item.function_object.learning_rate is None):
                 item.function_object.learning_rate = self.learning_rate
 
-            mech_tuple = self._allMechanisms._get_tuple_for_mech(item)
-            if not mech_tuple in self._monitoring_mech_tuples:
-                self._monitoring_mech_tuples.append(mech_tuple)
-            if isinstance(item, ObjectiveMechanism) and not mech_tuple in self._target_mech_tuples:
-                self._target_mech_tuples.append(mech_tuple)
-        self.monitoringMechanisms = MechanismList(self, self._monitoring_mech_tuples)
-        self.targetMechanisms = MechanismList(self, self._target_mech_tuples)
+            # object_item = self._allMechanisms._get_tuple_for_mech(item)
+            if not item in self._monitoring_mechs:
+                self._monitoring_mechs.append(item)
+            if isinstance(item, ObjectiveMechanism) and not item in self._target_mechs:
+                self._target_mechs.append(item)
+        self.monitoringMechanisms = MechanismList(self, self._monitoring_mechs)
+        self.targetMechanisms = MechanismList(self, self._target_mechs)
         # MODIFIED 3/12/17 END
 
         # Instantiate TargetInputStates
@@ -1815,7 +1816,7 @@ def _instantiate_target_inputs(self, context=None):
         for i, target_mech in zip(range(len(self.targetMechanisms)), self.targetMechanisms):
 
             # Create ProcessInputState for each target and assign to targetMechanism's target inputState
-            target_mech_TARGET_input_state = target_mech.inputStates[TARGET]
+            target_mech_TARGET_input_state = target_mech.input_states[TARGET]
 
             # Check, for each TARGET mechanism, that the length of the corresponding item of targets matches the length
             #    of the TARGET (ComparatorMechanism) target inputState's variable attribute
@@ -1834,7 +1835,7 @@ def _instantiate_target_inputs(self, context=None):
             self.targetInputStates.append(system_target_input_state)
 
             # Add MappingProjection from system_target_input_state to TARGET mechainsm's target inputState
-            from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
+            from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
             MappingProjection(sender=system_target_input_state,
                     receiver=target_mech_TARGET_input_state,
                     name=self.name+' Input Projection to '+target_mech_TARGET_input_state.name)
@@ -1842,7 +1843,7 @@ def _instantiate_target_inputs(self, context=None):
     def _assign_output_states(self):
         """Assign outputStates for System (the values of which will comprise System.value)
 
-        Assign the outputs of terminal Mechanisms in the graph to the system's outputValue
+        Assign the outputs of terminal Mechanisms in the graph to the system's output_values
 
         Note:
         * Current implementation simply assigns terminal mechanisms as outputStates
@@ -1850,7 +1851,7 @@ def _assign_output_states(self):
 
         """
         for mech in self.terminalMechanisms.mechanisms:
-            self.outputStates[mech.name] = mech.outputStates
+            self.output_states[mech.name] = mech.output_states
 
     def initialize(self):
         """Assign :py:data:`initial_values <System_Base.initialize>` to mechanisms designated as \
@@ -1870,6 +1871,8 @@ def execute(self,
                 execution_id=None,
                 clock=CentralClock,
                 time_scale=None,
+                termination_processing=None,
+                termination_learning=None,
                 # time_scale=TimeScale.TRIAL
                 context=None):
         """Execute mechanisms in system at specified :ref:`phases <System_Execution_Phase>` in order \
@@ -1910,6 +1913,11 @@ def execute(self,
             Each item is a 2d array that contains arrays for each outputState.value of each TERMINAL mechanism
 
         """
+        if self.scheduler_processing is None:
+            self.scheduler_processing = Scheduler(system=self)
+
+        if self.scheduler_learning is None:
+            self.scheduler_learning = Scheduler(nodes=self.learningExecutionList, toposort_ordering=toposort(self.learningGraph))
 
         if not context:
             context = EXECUTING + " " + SYSTEM + " " + self.name
@@ -1919,14 +1927,14 @@ def execute(self,
         self._execution_id = execution_id or _get_unique_id()
         # FIX: GO THROUGH LEARNING GRAPH HERE AND ASSIGN EXECUTION TOKENS FOR ALL MECHANISMS IN IT
         # self.learningExecutionList
-        for mech in self.execution_graph_mechs:
+        for mech in self.executionGraph:
             mech._execution_id = self._execution_id
         for learning_mech in self.learningExecutionList:
             learning_mech._execution_id = self._execution_id
         self.controller._execution_id = self._execution_id
-        if self.controller.inputStates:
-            for state in self.controller.inputStates.values():
-                for projection in state.receivesFromProjections:
+        if self.enable_controller and self.controller.input_states:
+            for state in self.controller.input_states:
+                for projection in state.all_afferents:
                     projection.sender.owner._execution_id = self._execution_id
 
         self._report_system_output = self.prefs.reportOutputPref and context and EXECUTING in context
@@ -1965,10 +1973,10 @@ def execute(self,
             # Get SystemInputState that projects to each ORIGIN mechanism and assign input to it
             for i, origin_mech in zip(range(num_origin_mechs), self.originMechanisms):
                 # For each inputState of the ORIGIN mechansim
-                input_states = list(origin_mech.inputStates.values())
-                for j, input_state in zip(range(len(origin_mech.inputStates)), input_states):
+                for j in range(len(origin_mech.input_states)):
                    # Get the input from each projection to that inputState (from the corresponding SystemInputState)
-                    system_input_state = next(projection.sender for projection in input_state.receivesFromProjections
+                    system_input_state = next(projection.sender
+                                              for projection in origin_mech.input_states[j].path_afferents
                                               if isinstance(projection.sender, SystemInputState))
                     if system_input_state:
                         system_input_state.value = input[i][j]
@@ -1976,6 +1984,10 @@ def execute(self,
                         raise SystemError("Failed to find expected SystemInputState for {}".format(origin_mech.name))
 
         self.input = input
+        if termination_processing is not None:
+            self.termination_processing = termination_processing
+        if termination_learning is not None:
+            self.termination_learning = termination_learning
         #endregion
 
         if self._report_system_output:
@@ -1987,7 +1999,7 @@ def execute(self,
         # TEST PRINT:
         # for i in range(len(self.executionList)):
         #     print(self.executionList[i][0].name)
-        # sorted_list = list(mech_tuple[0].name for mech_tuple in self.executionList)
+        # sorted_list = list(object_item[0].name for object_item in self.executionList)
 
         # Execute system without learning on projections (that will be taken care of in _execute_learning()
         self._execute_processing(clock=clock, context=context)
@@ -1997,7 +2009,7 @@ def execute(self,
 
         # Don't execute learning for simulation runs
         if not EVC_SIMULATION in context and self.learning:
-            self._execute_learning(clock=clock, context=context + LEARNING)
+            self._execute_learning(clock=clock, context=context + ' ' + LEARNING)
         # endregion
 
 
@@ -2018,7 +2030,8 @@ def execute(self,
 
             except AttributeError as error_msg:
                 if not 'INIT' in context:
-                    raise SystemError("Problem executing controller for {}: {}".format(self.name, error_msg))
+                    raise SystemError("PROGRAM ERROR: Problem executing controller for {}: {}".
+                                      format(self.name, error_msg))
         #endregion
 
         # Report completion of system execution and value of designated outputs
@@ -2030,14 +2043,23 @@ def execute(self,
     def _execute_processing(self, clock=CentralClock, context=None):
     # def _execute_processing(self, clock=CentralClock, time_scale=TimeScale.Trial, context=None):
         # Execute each Mechanism in self.executionList, in the order listed during its phase
-        for i in range(len(self.executionList)):
 
-            mechanism, params, phase_spec = self.executionList[i]
 
             # Only update Mechanism on time_step(s) determined by its phaseSpec (specified in Mechanism's Process entry)
 # FIX: NEED TO IMPLEMENT FRACTIONAL UPDATES (IN Mechanism.update()) FOR phaseSpec VALUES THAT HAVE A DECIMAL COMPONENT
-            if phase_spec == (clock.time_step % self.numPhases):
-                # Note:  DON'T include input arg, as that will be resolved by mechanism from its sender projections
+        if self.scheduler_processing is None:
+            raise SystemError('System.py:_execute_processing - {0}\'s scheduler is None, must be initialized before execution'.format(self.name))
+        logger.debug('{0}.scheduler processing termination conditions: {1}'.format(self, self.termination_processing))
+        for next_execution_set in self.scheduler_processing.run(termination_conds=self.termination_processing):
+            logger.debug('Running next_execution_set {0}'.format(next_execution_set))
+            i = 0
+            for mechanism in next_execution_set:
+                logger.debug('\tRunning mechanism {0}'.format(mechanism))
+                for p in self.processes:
+                    try:
+                        rt_params = p.runtime_params_dict[mechanism]
+                    except:
+                        rt_params = None
 
                 processes = list(mechanism.processes.keys())
                 process_keys_sorted = sorted(processes, key=lambda i : processes[processes.index(i)].name)
@@ -2046,7 +2068,7 @@ def _execute_processing(self, clock=CentralClock, context=None):
                 mechanism.execute(clock=clock,
                                   time_scale=self.timeScale,
                                   # time_scale=time_scale,
-                                  runtime_params=params,
+                                  runtime_params=rt_params,
                                   context=context +
                                           "| mechanism: " + mechanism.name +
                                           " [in processes: " + str(process_names) + "]")
@@ -2061,7 +2083,7 @@ def _execute_processing(self, clock=CentralClock, context=None):
                     process_keys_sorted = sorted(processes, key=lambda i : processes[processes.index(i)].name)
                     for process in process_keys_sorted:
                         if mechanism.processes[process] in {ORIGIN, SINGLETON} and process.reportOutputPref:
-                            process._report_process_initiation(input=mechanism.inputValue[0])
+                            process._report_process_initiation(input=mechanism.input_values[0])
 
                     # REPORT COMPLETION OF PROCESS IF TERMINAL:
                     # Report completion of process(es) for which mechanism is a TERMINAL
@@ -2074,7 +2096,7 @@ def _execute_processing(self, clock=CentralClock, context=None):
                         if mechanism.processes[process] == TERMINAL and process.reportOutputPref:
                             process._report_process_completion()
 
-            if not i:
+            if i == 0:
                 # Zero input to first mechanism after first run (in case it is repeated in the pathway)
                 # IMPLEMENTATION NOTE:  in future version, add option to allow Process to continue to provide input
                 # FIX: USE clamp_input OPTION HERE, AND ADD HARD_CLAMP AND SOFT_CLAMP
@@ -2100,63 +2122,74 @@ def _execute_learning(self, clock=CentralClock, context=None):
             self.targetInputStates[i].value = self.current_targets[i]
 
         # NEXT, execute all components involved in learning
-        for component in self.learningExecutionList:
+        if self.scheduler_learning is None:
+            raise SystemError('System.py:_execute_learning - {0}\'s scheduler is None, must be initialized before execution'.format(self.name))
+        logger.debug('{0}.scheduler learning termination conditions: {1}'.format(self, self.termination_learning))
+        for next_execution_set in self.scheduler_learning.run(termination_conds=self.termination_learning):
+            logger.debug('Running next_execution_set {0}'.format(next_execution_set))
+            for component in next_execution_set:
+                logger.debug('\tRunning component {0}'.format(component))
 
-            from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
-            if isinstance(component, MappingProjection):
-                continue
+                from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
+                if isinstance(component, MappingProjection):
+                    continue
 
-            params = None
+                params = None
 
-            component_type = component.componentType
+                component_type = component.componentType
 
-            processes = list(component.processes.keys())
+                processes = list(component.processes.keys())
 
-            # Sort for consistency of reporting:
-            process_keys_sorted = sorted(processes, key=lambda i : processes[processes.index(i)].name)
-            process_names = list(p.name for p in process_keys_sorted)
-
-            context_str = str("{} | {}: {} [in processes: {}]".
-                              format(context,
-                                     component_type,
-                                     component.name,
-                                     re.sub('[\[,\],\n]','',str(process_names))))
-
-            # Note:  DON'T include input arg, as that will be resolved by mechanism from its sender projections
-            component.execute(clock=clock,
-                              time_scale=self.timeScale,
-                              runtime_params=params,
-                              # time_scale=time_scale,
-                              context=context_str)
-            # # TEST PRINT:
-            # print ("EXECUTING MONITORING UPDATES: ", component.name)
+                # Sort for consistency of reporting:
+                process_keys_sorted = sorted(processes, key=lambda i : processes[processes.index(i)].name)
+                process_names = list(p.name for p in process_keys_sorted)
+
+                context_str = str("{} | {}: {} [in processes: {}]".
+                                  format(context,
+                                         component_type,
+                                         component.name,
+                                         re.sub(r'[\[,\],\n]','',str(process_names))))
+
+                # Note:  DON'T include input arg, as that will be resolved by mechanism from its sender projections
+                component.execute(
+                    clock=clock,
+                    time_scale=self.timeScale,
+                    runtime_params=params,
+                    # time_scale=time_scale,
+                    context=context_str
+                )
+                # # TEST PRINT:
+                # print ("EXECUTING MONITORING UPDATES: ", component.name)
 
         # THEN update all MappingProjections
-        for component in self.learningExecutionList:
+        for next_execution_set in self.scheduler_learning.run(termination_conds=self.termination_learning):
+            logger.debug('Running next_execution_set {0}'.format(next_execution_set))
+            for component in next_execution_set:
+                logger.debug('\tRunning component {0}'.format(component))
 
-            if isinstance(component, (LearningMechanism, ObjectiveMechanism)):
-                continue
-            if not isinstance(component, MappingProjection):
-                raise SystemError("PROGRAM ERROR:  Attempted learning on non-MappingProjection")
+                if isinstance(component, (LearningMechanism, ObjectiveMechanism)):
+                    continue
+                if not isinstance(component, MappingProjection):
+                    raise SystemError("PROGRAM ERROR:  Attempted learning on non-MappingProjection")
 
-            component_type = "mappingProjection"
-            processes = list(component.sender.owner.processes.keys())
+                component_type = "mappingProjection"
+                processes = list(component.sender.owner.processes.keys())
 
 
-            # Sort for consistency of reporting:
-            process_keys_sorted = sorted(processes, key=lambda i : processes[processes.index(i)].name)
-            process_names = list(p.name for p in process_keys_sorted)
+                # Sort for consistency of reporting:
+                process_keys_sorted = sorted(processes, key=lambda i : processes[processes.index(i)].name)
+                process_names = list(p.name for p in process_keys_sorted)
 
-            context_str = str("{} | {}: {} [in processes: {}]".
-                              format(context,
-                                     component_type,
-                                     component.name,
-                                     re.sub('[\[,\],\n]','',str(process_names))))
+                context_str = str("{} | {}: {} [in processes: {}]".
+                                  format(context,
+                                         component_type,
+                                         component.name,
+                                         re.sub(r'[\[,\],\n]','',str(process_names))))
 
-            component.parameterStates[MATRIX].update(time_scale=TimeScale.TRIAL, context=context_str)
+                component._parameter_states[MATRIX].update(time_scale=TimeScale.TRIAL, context=context_str)
 
-            # TEST PRINT:
-            # print ("EXECUTING WEIGHT UPDATES: ", component.name)
+                # TEST PRINT:
+                # print ("EXECUTING WEIGHT UPDATES: ", component.name)
 
         # FINALLY report outputs
         if self._report_system_output and self._report_process_output:
@@ -2169,10 +2202,11 @@ def _execute_learning(self, clock=CentralClock, context=None):
                 process_keys_sorted = sorted(processes, key=lambda i : processes[processes.index(i)].name)
                 process_names = list(p.name for p in process_keys_sorted)
                 # print("\n\'- Target: {}' error: {} (processes: {})".
-                print("- error for target {}': {}".
-                      format(append_type_to_name(target_mech),
-                             re.sub('[\[,\],\n]','',str([float("{:0.3}".format(float(i)))
-                                                         for i in target_mech.outputState.value])),
+                print("- error for target ({}): {}".
+                      # format(append_type_to_name(target_mech),
+                      format(target_mech.name,
+                             re.sub(r'[\[,\],\n]','',str([float("{:0.3}".format(float(i)))
+                                                         for i in target_mech.output_state.value])),
                              ))
                              # process_names))
 
@@ -2189,6 +2223,8 @@ def run(self,
             call_after_time_step=None,
             clock=CentralClock,
             time_scale=None,
+            termination_processing=None,
+            termination_learning=None,
             context=None):
         """Run a sequence of executions
 
@@ -2241,6 +2277,14 @@ def run(self,
             list of the OutputValue for each `TERMINAL` mechanism of the system returned for each execution.
 
         """
+        if self.scheduler_processing is None:
+            self.scheduler_processing = Scheduler(system=self)
+
+        if self.scheduler_learning is None:
+            self.scheduler_learning = Scheduler(nodes=self.learningExecutionList, toposort_ordering=toposort(self.learningGraph))
+
+        logger.debug(inputs)
+
         from PsyNeuLink.Globals.Run import run
         return run(self,
                    inputs=inputs,
@@ -2254,6 +2298,8 @@ def run(self,
                    call_before_time_step=call_before_time_step,
                    call_after_time_step=call_after_time_step,
                    time_scale=time_scale,
+                   termination_processing=termination_processing,
+                   termination_learning=termination_learning,
                    clock=clock,
                    context=context)
 
@@ -2282,7 +2328,7 @@ def _report_system_initiation(self, clock=CentralClock):
                   format(self.name, system_string, clock.time_step))
 
     def _report_system_completion(self, clock=CentralClock):
-        """Prints completion message and outputValue of system
+        """Prints completion message and output_values of system
         """
 
         if 'system' in self.name or 'System' in self.name:
@@ -2293,17 +2339,20 @@ def _report_system_completion(self, clock=CentralClock):
         # Print output value of primary (first) outputState of each terminal Mechanism in System
         # IMPLEMENTATION NOTE:  add options for what to print (primary, all or monitored outputStates)
         print("\n\'{}\'{} completed ***********(time_step {})".format(self.name, system_string, clock.time_step))
-        for mech_tuple in self._terminal_mech_tuples:
-            if mech_tuple.mechanism.phaseSpec == (clock.time_step % self.numPhases):
-                print("- output for {0}: {1}".
-                      format(mech_tuple.mechanism.name,
-                             re.sub('[\[,\],\n]','',str(["{:0.3}".
-                                                format(float(i)) for i in mech_tuple.mechanism.outputState.value]))))
+        # for object_item in self._terminal_mechs:
+        #     if object_item.mechanism.phaseSpec == (clock.time_step % self.numPhases):
+        #         print("- output for {0}: {1}".
+        #               format(object_item.mechanism.name,
+        #                      re.sub('[\[,\],\n]','',str(["{:0.3}".
+        #                                         format(float(i)) for i in object_item.mechanism.output_state.value]))))
         if self.learning:
-            from PsyNeuLink.Components.Projections.LearningProjection import TARGET_MSE
+            from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ObjectiveMechanisms.ComparatorMechanism \
+                import MSE
             for mech in self.targetMechanisms:
+                if not MSE in mech.output_states:
+                    continue
                 print("\n- MSE: {:0.3}".
-                      format(float(mech.outputStates[TARGET_MSE].value)))
+                      format(float(mech.output_states[MSE].value)))
 
 
     # TBI:
@@ -2370,8 +2419,8 @@ def show(self, options=None):
         # for i in range(len(self.execution_sets)):
             print ("\t\tSet {0}:\n\t\t\t".format(i),end='')
             print("{ ",end='')
-            sorted_mechs_names_in_set = sorted(list(mech_tuple.mechanism.name
-                                                    for mech_tuple in self.execution_sets[i]))
+            sorted_mechs_names_in_set = sorted(list(object_item.name
+                                                    for object_item in self.execution_sets[i]))
             for name in sorted_mechs_names_in_set:
                 print("{0} ".format(name), end='')
             print("}")
@@ -2383,43 +2432,43 @@ def show(self, options=None):
 
 
         # Sort by phaseSpec and, within each phase, by mechanism name
-        sorted_execution_list.sort(key=lambda mech_tuple: mech_tuple.phase)
+#        sorted_execution_list.sort(key=lambda object_item: object_item.phase)
 
 
         # Add controller to execution list for printing if enabled
         if self.enable_controller:
-            sorted_execution_list.append(MechanismTuple(self.controller, None, self.controller.phaseSpec))
-
-
-        mech_names_from_exec_list = list(mech_tuple.mechanism.name for mech_tuple in self.executionList)
-        mech_names_from_sorted_exec_list = list(mech_tuple.mechanism.name for mech_tuple in sorted_execution_list)
+            sorted_execution_list.append(self.controller)
 
-        print ("\n\tExecution list: ".format(self.name))
-        phase = 0
-        print("\t\tPhase {}:".format(phase))
-        for mech_tuple in sorted_execution_list:
-            if mech_tuple.phase != phase:
-                phase = mech_tuple.phase
-                print("\t\tPhase {}:".format(phase))
-            print ("\t\t\t{}".format(mech_tuple.mechanism.name))
 
-        print ("\n\tOrigin mechanisms: ".format(self.name))
-        for mech_tuple in self.originMechanisms.mech_tuples_sorted:
-            print("\t\t{0} (phase: {1})".format(mech_tuple.mechanism.name, mech_tuple.phase))
+        mech_names_from_exec_list = list(object_item.name for object_item in self.executionList)
+        mech_names_from_sorted_exec_list = list(object_item.name for object_item in sorted_execution_list)
 
-        print ("\n\tTerminal mechanisms: ".format(self.name))
-        for mech_tuple in self.terminalMechanisms.mech_tuples_sorted:
-            print("\t\t{0} (phase: {1})".format(mech_tuple.mechanism.name, mech_tuple.phase))
-            for output_state_name in mech_tuple.mechanism.outputStates:
-                print("\t\t\t{0}".format(output_state_name))
-
-        # if any(process.learning for process in self.processes):
-        if self.learning:
-            print ("\n\tTarget mechanisms: ".format(self.name))
-            for mech_tuple in self.targetMechanisms.mech_tuples:
-                print("\t\t{0} (phase: {1})".format(mech_tuple.mechanism.name, mech_tuple.phase))
-
-        print ("\n---------------------------------------------------------")
+        # print ("\n\tExecution list: ".format(self.name))
+        # phase = 0
+        # print("\t\tPhase {}:".format(phase))
+        # for object_item in sorted_execution_list:
+        #     if object_item.phase != phase:
+        #         phase = object_item.phase
+        #         print("\t\tPhase {}:".format(phase))
+        #     print ("\t\t\t{}".format(object_item.mechanism.name))
+        #
+        # print ("\n\tOrigin mechanisms: ".format(self.name))
+        # for object_item in self.originMechanisms.mechs_sorted:
+        #     print("\t\t{0} (phase: {1})".format(object_item.mechanism.name, object_item.phase))
+        #
+        # print ("\n\tTerminal mechanisms: ".format(self.name))
+        # for object_item in self.terminalMechanisms.mechs_sorted:
+        #     print("\t\t{0} (phase: {1})".format(object_item.mechanism.name, object_item.phase))
+        #     for output_state in object_item.mechanism.output_states:
+        #         print("\t\t\t{0}".format(output_state.name))
+        #
+        # # if any(process.learning for process in self.processes):
+        # if self.learning:
+        #     print ("\n\tTarget mechanisms: ".format(self.name))
+        #     for object_item in self.targetMechanisms.mechs:
+        #         print("\t\t{0} (phase: {1})".format(object_item.mechanism.name, object_item.phase))
+        #
+        # print ("\n---------------------------------------------------------")
 
 
     def inspect(self):
@@ -2477,28 +2526,28 @@ def inspect(self):
         output_state_names = []
         output_value_array = []
         for mech in list(self.terminalMechanisms.mechanisms):
-            output_value_array.append(mech.outputValue)
-            for name in mech.outputStates:
+            output_value_array.append(mech.output_values)
+            for name in mech.output_states:
                 output_state_names.append(name)
         output_value_array = np.array(output_value_array)
 
-        from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection
-        from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection
+        from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
+        from PsyNeuLink.Components.Projections.ModulatoryProjections.LearningProjection import LearningProjection
         learning_projections = []
         controlled_parameters = []
         for mech in list(self.mechanisms):
-            for parameter_state in mech.parameterStates:
+            for parameter_state in mech._parameter_states:
                 try:
-                    for projection in parameter_state.receivesFromProjections:
+                    for projection in parameter_state.mod_afferents:
                         if isinstance(projection, ControlProjection):
                             controlled_parameters.append(parameter_state)
                 except AttributeError:
                     pass
-            for output_state in mech.outputStates:
+            for output_state in mech.output_states:
                 try:
-                    for projection in output_state.sendsToProjections:
+                    for projection in output_state.efferents:
                         for parameter_state in projection.paramaterStates:
-                            for sender in parameter_state.receivesFromProjections:
+                            for sender in parameter_state.mod_afferents:
                                 if isinstance(sender, LearningProjection):
                                     learning_projections.append(projection)
                 except AttributeError:
@@ -2524,12 +2573,12 @@ def inspect(self):
 
         return inspect_dict
 
-    def _toposort_with_ordered_mech_tuples(self, data):
-        """Returns a single list of dependencies, sorted by mech_tuple[MECHANISM].name"""
+    def _toposort_with_ordered_mechs(self, data):
+        """Returns a single list of dependencies, sorted by object_item[MECHANISM].name"""
         result = []
         for dependency_set in toposort(data):
             d_iter = iter(dependency_set)
-            result.extend(sorted(dependency_set, key=lambda item : next(d_iter).mechanism.name))
+            result.extend(sorted(dependency_set, key=lambda item : next(d_iter).name))
         return result
 
     def _cache_state(self):
@@ -2585,15 +2634,15 @@ def mechanisms(self):
     # def processes(self):
     #     return sorted(self._processList.processes)
 
-    @property
-    def inputValue(self):
-        """Value of input to system
-
-        Returns
-        -------
-        value of input to system : ndarray
-        """
-        return self.variable
+    # @property
+    # def input_value(self):
+    #     """Value of input to system
+    #
+    #     Returns
+    #     -------
+    #     value of input to system : ndarray
+    #     """
+    #     return self.variable
 
     @property
     def numPhases(self):
@@ -2608,15 +2657,15 @@ def numPhases(self):
         """
         return self._phaseSpecMax + 1
 
-    @property
-    def execution_graph_mechs(self):
-        """Mechanisms whose mech_tuples appear as keys in self.executionGraph
-
-        :rtype: list of Mechanism objects
-        """
-        return list(mech_tuple[0] for mech_tuple in self.executionGraph)
+    # @property
+    # def execution_graph_mechs(self):
+    #     """Mechanisms whose mechs appear as keys in self.executionGraph
+    #
+    #     :rtype: list of Mechanism objects
+    #     """
+    #     return self.executionGraph
 
-    def show_graph(self, 
+    def show_graph(self,
                    direction = 'BT',
                    show_learning = False,
                    show_control = False,
@@ -2625,40 +2674,40 @@ def show_graph(self,
                    output_fmt='pdf',
                    ):
         """Generate a display of the graph structure of mechanisms and projections in the system.
-        
-        By default, only the `ProcessingMechanisms <ProcessingMechanism>` and `MappingProjections <MappingProjection>` 
-        in the `system's graph <System.graph>` are displayed.  However, the **show_learning** and 
+
+        By default, only the `ProcessingMechanisms <ProcessingMechanism>` and `MappingProjections <MappingProjection>`
+        in the `system's graph <System.graph>` are displayed.  However, the **show_learning** and
         **show_control** arguments can be used to also show the `learning <LearningMechanism>` and
-        `control <ControlMechanism>` components of the system, respectively.  `Mechanisms <Mechanism>` are always 
-        displayed as (oval) nodes.  `Projections <Projection>` are displayed as labelled arrows, unless 
-        **show_learning** is assigned **True**, in which case MappingProjections that receive a `LearningProjection` 
-        are displayed as diamond-shaped nodes. The numbers in parentheses within a mechanism node indicate its 
-        dimensionality.   
+        `control <ControlMechanism>` components of the system, respectively.  `Mechanisms <Mechanism>` are always
+        displayed as (oval) nodes.  `Projections <Projection>` are displayed as labelled arrows, unless
+        **show_learning** is assigned **True**, in which case MappingProjections that receive a `LearningProjection`
+        are displayed as diamond-shaped nodes. The numbers in parentheses within a mechanism node indicate its
+        dimensionality.
 
         Arguments
         ---------
 
-        direction : keyword : default 'BT' 
+        direction : keyword : default 'BT'
             'BT': bottom to top; 'TB': top to bottom; 'LR': left to right; and 'RL`: right to left.
 
-        show_learning : bool : default False 
-            determines whether or not to show the learning components of the system;  
+        show_learning : bool : default False
+            determines whether or not to show the learning components of the system;
             they will all be displayed in the color specified for **learning_color**.
             projections that receive a `LearningProjection` will be shown as a diamond-shaped node.
 
-        show_control :  bool : default False 
-            determines whether or not to show the control components of the system;  
+        show_control :  bool : default False
+            determines whether or not to show the control components of the system;
             they will all be displayed in the color specified for **control_color**.
 
         learning_color : keyword : default `green`
             determines the color in which the learning components are displayed
 
         control_color : keyword : default `blue`
-            determines the color in which the learning components are displayed (note: if the system's 
+            determines the color in which the learning components are displayed (note: if the system's
             `controller <System.controller>`) is an `EVCMechanism`, then a link is shown in red from the
-            `prediction mechanisms <EVCMechanism_Prediction_Mechanisms>` it creates to the corresponding 
-            `ORIGIN` mechanisms of the system, to indicate that although no projection are created for these, 
-            the prediction mechanisms determine the input to the `ORIGIN` mechanisms when the EVCMechanism 
+            `prediction mechanisms <EVCMechanism_Prediction_Mechanisms>` it creates to the corresponding
+            `ORIGIN` mechanisms of the system, to indicate that although no projection are created for these,
+            the prediction mechanisms determine the input to the `ORIGIN` mechanisms when the EVCMechanism
             `simulates execution <EVCMechanism_Execution>` of the system.
 
         output_fmt : keyword : default 'pdf'
@@ -2669,24 +2718,25 @@ def show_graph(self,
         Returns
         -------
 
-        display of system : `pdf` or Graphviz graph object 
-            'pdf' (placed in current directory) if :keyword:`output_fmt` arg is 'pdf'; 
+        display of system : `pdf` or Graphviz graph object
+            'pdf' (placed in current directory) if :keyword:`output_fmt` arg is 'pdf';
             Graphviz graph object if :keyword:`output_fmt` arg is 'jupyter'.
 
         """
 
-        from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ObjectiveMechanism import ObjectiveMechanism
+        from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ObjectiveMechanisms.ObjectiveMechanism import ObjectiveMechanism
         from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.LearningMechanisms.LearningMechanism import LearningMechanism
-        from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
+        from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
 
         import graphviz as gv
+        import numpy as np
 
         system_graph = self.graph
         learning_graph=self.learningGraph
 
         # build graph and configure visualisation settings
-        G = gv.Digraph(engine = "dot", 
-                       node_attr  = {'fontsize':'12', 'fontname': 'arial', 'shape':'oval'}, 
+        G = gv.Digraph(engine = "dot",
+                       node_attr  = {'fontsize':'12', 'fontname': 'arial', 'shape':'oval'},
                        edge_attr  = {'arrowhead':'halfopen', 'fontsize': '10', 'fontname': 'arial'},
                        graph_attr = {"rankdir" : direction} )
 
@@ -2695,29 +2745,34 @@ def show_graph(self,
         rcvrs = list(system_graph.keys())
         # loop through receivers
         for rcvr in rcvrs:
-            rcvr_name = rcvr[0].name
-            rcvr_shape = rcvr[0].variable.shape[1]
+            # rcvr_name = rcvr[0].name
+            # rcvr_shape = rcvr[0].variable.shape[1]
+            rcvr_name = rcvr.name
+            rcvr_shape = rcvr.variable.shape[1]
             rcvr_label = rcvr_name
 
 
             # loop through senders
             sndrs = system_graph[rcvr]
             for sndr in sndrs:
-                sndr_name = sndr[0].name
-                sndr_shape = sndr[0].variable.shape[1]
+                sndr_name = sndr.name
+                sndr_shape = sndr.variable.shape[1]
                 sndr_label = sndr_name
 
                 # find edge name
-                projs = sndr[0].outputState.sendsToProjections
+                projs = sndr.output_state.efferents
                 for proj in projs:
-                    if proj.receiver.owner == rcvr[0]:
+                    if proj.receiver.owner == rcvr:
                         edge_name = proj.name
-                        edge_shape = proj.matrix.shape
-                        has_learning = proj.has_learning_projection
+                        # edge_shape = proj.matrix.shape
+                        try:
+                            has_learning = proj.has_learning_projection
+                        except AttributeError:
+                            has_learning = None
                 edge_label = edge_name
                 #### CHANGE MADE HERE ###
                 # if rcvr is learning mechanism, draw arrow with learning color
-                if isinstance(rcvr[0], LearningMechanism) or isinstance(rcvr[0], ObjectiveMechanism):
+                if isinstance(rcvr, LearningMechanism) or isinstance(rcvr, ObjectiveMechanism):
                     break
                 else:
                     arrow_color="black"
@@ -2735,31 +2790,31 @@ def show_graph(self,
         # add learning graph if show_learning
         if show_learning:
             rcvrs = list(learning_graph.keys())
-
             for rcvr in rcvrs:
-                    # if rcvr is projection
-                    if isinstance(rcvr, MappingProjection):
-                        # for each sndr of rcvr
-                        sndrs = learning_graph[rcvr]
-                        for sndr in sndrs:
-                            edge_label = rcvr.parameterStates['matrix'].receivesFromProjections[0].name
-                            G.edge(sndr.name, rcvr.name, color=learning_color, label = edge_label)
-                    else:
-                        sndrs = learning_graph[rcvr]
-                        for sndr in sndrs:
-                            projs = sndr.outputState.sendsToProjections
-                            for proj in projs:
-                                if proj.receiver.owner == rcvr:
-                                    edge_name = proj.name
-                            G.node(rcvr.name, color=learning_color)
-                            G.node(sndr.name, color=learning_color)
-                            G.edge(sndr.name, rcvr.name, color=learning_color, label=edge_name)
+                # if rcvr is projection
+                if isinstance(rcvr, MappingProjection):
+                    # for each sndr of rcvr
+                    sndrs = learning_graph[rcvr]
+                    for sndr in sndrs:
+                        edge_label = rcvr._parameter_states['matrix'].mod_afferents[0].name
+                        G.edge(sndr.name, rcvr.name, color=learning_color, label = edge_label)
+                else:
+                    sndrs = list(learning_graph[rcvr])
+                    for sndr in sndrs:
+                        projs = sndr.input_state.path_afferents
+
+                        for proj in projs:
+                            edge_name=proj.name
+                        G.node(rcvr.name, color=learning_color)
+                        G.node(sndr.name, color=learning_color)
+                        G.edge(sndr.name, rcvr.name, color=learning_color, label=edge_name)
+
 
         # add control graph if show_control
         if show_control:
             controller = self.controller
 
-            connector = controller.inputState.receivesFromProjections[0]
+            connector = controller.input_state.path_afferents[0]
             objmech = connector.sender.owner
 
             # main edge
@@ -2768,21 +2823,21 @@ def show_graph(self,
             G.edge(objmech.name, controller.name, label=connector.name, color=control_color)
 
             # outgoing edges
-            for output_state in controller.controlSignals:
-                for projection in output_state.sendsToProjections:
+            for output_state in controller.control_signals:
+                for projection in output_state.efferents:
                     edge_name
                     rcvr_name = projection.receiver.owner.name
                     G.edge(controller.name, rcvr_name, label=projection.name, color=control_color)
 
             # incoming edges
-            for istate in objmech.inputStates.values():
-                for proj in istate.receivesFromProjections:
+            for istate in objmech.input_states:
+                for proj in istate.path_afferents:
                     sndr_name = proj.sender.owner.name
                     G.edge(sndr_name, objmech.name, label=proj.name, color=control_color)
 
             # prediction mechanisms
-            for mech_tuple in self.executionList:
-                mech = mech_tuple[0]
+            for object_item in self.executionList:
+                mech = object_item[0]
                 if mech._role is CONTROL:
                     G.node(mech.name, color=control_color)
                     recvr = mech.origin_mech
@@ -2822,6 +2877,6 @@ def __init__(self, owner=None, variable=None, name=None, prefs=None):
         else:
             self.name = owner.name + "_" + name
         self.prefs = prefs
-        self.sendsToProjections = []
+        self.efferents = []
         self.owner = owner
-        self.value = variable
\ No newline at end of file
+        self.value = variable
diff --git a/PsyNeuLink/Components/__init__.py b/PsyNeuLink/Components/__init__.py
index 0051a24a4a1..8f4fb784aa5 100644
--- a/PsyNeuLink/Components/__init__.py
+++ b/PsyNeuLink/Components/__init__.py
@@ -34,49 +34,35 @@ def __str__(self):
 
 from PsyNeuLink.Components.Mechanisms.Mechanism import Mechanism_Base
 from PsyNeuLink.Components.Mechanisms.Mechanism import MechanismRegistry
-from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DefaultProcessingMechanism import DefaultProcessingMechanism_Base
-from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.DefaultControlMechanism import DefaultControlMechanism
-from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.EVCMechanism import EVCMechanism
-
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DefaultProcessingMechanism \
+    import DefaultProcessingMechanism_Base
+from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.EVCMechanism \
+    import EVCMechanism
+from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.ControlMechanism \
+    import ControlMechanism_Base
+register_category(entry=ControlMechanism_Base,
+                  base_class=Mechanism_Base,
+                  registry=MechanismRegistry,
+                  context=kwInitPy)
 
-# DDM ------------------------------------------------------------------------------------------------------------------
+from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.DefaultControlMechanism \
+    import DefaultControlMechanism
+register_category(entry=DefaultControlMechanism,
+                  base_class=Mechanism_Base,
+                  registry=MechanismRegistry,
+                  context=kwInitPy)
 
+# DDM (used as DefaultMechanism)
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import DDM
-# DDM.register_category(DDM)
 register_category(entry=DDM,
                   base_class=Mechanism_Base,
                   registry=MechanismRegistry,
                   context=kwInitPy)
-# DDM_MECHANISM = DDM.__name__
-
-# # ControlMechanisms ----------------------------------------------------------------------------------------------
-#
-# # ControlMechanism
-# from Components.Mechanisms.ControlMechanism import ControlMechanism_Base
-# from Components.Mechanisms.ControlMechanism import ControlMechanismRegistry
-#
-# # DefaultControlMechanism
-# from Components.Mechanisms.DefaultControlMechanism import DefaultControlMechanism
-# register_category(DefaultControlMechanism,
-#                   ControlMechanism_Base,
-#                   ControlMechanismRegistry,
-#                   context=kwInitPy)
-# # DEFAULT_CONTROL_MECHANISM = DefaultControlMechanism.__name__
-#
-# # EVCMechanism
-# from Components.Mechanisms.EVCMechanism  import EVCMechanism
-# register_category(EVCMechanism,
-#                   ControlMechanism_Base,
-#                   ControlMechanismRegistry,
-#                   context=kwInitPy)
-# # EVC_MECHANISM = EVCMechanism.__name__
-#
 
 #endregion
 
 #region *************************************** ASSIGN DEFAULT MECHANISMS **********************************************
 
-
 # Use as default Mechanism in Process and in calls to mechanism()
 # Note: this must be a class (i.e., not an instantiated object)
 Mechanism_Base.defaultMechanism = MechanismRegistry[Mechanism_Base.defaultMechanism].subclass
@@ -85,59 +71,17 @@ def __str__(self):
 # Note: this must be an instantiated object
 DefaultProcessingMechanism = DefaultProcessingMechanism_Base(name=DEFAULT_PROCESSING_MECHANISM)
 
-# MODIFIED 2/22/17 OLD:
-# # Use as DefaultPreferenceSetOwner if owner is not specified for ComponentPreferenceSet (in ComponentPreferenceSet)
-# # Note: this must be an instantiated object
-# DefaultMonitoringMechanism = ComparatorMechanism(name=DEFAULT_MONITORING_MECHANISM)
-# MODIFIED 2/22/17 END
-
-# Use as PROJECTION_SENDER (default sender for ControlProjections) if sender is not specified (in ControlProjection)
-
-# Instantiates DefaultController (ControlMechanism):
-# - automatically assigned as the sender of default ControlProjections (that use the CONTROL_PROJECTION keyword)
-# - instantiated before a System and/or any (other) ControlMechanism (e.g., EVC) has been instantiated
-# - can be overridden in System by CONTROL_MECHANISM
-# - uses the defaultControlAllocation (specified in Globals.Defaults) to assign ControlProjection intensities
-# Note: this is an instantiated object
-DefaultController = DefaultControlMechanism(name=SYSTEM_DEFAULT_CONTROLLER)
-
-# Specifies subclass of ControlMechanism used as the default class of control mechanism to instantiate and assign,
-#    in place of DefaultController, when instantiating a System for which an existing control mech is specified
-# - if it is either not specified or is None, DefaultController will (continue to) be used (see above)
-# - if it is assigned to another subclass of ControlMechanism, its instantiation moves all of the
-#     existing ControlProjections from DefaultController to that instance of the specified subclass
+# Specifies subclass used to instantiate a ControlMechanism if it is not specified for a System being instantiated
 # Note: must be a class
 # SystemDefaultControlMechanism = EVCMechanism
 SystemDefaultControlMechanism = DefaultControlMechanism
 
-# MODIFIED 6/28/16 NEW:
-# FIX:  CAN'T INSTANTIATE OBJECT HERE, SINCE system IS NOT YET KNOWN
-#       COULD USE CLASS REFERENCE (HERE AND ABOVE), BUT THEN HAVE TO INSURE A SINGLE OBJECT IS INSTANTIATED
-#       AT SOME POINT AND THAT THAT IS THE ONLY ONE USED THEREAFTER;  WHERE TO DO THAT INSTANTIATION?
-#       WHEN CONTROLLER IS ASSIGNED TO SYSTEM??
-# DefaultController = EVCMechanism(name=EVC_MECHANISM)
-# DefaultController = EVCMechanism
-# MODIFIED END:
 
-# # # MODIFIED 6/28/16: EVC -- COMMENT OUT TO RUN
-# from Components.System import System_Base
-# # Use as default System (by EVC)
-# DefaultSystem = System_Base(name = DEFAULT_SYSTEM)
-
-
-# # Assign default inputState for Mechanism subclasses
-# for mechanism in MechanismRegistry:
-#     try:
-#         mechanism.inputStateTypeDefault = MechanismRegistry[mechanism.inputStateDefault].subclass
-#     except AttributeError:
-#         raise InitError("inputStateDefault not defined for".format(mechanism))
-#     except (KeyError, NameError):
-#         raise InitError("{0} not found in MechanismRegistry".format(mechanism))
-#     else:
-#         if not isinstance(mechanism.inputStateDefault, Mechanism_Base):
-#             raise InitError("inputStateDefault for {0} ({1}) must be a InputState".
-#                             format(mechanism, mechanism.inputStateDefault))
-#endregion
+# MODIFIED 2/22/17 OLD:
+# # Use as DefaultPreferenceSetOwner if owner is not specified for ComponentPreferenceSet (in ComponentPreferenceSet)
+# # Note: this must be an instantiated object
+# DefaultMonitoringMechanism = ComparatorMechanism(name=DEFAULT_MONITORING_MECHANISM)
+# MODIFIED 2/22/17 END
 
 #region ****************************************** REGISTER SUBCLASSES *************************************************
 
@@ -163,7 +107,6 @@ def __str__(self):
                   base_class=State_Base,
                   registry=StateRegistry,
                   context=kwInitPy)
-# INPUT_STATE = InputState.__name__
 
 # OutputState
 from PsyNeuLink.Components.States.OutputState import OutputState
@@ -171,7 +114,6 @@ def __str__(self):
                   base_class=State_Base,
                   registry=StateRegistry,
                   context=kwInitPy)
-# OUTPUT_STATE = OutputState.__name__
 
 # ParameterState
 from PsyNeuLink.Components.States.ParameterState import ParameterState
@@ -179,10 +121,6 @@ def __str__(self):
                   base_class=State_Base,
                   registry=StateRegistry,
                   context=kwInitPy)
-# PARAMETER_STATE = ParameterState.__name__
-
-# MODIFIED 9/11/16 END
-
 
 # Projection -----------------------------------------------------------------------------------------------------------
 
@@ -191,28 +129,25 @@ def __str__(self):
 from PsyNeuLink.Components.Projections.Projection import ProjectionRegistry
 
 # MappingProjection
-from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
 register_category(entry=MappingProjection,
                   base_class=Projection_Base,
                   registry=ProjectionRegistry,
                   context=kwInitPy)
-# MAPPING_PROJECTION = MappingProjection.__name__
 
 # ControlProjection
-from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection
+from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
 register_category(entry=ControlProjection,
                   base_class=Projection_Base,
                   registry=ProjectionRegistry,
                   context=kwInitPy)
-# CONTROL_PROJECTION = ControlProjection.__name__
 
 # LearningProjection
-from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection
+from PsyNeuLink.Components.Projections.ModulatoryProjections.LearningProjection import LearningProjection
 register_category(entry=LearningProjection,
                   base_class=Projection_Base,
                   registry=ProjectionRegistry,
                   context=kwInitPy)
-# LEARNING_PROJECTION = LearningProjection.__name__
 
 #endregion
 
@@ -329,6 +264,7 @@ def __str__(self):
                                              level=PreferenceLevel.TYPE,
                                              context=".__init__.py")
 
+
 from PsyNeuLink.Components.States.State import State
 State.classPreferences = ComponentPreferenceSet(owner=State,
                                                prefs=ComponentDefaultPrefDicts[PreferenceLevel.CATEGORY],
diff --git a/PsyNeuLink/Globals/Defaults.py b/PsyNeuLink/Globals/Defaults.py
index 29681c354f6..5d41918616d 100644
--- a/PsyNeuLink/Globals/Defaults.py
+++ b/PsyNeuLink/Globals/Defaults.py
@@ -35,3 +35,11 @@ class DefaultControlAllocationMode(Enum):
     BADGER_MODE = 1.0
     TEST_MODE = 240
 defaultControlAllocation = [DefaultControlAllocationMode.BADGER_MODE.value] #: This is a string
+
+# Default gating policy mode values:
+class DefaultGatingPolicyMode(Enum):
+    PHASIC_MODE = 1.0
+    TONIC_MODE = 0.5
+    SLEEP_MODE = 0.0
+    TEST_MODE = 240
+defaultGatingPolicy = [DefaultGatingPolicyMode.TONIC_MODE.value]
\ No newline at end of file
diff --git a/PsyNeuLink/Globals/Keywords.py b/PsyNeuLink/Globals/Keywords.py
index 80060b827b0..d602af4cfd6 100644
--- a/PsyNeuLink/Globals/Keywords.py
+++ b/PsyNeuLink/Globals/Keywords.py
@@ -30,7 +30,7 @@ class Keywords:
         receive projections from other processes in the system. The `ORIGIN` mechanisms of a process or
         system are listed in its :keyword:`originMechanisms` attribute, and can be displayed using its :keyword:`show`
         method.  For additional details about `ORIGIN` mechanisms in processes, see
-        `Process Mechanisms <Process_Mechanisms>` and `Process Input and Output <Process_Input_And_Ouput>`;
+        `Process Mechanisms <Process_Mechanisms>` and `Process Input and Output <Process_Input_And_Output>`;
         and for systems see `System Mechanisms <System_Mechanisms>` and
         `System Input and Initialization <System_Execution_Input_And_Initialization>`.
 
@@ -58,7 +58,7 @@ class Keywords:
         as it may send projections to other processes in the system.  The `TERMINAL` mechanisms of a process
         or system are listed in its :keyword:`terminalMechanisms` attribute, and can be displayed using its
         :keyword:`show` method.  For additional details about `TERMINAL` mechanisms in processes, see
-        `Process_Mechanisms` and `Process_Input_And_Ouput`; and for systems see `System_Mechanisms`.
+        `Process_Mechanisms` and `Process_Input_And_Output`; and for systems see `System_Mechanisms`.
 
     SINGLETON
         A `ProcessingMechanism` that is the only mechanism in a process and/or system.  It can serve the functions
@@ -100,7 +100,7 @@ class MatrixKeywords:
         a square matrix of 1's along the diagnoal, 0's elsewhere; this requires that the length of the sender and 
         receiver values are the same.
 
-    OFF_DIAGONAL_MATRIX
+    HOLLOW_MATRIX
         a square matrix of 0's along the diagnoal, 1's elsewhere; this requires that the length of the sender and 
         receiver values are the same.
 
@@ -124,7 +124,7 @@ class MatrixKeywords:
     def __init__(self):
         self.MATRIX = MATRIX
         self.IDENTITY_MATRIX = IDENTITY_MATRIX
-        self.OFF_DIAGONAL = OFF_DIAGNOAL_MATRIX
+        self.HOLLOW_MATRIX = HOLLOW_MATRIX
         self.FULL_CONNECTIVITY_MATRIX = FULL_CONNECTIVITY_MATRIX
         self.RANDOM_CONNECTIVITY_MATRIX = RANDOM_CONNECTIVITY_MATRIX
         self.AUTO_ASSIGN_MATRIX = AUTO_ASSIGN_MATRIX
@@ -133,12 +133,15 @@ def __init__(self):
     def _values(self):
         return list(self.__dict__.values())
 
+    def _set(self):
+        return set(self.__dict__.values())
+
     def _names(self):
         return list(self.__dict__)
 
 MATRIX = "matrix"
 IDENTITY_MATRIX = "IdentityMatrix"
-OFF_DIAGNOAL_MATRIX = "OffDiagonalMatrix"
+HOLLOW_MATRIX = "HollowMatrix"
 FULL_CONNECTIVITY_MATRIX = "FullConnectivityMatrix"
 RANDOM_CONNECTIVITY_MATRIX = "RandomConnectivityMatrix"
 AUTO_ASSIGN_MATRIX = 'AutoAssignMatrix'
@@ -146,11 +149,13 @@ def _names(self):
 DEFAULT_MATRIX = IDENTITY_MATRIX
 
 MATRIX_KEYWORDS = MatrixKeywords()
+MATRIX_KEYWORD_SET = MATRIX_KEYWORDS._set()
 MATRIX_KEYWORD_VALUES = MATRIX_KEYWORDS._values()
 MATRIX_KEYWORD_NAMES = MATRIX_KEYWORDS._names()
 # MATRIX_KEYWORD_VALUES = list(MATRIX_KEYWORDS.__dict__.values())
 # MATRIX_KEYWORD_NAMES = list(MATRIX_KEYWORDS.__dict__)
 
+
 # **********************************************************************************************************************
 # ******************************************    CONSTANTS  *************************************************************
 # **********************************************************************************************************************
@@ -160,6 +165,7 @@ def _names(self):
 DEFAULT = False
 AUTO = True
 
+
 # Used by initDirective
 INIT_FULL_EXECUTE_METHOD = 'init using the full base class execute method'
 INIT__EXECUTE__METHOD_ONLY = 'init using only the subclass _execute method'
@@ -177,12 +183,13 @@ def _names(self):
 INITIALIZING = " INITIALIZING "  # Used as context for Log
 kwInstantiate = " INSTANTIATING "  # Used as context for Log
 EXECUTING = " EXECUTING " # Used in context for Log and ReportOutput pref
-kwAssign = ': Assign' # Used in context for Log
+kwAssign = '| Assign' # Used in context for Log
 ASSIGN_VALUE = ': Assign value'
 kwAggregate = ': Aggregate' # Used in context for Log
-kwReceiver = "receiver"
+RECEIVER = "receiver"
 VALIDATE = 'Validate'
 COMMAND_LINE = "COMMAND_LINE"
+SET_ATTRIBUTE = "SET ATTRIBUTE"
 kwParams = 'params'
 CHANGED = 'CHANGED'
 UNCHANGED = 'UNCHANGED'
@@ -234,12 +241,14 @@ def _names(self):
 
 #region ----------------------------------------------   COMPONENT   ---------------------------------------------------
 
-# General:
-PREFS_ARG = "prefs"
+# Standard arg / attribute names:
 VARIABLE = "variable"
-NAME = "name"
+VALUE = "value"
 PARAMS = "params"
+NAME = "name"
+PREFS_ARG = "prefs"
 CONTEXT = "context"
+STANDARD_ARGS = {NAME, VARIABLE, VALUE, PARAMS, PREFS_ARG, CONTEXT}
 
 INITIAL_VALUES = 'initial_values'
 
@@ -251,6 +260,7 @@ def _names(self):
 
 COMPONENT_INIT = 'Component.__init__'
 DEFERRED_INITIALIZATION = 'Deferred Init'
+DEFERRED_ASSIGNMENT = 'Deferred Assignment'
 DEFERRED_DEFAULT_NAME = 'DEFERRED_DEFAULT_NAME'
 USER_PARAMS = 'user_params' # Params available to user for inspection in user_params dict
 FUNCTION = "function" # Param name for function, method, or type to instantiate and assign to self.execute
@@ -262,9 +272,8 @@ def _names(self):
                                                    #    in general, this includes params specifed as arg in a
                                                    #    to Function.execute;  however, there are some exceptions
                                                    #    in which those are kept separate from paramsCurrent (see DDM)
-
 FUNCTION_CHECK_ARGS = 'super._check_args' # Use for "context" arg
-kwFunctionOutputTypeConversion = "FunctionOutputTypeConversion" # Used in Function Components to set output type
+FUNCTION_OUTPUT_TYPE_CONVERSION = "FunctionOutputTypeConversion" # Used in Function Components to set output type
 
 #endregion
 
@@ -283,20 +292,26 @@ def _names(self):
 
 # Mechanisms:
 PROCESSING_MECHANISM = "ProcessingMechanism"
-MONITORING_MECHANISM = "MonitoringMechanism"
-CONTROL_MECHANISM = "ControlMechanism"
+ADAPTIVE_MECHANISM = "AdpativeMechanism"
 LEARNING_MECHANISM = "LearningMechanism"
+CONTROL_MECHANISM = "ControlMechanism"
+GATING_MECHANISM = 'GatingMechanism'
 
 # States:
 INPUT_STATE = "InputState"
-OUTPUT_STATE = "OutputState"
 PARAMETER_STATE = "ParameterState"
+OUTPUT_STATE = "OutputState"
+MODULATORY_SIGNAL = 'ModulatorySignal'
 
 # Projections:
 MAPPING_PROJECTION = "MappingProjection"
 LEARNING_PROJECTION = "LearningProjection"
 CONTROL_PROJECTION = "ControlProjection"
 GATING_PROJECTION = "GatingProjection"
+TRANSMISSIVE_PROJECTION = "TransmissiveProjection"
+MODULATORY_PROJECTION = "ModulatoryProjection"
+MODULATORY_PROJECTIONS = "ModulatoryProjections"
+
 
 # Function:
 EXAMPLE_FUNCTION_TYPE = "EXAMPLE FUNCTION"
@@ -304,10 +319,10 @@ def _names(self):
 COMBINATION_FUNCTION_TYPE = "COMBINATION FUNCTION TYPE"
 DIST_FUNCTION_TYPE = "DIST FUNCTION TYPE"
 INTEGRATOR_FUNCTION_TYPE = "INTEGRATOR FUNCTION TYPE"
-TRANFER_FUNCTION_TYPE = "TRANSFER FUNCTION TYPE"
-LEARNING_FUNCTION_TYPE = 'LEARNING FUNCTION TYPE'
+TRANSFER_FUNCTION_TYPE = "TRANSFER FUNCTION TYPE"
 DISTRIBUTION_FUNCTION_TYPE = "DISTRIBUTION FUNCTION TYPE"
-
+OBJECTIVE_FUNCTION_TYPE = "OBJECTIVE FUNCTION TYPE"
+LEARNING_FUNCTION_TYPE = 'LEARNING FUNCTION TYPE'
 
 
 # Component SUBTYPES -----------------
@@ -317,45 +332,67 @@ def _names(self):
 EVC_MECHANISM = "EVCMechanism"
 
 # MonitoringMechanisms:
-COMPARATOR_MECHANISM = "ComparatorMechanism"
-WEIGHTED_ERROR_MECHANISM = "WeightedErrorMechanism"
 OBJECTIVE_MECHANISM = "ObjectiveMechanism"
+COMPARATOR_MECHANISM = "ComparatorMechanism"
+MONITORING_MECHANISM = "MonitoringMechanism"
 
 # ProcessingMechanisms:
-DDM_MECHANISM = "DDM"
 TRANSFER_MECHANISM = "TransferMechanism"
-INTEGRATOR_MECHANISM = "IntegratorMechanmism"
+RECURRENT_TRANSFER_MECHANISM = "RecurrentTransferMechanism"
+LCA = "LCA"
+INTEGRATOR_MECHANISM = "IntegratorMechanism"
+DDM_MECHANISM = "DDM"
 
-# Function:
-ARGUMENT_THERAPY_FUNCTION = "Contradiction"
+# Functions:
+ARGUMENT_THERAPY_FUNCTION = "Contradiction Function"
 USER_DEFINED_FUNCTION = "USER DEFINED FUNCTION"
-REDUCE_FUNCTION = "Reduce"
-LINEAR_COMBINATION_FUNCTION = "LinearCombination"
-WEIGHTED_ERROR_FUNCTION = "WeighedErrorFunction"
-LINEAR_FUNCTION = "Linear"
-EXPONENTIAL_FUNCTION = "Exponential"
-LOGISTIC_FUNCTION = "Logistic"
-SOFTMAX_FUNCTION = 'SoftMax'
-INTEGRATOR_FUNCTION = "Integrator"
-LINEAR_MATRIX_FUNCTION = "Linear Matrix"
-BACKPROPAGATION_FUNCTION = 'Backpropagation Learning Algorithm'
-RL_FUNCTION = 'Reinforcement Learning Algorithm'
-ERROR_DERIVATIVE_FUNCTION = 'Error Derivative'
-
-#Distribution functions 
-
-NORMAL_DIST_FUNCTION = "Normal Distribution"
-UNIFORM_DIST_FUNCTION = "Uniform Distribution"
-EXPONENTIAL_DIST_FUNCTION = "Exponential Distribution"
-GAMMA_DIST_FUNCTION = "Gamma Distribution"
-WALD_DIST_FUNCTION = "Wald Distribution"
+REDUCE_FUNCTION = "Reduce Function"
+LINEAR_COMBINATION_FUNCTION = "LinearCombination Function"
+LINEAR_FUNCTION = "Linear Function"
+EXPONENTIAL_FUNCTION = "Exponential Function"
+LOGISTIC_FUNCTION = "Logistic Function"
+SOFTMAX_FUNCTION = 'SoftMax Function'
+INTEGRATOR_FUNCTION = "Integrator Function"
+SIMPLE_INTEGRATOR_FUNCTION = "SimpleIntegrator Function"
+CONSTANT_INTEGRATOR_FUNCTION = "ConstantIntegrator Function"
+ACCUMULATOR_INTEGRATOR_FUNCTION = "AccumualtorIntegrator Function"
+ADAPTIVE_INTEGRATOR_FUNCTION = "AdaptiveIntegrator Function"
+DRIFT_DIFFUSION_INTEGRATOR_FUNCTION = "DriftDiffusionIntegrator Function"
+ORNSTEIN_UHLENBECK_INTEGRATOR_FUNCTION = "OU Integrator Function"
+LINEAR_MATRIX_FUNCTION = "LinearMatrix Function"
+BACKPROPAGATION_FUNCTION = 'Backpropagation Learning Function'
+RL_FUNCTION = 'ReinforcementLearning Function'
+ERROR_DERIVATIVE_FUNCTION = 'Error Derivative Function'
+
+# Distribution functions
+
+NORMAL_DIST_FUNCTION = "Normal Distribution Function"
+UNIFORM_DIST_FUNCTION = "Uniform Distribution Function"
+EXPONENTIAL_DIST_FUNCTION = "Exponential Distribution Function"
+GAMMA_DIST_FUNCTION = "Gamma Distribution Function"
+WALD_DIST_FUNCTION = "Wald Distribution Function"
+
+# Objective functions
+STABILITY_FUNCTION = 'Stability Function'
+DISTANCE_FUNCTION = 'Distance Function'
+
+ENERGY = 'energy'
+ENTROPY = 'entropy'
 
+DIFFERENCE = 'difference'
+EUCLIDEAN = 'euclidean'
+ANGLE = 'angle'
+CORRELATION = 'correlation'
+PEARSON = 'Pearson'
+CROSS_ENTROPY = 'cross-entropy'
+DISTANCE_METRICS = {DIFFERENCE, EUCLIDEAN, ANGLE, CORRELATION, PEARSON, CROSS_ENTROPY}
 
 #endregion
 
 #region ----------------------------------------------    SYSTEM   ----------------------------------------------------
 
 SYSTEM = "System"
+SCHEDULER = "scheduler"
 SYSTEM_INIT = 'System.__init__'
 DEFAULT_SYSTEM = "DefaultSystem"
 CONTROLLER = "controller"
@@ -378,6 +415,7 @@ def _names(self):
 LEARNING = 'learning'
 LEARNING_RATE = "learning_rate"
 CONTROL = 'control'
+GATING = 'gating'
 kwProjections = "projections"
 kwProcessDefaultProjectionFunction = "Default Projection Function"
 kwProcessExecute = "ProcessExecute"
@@ -387,7 +425,6 @@ def _names(self):
 #region ---------------------------------------------    MECHANISM   ---------------------------------------------------
 
 MECHANISM = 'MECHANISM'
-kwMechanism = "MECHANISM"
 kwMechanismName = "MECHANISM NAME"
 kwMechanismDefault = "DEFAULT MECHANISM"
 DEFAULT_PROCESSING_MECHANISM = "DefaultProcessingMechanism"
@@ -408,12 +445,17 @@ def _names(self):
 SAMPLE = 'SAMPLE'
 TARGET = 'TARGET'
 
-kwStateValue = "State value"   # Used in State specification dict
+RESULT = 'RESULT'
+MEAN = 'MEAN'
+MEDIAN = 'MEDIAN'
+VARIANCE = 'VARIANCE'
+
+STATE_VALUE = "State value"   # Used in State specification dict
                                                  #  to specify State value
 STATE_PARAMS = "State params" # Used in State specification dict
 
 # ParamClassDefaults:
-kwMechanismTimeScale = "Mechanism Time Scale"
+MECHANISM_TIME_SCALE = "Mechanism Time Scale"
 kwMechanismExecutionSequenceTemplate = "Mechanism Execution Sequence Template"
 
 # Entries for output OrderedDict, describing the current state of the Mechanism
@@ -428,18 +470,32 @@ def _names(self):
 kwMechanismInterrogateFunction = "MECHANISM INTERROGATE FUNCTION"
 kwMechanismTerminateFunction = "MECHANISM TERMINATE FUNCTION"
 # TBI: kwMechanismAccuracyFunction = "MECHANISM ACCURACY FUNCTION"
+
+#DDM
+kwThreshold = 'thresh'
+kwInitialPoint = 'initial_point'
 #endregion
 
 
-#region ------------------------------------------    CONTROL MECHANISM   ----------------------------------------------
+#region ----------------------------------------    MODULATORY MECHANISMS ----------------------------------------------
+
+MODULATION = 'modulation'
 
+# ControlMechanism / EVCMechanism
 MAKE_DEFAULT_CONTROLLER = "make_default_controller"
 MONITOR_FOR_CONTROL = "monitor_for_control"
 PREDICTION_MECHANISM = "Prediction Mechanism"
 PREDICTION_MECHANISM_TYPE = "prediction_mechanism_type"
 PREDICTION_MECHANISM_PARAMS = "prediction_mechanism_params"
 PREDICTION_MECHANISM_OUTPUT = "PredictionMechanismOutput"
+LEARNING_SIGNAL = 'learning_signal'
+LEARNING_SIGNALS = 'learning_signals'
+LEARNING_SIGNAL_SPECS = 'LEARNING_SIGNAL_SPECS'
+LEARNED_PARAM = 'learned_param'
 CONTROL_SIGNAL = 'control_signal'
+CONTROL_SIGNALS = 'control_signals'
+CONTROL_SIGNAL_SPECS = 'CONTROL_SIGNAL_SPECS'
+CONTROLLED_PARAM = 'controlled_param'
 CONTROL_PROJECTIONS = 'ControlProjections'
 OUTCOME_FUNCTION = 'outcome_function'
 COST_FUNCTION = 'cost_function'
@@ -450,28 +506,52 @@ def _names(self):
 EVC_SIMULATION = 'SIMULATING'
 ALLOCATION_SAMPLES = "allocation_samples"
 
+# GatingMechanism
+MAKE_DEFAULT_GATING_MECHANISM = "make_default_gating_mechanism"
+GATING_SIGNAL = 'gating_signal'
+GATING_SIGNALS = 'gating_signals'
+GATING_SIGNAL_SPECS = 'GATING_SIGNAL_SPECS'
+GATE = 'GATE'
+GATED_STATE = "gated_state"
+GATING_PROJECTIONS = 'GatingProjections'
+GATING_POLICY = 'gating_policy'
+
 #endregion
 
 #region ----------------------------------------------    STATES  ------------------------------------------------------
 
-kwState = "State"
-# These are use for dict specification of State
+STATE = "State"
+# These are used as keys in State specification dictionaries
+STATES = "STATES"
 STATE_PROJECTIONS = "StateProjections"  # Used to specify projection list to State
 kwStateName = "StateName"
 kwStatePrefs = "StatePrefs"
 kwStateContext = "StateContext"
+kwAddInputState = 'kwAddNewInputState'     # Used by Mechanism._add_projection_to()
+kwAddOutputState = 'kwAddNewOutputState'   # Used by Mechanism._add_projection_from()
+FULL = 'FULL'
+OWNER = 'owner'
+REFERENCE_VALUE = 'reference_value'
 
+# InputStates:
+PRIMARY = 'Primary'
 INPUT_STATES = 'input_states'
 INPUT_STATE_PARAMS = 'input_state_params'
-kwAddInputState = 'kwAddNewInputState'     # Used by Mechanism._add_projection_to()
-kwAddOutputState = 'kwAddNewOutputState'   # Used by Mechanism._add_projection_from()
+WEIGHT = 'weight'
+EXPONENT = 'exponent'
+
+# ParameterStates:
 PARAMETER_STATES = 'parameter_states'
 PARAMETER_STATE_PARAMS = 'parameter_state_params'
-PARAMETER_MODULATION_OPERATION = 'parameter_modulation_operation'
+
+# OutputStates:
 OUTPUT_STATES = 'output_states'
 OUTPUT_STATE_PARAMS = 'output_states_params'
+STANDARD_OUTPUT_STATES = 'standard_output_states'
 INDEX = 'index'
 CALCULATE = 'calculate'
+
+
 #endregion
 
 #region ---------------------------------------------    PROJECTION  ---------------------------------------------------
@@ -481,10 +561,11 @@ def _names(self):
 PROJECTION_TYPE = "ProjectionType"
 PROJECTION_PARAMS = "ProjectionParams"
 MAPPING_PROJECTION_PARAMS = "MappingProjectionParams"
-CONTROL_PROJECTION_PARAMS = "ControlProjectionParams"
 LEARNING_PROJECTION_PARAMS = 'LearningProjectionParams'
+CONTROL_PROJECTION_PARAMS = "ControlProjectionParams"
+GATING_PROJECTION_PARAMS = 'GatingProjectionParams'
 PROJECTION_SENDER = 'projection_sender'
-kwSenderArg = 'sender'
+SENDER = 'sender'
 PROJECTION_SENDER_VALUE =  "projection_sender_value"
 kwProjectionReceiver = 'projection_receiver'
 kwReceiverArg = 'receiver'
@@ -497,8 +578,10 @@ def _names(self):
 #region ----------------------------------------------    FUNCTION   ---------------------------------------------------
 
 
+FUNCTION_OUTPUT_TYPE = 'functionOutputType'
+
 SUM = 'sum'
-DIFFERENCE = 'difference'
+DIFFERENCE = DIFFERENCE # Defined above for DISTANCE_METRICS
 PRODUCT = 'product'
 QUOTIENT = 'quotient'
 SUBTRACTION = 'subtraction'
@@ -516,19 +599,21 @@ def _names(self):
 
 AUTO_DEPENDENT='auto_dependent'
 DRIFT_RATE = 'drift_rate'
+INCREMENT = 'increment'
 INTEGRATOR_FUNCTION = 'integrator_function'
 INTEGRATION_TYPE = "integration_type"
 TIME_STEP_SIZE = 'time_step_size'
-
-MEAN = 'mean'
-STANDARD_DEV = 'standard_dev'
+DECAY = 'decay'
 
 LOW = 'low'
 HIGH = 'high'
 
 BETA = 'beta'
 
-SHAPE = 'shape'
+DIST_SHAPE = 'dist_shape'
+
+STANDARD_DEV = 'standard_dev'
+DIST_MEAN = 'mean'
 
 OUTPUT_TYPE = 'output'
 ALL = 'all'
diff --git a/PsyNeuLink/Globals/Preferences/ComponentPreferenceSet.py b/PsyNeuLink/Globals/Preferences/ComponentPreferenceSet.py
index 81acb9a3636..46752544ed1 100644
--- a/PsyNeuLink/Globals/Preferences/ComponentPreferenceSet.py
+++ b/PsyNeuLink/Globals/Preferences/ComponentPreferenceSet.py
@@ -10,7 +10,7 @@
 #
 #
 from PsyNeuLink.Globals.Log import *
-from PsyNeuLink.Globals.Utilities import ModulationOperation
+from PsyNeuLink.Globals.Utilities import Modulation
 from PsyNeuLink.Globals.Preferences.PreferenceSet import *
 
 # Keypaths for preferences:
@@ -45,7 +45,7 @@
     kpParamValidationPref: PreferenceEntry(True, PreferenceLevel.SYSTEM),
     kpReportOutputPref: PreferenceEntry(False, PreferenceLevel.SYSTEM),
     kpLogPref: PreferenceEntry(LogLevel.OFF, PreferenceLevel.CATEGORY),
-    kpRuntimeParamModulationPref: PreferenceEntry(ModulationOperation.MULTIPLY, PreferenceLevel.SYSTEM),
+    kpRuntimeParamModulationPref: PreferenceEntry(Modulation.MULTIPLY, PreferenceLevel.SYSTEM),
     kpRuntimeParamStickyAssignmentPref: PreferenceEntry(False, PreferenceLevel.SYSTEM)}
 
 CategoryDefaultPreferencesDict = {
@@ -54,7 +54,7 @@
     kpParamValidationPref: PreferenceEntry(True, PreferenceLevel.CATEGORY),
     kpReportOutputPref: PreferenceEntry(False, PreferenceLevel.CATEGORY),
     kpLogPref: PreferenceEntry(LogLevel.VALUE_ASSIGNMENT, PreferenceLevel.CATEGORY),
-    kpRuntimeParamModulationPref: PreferenceEntry(ModulationOperation.MULTIPLY,PreferenceLevel.CATEGORY),
+    kpRuntimeParamModulationPref: PreferenceEntry(Modulation.MULTIPLY,PreferenceLevel.CATEGORY),
     kpRuntimeParamStickyAssignmentPref: PreferenceEntry(False, PreferenceLevel.CATEGORY)}
 
 TypeDefaultPreferencesDict = {
@@ -63,7 +63,7 @@
     kpParamValidationPref: PreferenceEntry(True, PreferenceLevel.TYPE),
     kpReportOutputPref: PreferenceEntry(False, PreferenceLevel.TYPE),
     kpLogPref: PreferenceEntry(LogLevel.OFF, PreferenceLevel.CATEGORY),   # This gives control to Mechanisms
-    kpRuntimeParamModulationPref: PreferenceEntry(ModulationOperation.ADD,PreferenceLevel.TYPE),
+    kpRuntimeParamModulationPref: PreferenceEntry(Modulation.ADD,PreferenceLevel.TYPE),
     kpRuntimeParamStickyAssignmentPref: PreferenceEntry(False, PreferenceLevel.TYPE)}
 
 SubtypeDefaultPreferencesDict = {
@@ -72,7 +72,7 @@
     kpParamValidationPref: PreferenceEntry(True, PreferenceLevel.SUBTYPE),
     kpReportOutputPref: PreferenceEntry(False, PreferenceLevel.SUBTYPE),
     kpLogPref: PreferenceEntry(LogLevel.OFF, PreferenceLevel.CATEGORY),   # This gives control to Mechanisms
-    kpRuntimeParamModulationPref: PreferenceEntry(ModulationOperation.ADD,PreferenceLevel.SUBTYPE),
+    kpRuntimeParamModulationPref: PreferenceEntry(Modulation.ADD,PreferenceLevel.SUBTYPE),
     kpRuntimeParamStickyAssignmentPref: PreferenceEntry(False, PreferenceLevel.SUBTYPE)}
 
 InstanceDefaultPreferencesDict = {
@@ -81,7 +81,7 @@
     kpParamValidationPref: PreferenceEntry(False, PreferenceLevel.INSTANCE),
     kpReportOutputPref: PreferenceEntry(False, PreferenceLevel.INSTANCE),
     kpLogPref: PreferenceEntry(LogLevel.OFF, PreferenceLevel.CATEGORY),   # This gives control to Mechanisms
-    kpRuntimeParamModulationPref: PreferenceEntry(ModulationOperation.OVERRIDE, PreferenceLevel.INSTANCE),
+    kpRuntimeParamModulationPref: PreferenceEntry(Modulation.OVERRIDE, PreferenceLevel.INSTANCE),
     kpRuntimeParamStickyAssignmentPref: PreferenceEntry(False, PreferenceLevel.INSTANCE)}
 
 # Dict of default dicts
@@ -119,7 +119,7 @@ class ComponentPreferenceSet(PreferenceSet):
             - paramValidation (bool):  enables/disables run-time validation of the execute method of a Function object
             - reportOutput (bool): enables/disables reporting of execution of execute method
             - log (bool): enables/disables logging for a given object
-            - functionRunTimeParams (ModulationOperation): uses run-time params to modulate execute method params
+            - functionRunTimeParams (Modulation): uses run-time params to modulate execute method params
         Implement the following preference levels:
             - SYSTEM: system level default settings (Function.classPreferences)
             - CATEGORY: category-level default settings:
@@ -218,7 +218,7 @@ class ComponentPreferenceSet(PreferenceSet):
             kpParamValidationPref: PreferenceEntry(True, PreferenceLevel.SYSTEM),
             kpReportOutputPref: PreferenceEntry(True, PreferenceLevel.SYSTEM),
             kpLogPref: PreferenceEntry(LogLevel.OFF, PreferenceLevel.CATEGORY),
-            kpRuntimeParamModulationPref: PreferenceEntry(ModulationOperation.MULTIPLY, PreferenceLevel.SYSTEM),
+            kpRuntimeParamModulationPref: PreferenceEntry(Modulation.MULTIPLY, PreferenceLevel.SYSTEM),
             kpRuntimeParamStickyAssignmentPref: PreferenceEntry(False, PreferenceLevel.SYSTEM)
 
     }
diff --git a/PsyNeuLink/Globals/Preferences/MechanismPreferenceSet.py b/PsyNeuLink/Globals/Preferences/MechanismPreferenceSet.py
index c4745df6878..4cf5d4f8278 100644
--- a/PsyNeuLink/Globals/Preferences/MechanismPreferenceSet.py
+++ b/PsyNeuLink/Globals/Preferences/MechanismPreferenceSet.py
@@ -10,7 +10,7 @@
 #
 #
 from PsyNeuLink.Globals.Preferences.ComponentPreferenceSet import *
-from PsyNeuLink.Globals.Utilities import ModulationOperation
+from PsyNeuLink.Globals.Utilities import Modulation
 from PsyNeuLink.Globals.Preferences import *
 
 # MODIFIED 11/29/16 OLD:
@@ -19,9 +19,9 @@
 # MODIFIED 11/29/16 END
 
 # Default PreferenceSets:
-runtimeParamModulationPrefInstanceDefault = PreferenceEntry(ModulationOperation.OVERRIDE, PreferenceLevel.INSTANCE)
-runtimeParamModulationPrefTypeDefault = PreferenceEntry(ModulationOperation.ADD, PreferenceLevel.TYPE)
-# runtimeParamModulationPrefCategoryDefault = PreferenceEntry(ModulationOperation.MULTIPLY, PreferenceLevel.CATEGORY)
+runtimeParamModulationPrefInstanceDefault = PreferenceEntry(Modulation.OVERRIDE, PreferenceLevel.INSTANCE)
+runtimeParamModulationPrefTypeDefault = PreferenceEntry(Modulation.ADD, PreferenceLevel.TYPE)
+# runtimeParamModulationPrefCategoryDefault = PreferenceEntry(Modulation.MULTIPLY, PreferenceLevel.CATEGORY)
 runtimeParamModulationPrefCategoryDefault = PreferenceEntry(False, PreferenceLevel.CATEGORY)
 
 runtimeParamStickyAssignmentPrefInstanceDefault = PreferenceEntry(False, PreferenceLevel.INSTANCE)
@@ -156,7 +156,7 @@ def runtimeParamModulationPref(self, setting):
             print("setting of runtimeParamModulation preference ({0}) must be a {1} or a function;"
                   " it will remain unchanged ({2})".
                   format(setting,
-                         ModulationOperation.__class__.__name__,
+                         Modulation.__class__.__name__,
                          self._runtime_param_modulation_pref.setting))
             return
 
@@ -232,7 +232,7 @@ def runtimeParamStickyAssignmentPref(self, setting):
             print("setting of runtimeParamStickyAssignment preference ({0}) must be a {1} or a function;"
                   " it will remain unchanged ({2})".
                   format(setting,
-                         ModulationOperation.__class__.__name__,
+                         Modulation.__class__.__name__,
                          self._runtime_param_sticky_assignment_pref.setting))
             return
 
diff --git a/PsyNeuLink/Globals/Preferences/PreferenceSet.py b/PsyNeuLink/Globals/Preferences/PreferenceSet.py
index 3584bb0d111..1cc49004340 100644
--- a/PsyNeuLink/Globals/Preferences/PreferenceSet.py
+++ b/PsyNeuLink/Globals/Preferences/PreferenceSet.py
@@ -678,9 +678,9 @@ def get_pref_setting_for_level(self, pref_ivar_name, requested_level=None):
         Arguments:
         - pref_ivar_name (str): name of ivar for preference attribute for which to return the setting;
         - requested_level (PreferenceLevel): preference level for which the setting should be returned
-
-        :param requested_level: (PreferenceLevel)
-        :return PreferenceEntry.setting, str:
+        
+        Returns:
+        - PreferenceEntry.setting, str:
         """
         pref_entry = getattr(self, pref_ivar_name)
 
@@ -698,14 +698,14 @@ def get_pref_setting_for_level(self, pref_ivar_name, requested_level=None):
             elif requested_level > self.owner.__class__.classPreferenceLevel:
                 # IMPLEMENTATION NOTE: REMOVE HACK BELOW, ONCE ALL CLASSES ARE ASSIGNED classPreferences ON INIT
                 next_level = self.owner.__class__.__bases__[0]
-                try:
-                    next_level.classPreferences
-                except AttributeError:
-                    # If classPreferences for level have not been assigned, assign them
+                # MODIFIED ~4/30/17 NEW:
+                # If classPreferences for level have not yet been assigned as PreferenceSet, assign them
+                if (not hasattr(next_level, 'classPreferences') or
+                        not isinstance(next_level.classPreferences, PreferenceSet)):
                     from PsyNeuLink.Globals.Preferences.ComponentPreferenceSet import ComponentPreferenceSet
                     ComponentPreferenceSet(owner=next_level, level=next_level.classPreferenceLevel)
-                return_val = next_level.classPreferences.get_pref_setting_for_level(pref_ivar_name,
-                                                                                    requested_level)
+                # MODIFIED ~4/30/17 END
+                return_val = next_level.classPreferences.get_pref_setting_for_level(pref_ivar_name, requested_level)
                 return return_val[0],return_val[1]
             # Otherwise, return value for current level
             else:
@@ -758,6 +758,14 @@ def get_pref_setting_for_level(self, pref_ivar_name, requested_level=None):
                                           pref_entry.level.__class__.__name__+'.'+pref_entry.level.name))
                         return pref_value, err_msg
                 else:
+                    # MODIFIED 5/2/17 NEW:
+                    # If classPreferences for level have not yet been assigned as PreferenceSet, assign them
+                    next_level = self.owner.__bases__[0]
+                    if (not hasattr(next_level, 'classPreferences') or
+                            not isinstance(next_level.classPreferences, PreferenceSet)):
+                        from PsyNeuLink.Globals.Preferences.ComponentPreferenceSet import ComponentPreferenceSet
+                        ComponentPreferenceSet(owner=next_level, level=next_level.classPreferenceLevel)
+                    # MODIFIED 5/2/17 END
                     return_val = self.owner.__bases__[0].classPreferences.get_pref_setting_for_level(pref_ivar_name,
                                                                                                requested_level)
                     return return_val[0], return_val[1]
@@ -796,17 +804,17 @@ def show(self, type=None):
                 # GET TABLE INFO
                 # Get base_value of pref
                 base_value, level = self.__dict__[pref_name]
-                # This is needed because value of ModulationOperation is callable (lambda function)
+                # This is needed because value of Modulation is callable (lambda function)
                 if inspect.isfunction(base_value):
-                    if 'ModulationOperation' in repr(base_value):
+                    if 'Modulation' in repr(base_value):
                         base_value = get_modulationOperation_name(base_value)
                 # Get current_value of pref
                 current_value, msg = self.get_pref_setting_for_level(pref_ivar_name=pref_name, requested_level=level)
                 if msg:
                     error_messages.append(msg)
-                # Get name of ModulationOperation (the value of which is a lambda function)
+                # Get name of Modulation (the value of which is a lambda function)
                 if inspect.isfunction(current_value):
-                    if 'ModulationOperation' in repr(current_value):
+                    if 'Modulation' in repr(current_value):
                         current_value = get_modulationOperation_name(current_value)
                 # Get name of any enums
                 if isinstance(base_value, (Enum, IntEnum)):
diff --git a/PsyNeuLink/Globals/Registry.py b/PsyNeuLink/Globals/Registry.py
index 92e08cba54a..5eba026888f 100644
--- a/PsyNeuLink/Globals/Registry.py
+++ b/PsyNeuLink/Globals/Registry.py
@@ -28,6 +28,7 @@
     INPUT_STATE: DEFAULT_REGISTRY_VERBOSITY,
     PARAMETER_STATE: DEFAULT_REGISTRY_VERBOSITY,
     OUTPUT_STATE: DEFAULT_REGISTRY_VERBOSITY,
+    GATING_SIGNAL: DEFAULT_REGISTRY_VERBOSITY,
     DDM_MECHANISM: DEFAULT_REGISTRY_VERBOSITY,
     kwProjectionComponentCategory: DEFAULT_REGISTRY_VERBOSITY,
     CONTROL_PROJECTION: DEFAULT_REGISTRY_VERBOSITY,
@@ -54,7 +55,6 @@ def register_category(entry,
                       name=None,
                       registry=None,
                       context='Registry'):
-# MODIFIED 9/10/16 END
 # DOCUMENT:
     """Maintains registry of subclasses for base_class, names instances incrementally (if duplicates), and sets default
 
@@ -123,10 +123,27 @@ def register_category(entry,
     # If entry is an instance (presumably of a component type of the base class):
     if isinstance(entry, base_class):
 
+        # MODIFIED 6/8/17 OLD:
+        # try:
+        #     component_type_name = entry.componentType
+        # except AttributeError:
+        #     component_type_name = entry.__class__.__name__
+        #
+        # # Component type is registered (i.e., there is an entry for component_type_name)
+        # if component_type_name in registry:
+        #     register_instance(entry=entry,
+        #                       name=name,
+        #                       base_class=base_class,
+        #                       registry=registry,
+        #                       sub_dict=component_type_name)
+        # MODIFIED 6/8/17 NEW:
         try:
-            component_type_name = entry.componentType
+            component_type_name = entry.componentName
         except AttributeError:
-            component_type_name = entry.__class__.__name__
+            try:
+                component_type_name = entry.componentType
+            except AttributeError:
+                component_type_name = entry.__class__.__name__
 
         # Component type is registered (i.e., there is an entry for component_type_name)
         if component_type_name in registry:
@@ -135,6 +152,7 @@ def register_category(entry,
                               base_class=base_class,
                               registry=registry,
                               sub_dict=component_type_name)
+        # MODIFIED 6/8/17 END
 
         # If component type is not already registered in registry, then:
         else:
diff --git a/PsyNeuLink/Globals/Run.py b/PsyNeuLink/Globals/Run.py
index bd4266f10b6..6b53a255685 100644
--- a/PsyNeuLink/Globals/Run.py
+++ b/PsyNeuLink/Globals/Run.py
@@ -107,7 +107,7 @@
     used for the format of the input for the execute method of a process or system.  This can be specified as a
     nested set of lists, or an ndarray.  The exact structure is determined by a number of factors, as described below.
     the number of `ORIGIN` mechanisms involved (a process has only one, but a system can have several), the
-    number of inputStates for each `ORIGIN` mechanism, and whether the input to those inputStates is
+    number of input_states for each `ORIGIN` mechanism, and whether the input to those input_states is
     single-element (such as scalars), multi-element (such as vectors) or a mix.  For the run method, the structure is
     further determined by whether only a single execution or multiple executions is specified.  Rather than specifying a
     single format structure that must be used for all purposes (which would necessarily be the most complex one),
@@ -115,7 +115,7 @@
     particular process or input, which can vary according to circumstance.  Examples are provided below.  In all cases,
     either nested lists or ndarrays can be used, in which the innermost level (highest axis of an ndarray) is used to
     specify the input values for a given inputState (if any are multi-element), the next nested level (second highest
-    axis) is used to specify the different inputStates of a given mechanism (if any have more than one), the level
+    axis) is used to specify the different input_states of a given mechanism (if any have more than one), the level
     (axis) after that is used to specify the different `ORIGIN` mechanisms (if there is more than one), and
     finally the outermost level (lowest axis) is used to specify different trials (if there is more than one to be run).
 
@@ -126,15 +126,15 @@
     an inputstae as a list or array (axis of an ndarray).
 COMMENT
 
-The :keyword:`run` function presents the inputs for each round of execution to the inputStates of the relevant
+The :keyword:`run` function presents the inputs for each round of execution to the input_states of the relevant
 mechanisms. These are specified in the **inputs** argument of the :keyword:`execute` or :keyword:`run` method.
-For a mechanism, they comprise the input value for each of the mechanism's `inputStates <InputState>`.  For a process
+For a mechanism, they comprise the input value for each of the mechanism's `input_states <InputState>`.  For a process
 or system, they comprise the input values for the inputState(s) of the `ORIGIN` mechanism(s).  Input values can be
 specified in one of two ways: `sequence format <Run_Inputs_Sequence_Format>` and `mechanism format <Run_Dict_format>`.
 Sequence format is more complex, but does not require the specification of mechanisms by name, and thus may better
 suited for automated means of generating inputs.  Mechanism format requires that inputs be assigned to mechanisms by
 name, but is easier to use (as the order in which the mechanisms are specified does not matter).  Both formats require
-that inputs be specified as nested lists or ndarrays, that define the number of executions, mechanisms, inputStates
+that inputs be specified as nested lists or ndarrays, that define the number of executions, mechanisms, input_states
 and elements of each input value.  These factors determine the levels of nesting required for a list, or
 the dimensionality (number of axes) for an ndarray.  They are described below, followed by a description of the two
 formats.
@@ -152,10 +152,10 @@
   has only one `ORIGIN` mechanism.  It is also not relevant for the `mechanism format <Run_Inputs_Mechanism_Format>`,
   since that separates the inputs for each mechanism into separate entries of a dictionary.
 ..
-* **Number of inputStates.** In general, mechanisms have a single ("primary") inputState; however, some types of
+* **Number of input_states.** In general, mechanisms have a single ("primary") inputState; however, some types of
   mechanisms can have more than one (see `Mechanism_InputStates`).  If any `ORIGIN` mechanism in a process or
   system has more than one inputState, then the next level of nesting of a list, or next higher axis of an ndarray,
-  is used for the set of inputStates for each mechanism.
+  is used for the set of input_states for each mechanism.
 ..
 * **Number of elements for the value of an inputState.** The input for an inputState can be a single element (e.g.,
   a scalar) or have multiple elements (e.g., a vector).  By convention, even if the input to an inputState is only a
@@ -187,8 +187,8 @@
     *Lists:* if there is more than one round, then the outermost level of the list is used for the sequence of
     executions.  If there is only one `ORIGIN` mechanism and it has only one inputState (the most common
     case), then a single sublist is used for the input of each round.  If the `ORIGIN` mechanism has more
-    than one inputState, then the entry for each round is a sublist of the inputStates, each entry of which is a 
-    sublist containing the input for that inputState.  If there is more than one mechanism, but none have more than 
+    than one inputState, then the entry for each round is a sublist of the input_states, each entry of which is a
+    sublist containing the input for that inputState.  If there is more than one mechanism, but none have more than
     one inputState, then a sublist is used for each mechanism in each round, within which a sublist is used for the
     input for that mechanism.  If there is more than one mechanism, and any have more than one inputState,
     then a sublist is used for each mechanism for each round, within which a sublist is used for each
@@ -199,10 +199,10 @@
     more than one round, only one `ORIGIN` mechanism, and that has only one inputState (the most common case),
     then axis 0 is used for round, and axis 1 for inputs per round.  In the extreme, if there are multiple rounds,
     more than one `ORIGIN` mechanism, and more than one inputState for one or more of the `ORIGIN` mechanisms,
-    then axis 0 is used for rounds, axis 1 for mechanisms within round, axis 2 for inputStates of each mechanism, and
+    then axis 0 is used for rounds, axis 1 for mechanisms within round, axis 2 for input_states of each mechanism, and
     axis 3 for the input to each inputState of a mechanism.  Note that if *any* mechanism being run (directly, or as
     one of the `ORIGIN` mechanisms of a process or system) has more than one inputState, then an axis must be
-    committed to inputStates, and the input to every inputState of every mechanism must be specified in that axis
+    committed to input_states, and the input to every inputState of every mechanism must be specified in that axis
     (i.e., even for those mechanisms that have a single inputState).
 
     .. _Run_Sequence_Format_Fig:
@@ -227,9 +227,9 @@
 process or system being run.  The key for each entry is the `ORIGIN` mechanism, and the value contains either a list
 or ndarray specifying the sequence of inputs for that mechanism, one for each round of execution.  If a list is used,
 and the mechanism has more than one inputState, then a sublist is used in each item of the list to specify the inputs
-for each of the mechanism's inputStates for that round.  If an ndarray is used, axis 0 is used for the sequence of
+for each of the mechanism's input_states for that round.  If an ndarray is used, axis 0 is used for the sequence of
 rounds. If the mechanism has a single inputState, then axis 1 is used for the input for each round.  If the mechanism
-has multiple inputStates, then axis 1 is used for the inputStates, and axis 2 is used for the input to each
+has multiple input_states, then axis 1 is used for the input_states, and axis 2 is used for the input to each
 inputState for each round.
 
     .. figure:: _static/Mechanism_format_input_specs_fig.*
@@ -249,7 +249,7 @@
 specified in the :keyword:`initial_values` argument of :keyword:`run`, as a dictionary. The key for each entry must
 be a mechanism designated as `INITIALIZE_CYCLE`, and its value an input for the mechanism to be used as its initial
 value.  The size of the input (length of the outermost level if it is a list, or axis 0 if it is an np.ndarray),
-must equal the number of inputStates of the mechanism, and the size of each value must match (in number and type of
+must equal the number of input_states of the mechanism, and the size of each value must match (in number and type of
 elements) that of the `variable <InputState.InputState.variable>` for the corresponding inputState.
 
 .. _Run_Targets:
@@ -263,7 +263,7 @@
 any of three formats.  The two formats used for :keyword:`inputs` (`sequence <Run_Inputs_Sequence_Format>` and
 `mechanism <Run_Inputs_Mechanism_Format>` format) can also be used for targets.  However, the format of the lists or
 ndarrays is simpler, since each `TARGET` mechanism is assigned only a single target value, so there is never the need
-for the extra level of nesting (or dimension of ndarray) used for inputStates in the specification of :keyword:`inputs`.
+for the extra level of nesting (or dimension of ndarray) used for input_states in the specification of :keyword:`inputs`.
 Details concerning the use of the `sequence <Run_Targets_Sequence_Format>`  and
 `mechanism <Run_Targets_Mechanism_Format>` formats for targets is described below. Targets can also be specified
 as a `function <Run_Targets_Function_Format>` (for example, to allow the target to depend on the outcome of processing).
@@ -338,14 +338,13 @@
 
 """
 
-
-import numpy as np
 from collections import Iterable
-from PsyNeuLink.Globals.Utilities import *
+
 from PsyNeuLink.Components.Component import function_type
-from PsyNeuLink.Components.System import System
-from PsyNeuLink.Components.Process import Process, ProcessInputState
 from PsyNeuLink.Components.Mechanisms.Mechanism import Mechanism
+from PsyNeuLink.Components.Process import Process, ProcessInputState
+from PsyNeuLink.Components.System import System
+from PsyNeuLink.Globals.Utilities import *
 
 HOMOGENOUS = 1
 HETEROGENOUS = 0
@@ -393,12 +392,14 @@ def run(object,
         intial_values:tc.optional(tc.any(list, np.ndarray))=None,
         targets:tc.optional(tc.any(list, dict, np.ndarray, function_type))=None,
         learning:tc.optional(bool)=None,
-        call_before_trial:tc.optional(function_type)=None,
-        call_after_trial:tc.optional(function_type)=None,
-        call_before_time_step:tc.optional(function_type)=None,
-        call_after_time_step:tc.optional(function_type)=None,
+        call_before_trial:tc.optional(callable)=None,
+        call_after_trial:tc.optional(callable)=None,
+        call_before_time_step:tc.optional(callable)=None,
+        call_after_time_step:tc.optional(callable)=None,
         clock=CentralClock,
         time_scale:tc.optional(tc.enum(TimeScale.TRIAL, TimeScale.TIME_STEP))=None,
+        termination_processing=None,
+        termination_learning=None,
         context=None):
     """run(                         \
     inputs,                      \
@@ -493,6 +494,7 @@ def run(object,
     """
 
     inputs = _construct_stimulus_sets(object, inputs)
+
     if targets:
         targets = _construct_stimulus_sets(object, targets, is_target=True)
 
@@ -546,10 +548,9 @@ def run(object,
 
     # SET LEARNING_RATE, if specified, for all learningProjections in process or system
     if object.learning_rate is not None:
-        from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ObjectiveMechanism import ObjectiveMechanism
-        from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection
+        from PsyNeuLink.Components.Projections.ModulatoryProjections.LearningProjection import LearningProjection
         for learning_mech in object.monitoringMechanisms.mechanisms:
-            for projection in learning_mech.outputState.sendsToProjections:
+            for projection in learning_mech.output_state.efferents:
                 if isinstance(projection, LearningProjection):
                     projection.function_object.learning_rate = object.learning_rate
 
@@ -636,6 +637,8 @@ def run(object,
                                     execution_id=execution_id,
                                     clock=clock,
                                     time_scale=time_scale,
+                                    termination_processing=termination_processing,
+                                    termination_learning=termination_learning,
                                     context=context)
 
             if call_after_time_step:
@@ -693,8 +696,8 @@ def _construct_stimulus_sets(object, stimuli, is_target=False):
        axis 0: num_input_sets
        axis 1: object._phaseSpecMax
        axis 2: len(object.originMechanisms)
-       axis 3: len(mech.inputStates)
-       axis 4: items of inputStates
+       axis 3: len(mech.input_states)
+       axis 4: items of input_states
 
     Notes:
     * Construct as lists and then convert to np.array, since size of inputs can be different for different mechs
@@ -728,7 +731,6 @@ def _construct_stimulus_sets(object, stimuli, is_target=False):
     return stim_list_array
 
 def _construct_from_stimulus_list(object, stimuli, is_target, context=None):
-
     object_type = _get_obect_type(object)
 
     # Check for header
@@ -776,26 +778,31 @@ def _construct_from_stimulus_list(object, stimuli, is_target, context=None):
     execution_offset = 0  # Used for indexing w/ headers
     stim_list = []
 
+    # NOTE: never happens as guessed
+    # if object.numPhases > 1:
+    #     print('NUM PHASES: {0}'.format(object.numPhases))
+    #     import code
+    #     code.interact(local=locals())
+
     for execution in range(num_input_sets):
         execution_len = 0  # Used for indexing w/ headers
         stimuli_in_execution = []
         for phase in range(object.numPhases):
             stimuli_in_phase = []
             for mech_num in range(num_mechs):
-                mech, runtime_params, phase_spec = list(object.originMechanisms.mech_tuples)[mech_num]
+                mech = list(object.originMechanisms.mechs)[mech_num]
                 mech_len = np.size(mechs[mech_num].variable)
                 # Assign stimulus of appropriate size for mech and fill with 0's
                 stimulus = np.zeros(mech_len)
                 # Assign input elements to stimulus if phase is correct one for mech
-                if phase == phase_spec:
-                    for stim_elem in range(mech_len):
-                        if headers:
-                            input_index = headers.index(mech) + execution_offset
-                        else:
-                            input_index = input_elem
-                        stimulus[stim_elem] = inputs_flattened[input_index]
-                        input_elem += 1
-                        execution_len += 1
+                for stim_elem in range(mech_len):
+                    if headers:
+                        input_index = headers.index(mech) + execution_offset
+                    else:
+                        input_index = input_elem
+                    stimulus[stim_elem] = inputs_flattened[input_index]
+                    input_elem += 1
+                    execution_len += 1
                 # Otherwise, assign vector of 0's with proper length
                 stimuli_in_phase.append(stimulus)
             stimuli_in_execution.append(stimuli_in_phase)
@@ -835,11 +842,11 @@ def _construct_from_stimulus_dict(object, stimuli, is_target):
         for target in object.targetMechanisms:
             # If any projection to a target does not have a sender in the stimulus dict, raise an exception
             if not any(mech is projection.sender.owner for
-                       projection in target.inputStates[SAMPLE].receivesFromProjections
+                       projection in target.input_states[SAMPLE].path_afferents
                        for mech in stimuli.keys()):
                     raise RunError("Entry for {} is missing from specification of targets for run of {}".
-                                   format(target.inputStates[SAMPLE].
-                                          receivesFromProjections[0].sender.owner.name,
+                                   format(target.input_states[SAMPLE].
+                                          afferents[0].sender.owner.name,
                                           object.name))
 
         # FIX: COULD JUST IGNORE THOSE, OR WARN ABOUT THEM IF VERBOSE?
@@ -850,11 +857,11 @@ def _construct_from_stimulus_dict(object, stimuli, is_target):
         for mech in stimuli.keys():
             # If any mechanism in the stimulus dict does not have a projection to the target, raise an exception
             if not any(target is projection.receiver.owner for
-                       projection in mech.outputState.sendsToProjections
+                       projection in mech.output_state.efferents
                        for target in object.targetMechanisms):
                 raise RunError("{} is not a target mechanism in {}".format(mech.name, object.name))
             # Get target mech (comparator) for each entry in stimuli dict:
-            terminal_to_target_mapping[mech] = mech.outputState.sendsToProjections[0]
+            terminal_to_target_mapping[mech] = mech.output_state.efferents[0]
 
         # Insure that target lists in dict are accessed in the same order as the
         #   targets in the system's targetMechanisms list, by reassigning targets to an OrderedDict:
@@ -864,7 +871,7 @@ def _construct_from_stimulus_dict(object, stimuli, is_target):
             # Get the process to which the TARGET mechanism belongs:
             try:
                 process = next(projection.sender.owner for
-                               projection in target.inputStates[TARGET].receivesFromProjections if
+                               projection in target.input_states[TARGET].path_afferents if
                                isinstance(projection.sender, ProcessInputState))
             except StopIteration:
                 raise RunError("PROGRAM ERROR: No process found for target mechanism ({}) "
@@ -925,6 +932,12 @@ def _construct_from_stimulus_dict(object, stimuli, is_target):
                 stims_in_execution.append(stimuli[mech][i])
             stim_list.append(stims_in_execution)
 
+    # NOTE: never happens as guessed
+    # if object.numPhases > 1:
+    #     print('NUM PHASES: {0}'.format(object.numPhases))
+    #     import code
+    #     code.interact(local=locals())
+
     # Otherwise, for inputs to a system, construct stimulus from dict with phases
     elif object_type is SYSTEM:
         for execution in range(num_input_sets):
@@ -933,19 +946,14 @@ def _construct_from_stimulus_dict(object, stimuli, is_target):
                 stimuli_in_phase = []
                 # Only assign inputs to originMechanisms
                 #    and assign them in the order they appear in originMechanisms and fill out each phase
-                for mech, runtime_params, phase_spec in object.originMechanisms.mech_tuples:
+                for mech in object.originMechanisms.mechs:
                     # Assign input elements to stimulus if phase is correct one for mech
-                    if phase == phase_spec:
-                        # Get stimulus for mech for current execution, and enforce 2d to accomodate inputStates per mech
-                        stimulus = np.atleast_2d(stimuli[mech][execution])
-                        if not isinstance(stimulus, Iterable):
-                            stimulus = np.atleast_2d([stimulus])
-                    # Otherwise, pad stimulus for this phase with zeros
-                    else:
-                        if not isinstance(stimuli[mech][execution], Iterable):
-                            stimulus = np.atleast_2d(np.zeros(1))
-                        else:
-                            stimulus = np.atleast_2d(np.zeros(len(stimuli[mech][execution])))
+
+                    # Get stimulus for mech for current execution, and enforce 2d to accomodate input_states per mech
+                    stimulus = np.atleast_2d(stimuli[mech][execution])
+                    if not isinstance(stimulus, Iterable):
+                        stimulus = np.atleast_2d([stimulus])
+
                     stimuli_in_phase.append(stimulus)
 
                 stimuli_in_execution.append(stimuli_in_phase)
@@ -1028,13 +1036,13 @@ def _validate_inputs(object, inputs=None, is_target=False, num_phases=None, cont
         elif inputs.dtype is np.dtype('O'):
             input_homogenity = HETEROGENOUS
             # Determine whether the number of states/mech is homogenous
-            num_states_in_first_mech = len(object.originMechanisms[0].inputStates)
-            if any(len(mech.inputStates) != num_states_in_first_mech for mech in object.originMechanisms):
+            num_states_in_first_mech = len(object.originMechanisms[0].input_states)
+            if any(len(mech.input_states) != num_states_in_first_mech for mech in object.originMechanisms):
                 states_per_mech_heterog = True
             # Determine whether the size of all states is homogenous
-            size_of_first_state = len(list(object.originMechanisms[0].inputStates.values())[0].value)
+            size_of_first_state = len(object.originMechanisms[0].input_states[0].value)
             for origin_mech in object.originMechanisms:
-                if any(len(state.value) != size_of_first_state for state in origin_mech.inputStates.values()):
+                if any(len(state.value) != size_of_first_state for state in origin_mech.input_states):
                     size_of_states_heterog = True
         else:
             raise RunError("Unknown data type for inputs in {}".format(object.name))
@@ -1058,7 +1066,7 @@ def _validate_inputs(object, inputs=None, is_target=False, num_phases=None, cont
             elif size_of_states_heterog:
                 expected_dim = 4
             else:
-                raise RunError("PROGRAM ERROR: Unexepcted shape of intputs: {}".format(inputs.shape))
+                raise RunError("PROGRAM ERROR: Unexpected shape of inputs: {}".format(inputs.shape))
 
         if inputs.ndim != expected_dim:
             raise RunError("inputs arg in call to {}.run() must be a {}d np.array or comparable list".
@@ -1088,19 +1096,19 @@ def _validate_inputs(object, inputs=None, is_target=False, num_phases=None, cont
                                    format(len(inputs_for_phase), phase_num, num_mechs))
                 for mech_num in range(num_mechs):
                     input_for_mech = inputs_for_phase[mech_num]
-                    if len(input_for_mech) != len(mechs[mech_num].inputValue):
+                    if len(input_for_mech) != len(mechs[mech_num].input_values):
                         raise RunError("Number of states ({}) in input for {} should be {}".
                                        format(len(input_for_mech),
                                               mechs[mech_num].name,
-                                              len(mechs[mech_num].inputValue)))
+                                              len(mechs[mech_num].input_values)))
                     for state_num in range(len(input_for_mech)):
-                        input_for_state = mechs[mech_num].inputValue[state_num]
-                        if len(input_for_state) != len(mechs[mech_num].inputValue[state_num]):
+                        input_for_state = mechs[mech_num].input_values[state_num]
+                        if len(input_for_state) != len(mechs[mech_num].input_values[state_num]):
                             raise RunError("Length of state {} ({}) in input for {} should be {}".
-                                           format(list(mechs[mech_num].inputStates)[state_num],
+                                           format(list(mechs[mech_num].input_states)[state_num],
                                                   len(input_for_state),
                                                   mechs[mech_num].name,
-                                                  len(mechs[mech_num].inputValue[state_num])))
+                                                  len(mechs[mech_num].input_values[state_num])))
         return num_execution_sets
 
     else:
@@ -1129,7 +1137,7 @@ def _validate_targets(object, targets, num_input_sets, context=None):
         # Check that each target generated is compatible with the targetMechanism for which it is intended
         for target, targetMechanism in zip(generated_targets, object.targetMechanisms):
             target_len = np.size(target)
-            if target_len != np.size(targetMechanism.inputStates[TARGET].variable):
+            if target_len != np.size(targetMechanism.input_states[TARGET].variable):
                 if num_target_sets > 1:
                     plural = 's'
                 else:
@@ -1148,7 +1156,7 @@ def _validate_targets(object, targets, num_input_sets, context=None):
             target_len = np.size(target_array[0])
             num_target_sets = np.size(target_array, 0)
 
-            if target_len != np.size(object.targetMechanism.inputStates[TARGET].variable):
+            if target_len != np.size(object.targetMechanism.input_states[TARGET].variable):
                 if num_target_sets > 1:
                     plural = 's'
                 else:
@@ -1218,7 +1226,7 @@ def _validate_targets(object, targets, num_input_sets, context=None):
 
             for target, targetMechanism in zip(targets, object.targetMechanisms):
                 target_len = np.size(target)
-                if target_len != np.size(targetMechanism.inputStates[TARGET].variable):
+                if target_len != np.size(targetMechanism.input_states[TARGET].variable):
                     if num_targets_per_set > 1:
                         plural = 's'
                     else:
@@ -1226,7 +1234,7 @@ def _validate_targets(object, targets, num_input_sets, context=None):
                     raise RunError("Length ({}) of target{} specified for run of {}"
                                        " does not match expected target length of {}".
                                        format(target_len, plural, append_type_to_name(object),
-                                              np.size(targetMechanism.inputStates[TARGET].variable)))
+                                              np.size(targetMechanism.input_states[TARGET].variable)))
 
                 if any(np.size(target) != target_len for target in target_array):
                     raise RunError("Not all of the targets specified for {} are of the same length".
@@ -1252,7 +1260,7 @@ def _get_obect_type(object):
         return SYSTEM
     else:
         raise RunError("{} type not supported by Run module".format(object.__class__.__name__))
-    
+
 
 import uuid
 def _get_unique_id():
diff --git a/PsyNeuLink/Globals/TimeScale.py b/PsyNeuLink/Globals/TimeScale.py
index 6991d81221d..77ef1d977c8 100644
--- a/PsyNeuLink/Globals/TimeScale.py
+++ b/PsyNeuLink/Globals/TimeScale.py
@@ -9,13 +9,37 @@
 from enum import Enum
 from PsyNeuLink.Globals.Keywords import CENTRAL_CLOCK
 
+# ********************************************* TimeScale ***************************************************************
+
+"""
+TimeScale (Enum) represents the various divisions of time used elsewhere in PsyNeuLink, and are defined as follows
+(in order of increasingly coarse granularity):
+
+    - TIME_STEP
+        The nuclear unit of time, consisting of a set of mechanisms that are considered to run simultaneously
+    - PASS
+        A PASS consists of an entire iteration through the `Scheduler`<Scheduler>'s consideration queue (i.e. its
+        toposort ordering), during which zero or more TIME_STEPs will occur and mechanisms set to execute
+    - TRIAL
+        An open-ended unit of time consisting of all activity occurring within the scope of a single input to a
+        `System`<System> (or similar composition)
+    - RUN
+        A loosely-defined unit of time consisting of zero or more TRIALs
+    - LIFE
+        LIFE consists of all time since the creation of an object
+
+"""
+
 # Time scale modes
 class TimeScale(Enum):
     """Values used to specify ``time_scale`` argument for mechanisms, processes, and systems.
     """
     TIME_STEP = 0
-    TRIAL = 1
-    BOUNDS = 2 # Used for type checking where TimeScale value is passed
+    PASS = 1
+    TRIAL = 2
+    RUN = 3
+    LIFE = 4
+    BOUNDS = 5 # Used for type checking where TimeScale value is passed
 
 
 class Clock:
diff --git a/PsyNeuLink/Globals/Utilities.py b/PsyNeuLink/Globals/Utilities.py
index bd1e62a200f..a8108b36d11 100644
--- a/PsyNeuLink/Globals/Utilities.py
+++ b/PsyNeuLink/Globals/Utilities.py
@@ -26,15 +26,22 @@
 .. note::
    PsyNeuLink-specific typechecking functions are in the `Component` module
 
-* `is_numeric_or_none`
+* `parameter_spec`
+* `optional_parameter_spec`
+* `is_matrix
+* `is_matrix_spec`
 * `is_numeric`
+* `is_numeric_or_none`
 * `iscompatible`
+* `is_value_spec`
+* `is_unit_interval`
+* `is_same_function_spec`
 
 ENUM
 ~~~~
 
 * `Autonumber`
-* `ModulationOperation`
+* `Modulation`
 * `get_modulationOperation_name`
 
 KVO
@@ -57,6 +64,9 @@
 * `underscore_to_camelCase`
 * `append_type_to_name`
 * `ReadOnlyOrderedDict`
+* `ContentAddressableList`
+* `make_readonly_property`
+* `get_class_attributes`
 
 """
 
@@ -65,11 +75,11 @@
 warnings.filterwarnings("error")
 
 import numbers
-from random import random
 import numpy as np
 from enum import EnumMeta
 from enum import IntEnum
 import typecheck as tc
+import inspect
 
 from PsyNeuLink.Globals.Defaults import *
 from PsyNeuLink.Globals.Keywords import *
@@ -85,20 +95,35 @@ def __str__(self):
         return repr(self.error_value)
 
 
-class ModulationOperation(Enum):
-    DISABLED = 0
-    ADD = lambda runtime, default : runtime + default
+MODULATION_OVERRIDE = 'Modulation.OVERRIDE'
+MODULATION_MULTIPLY = 'Modulation.MULTIPLY'
+MODULATION_ADD = 'Modulation.ADD'
+
+
+class Modulation(Enum):
     MULTIPLY = lambda runtime, default : runtime * default
+    ADD = lambda runtime, default : runtime + default
     OVERRIDE = lambda runtime, default : runtime
+    DISABLE = 0
+
+def is_modulation_operation(val):
+    # try:
+    #     val(0,0)
+    #     return True
+    # except:
+    #     return False
+    return get_modulationOperation_name(val)
 
 def get_modulationOperation_name(operation):
         x = operation(1,2)
         if x is 1:
-            return 'ModulationOperation.OVERRIDE'
+            return MODULATION_OVERRIDE
         elif x is 2:
-            return 'ModulationOperation.MULTIPLY'
+            return MODULATION_MULTIPLY
         elif x is 3:
-            return 'ModulationOperation.ADD'
+            return MODULATION_ADD
+        else:
+            return False
 
 
 
@@ -124,12 +149,12 @@ class NumberedList(AutoNumber):
     * Start of numbering changed to 0 (from 1 in example)
     * obj based on int rather than object
     """
-    def __new__(cls):
+    def __new__(component_type):
         # Original example:
-        # value = len(cls.__members__) + 1
-        # obj = object.__new__(cls)
-        value = len(cls.__members__)
-        obj = int.__new__(cls)
+        # value = len(component_type.__members__) + 1
+        # obj = object.__new__(component_type)
+        value = len(component_type.__members__)
+        obj = int.__new__(component_type)
         obj._value_ = value
         return obj
 
@@ -138,14 +163,88 @@ def __new__(cls):
 
 TEST_CONDTION = False
 
+
+def optional_parameter_spec(param):
+    """Test whether param is a legal PsyNeuLink parameter specification or `None`
+
+    Calls parameter_spec if param is not `None`
+    Used with typecheck
+
+    Returns
+    -------
+    `True` if it is a legal parameter or `None`.
+    `False` if it is neither.
+
+
+    """
+    if not param:
+        return True
+    return parameter_spec(param)
+
+
+def parameter_spec(param):
+    """Test whether param is a legal PsyNeuLink parameter specification
+
+    Used with typecheck
+
+    Returns
+    -------
+    `True` if it is a legal parameter.
+    `False` if it is not.
+    """
+    # if isinstance(param, property):
+    #     param = ??
+    # if is_numeric(param):
+    from PsyNeuLink.Components.Functions.Function import function_type
+    from PsyNeuLink.Components.Projections.Projection import Projection
+    from PsyNeuLink.Components.Component import parameter_keywords
+
+    if (isinstance(param, (numbers.Number,
+                           np.ndarray,
+                           list,
+                           tuple,
+                           dict,
+                           function_type,
+                           Projection)) or
+        (inspect.isclass(param) and issubclass(param, Projection)) or
+        param in parameter_keywords):
+        return True
+    return False
+
+
 def is_numeric_or_none(x):
     if x is None:
         return True
     return is_numeric(x)
 
+
 def is_numeric(x):
     return iscompatible(x, **{kwCompatibilityNumeric:True, kwCompatibilityLength:0})
 
+
+def is_matrix_spec(m):
+    return isinstance(m, str) and m in MATRIX_KEYWORD_VALUES
+
+
+def is_matrix(m):
+    if is_matrix_spec(m):
+        return True
+    if isinstance(m, (list, np.ndarray, np.matrix)):
+        return True
+    if callable(m):
+        try:
+            return is_matrix(m())
+        except:
+            return False
+
+
+def is_distance_metric(s):
+    if s in DISTANCE_METRICS:
+        return True
+    else:
+        return False
+
+
 kwCompatibilityType = "type"
 kwCompatibilityLength = "length"
 kwCompatibilityNumeric = "numeric"
@@ -261,6 +360,12 @@ def iscompatible(candidate, reference=None, **kargs):
               format(candidate, kargs, match_length))
         match_length = 0
 
+    # # FIX??
+    # # Reference is a matrix or a keyword specification for one
+    # # from PsyNeuLink.Components.Functions.Function import matrix_spec
+    if is_matrix_spec(reference):
+        return is_matrix(candidate)
+
     # IMPLEMENTATION NOTE:
     #   modified to allow numeric type mismatches (e.g., int and float;
     #   should be added as option in future (i.e., to disallow it)
@@ -269,6 +374,8 @@ def iscompatible(candidate, reference=None, **kargs):
             (isinstance(candidate, numbers.Number) and issubclass(match_type,numbers.Number)) or
             # IMPLEMENTATION NOTE: Allow UserDict types to match dict (make this an option in the future)
             (isinstance(candidate, UserDict) and match_type is dict) or
+            # IMPLEMENTATION NOTE: Allow UserList types to match list (make this an option in the future)
+            (isinstance(candidate, UserList) and match_type is list) or
             # IMPLEMENTATION NOTE: This is needed when kwCompatiblityType is not specified
             #                      and so match_type==list as default
             (isinstance(candidate, numbers.Number) and issubclass(match_type,list)) or
@@ -472,7 +579,13 @@ def type_match(value, value_type):
         return list(value)
     if value_type is None:
         return None
-    raise UtilitiesError("Type of {} not recognized".format(value))
+    if value_type is type(None):
+        # # MODIFIED 6/9/17 OLD:
+        # raise UtilitiesError("PROGRAM ERROR: template provided to type_match for {} is \'None\'".format(value))
+        # MODIFIED 6/9/17 NEW:
+        return value
+        # MODIFIED 6/9/17 END
+    raise UtilitiesError("Type of {} not recognized".format(value_type))
 
 def get_value_from_array(array):
     """Extract numeric value from array, preserving numeric type
@@ -544,7 +657,296 @@ def __additem__(self, key, value):
         self.data[key] = value
         if not key in self._ordered_keys:
             self._ordered_keys.append(key)
+    def __deleteitem__(self, key):
+        del self.data[key]
     def keys(self):
         return self._ordered_keys
     def copy(self):
         return self.data.copy()
+
+from collections import UserList
+class ContentAddressableList(UserList):
+    """
+    ContentAddressableList( component_type, key=None, list=None)
+    
+    Implements dict-like list, that can be keyed by the names of the `compoments <Component>` in its entries.
+
+    Supports:
+      * getting and setting entries in the list using keys (string), in addition to numeric indices.
+        the key to use is specified by the **key** arg of the constructor, and must be a string attribute;
+        * for getting an entry:
+          the key must match the keyed attribute of a component in the list; otherwise an exception is raised;
+        * for setting an entry:
+            - the key must match the key of the component being assigned;
+            - if there is already a component in the list the keyed vaue of which matches the key, it is replaced;
+            - if there is no component in the list the keyed attribute of which matches the key,
+              the component is appended to the list;
+        * for getting lists of the names, values of the keyed attributes, and values of the `value <Component.value>`
+            attributes of components in the list.
+
+    IMPLEMENTATION NOTE:
+        This class allows components to be maintained in lists, while providing ordered storage
+        and the convenience access and assignment by name (e.g., akin to a dict).
+        Lists are used (instead of a dict or OrderedDict) since:
+            - ordering is in many instances convenient, and in some critical (e.g., for consistent mapping from
+                collections of states to other variables, such as lists of their values);
+            - they are most commonly accessed either exhaustively (e.g., in looping through them during execution),
+                or by key (e.g., to get the first, "primary" one), which makes the efficiencies of a dict for
+                accessing by key/name less critical;
+            - the number of states in a collection for a given mechanism is likely to be small so that, even when
+                accessed by key/name, the inefficiencies of searching a list are likely to be inconsequential.
+
+    Arguments
+    ---------
+
+    component_type : Class
+        specifies the class of the items in the list.
+
+    name : str : 'ContentAddressableList'
+        name to use for ContentAddressableList
+
+    key : str : default `name`
+        specifies the attribute of **component_type** used to key items in the list by content;
+        **component_type** must have this attribute or, if it is not provided, an attribute with the name 'name'.
+
+    list : List : default None
+        specifies a list used to initialize the list;
+        all of the items must be of type **component_type** and have the **key** attribute.
+
+    Attributes
+    ----------
+
+    component_type : Class
+        the class of the items in the list.
+
+    name: str
+        name if provided as arg, else name of of ContentAddressableList class
+
+    key : str
+        the attribute of `component_type <ContentAddressableList.component_type>` used to key items in the list by content;
+
+    data : List (property)
+        the actual list of items.
+
+    names : List (property)
+        values of the `name <Component>` attribute of components in the list.
+
+    key_values : List (property)
+        values of the keyed attribute of each component in the list.
+
+    values : List (property)
+        values of the `value <Component>` attribute of components in the list.
+
+    """
+
+    def __init__(self, component_type, key=None, list=None, name=None, **kwargs):
+        self.component_type = component_type
+        self.key = key or 'name'
+        self.name = name or component_type.__name__
+        if not isinstance(component_type, type):
+            raise UtilitiesError("component_type arg for {} ({}) must be a class"
+                                 .format(self.name, component_type))
+        if not isinstance(self.key, str):
+            raise UtilitiesError("key arg for {} ({}) must be a string".
+                                 format(self.name, self.key))
+        if not hasattr(component_type, self.key):
+            raise UtilitiesError("key arg for {} (\'{}\') must be an attribute of {}".
+                                 format(self.name,
+                                        self.key,
+                                        component_type.__name__))
+        if list is not None:
+            if not all(isinstance(obj, self.component_type) for obj in list):
+                raise UtilitiesError("All of the items in the list arg for {} "
+                                     "must be of the type specified in the component_type arg ({})"
+                                     .format(self.name, self.component_type.__name__))
+        UserList.__init__(self, list, **kwargs)
+
+    def __repr__(self):
+        return '[\n\t{0}\n]'.format('\n\t'.join(['{0}\t{1}\t{2}'.format(i, self[i].name, repr(self[i].value)) for i in range(len(self))]))
+
+    def __getitem__(self, key):
+        if key is None:
+            raise KeyError("None is not a legal key for {}".format(self.name))
+        try:
+            return self.data[key]
+        except TypeError:
+            key_num = self._get_key_for_item(key)
+            if key_num is None:
+                # raise TypeError("\'{}\' is not a key in the {} being addressed".
+                                # format(key, self.__class__.__name__))
+                raise TypeError("\'{}\' is not a key in {}".
+                                format(key, self.name))
+            return self.data[key_num]
+
+
+    def __setitem__(self, key, value):
+        # For efficiency, first assume the key is numeric (duck typing in action!)
+        try:
+            self.data[key] = value
+        # If it is not
+        except TypeError:
+            # It must be a string
+            if not isinstance(key, str):
+                raise UtilitiesError("Non-numer key used for {} ({})must be a string)".
+                                      format(self.name, key))
+            # The specified string must also match the value of the attribute of the class used for addressing
+            if not key == value.name:
+                raise UtilitiesError("The key of the entry for {} {} ({}) "
+                                     "must match the value of its {} attribute ({})".
+                                      format(self.name,
+                                             value.__class__.__name__,
+                                             key,
+                                             self.key,
+                                             getattr(value, self.key)))
+            key_num = self._get_key_for_item(key)
+            if key_num is not None:
+                self.data[key_num] = value
+            else:
+                self.data.append(value)
+
+    def __contains__(self, item):
+        if super().__contains__(item):
+            return True
+        else:
+            return any(item == obj.name for obj in self.data)
+
+    def _get_key_for_item(self, key):
+        if isinstance(key, str):
+            obj = next((obj for obj in self.data if obj.name == key), None)
+            if obj is None:
+                return None
+            else:
+                return self.data.index(obj)
+        elif isinstance(key, self.component_type):
+            return self.data.index(key)
+        else:
+            raise UtilitiesError("{} is not a legal key for {} (must be number, string or State)".
+                                  format(key, self.key))
+
+    def __delitem__(self, key):
+        if key is None:
+            raise KeyError("None is not a legal key for {}".format(self.name))
+        try:
+            del self.data[key]
+        except TypeError:
+            key_num = self._get_key_for_item(key)
+            del self.data[key_num]
+
+    def clear(self):
+        super().clear()
+
+    # def pop(self, key, *args):
+    #     raise UtilitiesError("{} is read-only".format(self.name))
+    #
+    # def popitem(self):
+    #     raise UtilitiesError("{} is read-only".format(self.name))
+
+    def __additem__(self, key, value):
+        if key >= len(self.data):
+            self.data.append(value)
+        else:
+            self.data[key] = value
+
+    def copy(self):
+        return self.data.copy()
+
+    @property
+    def names(self):
+        """Return list of `values <Component.value>` of the name attribute of components in the list.
+        Returns
+        -------
+        names :  list
+            list of the values of the `name <Component.name>` attributes of components in the list.
+        """
+        return [getattr(item, NAME) for item in self.data]
+
+    @property
+    def key_values(self):
+        """Return list of `values <Component.value>` of the keyed attribute of components in the list.
+        Returns
+        -------
+        key_values :  list
+            list of the values of the `keyed <Component.name>` attributes of components in the list.
+        """
+        return [getattr(item, self.key) for item in self.data]
+
+    @property
+    def values(self):
+        """Return list of values of components in the list.
+        Returns
+        -------
+        values :  list
+            list of the values of the `value <Component.value>` attributes of components in the list.
+        """
+        return [getattr(item, VALUE) for item in self.data]
+
+    @property
+    def values_as_lists(self):
+        """Return list of values of components in the list, each converted to a list.
+        Returns
+        -------
+        values :  list
+            list of list values of the `value <Component.value>` attributes of components in the list,
+        """
+        return [np.ndarray.tolist(getattr(item, VALUE)) for item in self.data]
+
+
+def is_value_spec(spec):
+    if isinstance(spec, (int, float, list, np.ndarray)):
+        return True
+    else:
+        return False
+
+
+def is_unit_interval(spec):
+    if isinstance(spec, (int, float)) and 0 <= spec <= 1:
+        return True
+    else:
+        return False
+
+
+def is_same_function_spec(fct_spec_1, fct_spec_2):
+    """Compare two function specs and return True if both are for the same class of Function
+
+    Arguments can be either a class or instance of a PsyNeuLink Function, or any other callable method or function
+    Return True only if both are instances or classes of a PsyNeuLink Function;  otherwise, return False
+    """
+    from PsyNeuLink.Components.Functions.Function import Function
+
+    def _convert_to_type(fct):
+        if isinstance(fct, Function):
+            return type(fct)
+        elif inspect.isclass(fct) and issubclass(fct, Function):
+            return fct
+        else:
+            return None
+
+    fct_1 = _convert_to_type(fct_spec_1)
+    fct_2 = _convert_to_type(fct_spec_2)
+
+    if fct_1 and fct_2 and fct_1 == fct_2:
+        return True
+    else:
+        return False
+
+
+def make_readonly_property(val):
+    """Return property that provides read-only access to its value
+    """
+
+    def getter(self):
+        return val
+
+    def setter(self, val):
+        raise UtilitiesError("{} is read-only property of {}".format(val, self.__class__.__name__))
+
+    # Create the property
+    prop = property(getter).setter(setter)
+    return prop
+
+
+def get_class_attributes(cls):
+    boring = dir(type('dummy', (object,), {}))
+    return [item
+            for item in inspect.getmembers(cls)
+            if item[0] not in boring]
\ No newline at end of file
diff --git a/PsyNeuLink/__init__.py b/PsyNeuLink/__init__.py
index c280f3877d7..fd1b71c149e 100644
--- a/PsyNeuLink/__init__.py
+++ b/PsyNeuLink/__init__.py
@@ -7,6 +7,7 @@
 #
 #
 # ***********************************************  Init ****************************************************************
+import logging
 
 from PsyNeuLink.Components.Functions.Function import *
 from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.EVCMechanism import EVCMechanism
@@ -14,14 +15,39 @@
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.IntegratorMechanism import IntegratorMechanism
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
 from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection
-from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection
-from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
+from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
+from PsyNeuLink.Components.Projections.ModulatoryProjections.LearningProjection import LearningProjection
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
 from PsyNeuLink.Components.System import System, system
 from PsyNeuLink.Globals.Defaults import DefaultControlAllocationMode
 from PsyNeuLink.Globals.Keywords import *
 from PsyNeuLink.Globals.Preferences.ComponentPreferenceSet import ComponentPreferenceSet
 
+
+# https://stackoverflow.com/a/17276457/3131666
+class Whitelist(logging.Filter):
+    def __init__(self, *whitelist):
+        self.whitelist = [logging.Filter(name) for name in whitelist]
+
+    def filter(self, record):
+        return any(f.filter(record) for f in self.whitelist)
+
+
+class Blacklist(Whitelist):
+    def filter(self, record):
+        return not Whitelist.filter(self, record)
+
+
+logging.basicConfig(
+    level=logging.ERROR,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+for handler in logging.root.handlers:
+    handler.addFilter(Blacklist(
+        'PsyNeuLink.scheduling.Scheduler',
+        'PsyNeuLink.scheduling.condition',
+    ))
+
 __all__ = ['System',
            'system',
            'process',
@@ -60,7 +86,7 @@
            'SCALE',
            'MATRIX',
            'IDENTITY_MATRIX',
-           'OFF_DIAGNOAL_MATRIX',
+           'HOLLOW_MATRIX',
            'FULL_CONNECTIVITY_MATRIX',
            'DEFAULT_MATRIX',
            'ALL',
diff --git a/PsyNeuLink/mechanismGenerator.py b/PsyNeuLink/mechanismGenerator.py
new file mode 100644
index 00000000000..43b6aaff57b
--- /dev/null
+++ b/PsyNeuLink/mechanismGenerator.py
@@ -0,0 +1,21 @@
+
+# Create a generator that simply returns the next mechanism from a list
+
+class mechanismGenerator(object):
+
+    # generator takes in a list of mechanisms
+    def __init__(self, mechList):
+
+        self.mechList = mechList
+
+    # generator yields one mechanism from the list on each call
+    def yield_mech(self):
+
+        for i in self.mechList:
+            yield(i)
+
+# Test the generator
+# for mech in mechanismGenerator([1,2,3,4,5]).yieldMech():
+#     print(mech)
+
+
diff --git a/PsyNeuLink/scheduling/Scheduler.py b/PsyNeuLink/scheduling/Scheduler.py
new file mode 100644
index 00000000000..0c6bfb2d089
--- /dev/null
+++ b/PsyNeuLink/scheduling/Scheduler.py
@@ -0,0 +1,406 @@
+# Princeton University licenses this file to You under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.  You may obtain a copy of the License at:
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed
+# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and limitations under the License.
+
+
+# ********************************************* Scheduler ***************************************************************
+
+"""
+
+Overview
+--------
+
+A scheduler is an object that can be used to generate an order in which components are to be run, using a set of
+arbitrary `Condition`<Condition>s (`ConditionSet`<ConditionSet>). These Conditions can be specified relative
+to other components, for example, to generate patterns in which some components run at higher frequency than
+others, or in which components wait for others to reach some stopping condition. However, the scheduler is
+designed to be compatible with any Conditions.
+
+Creating a Scheduler
+--------------------
+
+A scheduler is created either explicitly, or implicitly during the creation of a System. When creating a scheduler
+explicitly, you must either provide a System, or a set of components (i.e. vertices) and a topological ordering of
+these components that represents the graph structure of these components, produced by the toposort module. This
+graph structure must be directed and acyclic. When providing a System, the scheduler is created using the System's
+executionList as components and its executionGraph as the topological ordering. Additionally, a ConditionSet may be
+assigned at creation; otherwise, a blank set is initialized. Conditions may be added directly to the scheduler's
+ConditionSet later using the wrapper methods add_condition and add_condition_set.
+If both a System and a set of nodes are specified, the scheduler will initialize based on the System.
+
+Running a Scheduler
+-------------------
+
+Using the run method, which is a generator, the scheduler can specify the ordering in which its nodes should be run
+to meet the conditions specified in the condition set. When calling the run method, if any nodes do not have run
+conditions specified, by default they will be assigned the condition Always, which allows them to run at any time
+they are under consideration. If termination conditions are not specified, by default the scheduler will terminate
+using the AllHaveRun condition, which is true when all of the nodes in the scheduler have been told to run
+at least once. Each generation from the run method will consist of a set of all nodes that can run in the current
+time step, and this set itself represents the time step.
+
+
+Algorithm
+---------
+
+A scheduler first constructs a consideration queue of its nodes using the topological ordering. This consideration
+queue consists of a list of sets of nodes grouped based on their graph dependencies. The first set consists of
+source nodes only. The second set consists of all nodes that have incoming edges only from nodes in the first set.
+The third set consists of all other nodes that have incoming edges only from nodes in the first two sets, and so on.
+
+When running, the scheduler maintains internal state about the number of times its nodes were set to be executed, and
+the total number of steps in each TimeScale that have happened. This information is used by Conditions. The scheduler
+checks each set in order in the consideration queue, and determines which nodes in this set are allowed to run based on
+whether their associated conditions were met; all nodes within a consideration set that are allowed to run comprise a
+`time step`<TimeScale.TIME_STEP>. These nodes are considered to be run simultaneously, and so the running of a node
+within a time step may trigger the running of another node within its consideration set. The ordering of these nodes
+is irrelevant, as it is necessarily the case that no parent and child nodes are within the same consideration set. A
+key feature is that all parents have the chance to run (even if they do not actually run) before their children.
+
+At the beginning of each time step, the scheduler checks whether the specified termination conditions have been met,
+and terminates if so.
+
+Examples
+--------
+
+Please see `Condition`<Condition> documentation for a list of all included Conditions and their behavior.
+
+*Basic phasing in a linear process:*
+    A = TransferMechanism(function = Linear(), name = 'A')
+    B = TransferMechanism(function = Linear(), name = 'B')
+    C = TransferMechanism(function = Linear(), name = 'C')
+
+    p = process(
+        pathway=[A, B, C],
+        name = 'p',
+    )
+    s = system(
+        processes=[p],
+        name='s',
+    )
+    sched = Scheduler(system=s)
+
+    #impicit condition of Always for A
+
+    sched.add_condition(B, EveryNCalls(A, 2))
+    sched.add_condition(C, EveryNCalls(B, 3))
+
+    # implicit AllHaveRun condition
+    output = list(sched.run())
+
+    # output will produce
+    # [A, A, B, A, A, B, A, A, B, C]
+
+*Alternate basic phasing in a linear process:*
+    A = TransferMechanism(function = Linear(), name = 'A')
+    B = TransferMechanism(function = Linear(), name = 'B')
+
+    p = process(
+        pathway=[A, B],
+        name = 'p',
+    )
+    s = system(
+        processes=[p],
+        name='s',
+    )
+    sched = Scheduler(system=s)
+
+    sched.add_condition(A, Any(AtPass(0), EveryNCalls(B, 2)))
+    sched.add_condition(B, Any(EveryNCalls(A, 1), EveryNCalls(B, 1)))
+
+    termination_conds = {ts: None for ts in TimeScale}
+    termination_conds[TimeScale.TRIAL] = AfterNCalls(B, 4, time_scale=TimeScale.TRIAL)
+    output = list(sched.run(termination_conds=termination_conds))
+
+    # output will produce
+    # [A, B, B, A, B, B]
+
+*Basic phasing in two processes:*
+    A = TransferMechanism(function = Linear(), name = 'A')
+    B = TransferMechanism(function = Linear(), name = 'B')
+    C = TransferMechanism(function = Linear(), name = 'C')
+
+    p = process(
+        pathway=[A, C],
+        name = 'p',
+    )
+    q = process(
+        pathway=[B, C],
+        name = 'q',
+    )
+    s = system(
+        processes=[p, q],
+        name='s',
+    )
+    sched = Scheduler(system=s)
+
+    sched.add_condition(A, EveryNPasses(1))
+    sched.add_condition(B, EveryNCalls(A, 2))
+    sched.add_condition(C, Any(AfterNCalls(A, 3), AfterNCalls(B, 3)))
+
+    termination_conds = {ts: None for ts in TimeScale}
+    termination_conds[TimeScale.TRIAL] = AfterNCalls(C, 4, time_scale=TimeScale.TRIAL)
+    output = list(sched.run(termination_conds=termination_conds))
+
+    # output will produce
+    # [A, set([A,B]), A, C, set([A,B]), C, A, C, set([A,B]), C]
+
+"""
+
+import logging
+
+from toposort import toposort
+
+from PsyNeuLink.Globals.TimeScale import TimeScale
+from PsyNeuLink.scheduling.condition import AllHaveRun, Always, ConditionSet
+
+logger = logging.getLogger(__name__)
+
+
+class SchedulerError(Exception):
+    def __init__(self, error_value):
+        self.error_value = error_value
+
+    def __str__(self):
+        return repr(self.error_value)
+
+
+class Scheduler(object):
+    """
+        An object that can be used to generate an order in which components are to be run, using a set of
+        arbitrary `Condition`<Condition>s (`ConditionSet`<ConditionSet>).
+
+        Attributes:
+            condition_set (ConditionSet): the set of Conditions this scheduler will use when running
+            execution_list (list): the full history of time steps this scheduler has produced
+            consideration_queue (list): a list form of the scheduler's toposort ordering of its nodes
+            termination_conds (dict): a mapping from :keyword:`TimeScale`s to :keyword:`Condition`s that when met terminate the execution of the specified :keyword:`TimeScale`
+    """
+    def __init__(self, system=None, condition_set=None, nodes=None, toposort_ordering=None):
+        '''
+        :param self:
+        :param condition_set: (ConditionSet) - a :keyword:`ConditionSet` to be scheduled
+        '''
+        self.condition_set = condition_set if condition_set is not None else ConditionSet(scheduler=self)
+        # stores the in order list of self.run's yielded outputs
+        self.execution_list = []
+        self.consideration_queue = []
+        self.termination_conds = None
+
+        if system is not None:
+            self.nodes = [m for m in system.executionList]
+            self._init_consideration_queue_from_system(system)
+        elif nodes is not None:
+            self.nodes = nodes
+            if toposort_ordering is None:
+                raise SchedulerError('Instantiating Scheduler by list of nodes requires a toposort ordering (kwarg toposort_ordering)')
+            self.consideration_queue = list(toposort_ordering)
+        else:
+            raise SchedulerError('Must instantiate a Scheduler with either a System (kwarg system), or a list of Mechanisms (kwarg nodes) and and a toposort ordering over them (kwarg toposort_ordering)')
+
+        self._init_counts()
+
+    # the consideration queue is the ordered list of sets of nodes in the composition graph, by the
+    # order in which they should be checked to ensure that all parents have a chance to run before their children
+    def _init_consideration_queue_from_system(self, system):
+        dependencies = []
+        for dependency_set in list(toposort(system.executionGraph)):
+            new_set = set()
+            for d in dependency_set:
+                new_set.add(d)
+            dependencies.append(new_set)
+        self.consideration_queue = dependencies
+        logger.debug('Consideration queue: {0}'.format(self.consideration_queue))
+
+    def _init_counts(self):
+        # self.times[p][q] stores the number of TimeScale q ticks that have happened in the current TimeScale p
+        self.times = {ts: {ts: 0 for ts in TimeScale} for ts in TimeScale}
+        # stores total the number of occurrences of a node through the time scale
+        # i.e. the number of times node has ran/been queued to run in a trial
+        self.counts_total = {ts: None for ts in TimeScale}
+        # counts_useable is a dictionary intended to store the number of available "instances" of a certain node that
+        # are available to expend in order to satisfy conditions such as "run B every two times A runs"
+        # specifically, counts_useable[a][b] = n indicates that there are n uses of a that are available for b to expend
+        # so, in the previous example B would check to see if counts_useable[A][B] is 2, in which case B can run
+        self.counts_useable = {node: {n: 0 for n in self.nodes} for node in self.nodes}
+
+        for ts in TimeScale:
+            self.counts_total[ts] = {n: 0 for n in self.nodes}
+
+    def _reset_count(self, count, time_scale):
+        for c in count[time_scale]:
+            count[time_scale][c] = 0
+
+    def _increment_time(self, time_scale):
+        for ts in TimeScale:
+            self.times[ts][time_scale] += 1
+
+    def _reset_time(self, time_scale):
+        for ts in TimeScale:
+            self.times[time_scale][ts] = 0
+
+    ################################################################################
+    # Wrapper methods
+    #   to allow the user to ignore the ConditionSet internals
+    ################################################################################
+    def __contains__(self, item):
+        return self.condition_set.__contains__(item)
+
+    def add_condition(self, owner, condition):
+        '''
+        :param: self:
+        :param owner: the :keyword:`Component` that is dependent on the :param conditions:
+        :param conditions: a :keyword:`Condition` (including All or Any)
+        '''
+        self.condition_set.add_condition(owner, condition)
+
+    def add_condition_set(self, conditions):
+        '''
+        :param: self:
+        :param conditions: a :keyword:`dict` mapping :keyword:`Component`s to :keyword:`Condition`s, can be added later with :keyword:`add_condition`
+        '''
+        self.condition_set.add_condition_set(conditions)
+
+    ################################################################################
+    # Validation methods
+    #   to provide the user with info if they do something odd
+    ################################################################################
+    def _validate_run_state(self):
+        self._validate_condition_set()
+        self._validate_termination()
+
+    def _validate_condition_set(self):
+        unspecified_nodes = []
+        for node in self.nodes:
+            if node not in self.condition_set:
+                self.condition_set.add_condition(node, Always())
+                unspecified_nodes.append(node)
+        if len(unspecified_nodes) > 0:
+            logger.warning('These nodes have no Conditions specified, and will be scheduled with condition Always: {0}'.format(unspecified_nodes))
+
+    def _validate_termination(self):
+        if self.termination_conds is None:
+            logger.warning('A termination Condition dict (termination_conds[<time_step>]: Condition) was not specified, and so the termination conditions for all TimeScale will be set to AllHaveRun()')
+            self.termination_conds = {ts: AllHaveRun() for ts in TimeScale}
+        for tc in self.termination_conds:
+            if self.termination_conds[tc] is None:
+                if tc in [TimeScale.TRIAL]:
+                    raise SchedulerError('Must specify a {0} termination Condition (termination_conds[{0}]'.format(tc))
+            else:
+                if self.termination_conds[tc].scheduler is None:
+                    logger.debug('Setting scheduler of {0} to self ({1})'.format(self.termination_conds[tc], self))
+                    self.termination_conds[tc].scheduler = self
+
+    ################################################################################
+    # Run methods
+    ################################################################################
+
+    def run(self, termination_conds=None):
+        '''
+        :param self:
+        :param termination_conds: (dict) - a mapping from :keyword:`TimeScale`s to :keyword:`Condition`s that when met terminate the execution of the specified :keyword:`TimeScale`
+        '''
+        self.termination_conds = termination_conds
+        self._validate_run_state()
+
+        logger.info('termination_conds: {0}, self.termination_conds: {1}'.format(termination_conds, self.termination_conds))
+
+        def has_reached_termination(self, time_scale=None):
+            term = True
+            if time_scale is None:
+                for ts in self.termination_conds:
+                    term = term and self.termination_conds[ts].is_satisfied()
+            else:
+                term = term and self.termination_conds[time_scale].is_satisfied()
+
+            return term
+
+        self.counts_useable = {node: {n: 0 for n in self.nodes} for node in self.nodes}
+        self._reset_count(self.counts_total, TimeScale.TRIAL)
+        self._reset_time(TimeScale.TRIAL)
+
+        while not self.termination_conds[TimeScale.TRIAL].is_satisfied():
+            self._reset_count(self.counts_total, TimeScale.PASS)
+            self._reset_time(TimeScale.PASS)
+
+            execution_list_has_changed = False
+            cur_index_consideration_queue = 0
+
+            while (
+                cur_index_consideration_queue < len(self.consideration_queue)
+                and not self.termination_conds[TimeScale.TRIAL].is_satisfied()
+            ):
+                # all nodes to be added during this time step
+                cur_time_step_exec = set()
+                # the current "layer/group" of nodes that MIGHT be added during this time step
+                cur_consideration_set = self.consideration_queue[cur_index_consideration_queue]
+                try:
+                    iter(cur_consideration_set)
+                except TypeError as e:
+                    raise SchedulerError('cur_consideration_set is not iterable, did you ensure that this Scheduler was instantiated with an actual toposort output for param toposort_ordering? err: {0}'.format(e))
+                logger.debug('trial, num passes in trial {0}, consideration_queue {1}'.format(self.times[TimeScale.TRIAL][TimeScale.PASS], ' '.join([str(x) for x in cur_consideration_set])))
+
+                # do-while, on cur_consideration_set_has_changed
+                # we check whether each node in the current consideration set is allowed to run,
+                # and nodes can cause cascading adds within this set
+                while True:
+                    cur_consideration_set_has_changed = False
+                    for current_node in cur_consideration_set:
+                        logger.debug('cur time_step exec: {0}'.format(cur_time_step_exec))
+                        for n in self.counts_useable:
+                            logger.debug('Counts of {0} useable by'.format(n))
+                            for n2 in self.counts_useable[n]:
+                                logger.debug('\t{0}: {1}'.format(n2, self.counts_useable[n][n2]))
+
+                        # only add each node once during a single time step, this also serves
+                        # to prevent infinitely cascading adds
+                        if current_node not in cur_time_step_exec:
+                            if self.condition_set.conditions[current_node].is_satisfied():
+                                logger.debug('adding {0} to execution list'.format(current_node))
+                                logger.debug('cur time_step exec pre add: {0}'.format(cur_time_step_exec))
+                                cur_time_step_exec.add(current_node)
+                                logger.debug('cur time_step exec post add: {0}'.format(cur_time_step_exec))
+                                execution_list_has_changed = True
+                                cur_consideration_set_has_changed = True
+
+                                for ts in TimeScale:
+                                    self.counts_total[ts][current_node] += 1
+                                # current_node's node is added to the execution queue, so we now need to
+                                # reset all of the counts useable by current_node's node to 0
+                                for n in self.counts_useable:
+                                    self.counts_useable[n][current_node] = 0
+                                # and increment all of the counts of current_node's node useable by other
+                                # nodes by 1
+                                for n in self.counts_useable:
+                                    self.counts_useable[current_node][n] += 1
+                    # do-while condition
+                    if not cur_consideration_set_has_changed:
+                        break
+
+                # add a new time step at each step in a pass, if the time step would not be empty
+                if len(cur_time_step_exec) >= 1:
+                    self.execution_list.append(cur_time_step_exec)
+                    yield self.execution_list[-1]
+
+                    self._increment_time(TimeScale.TIME_STEP)
+
+                cur_index_consideration_queue += 1
+
+            # if an entire pass occurs with nothing running, add an empty time step
+            if not execution_list_has_changed:
+                self.execution_list.append(set())
+                yield self.execution_list[-1]
+
+                self._increment_time(TimeScale.TIME_STEP)
+
+            # can execute the execution_list here
+            logger.info(self.execution_list)
+            logger.debug('Execution list: [{0}]'.format(' '.join([str(x) for x in self.execution_list])))
+            self._increment_time(TimeScale.PASS)
+
+        self._increment_time(TimeScale.TRIAL)
+
+        return self.execution_list
diff --git a/PsyNeuLink/scheduling/__init__.py b/PsyNeuLink/scheduling/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/PsyNeuLink/scheduling/condition.py b/PsyNeuLink/scheduling/condition.py
new file mode 100644
index 00000000000..3800a867344
--- /dev/null
+++ b/PsyNeuLink/scheduling/condition.py
@@ -0,0 +1,884 @@
+# Princeton University licenses this file to You under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.  You may obtain a copy of the License at:
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed
+# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and limitations under the License.
+
+
+# ********************************************* Condition ***************************************************************
+
+"""
+
+Overview
+--------
+
+`Condition`<Condition>s represent any conditions that can be satisfied. Each Condition is associated with an owner (a
+`Component`<Component> that the Condition "belongs" to, relevant for relative conditions such as `EveryNCalls`), and
+a `Scheduler`<Scheduler>, which maintains most of the data required to test satisfaction. These properties can usually
+be determined automatically based on the context in which Conditions are created.
+
+
+Creating new Conditions
+-----------------------
+
+Each Condition must
+    - be a subclass of `Condition`<Condition>
+    - pass `dependencies` as the first argument to the __init__ function of Condition
+    - pass `func` as the second argument to the __init__ function of Condition
+
+In determining whether a Condition is satisfied, `func` is called with `dependencies` as parameter (and optionally,
+additional named and unnamed arguments).
+
+Hint:
+    If you do not want to use the dependencies parameter, and instead want to use only args or kwargs, you may
+    pass a dummy variable for dependencies. See `AfterNCallsCombined`<AfterNCallsCombined> for reference:
+
+    class AfterNCallsCombined(Condition):
+        def __init__(self, *dependencies, n=None, time_scale=TimeScale.TRIAL):
+            def func(_none, *dependencies, n=None):
+                if self.scheduler is None:
+                    raise ConditionError('{0}: self.scheduler is None - scheduler must be assigned'.format(type(self).__name__))
+                if n is None:
+                    raise ConditionError('{0}: keyword argument n is None'.format(type(self).__name__))
+                count_sum = 0
+                for d in dependencies:
+                    count_sum += self.scheduler.counts_total[time_scale][d]
+                return count_sum >= n
+            super().__init__(None, func, *dependencies, n=n)
+
+"""
+
+import logging
+
+from PsyNeuLink.Globals.TimeScale import TimeScale
+
+logger = logging.getLogger(__name__)
+
+
+class ConditionError(Exception):
+    def __init__(self, error_value):
+        self.error_value = error_value
+
+    def __str__(self):
+        return repr(self.error_value)
+
+
+class ConditionSet(object):
+    """
+    An object used in conjunction with `Scheduler`<Scheduler> to store all the associated conditions with their owners.
+    """
+    def __init__(self, scheduler=None, conditions=None):
+        """
+        :param self:
+        :param scheduler: a :keyword:`Scheduler` that these conditions are associated with, which maintains any state necessary for these conditions
+        :param conditions: a :keyword:`dict` mapping :keyword:`Component`s to :keyword:`iterable`s of :keyword:`Condition`s, can be added later with :keyword:`add_condition`
+        """
+        self.conditions = conditions if conditions is not None else {}
+        self.scheduler = scheduler
+
+    def __contains__(self, item):
+        return item in self.conditions
+
+    @property
+    def scheduler(self):
+        return self._scheduler
+
+    @scheduler.setter
+    def scheduler(self, value):
+        logger.debug('ConditionSet ({0}) setting scheduler to {1}'.format(type(self).__name__, value))
+        self._scheduler = value
+
+        for owner, cond in self.conditions.items():
+            cond.scheduler = value
+
+    def add_condition(self, owner, condition):
+        """
+        :param self:
+        :param owner: the :keyword:`Component` that is dependent on the :param conditions:
+        :param conditions: a :keyword:`Condition` (including All or Any)
+        """
+        logger.debug('add_condition: Setting scheduler of {0}, (owner {2}) to self.scheduler ({1})'.format(condition, self.scheduler, owner))
+        condition.owner = owner
+        condition.scheduler = self.scheduler
+        self.conditions[owner] = condition
+
+    def add_condition_set(self, conditions):
+        """
+        :param self:
+        :param conditions: a :keyword:`dict` mapping :keyword:`Component`s to :keyword:`Condition`s, can be added later with :keyword:`add_condition`
+        """
+        for owner in conditions:
+            conditions[owner].owner = owner
+            conditions[owner].scheduler = self.scheduler
+            self.conditions[owner] = conditions[owner]
+
+
+class Condition(object):
+    """
+    An object used in conjunction with `Scheduler`<Scheduler> to specify a running pattern for PNL objects.
+    Each Condition will be satisfied based on some criteria.
+
+    Properties:
+        scheduler (Scheduler): a Scheduler object that this condition is associated with. This condition will
+            use scheduler's state for its satisfaction checks
+        owner (Component): the PNL object this condition is associated with.
+
+    Attributes:
+        dependencies: one or more PNL objects over which func is evaluated to determine satisfaction of the :keyword:`Condition`
+            user must ensure that dependencies are suitable as func parameters
+        func: parameters over which func is evaluated to determine satisfaction of the :keyword:`Condition`
+        args: additional formal arguments passed to func
+        kwargs: additional keyword arguments passed to func
+    """
+    def __init__(self, dependencies, func, *args, **kwargs):
+        """
+        :param self:
+        :param dependencies: one or more PNL objects over which func is evaluated to determine satisfaction of the :keyword:`Condition`
+            user must ensure that dependencies are suitable as func parameters
+        :param func: parameters over which func is evaluated to determine satisfaction of the :keyword:`Condition`
+        :param args: additional formal arguments passed to func
+        :param kwargs: additional keyword arguments passed to func
+        """
+        self.dependencies = dependencies
+        self.func = func
+        self.args = args
+        self.kwargs = kwargs
+
+        self._scheduler = None
+        self._owner = None
+        # logger.debug('{1} dependencies: {0}'.format(dependencies, type(self).__name__))
+
+    @property
+    def scheduler(self):
+        return self._scheduler
+
+    @scheduler.setter
+    def scheduler(self, value):
+        logger.debug('Condition ({0}) setting scheduler to {1}'.format(type(self).__name__, value))
+        self._scheduler = value
+
+    @property
+    def owner(self):
+        return self._owner
+
+    @owner.setter
+    def owner(self, value):
+        logger.debug('Condition ({0}) setting owner to {1}'.format(type(self).__name__, value))
+        self._owner = value
+
+    def is_satisfied(self):
+        logger.debug('Condition ({0}) using scheduler {1}'.format(type(self).__name__, self.scheduler))
+        has_args = len(self.args) > 0
+        has_kwargs = len(self.kwargs) > 0
+
+        if has_args and has_kwargs:
+            return self.func(self.dependencies, *self.args, **self.kwargs)
+        if has_args:
+            return self.func(self.dependencies, *self.args)
+        if has_kwargs:
+            return self.func(self.dependencies, **self.kwargs)
+        return self.func(self.dependencies)
+
+#########################################################################################################
+# Included Conditions
+#########################################################################################################
+
+######################################################################
+# Static Conditions
+#   - independent of components and time
+######################################################################
+
+
+class Always(Condition):
+    """
+    Always
+
+    Parameters:
+
+    Satisfied when:
+        - always satisfied
+
+    Notes:
+
+    """
+    def __init__(self):
+        super().__init__(True, lambda x: x)
+
+
+class Never(Condition):
+    """
+    Never
+
+    Parameters:
+
+    Satisfied when:
+        - never satisfied
+
+    Notes:
+
+    """
+    def __init__(self):
+        super().__init__(False, lambda x: x)
+
+######################################################################
+# Relative Conditions
+#   - based on other Conditions
+######################################################################
+
+# TODO: create this class to subclass All and Any from
+# class CompositeCondition(Condition):
+    # def
+
+
+class All(Condition):
+    """
+    All
+
+    Parameters:
+        - args (argtuple): one or more :keyword:`Condition`s
+
+    Satisfied when:
+        - All args are satisfied
+
+    Notes:
+        To initialize with a list (for example),
+            conditions = [AfterNCalls(mechanism, 5) for mechanism in mechanism_list]
+        unpack the list to supply its members as args
+            composite_condition = All(*conditions)
+    """
+    def __init__(self, *args):
+        """
+        :param self:
+        :param args: one or more :keyword:`Condition`s, all of which must be satisfied to satisfy this composite condition
+        """
+        super().__init__(args, self.satis)
+
+    @Condition.scheduler.setter
+    def scheduler(self, value):
+        for cond in self.dependencies:
+            logger.debug('schedule setter: Setting scheduler of {0} to ({1})'.format(cond, value))
+            if cond.scheduler is None:
+                cond.scheduler = value
+
+    @Condition.owner.setter
+    def owner(self, value):
+        for cond in self.dependencies:
+            logger.debug('owner setter: Setting owner of {0} to ({1})'.format(cond, value))
+            if cond.owner is None:
+                cond.owner = value
+
+    def satis(self, conds):
+        for cond in conds:
+            if not cond.is_satisfied():
+                return False
+        return True
+
+
+class Any(Condition):
+    """
+    Any
+
+    Parameters:
+        - args: one or more :keyword:`Condition`s
+
+    Satisfied when:
+        - All args are satisfied
+
+    Notes:
+        To initialize with a list (for example), conditions = [AfterNCalls(mechanism, 5) for mechanism in mechanism_list], unpack the list to supply its members as args
+
+        composite_condition = Any(*conditions)
+    """
+    def __init__(self, *args):
+        """
+        :param self:
+        :param args: one or more :keyword:`Condition`s, any of which must be satisfied to satisfy this composite condition
+        """
+        super().__init__(args, self.satis)
+
+    @Condition.scheduler.setter
+    def scheduler(self, value):
+        logger.debug('Any setter args: {0}'.format(self.dependencies))
+        for cond in self.dependencies:
+            logger.debug('schedule setter: Setting scheduler of {0} to ({1})'.format(cond, value))
+            if cond.scheduler is None:
+                cond.scheduler = value
+
+    @Condition.owner.setter
+    def owner(self, value):
+        for cond in self.dependencies:
+            logger.debug('owner setter: Setting owner of {0} to ({1})'.format(cond, value))
+            if cond.owner is None:
+                cond.owner = value
+
+    def satis(self, conds):
+        for cond in conds:
+            if cond.is_satisfied():
+                return True
+        return False
+
+
+class Not(Condition):
+    """
+    Not
+
+    Parameters:
+        - condition (Condition): a :keyword:`Condition`
+
+    Satisfied when:
+        - condition is not satisfied
+
+    Notes:
+
+    """
+    def __init__(self, condition):
+        super().__init__(condition, lambda c: not c.is_satisfied())
+
+    @Condition.scheduler.setter
+    def scheduler(self, value):
+        self.dependencies.scheduler = value
+
+    @Condition.owner.setter
+    def owner(self, value):
+        self.dependencies.owner = value
+
+######################################################################
+# Time-based Conditions
+#   - satisfied based only on TimeScales
+######################################################################
+
+
+class BeforePass(Condition):
+    """
+    BeforePass
+
+    Parameters:
+        - n (int): the pass after which this condition will be satisfied
+        - time_scale (TimeScale): the TimeScale used as basis for counting passes. Defaults to TimeScale.TRIAL
+
+    Satisfied when:
+        - within the scope of time_scale, at most n-1 passes have occurred
+
+    Notes:
+        Counts of TimeScales are zero-indexed (that is, the first Pass is pass 0, the second Pass is pass 1, etc.). So,
+        BeforePass(2) is satisfied at pass 0 and pass 1
+
+    """
+    def __init__(self, n, time_scale=TimeScale.TRIAL):
+        def func(n, time_scale):
+            if self.scheduler is None:
+                raise ConditionError('{0}: self.scheduler is None - scheduler must be assigned'.format(type(self).__name__))
+            return self.scheduler.times[time_scale][TimeScale.PASS] < n
+        super().__init__(n, func, time_scale)
+
+
+class AtPass(Condition):
+    """
+    AtPass
+
+    Parameters:
+        - n (int): the pass at which this condition will be satisfied
+        - time_scale (TimeScale): the TimeScale used as basis for counting passes. Defaults to TimeScale.TRIAL
+
+    Satisfied when:
+        - within the scope of time_scale, exactly n passes have occurred
+
+    Notes:
+        Counts of TimeScales are zero-indexed (that is, the first Pass is pass 0, the second Pass is pass 1, etc.). So,
+        AtPass(1) is satisfied when one pass (pass 0) has already occurred.
+
+    """
+    def __init__(self, n, time_scale=TimeScale.TRIAL):
+        def func(n):
+            if self.scheduler is None:
+                raise ConditionError('{0}: self.scheduler is None - scheduler must be assigned'.format(type(self).__name__))
+            try:
+                return self.scheduler.times[time_scale][TimeScale.PASS] == n
+            except KeyError as e:
+                raise ConditionError('{0}: {1}, is time_scale set correctly? Currently: {2}'.format(type(self).__name__, e, time_scale))
+        super().__init__(n, func)
+
+
+class AfterPass(Condition):
+    """
+    AfterPass
+
+    Parameters:
+        - n (int): the pass after which this condition will be satisfied
+        - time_scale (TimeScale): the TimeScale used as basis for counting passes. Defaults to TimeScale.TRIAL
+
+    Satisfied when:
+        - within the scope of time_scale, at least n+1 passes have occurred
+
+    Notes:
+        Counts of TimeScales are zero-indexed (that is, the first Pass is pass 0, the second Pass is pass 1, etc.). So,
+        AfterPass(1) is satisfied after pass 1 has occurred, at pass 2, pass 3, pass 4, etc.
+
+    """
+    def __init__(self, n, time_scale=TimeScale.TRIAL):
+        def func(n, time_scale):
+            if self.scheduler is None:
+                raise ConditionError('{0}: self.scheduler is None - scheduler must be assigned'.format(type(self).__name__))
+            return self.scheduler.times[time_scale][TimeScale.PASS] > n
+        super().__init__(n, func, time_scale)
+
+
+class AfterNPasses(Condition):
+    """
+    AfterPass
+
+    Parameters:
+        - n (int): the number of TimeScale.PASSes after which this condition will be satisfied
+        - time_scale (TimeScale): the TimeScale used as basis for counting passes. Defaults to TimeScale.TRIAL
+
+    Satisfied when:
+        - the count of TimeScale.PASSes within time_scale is at least n
+
+    """
+    def __init__(self, n, time_scale=TimeScale.TRIAL):
+        def func(n, time_scale):
+            if self.scheduler is None:
+                raise ConditionError('{0}: self.scheduler is None - scheduler must be assigned'.format(type(self).__name__))
+            return self.scheduler.times[time_scale][TimeScale.PASS] >= n
+        super().__init__(n, func, time_scale)
+
+
+class EveryNPasses(Condition):
+    """
+    EveryNPasses
+
+    Parameters:
+        - n (int): the frequency of passes with which this condition will be satisfied
+        - time_scale (TimeScale): the TimeScale used as basis for counting passes. Defaults to TimeScale.TRIAL
+
+    Satisfied when:
+        - the number of passes that has occurred within time_scale is evenly divisible by n
+
+    Notes:
+        All EveryNPasses conditions will be satisfied at pass 0
+
+    """
+    def __init__(self, n, time_scale=TimeScale.TRIAL):
+        def func(n, time_scale):
+            if self.scheduler is None:
+                raise ConditionError('{0}: self.scheduler is None - scheduler must be assigned'.format(type(self).__name__))
+            return self.scheduler.times[time_scale][TimeScale.PASS] % n == 0
+        super().__init__(n, func, time_scale)
+
+
+class BeforeTrial(Condition):
+    """
+    BeforeTrial
+
+    Parameters:
+        - n (int): the trial after which this condition will be satisfied
+        - time_scale (TimeScale): the TimeScale used as basis for counting trials. Defaults to TimeScale.RUN
+
+    Satisfied when:
+        - within the scope of time_scale, at most n-1 trials have occurred
+
+    Notes:
+        Counts of TimeScales are zero-indexed (that is, the first Trial is trial 0, the second Trial is trial 1, etc.). So,
+        BeforeTrial(2) is satisfied at trial 0 and trial 1
+
+    """
+    def __init__(self, n, time_scale=TimeScale.RUN):
+        def func(n):
+            if self.scheduler is None:
+                raise ConditionError('{0}: self.scheduler is None - scheduler must be assigned'.format(type(self).__name__))
+            try:
+                return self.scheduler.times[time_scale][TimeScale.TRIAL] < n
+            except KeyError as e:
+                raise ConditionError('{0}: {1}, is time_scale set correctly? Currently: {2}'.format(type(self).__name__, e, time_scale))
+        super().__init__(n, func)
+
+
+class AtTrial(Condition):
+    """
+    AtTrial
+
+    Parameters:
+        - n (int): the trial at which this condition will be satisfied
+        - time_scale (TimeScale): the TimeScale used as basis for counting trials. Defaults to TimeScale.RUN
+
+    Satisfied when:
+        - within the scope of time_scale, exactly n trials have occurred
+
+    Notes:
+        Counts of TimeScales are zero-indexed (that is, the first Trial is trial 0, the second Trial is trial 1, etc.). So,
+        AtTrial(1) is satisfied when one trial (trial 0) has already occurred.
+
+    """
+    def __init__(self, n, time_scale=TimeScale.RUN):
+        def func(n):
+            if self.scheduler is None:
+                raise ConditionError('{0}: self.scheduler is None - scheduler must be assigned'.format(type(self).__name__))
+            try:
+                return self.scheduler.times[time_scale][TimeScale.TRIAL] == n
+            except KeyError as e:
+                raise ConditionError('{0}: {1}, is time_scale set correctly? Currently: {2}'.format(type(self).__name__, e, time_scale))
+        super().__init__(n, func)
+
+
+class AfterTrial(Condition):
+    """
+    AfterTrial
+
+    Parameters:
+        - n (int): the trial after which this condition will be satisfied
+        - time_scale (TimeScale): the TimeScale used as basis for counting trials. Defaults to TimeScale.RUN
+
+    Satisfied when:
+        - within the scope of time_scale, at least n+1 trials have occurred
+
+    Notes:
+        Counts of TimeScales are zero-indexed (that is, the first Trial is trial 0, the second Trial is trial 1, etc.). So,
+        AfterTrial(1) is satisfied after trial 1 has occurred, at trial 2, trial 3, trial 4, etc.
+
+    """
+    def __init__(self, n, time_scale=TimeScale.RUN):
+        def func(n):
+            if self.scheduler is None:
+                raise ConditionError('{0}: self.scheduler is None - scheduler must be assigned'.format(type(self).__name__))
+            try:
+                return self.scheduler.times[time_scale][TimeScale.TRIAL] > n
+            except KeyError as e:
+                raise ConditionError('{0}: {1}, is time_scale set correctly? Currently: {2}'.format(type(self).__name__, e, time_scale))
+        super().__init__(n, func)
+
+
+class AfterNTrials(Condition):
+    """
+    AfterNTrials
+
+    Parameters:
+        - n (int): the number of TimeScale.TRIALs after which this condition will be satisfied
+        - time_scale (TimeScale): the TimeScale used as basis for counting trials. Defaults to TimeScale.RUN
+
+    Satisfied when:
+        - the count of TimeScale.TRIALs within time_scale is at least n
+
+    Notes:
+
+    """
+    def __init__(self, n, time_scale=TimeScale.RUN):
+        def func(n, time_scale):
+            if self.scheduler is None:
+                raise ConditionError('{0}: self.scheduler is None - scheduler must be assigned'.format(type(self).__name__))
+            return self.scheduler.times[time_scale][TimeScale.TRIAL] >= n
+        super().__init__(n, func, time_scale)
+
+######################################################################
+# Component-based Conditions
+#   - satisfied based on executions or state of Components
+######################################################################
+
+
+class BeforeNCalls(Condition):
+    """
+    BeforeNCalls
+
+    Parameters:
+        - dependency (Component):
+        - n (int): the number of executions of dependency at which this condition will be satisfied
+        - time_scale (TimeScale): the TimeScale used as basis for counting executions of dependency. Defaults to TimeScale.TRIAL
+
+    Satisfied when:
+        - dependency has been executed exactly n times within the scope of time_scale
+
+    Notes:
+
+    """
+    def __init__(self, dependency, n, time_scale=TimeScale.TRIAL):
+        def func(dependency, n):
+            if self.scheduler is None:
+                raise ConditionError('{0}: self.scheduler is None - scheduler must be assigned'.format(type(self).__name__))
+            num_calls = self.scheduler.counts_total[time_scale][dependency]
+            logger.debug('{0} has reached {1} num_calls in {2}'.format(dependency, num_calls, time_scale.name))
+            return num_calls < n
+        super().__init__(dependency, func, n)
+
+# NOTE:
+# The behavior of AtNCalls is not desired (i.e. depending on the order mechanisms are checked, B running AtNCalls(A, x))
+# may run on both the xth and x+1st call of A; if A and B are not parent-child
+# A fix could invalidate key assumptions and affect many other conditions
+# Since this condition is unlikely to be used, it's best to leave it for now
+
+
+class AtNCalls(Condition):
+    """
+    AtNCalls
+
+    Parameters:
+        - dependency (Component):
+        - n (int): the number of executions of dependency at which this condition will be satisfied
+        - time_scale (TimeScale): the TimeScale used as basis for counting executions of dependency. Defaults to TimeScale.TRIAL
+
+    Satisfied when:
+        - dependency has been executed exactly n times within the scope of time_scale
+
+    Notes:
+
+    """
+    def __init__(self, dependency, n, time_scale=TimeScale.TRIAL):
+        def func(dependency, n):
+            if self.scheduler is None:
+                raise ConditionError('{0}: self.scheduler is None - scheduler must be assigned'.format(type(self).__name__))
+            num_calls = self.scheduler.counts_total[time_scale][dependency]
+            logger.debug('{0} has reached {1} num_calls in {2}'.format(dependency, num_calls, time_scale.name))
+            return num_calls == n
+        super().__init__(dependency, func, n)
+
+
+class AfterCall(Condition):
+    """
+    AfterCall
+
+    Parameters:
+        - dependency (Component):
+        - n (int): the number of executions of dependency after which this condition will be satisfied
+        - time_scale (TimeScale): the TimeScale used as basis for counting executions of dependency. Defaults to TimeScale.TRIAL
+
+    Satisfied when:
+        - dependency has been executed at least n+1 times within the scope of time_scale
+
+    Notes:
+
+    """
+    def __init__(self, dependency, n, time_scale=TimeScale.TRIAL):
+        def func(dependency, n):
+            if self.scheduler is None:
+                raise ConditionError('{0}: self.scheduler is None - scheduler must be assigned'.format(type(self).__name__))
+            num_calls = self.scheduler.counts_total[time_scale][dependency]
+            logger.debug('{0} has reached {1} num_calls in {2}'.format(dependency, num_calls, time_scale.name))
+            return num_calls > n
+        super().__init__(dependency, func, n)
+
+
+class AfterNCalls(Condition):
+    """
+    AfterNCalls
+
+    Parameters:
+        - dependency (Component):
+        - n (int): the number of executions of dependency after which this condition will be satisfied
+        - time_scale (TimeScale): the TimeScale used as basis for counting executions of dependency. Defaults to TimeScale.TRIAL
+
+    Satisfied when:
+        - dependency has been executed at least n+1 times within the scope of time_scale
+
+    Notes:
+
+    """
+    def __init__(self, dependency, n, time_scale=TimeScale.TRIAL):
+        def func(dependency, n):
+            if self.scheduler is None:
+                raise ConditionError('{0}: self.scheduler is None - scheduler must be assigned'.format(type(self).__name__))
+            num_calls = self.scheduler.counts_total[time_scale][dependency]
+            logger.debug('{0} has reached {1} num_calls in {2}'.format(dependency, num_calls, time_scale.name))
+            return num_calls >= n
+        super().__init__(dependency, func, n)
+
+
+class AfterNCallsCombined(Condition):
+    """
+    AfterNCallsCombined
+
+    Parameters:
+        - *dependencies (Components): variable length
+        - n (int): the number of executions of all dependencies after which this condition will be satisfied. Defaults to None
+        - time_scale (TimeScale): the TimeScale used as basis for counting executions of dependencies. Defaults to TimeScale.TRIAL
+
+    Satisfied when:
+        - Among all dependencies, there have been at least n+1 executions within the scope of time_scale
+
+    Notes:
+
+    """
+    def __init__(self, *dependencies, n=None, time_scale=TimeScale.TRIAL):
+        logger.debug('{0} args: deps {1}, n {2}, ts {3}'.format(type(self).__name__, dependencies, n, time_scale))
+
+        def func(_none, *dependencies, n=None):
+            if self.scheduler is None:
+                raise ConditionError('{0}: self.scheduler is None - scheduler must be assigned'.format(type(self).__name__))
+            if n is None:
+                raise ConditionError('{0}: keyword argument n is None'.format(type(self).__name__))
+            count_sum = 0
+            for d in dependencies:
+                count_sum += self.scheduler.counts_total[time_scale][d]
+                logger.debug('{0} has reached {1} num_calls in {2}'.format(d, self.scheduler.counts_total[time_scale][d], time_scale.name))
+            return count_sum >= n
+        super().__init__(None, func, *dependencies, n=n)
+
+
+class EveryNCalls(Condition):
+    """
+    EveryNCalls
+
+    Parameters:
+        - dependency (Component):
+        - n (int): the frequency of executions of dependency with which this condition will be satisfied
+
+    Satisfied when:
+        - since the last time this conditon's owner was called, the number of calls of dependency is at least n
+
+    Notes:
+        Whenever a Component is run, the Scheduler's count of each dependency that is "useable" by the Component is
+        reset to 0
+
+    """
+    def __init__(self, dependency, n):
+        def func(dependency, n):
+            if self.scheduler is None:
+                raise ConditionError('{0}: self.scheduler is None - scheduler must be assigned'.format(type(self).__name__))
+            num_calls = self.scheduler.counts_useable[dependency][self.owner]
+            logger.debug('{0} has reached {1} num_calls'.format(dependency, num_calls))
+            return num_calls >= n
+        super().__init__(dependency, func, n)
+
+
+class JustRan(Condition):
+    """
+    JustRan
+
+    Parameters:
+        - dependency (Component):
+
+    Satisfied when:
+        - dependency has been run (or told to run) in the previous TimeScale.TIME_STEP
+
+    Notes:
+        This condition can transcend divisions between TimeScales. That is, if A runs in the final time step in a trial,
+        JustRan(A) will be satisfied at the beginning of the next trial.
+
+    """
+    def __init__(self, dependency):
+        def func(dependency):
+            if self.scheduler is None:
+                raise ConditionError('{0}: self.scheduler is None - scheduler must be assigned'.format(type(self).__name__))
+            logger.debug('checking if {0} in previous execution step set'.format(dependency))
+            try:
+                return dependency in self.scheduler.execution_list[-1]
+            except TypeError:
+                return dependency == self.scheduler.execution_list[-1]
+        super().__init__(dependency, func)
+
+
+class AllHaveRun(Condition):
+    """
+    AllHaveRun
+
+    Parameters:
+        - *dependencies (Components): variable length
+        - time_scale (TimeScale): the TimeScale used as basis for counting executions of dependencies. Defaults to TimeScale.TRIAL
+
+    Satisfied when:
+        - All dependencies have been executed at least 1 time within the scope of time_scale
+
+    Notes:
+
+    """
+    def __init__(self, *dependencies, time_scale=TimeScale.TRIAL):
+        def func(_none, *dependencies):
+            if self.scheduler is None:
+                raise ConditionError('{0}: self.scheduler is None - scheduler must be assigned'.format(type(self).__name__))
+            if len(dependencies) == 0:
+                dependencies = self.scheduler.nodes
+            for d in dependencies:
+                if self.scheduler.counts_total[time_scale][d] < 1:
+                    return False
+            return True
+        super().__init__(None, func, *dependencies)
+
+
+class WhenFinished(Condition):
+    """
+    WhenFinished
+
+    Parameters:
+        - dependency (Component):
+
+    Satisfied when:
+        - dependency has "finished" (i.e. its is_finished attribute is True)
+
+    Notes:
+        This is a dynamic condition.
+        The is_finished concept varies among components, and is currently implemented in:
+            `DDM`<DDM>
+
+    """
+    def __init__(self, dependency):
+        def func(dependency):
+            try:
+                return dependency.is_finished
+            except AttributeError as e:
+                raise ConditionError('WhenFinished: Unsupported dependency type: {0}; ({1})'.format(type(dependency), e))
+
+        super().__init__(dependency, func)
+
+
+class WhenFinishedAny(Condition):
+    """
+    WhenFinishedAny
+
+    Parameters:
+        - *dependencies (Components): variable length
+
+    Satisfied when:
+        - any of the dependencies have "finished" (i.e. its is_finished attribute is True)
+
+    Notes:
+    This is a dynamic condition.
+        This is a convenience class; WhenFinishedAny(A, B, C) is equivalent to Any(WhenFinished(A), WhenFinished(B), WhenFinished(C))
+        If no dependencies are specified, the condition will default to checking all of its scheduler's Components.
+        The is_finished concept varies among components, and is currently implemented in:
+            `DDM`<DDM>
+
+    """
+    def __init__(self, *dependencies):
+        def func(_none, *dependencies):
+            if len(dependencies) == 0:
+                dependencies = self.scheduler.nodes
+            for d in dependencies:
+                try:
+                    if d.is_finished:
+                        return True
+                except AttributeError as e:
+                    raise ConditionError('WhenFinishedAny: Unsupported dependency type: {0}; ({1})'.format(type(d), e))
+            return False
+
+        super().__init__(None, func, *dependencies)
+
+
+class WhenFinishedAll(Condition):
+    """
+    WhenFinishedAll
+
+    Parameters:
+        - *dependencies (Components): variable length
+
+    Satisfied when:
+        - all of the dependencies have "finished" (i.e. its is_finished attribute is True)
+
+    Notes:
+        This is a dynamic condition.
+        This is a convenience class; WhenFinishedAll(A, B, C) is equivalent to All(WhenFinished(A), WhenFinished(B), WhenFinished(C))
+        If no dependencies are specified, the condition will default to checking all of its scheduler's Components.
+        The is_finished concept varies among components, and is currently implemented in:
+            `DDM`<DDM>
+
+    """
+    def __init__(self, *dependencies):
+        def func(_none, *dependencies):
+            if len(dependencies) == 0:
+                dependencies = self.scheduler.nodes
+            for d in dependencies:
+                try:
+                    if not d.is_finished:
+                        return False
+                except AttributeError as e:
+                    raise ConditionError('WhenFinishedAll: Unsupported dependency type: {0}; ({1})'.format(type(d), e))
+            return True
+
+        super().__init__(None, func, *dependencies)
diff --git a/SOFTWARE ARCHITECTURE b/SOFTWARE ARCHITECTURE
index 1470f23743d..f4ee50641ea 100644
--- a/SOFTWARE ARCHITECTURE	
+++ b/SOFTWARE ARCHITECTURE	
@@ -291,7 +291,7 @@ Keep components simple; put any necessary complexity (to make it easier for the
          every instance of State must be explicitly assigned an existing <state>.ownerMechanism (Mechanism)
          default projections can be implemented for a state (using <state>.defaultProjectionType);
              if their sender is not specified, a default one will be created (see Projection below)
-         <state>.receivesFromProjections is consulted when a State's update function is executed,
+         <state>.afferents is consulted when a State's update function is executed,
              and <state>.value is updated based on those
          subclasses must implement defaultProjectionType
      - Projection:
@@ -538,7 +538,7 @@ Keep components simple; put any necessary complexity (to make it easier for the
              - insures output of projection function is compatible with state.value
              - insures receiver for each projection is state
              - if spec is not valid, default is created of type determined by paramsCurrent[PROJECTION_TYPE]
-             - adds each to state.receivesFromProjections
+             - adds each to state.afferents
          9) Assign observers
 
      E) Projection:
@@ -651,14 +651,14 @@ Keep components simple; put any necessary complexity (to make it easier for the
      - Mechanism.update_states_and_execute:
          [TBI: calls each of the functions in its executionSequence (see Mechanism.execute):
          - calls self.inputState.() for each entry in self.inputStates, which:
-             + executes every self.inputState.receivesFromProjections.[<Projection>.execute()...]
+             + executes every self.inputState.afferents.[<Projection>.execute()...]
                  note:  for the first mechanism in the pathway, this includes a projection with Process input
              + aggregates them using self.inputState.params[FUNCTION]()
              + applies any runtime kwMechansimInputStateParams specified with mechanism in a tuple in the pathway
              + stores result in self.inputState.value
          - calls self._update_parameter_states, which calls every self.params[<ParameterState>].execute(),
              each of which:
-             + executes self.params[<ParameterState>].receivesFromProjections.[<Projection>.execute()...]
+             + executes self.params[<ParameterState>].afferents.[<Projection>.execute()...]
                  (usually this absent, or is a single ControlSignal projection from DefaultController)
                  with any runtime kwMechansimParameterStateParams specified with mechanism in tupel in pathway
              + aggregates results using self.params[<ParameterState>].params[FUNCTION]()
diff --git a/Scripts/DEBUGGING SCRIPTS/EVC Gratton Script - SEBASTIAN.py b/Scripts/DEBUGGING SCRIPTS/EVC Gratton Script - SEBASTIAN.py
index 2edfcf4b6b6..80c64d0736e 100644
--- a/Scripts/DEBUGGING SCRIPTS/EVC Gratton Script - SEBASTIAN.py	
+++ b/Scripts/DEBUGGING SCRIPTS/EVC Gratton Script - SEBASTIAN.py	
@@ -3,7 +3,7 @@
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import *
 from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection
+from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
 from PsyNeuLink.Components.System import system
 from PsyNeuLink.Globals.Keywords import *
 import numpy as np
@@ -28,7 +28,7 @@ def test_outcome_function(**kwargs):
                                       verbose_pref=PreferenceEntry(True,PreferenceLevel.INSTANCE))
 
 # Control Parameters
-signalSearchRange = np.arange(0, 10, 2)
+signalSearchRange = np.arange(1.0, 2.0, 0.2)
 
 # Stimulus Mechanisms
 Target_Stim = TransferMechanism(name='Target Stimulus', function=Linear(slope = 0.3324))
@@ -38,13 +38,15 @@ def test_outcome_function(**kwargs):
 Target_Rep = TransferMechanism(name='Target Representation',
                                function=Linear(slope=(1.0,
                                                      ControlProjection(function=Linear,
-                                                                       control_signal={ALLOCATION_SAMPLES:signalSearchRange}
+                                                                       control_signal_params={
+                                                                           ALLOCATION_SAMPLES:signalSearchRange}
                                                                        ))),
                                prefs=mechanism_prefs)
 Flanker_Rep = TransferMechanism(name='Flanker Representation',
                                function=Linear(slope=(1.0,
                                                      ControlProjection(function=Linear,
-                                                                       control_signal={ALLOCATION_SAMPLES:signalSearchRange}
+                                                                       control_signal_params={
+                                                                           ALLOCATION_SAMPLES:signalSearchRange}
                                                                        ))),
                                prefs=mechanism_prefs)
 
@@ -54,11 +56,14 @@ def test_outcome_function(**kwargs):
                                prefs=mechanism_prefs)
 
 # Decision Mechanisms
-Decision = DDM(function=BogaczEtAl(drift_rate=(1.0),
-                                   threshold=(0.2645),
-                                   noise=(0.5),
-                                   starting_point=(0),
+Decision = DDM(function=BogaczEtAl(drift_rate=1.0,
+                                   threshold=0.2645,
+                                   noise=0.5,
+                                   starting_point=0,
                                    t0=0.15),
+               output_states=[DECISION_VARIABLE,
+                              RESPONSE_TIME,
+                              PROBABILITY_UPPER_THRESHOLD],
                prefs = mechanism_prefs,
                name='Decision')
 
@@ -94,7 +99,7 @@ def test_outcome_function(**kwargs):
 
 RewardProcess = process(
     default_input_value=[0],
-    pathway=[(Reward, 1)],
+    pathway=[Reward],
     prefs = process_prefs,
     name = 'RewardProcess')
 
@@ -104,7 +109,7 @@ def test_outcome_function(**kwargs):
                              RewardProcess],
                   controller=EVCMechanism,
                   enable_controller=True,
-                  monitor_for_control=[Reward, DDM_PROBABILITY_UPPER_THRESHOLD],
+                  monitor_for_control=[Reward, Decision.PROBABILITY_UPPER_THRESHOLD],
                   # monitor_for_control=[Reward, DDM_PROBABILITY_UPPER_THRESHOLD, (DDM_RESPONSE_TIME, -1, 1)],
                   name='EVC Gratton System')
 
@@ -114,12 +119,13 @@ def test_outcome_function(**kwargs):
 mySystem.controller.show()
 
 # configure EVC components
-mySystem.controller.controlSignals[0].intensity_cost_function = Exponential(rate =  0.8046).function
-mySystem.controller.controlSignals[1].intensity_cost_function = Exponential(rate =  0.8046).function
+mySystem.controller.control_signals[0].intensity_cost_function = Exponential(rate =  0.8046).function
+mySystem.controller.control_signals[1].intensity_cost_function = Exponential(rate =  0.8046).function
 
 
 # Loop over the KEYS in this dict
-for mech in mySystem.controller.prediction_mechanisms.keys():
+# for mech in mySystem.controller.prediction_mechanisms.keys():
+for mech in mySystem.controller.predictionMechanisms.mechanisms:
 
     # mySystem.controller.prediction_mechanisms is dictionary organized into key-value pairs where the key is a
     # (transfer) mechanism, and the value is the corresponding prediction (integrator) mechanism
@@ -127,7 +133,7 @@ def test_outcome_function(**kwargs):
     # For example: the key which is a transfer mechanism with the name 'Flanker Stimulus'
     # acceses an integrator mechanism with the name 'Flanker Stimulus_PredictionMechanism'
 
-    if mech.name is 'Flanker Stimulus' or mech.name is 'Target Stimulus':
+    if mech.name == 'Flanker Stimulus Prediction Mechanism' or mech.name == 'Target Stimulus Prediction Mechanism':
 
         # when you find a key mechanism (transfer mechanism) with the correct name, print its name
         print(mech.name)
@@ -135,34 +141,47 @@ def test_outcome_function(**kwargs):
         # then use that key to access its *value* in the dictionary, which will be an integrator mechanism
         # that integrator mechanism is the one whose rate we want to change ( I think!)
         # mySystem.controller.prediction_mechanisms[mech].function_object.rate = 0.3481
-        # mySystem.controller.prediction_mechanisms[mech].parameterStates['rate'].baseValue = 0.3481
-        # mech.parameterStates['rate'].baseValue = 0.3481
-        mySystem.controller.prediction_mechanisms[mech].function_object.rate = 0.3481
+        # mySystem.controller.prediction_mechanisms[mech].parameterStates['rate'].base_value = 0.3481
+        # mech.parameterStates['rate'].base_value = 0.3481
+        # mySystem.controller.prediction_mechanisms[mech].function_object.rate = 0.3481
+        mech.function_object.rate = 1.0
+        x = mech.function_object.rate
 
-    if mech.name is 'Reward':
+    if 'Reward' in mech.name :
         print(mech.name)
-        mySystem.controller.prediction_mechanisms[mech].function_object.rate = 1.0
-        # mySystem.controller.prediction_mechanisms[mech].parameterStates['rate'].baseValue = 1.0
+        mech.function_object.rate = 1.0
+        # mySystem.controller.prediction_mechanisms[mech].parameterStates['rate'].base_value = 1.0
 
 
 
 
 print('new rate of integration mechanisms before system execution:')
-for mech in mySystem.controller.prediction_mechanisms.keys():
-    print( mySystem.controller.prediction_mechanisms[mech].name)
-    print( mySystem.controller.prediction_mechanisms[mech].function_object.rate)
+# for mech in mySystem.controller.prediction_mechanisms.keys():
+for mech in mySystem.controller.predictionMechanisms.mechanisms:
+    print(mech.name)
+    print(mech.function_object.rate)
     print('----')
 
 # generate stimulus environment
 
-nTrials = 20
-targetFeatures = [1]
-flankerFeatures = [-1] # for full simulation: flankerFeatures = [-1,1]
+# nTrials = 20
+# targetFeatures = [1]
+# flankerFeatures = [-1] # for full simulation: flankerFeatures = [-1,1]
+# reward = 100
+
+nTrials = 3
+targetFeatures = [1 , 1, 1]
+flankerFeatures = [1, -1, 1] # for full simulation: flankerFeatures = [-1,1]
 reward = 100
 
-targetInputList = np.random.choice(targetFeatures, nTrials).tolist()
-flankerInputList = np.random.choice(flankerFeatures, nTrials).tolist()
-rewardList = (np.ones(nTrials) * reward).tolist() #np.random.choice(reward, nTrials).tolist()
+
+# targetInputList = np.random.choice(targetFeatures, nTrials).tolist()
+# flankerInputList = np.random.choice(flankerFeatures, nTrials).tolist()
+# rewardList = (np.ones(nTrials) * reward).tolist() #np.random.choice(reward, nTrials).tolist()
+
+targetInputList = [1, 1, 1]
+flankerInputList = [1, -1, 1]
+rewardList = [100, 100, 100]
 
 stim_list_dict = {Target_Stim:targetInputList,
                   Flanker_Stim:flankerInputList,
diff --git a/Scripts/DEBUGGING SCRIPTS/EVC System Laming Validation Test Script [SPECIFY ControlSignals]].py b/Scripts/DEBUGGING SCRIPTS/EVC System Laming Validation Test Script [SPECIFY ControlSignals]].py
new file mode 100644
index 00000000000..2a955ff1beb
--- /dev/null
+++ b/Scripts/DEBUGGING SCRIPTS/EVC System Laming Validation Test Script [SPECIFY ControlSignals]].py	
@@ -0,0 +1,184 @@
+# from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.Deprecated.LinearMechanism import *
+from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.EVCMechanism import EVCMechanism
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import *
+from PsyNeuLink.Components.States.ModulatorySignals.ControlSignal import ControlSignal
+from PsyNeuLink.Components.Process import process
+from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
+from PsyNeuLink.Components.System import system
+from PsyNeuLink.Globals.Keywords import *
+from PsyNeuLink.scheduling.condition import AfterNCalls
+
+import random
+random.seed(0)
+np.random.seed(0)
+
+# Preferences:
+DDM_prefs = ComponentPreferenceSet(
+                prefs = {
+                    kpVerbosePref: PreferenceEntry(False,PreferenceLevel.INSTANCE),
+                    kpReportOutputPref: PreferenceEntry(True,PreferenceLevel.INSTANCE)})
+
+process_prefs = ComponentPreferenceSet(reportOutput_pref=PreferenceEntry(False,PreferenceLevel.INSTANCE),
+                                      verbose_pref=PreferenceEntry(True,PreferenceLevel.INSTANCE))
+
+
+# Mechanisms:
+Input = TransferMechanism(name='Input',
+
+                 )
+
+Reward = TransferMechanism(name='Reward',
+                 # params={MONITOR_FOR_CONTROL:[PROBABILITY_UPPER_THRESHOLD,(RESPONSE_TIME, -1, 1)]}
+                           output_states=[RESULT, MEAN, VARIANCE]
+                  )
+
+# # CONTROL SPECIFIED IN control_signals ARG OF System CONSTRUCTOR
+# Decision = DDM(function=BogaczEtAl(drift_rate=1.0,
+#                                    threshold=1.0,
+#                                    noise=0.5,
+#                                    starting_point=0,
+#                                    t0=0.45),
+#                output_states=[DECISION_VARIABLE,
+#                               RESPONSE_TIME,
+#                               PROBABILITY_UPPER_THRESHOLD],
+#                prefs = DDM_prefs,
+#                name='Decision')
+
+# # CONTROL SPECIFIED BY ControlProjections
+# Decision = DDM(function=BogaczEtAl(drift_rate=(1.0, ControlProjection(function=Linear,
+#                                                                       control_signal_params={
+#                                                                           ALLOCATION_SAMPLES:np.arange(0.1, 1.01, 0.3)},
+#                                                                       )),
+#                                    threshold=(1.0, ControlProjection(function=Linear,
+#                                                                      control_signal_params={
+#                                                                          ALLOCATION_SAMPLES:np.arange(0.1, 1.01, 0.3)}
+#                                                                      )),
+#                                    noise=(0.5),
+#                                    starting_point=(0),
+#                                    t0=0.45),
+#                output_states=[DECISION_VARIABLE,
+#                               RESPONSE_TIME,
+#                               PROBABILITY_UPPER_THRESHOLD],
+#                prefs = DDM_prefs,
+#                name='Decision')
+
+# CONTROL SPECIFIED BY ControlSignals
+Decision = DDM(function=BogaczEtAl(drift_rate=(1.0, ControlSignal(allocation_samples=np.arange(0.1, 1.01, 0.3))),
+                                   threshold=(1.0, ControlSignal(allocation_samples=np.arange(0.1, 1.01, 0.3))),
+                                   noise=(0.5),
+                                   starting_point=(0),
+                                   t0=0.45),
+               output_states=[DECISION_VARIABLE,
+                              RESPONSE_TIME,
+                              PROBABILITY_UPPER_THRESHOLD],
+               prefs = DDM_prefs,
+               name='Decision')
+
+# Processes:
+TaskExecutionProcess = process(
+    default_input_value=[0],
+    pathway=[Input, IDENTITY_MATRIX, Decision],
+    prefs = process_prefs,
+    name = 'TaskExecutionProcess')
+
+RewardProcess = process(
+    default_input_value=[0],
+    pathway=[Reward],
+    prefs = process_prefs,
+    name = 'RewardProcess')
+
+
+# System:
+mySystem = system(processes=[TaskExecutionProcess, RewardProcess],
+                  controller=EVCMechanism,
+                  # controller=EVCMechanism(monitor_for_control=[Reward,
+                  #                                              Decision.PROBABILITY_UPPER_THRESHOLD,
+                  #                                              Decision.RESPONSE_TIME],
+                  #                         outcome_function=LinearCombination(exponents=[1, 1, -1])),
+                  enable_controller=True,
+                  monitor_for_control=[Reward,
+                                       Decision.PROBABILITY_UPPER_THRESHOLD,
+                                       (Decision.RESPONSE_TIME, -1, 1)],
+                  # ParameterState state specification dictionary
+                  # control_signals=[{NAME: DRIFT_RATE,
+                  #                   MECHANISM: Decision,
+                  #                   ALLOCATION_SAMPLES:np.arange(0.1, 1.01, 0.3)},
+                  #                  {NAME: THRESHOLD,
+                  #                   MECHANISM: Decision}],
+                  # control_signals=[(DRIFT_RATE, Decision),
+                  #                  (THRESHOLD, Decision)],
+                  # monitor_for_control=[Input, PROBABILITY_UPPER_THRESHOLD,(RESPONSE_TIME, -1, 1)],
+                  # monitor_for_control=[MonitoredOutputStatesOption.ALL_OUTPUT_STATES],
+                  name='EVC Test System')
+
+# Show characteristics of system:
+mySystem.show()
+mySystem.controller.show()
+# mySystem.show_graph(show_control=True)
+
+# Specify stimuli for run:
+# #   two ways to do so:
+#
+# #   - as a dictionary of stimulus lists; for each entry:
+# #     key is name of an origin mechanism in the system
+# #     value is a list of its sequence of stimuli (one for each trial)
+# inputList = [0.5, 0.123]
+# rewardList = [20, 20]
+# # stim_list_dict = {Input:[0.5, 0.123],
+# #               Reward:[20, 20]}
+
+stim_list_dict = {Input:[0.5, 0.123],
+                  Reward:[20, 20]}
+
+# #   - as a list of trials;
+# #     each item in the list contains the stimuli for a given trial,
+# #     one for each origin mechanism in the system
+# trial_list = [[0.5, 20], [0.123, 20]]
+# reversed_trial_list = [[Reward, Input], [20, 0.5], [20, 0.123]]
+
+# Create printouts function (to call in run):
+def show_trial_header():
+    print("\n############################ TRIAL {} ############################".format(CentralClock.trial))
+
+def show_results():
+    import re
+    print('\nRESULTS (time step {}): '.format(CentralClock.time_step))
+    results_for_decision = [(state.name, state.value) for state in Decision.output_states]
+
+    print("\tDecision:")
+    print('\t\tControlSignal values:')
+    print ('\t\t\tDrift rate control signal (from EVC): {}'.
+           # format(re.sub('[\[,\],\n]','',str(float(Decision.parameterStates[DRIFT_RATE].value)))))
+           format(re.sub('[\[,\],\n]','',str("{:0.3}".format(float(Decision._parameter_states[DRIFT_RATE].value))))))
+    print ('\t\t\tThreshold control signal (from EVC): {}'.
+           format(re.sub('[\[,\],\n]','',str(float(Decision._parameter_states[THRESHOLD].value))),
+                  mySystem.controller.output_states['threshold_ControlSignal'].value,
+                  Decision._parameter_states[THRESHOLD].mod_afferents[0].value
+                  ))
+    print('\t\tOutput:')
+    for result in results_for_decision:
+        print("\t\t\t{}: {}".format(result[0],
+                                re.sub('[\[,\],\n]','',str("{:0.3}".format(float(result[1]))))))
+    results_for_reward = [(state.name, state.value) for state in Reward.output_states]
+    print("\tReward:\n\t\tOutput:")
+    for result in results_for_reward:
+        print("\t\t\t{}: {}".format(result[0],
+                                re.sub('[\[,\],\n]','',str("{:0.3}".format(float(result[1]))))))
+
+
+# Run system:
+
+mySystem.controller.reportOutputPref = False
+
+# mySystem.show_graph(direction='LR')
+# mySystem.show_graph(show_control=True)
+
+# mySystem.run(inputs=trial_list,
+# # mySystem.run(inputs=reversed_trial_list,
+mySystem.run(
+    inputs=stim_list_dict,
+    call_before_trial=show_trial_header,
+    # call_after_time_step=show_results
+    call_after_trial=show_results,
+)
diff --git a/Scripts/DEBUGGING SCRIPTS/Multilayer Learning Test Script [WITH GATING].py b/Scripts/DEBUGGING SCRIPTS/Multilayer Learning Test Script [WITH GATING].py
new file mode 100644
index 00000000000..a8c2212eabe
--- /dev/null
+++ b/Scripts/DEBUGGING SCRIPTS/Multilayer Learning Test Script [WITH GATING].py	
@@ -0,0 +1,218 @@
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
+from PsyNeuLink.Components.Process import process
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
+from PsyNeuLink.Components.System import system
+from PsyNeuLink.scheduling.condition import AfterNCalls
+from PsyNeuLink.Components.States.OutputState import *
+from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.GatingMechanisms.GatingMechanism import GatingMechanism
+
+# from PsyNeuLink.Globals.Run import run, construct_inputs
+
+Input_Layer = TransferMechanism(name='Input Layer',
+                                function=Logistic,
+                                default_input_value = np.zeros((2,)))
+
+Hidden_Layer_1 = TransferMechanism(name='Hidden Layer_1',
+                          function=Logistic(),
+                          default_input_value = np.zeros((5,)))
+
+Hidden_Layer_2 = TransferMechanism(name='Hidden Layer_2',
+                          function=Logistic(),
+                          default_input_value = [0,0,0,0])
+
+Output_Layer = TransferMechanism(name='Output Layer',
+                        function=Logistic,
+                        default_input_value = [0,0,0])
+
+random_weight_matrix = lambda sender, receiver : random_matrix(sender, receiver, .2, -.1)
+
+Gating_Mechanism = GatingMechanism(default_gating_policy=1.0,
+                                   gating_signals=[
+                                       # THIS GENERATES ONE GatingSignal WITH THREE Projections
+                                       {
+                                       # THIS NAMES THE GatingSignal EXPLICITLY, OVERRIDING THE KEY BELOW:
+                                       # NAME: 'GATING SIGNAL EXPLICIT NAME',
+                                       # THIS USES THE KEY AS THE NAME OF THE GatingSignal, UNLESS NAMED EXPLICITLY:
+                                        'GATE_ALL': [Hidden_Layer_1,
+                                                     Hidden_Layer_2,
+                                                     Output_Layer]
+                                       },
+                                       # THIS GENERATES THREE GatingSignals, EACH WITH ONE Projection:
+                                       # Hidden_Layer_1,
+                                       # Hidden_Layer_2,
+                                       # Output_Layer
+                                   ])
+
+
+Input_Weights_matrix = (np.arange(2*5).reshape((2, 5)) + 1)/(2*5)
+Middle_Weights_matrix = (np.arange(5*4).reshape((5, 4)) + 1)/(5*4)
+Output_Weights_matrix = (np.arange(4*3).reshape((4, 3)) + 1)/(4*3)
+
+
+# TEST PROCESS.LEARNING WITH:
+# CREATION OF FREE STANDING PROJECTIONS THAT HAVE NO LEARNING (Input_Weights, Middle_Weights and Output_Weights)
+# INLINE CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
+# NO EXPLICIT CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
+
+# This projection will be used by the process below by referencing it in the process' pathway;
+#    note: sender and receiver args don't need to be specified
+Input_Weights = MappingProjection(name='Input Weights',
+                        # sender=Input_Layer,
+                        # receiver=Hidden_Layer_1,
+                        # matrix=(random_weight_matrix, LearningProjection()),
+                        # matrix=random_weight_matrix,
+                        # matrix=(RANDOM_CONNECTIVITY_MATRIX, LearningProjection()),
+                        # matrix=RANDOM_CONNECTIVITY_MATRIX
+                        # matrix=FULL_CONNECTIVITY_MATRIX,
+                        matrix=Input_Weights_matrix
+                        )
+
+# This projection will be used by the process below by assigning its sender and receiver args
+#    to mechanismss in the pathway
+Middle_Weights = MappingProjection(name='Middle Weights',
+                                   sender=Hidden_Layer_1,
+                                   receiver=Hidden_Layer_2,
+                                   # matrix=(FULL_CONNECTIVITY_MATRIX, LearningProjection())
+                                   # matrix=FULL_CONNECTIVITY_MATRIX
+                                   # matrix=RANDOM_CONNECTIVITY_MATRIX
+                                   # # MODIFIED 6/11/17 OLD:
+                                   #  matrix=Middle_Weights_matrix
+                                   # # MODIFIED 6/11/17 NEW:
+                                   matrix={VALUE:Middle_Weights_matrix,
+                                           FUNCTION:Linear,
+                                           # FUNCTION:ConstantIntegrator,
+                                           # FUNCTION_PARAMS:{
+                                           #     INITIALIZER:Middle_Weights_matrix,
+                                           #     RATE:Middle_Weights_matrix},
+                                           # FUNCTION:ConstantIntegrator(rate=Middle_Weights_matrix)
+                                           # MODULATION:ADDITIVE_PARAM
+                                           }
+                                   # MODIFIED 6/11/17 END:
+                                   )
+
+# Commented lines in this projection illustrate variety of ways in which matrix and learning signals can be specified
+Output_Weights = MappingProjection(name='Output Weights',
+                         sender=Hidden_Layer_2,
+                         receiver=Output_Layer,
+                         # matrix=random_weight_matrix,
+                         # matrix=(random_weight_matrix, LEARNING_PROJECTION),
+                         # matrix=(random_weight_matrix, LearningProjection),
+                         # matrix=(random_weight_matrix, LearningProjection()),
+                         # matrix=(RANDOM_CONNECTIVITY_MATRIX),
+                         # matrix=(RANDOM_CONNECTIVITY_MATRIX, LearningProjection),
+                         # matrix=(FULL_CONNECTIVITY_MATRIX, LearningProjection)
+                         # matrix=FULL_CONNECTIVITY_MATRIX
+                         matrix=Output_Weights_matrix
+                         )
+
+z = process(default_input_value=[0, 0],
+            pathway=[Input_Layer,
+                           # The following reference to Input_Weights is needed to use it in the pathway
+                           #    since it's sender and receiver args are not specified in its declaration above
+                           Input_Weights,
+                           Hidden_Layer_1,
+                           # No projection specification is needed here since the sender arg for Middle_Weights
+                           #    is Hidden_Layer_1 and its receiver arg is Hidden_Layer_2
+                           # Middle_Weights,
+                           Hidden_Layer_2,
+                           # Output_Weights does not need to be listed for the same reason as Middle_Weights
+                           # If Middle_Weights and/or Output_Weights is not declared above, then the process
+                           #    will assign a default for missing projection
+                           # Output_Weights,
+                           Output_Layer],
+            clamp_input=SOFT_CLAMP,
+            learning=LEARNING,
+            learning_rate=1.0,
+            target=[0,0,1],
+            prefs={VERBOSE_PREF: False,
+                   REPORT_OUTPUT_PREF: True})
+
+g = process(default_input_value=[1.0],
+            pathway=[Gating_Mechanism])
+
+# Input_Weights.matrix = (np.arange(2*5).reshape((2, 5)) + 1)/(2*5)
+# Middle_Weights.matrix = (np.arange(5*4).reshape((5, 4)) + 1)/(5*4)
+# Output_Weights.matrix = (np.arange(4*3).reshape((4, 3)) + 1)/(4*3)
+
+
+# stim_list = {Input_Layer:[[-1, 30],[2, 10]]}
+# target_list = {Output_Layer:[[0, 0, 1],[0, 0, 1]]}
+# stim_list = {Input_Layer:[[-1, 30]]}
+# stim_list = {Input_Layer:[[-1, 30]]}
+stim_list = {Input_Layer:[[-1, 30]],
+             Gating_Mechanism:[0.0]}
+target_list = {Output_Layer:[[0, 0, 1]]}
+
+
+# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+# COMPOSITION = PROCESS
+COMPOSITION = SYSTEM
+# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+
+
+def print_header():
+    print("\n\n**** TRIAL: ", CentralClock.trial)
+
+def show_target():
+
+    if COMPOSITION is PROCESS:
+        i = composition.input
+        t = composition.target
+    elif COMPOSITION is SYSTEM:
+        i = composition.input
+        t = composition.targetInputStates[0].value
+    print ('\nOLD WEIGHTS: \n')
+    print ('- Input Weights: \n', Input_Weights.matrix)
+    print ('- Middle Weights: \n', Middle_Weights.matrix)
+    print ('- Output Weights: \n', Output_Weights.matrix)
+    print ('\nSTIMULI:\n\n- Input: {}\n- Target: {}\n'.format(i, t))
+    print ('ACTIVITY FROM OLD WEIGHTS: \n')
+    print ('- Middle 1: \n', Hidden_Layer_1.value)
+    print ('- Middle 2: \n', Hidden_Layer_2.value)
+    print ('- Output:\n', Output_Layer.value)
+    # print ('MSE: \n', Output_Layer.output_values[0])
+
+if COMPOSITION is PROCESS:
+    # z.execute()
+
+    composition = z
+
+    # PROCESS VERSION:
+    z.run(num_executions=10,
+          # inputs=[[-1, 30],[2, 10]],
+          # targets=[[0, 0, 1],[0, 0, 1]],
+          inputs=stim_list,
+          targets=target_list,
+          call_before_trial=print_header,
+          call_after_trial=show_target)
+
+elif COMPOSITION is SYSTEM:
+    # SYSTEM VERSION:
+    x = system(processes=[z, g],
+               targets=[0, 0, 1],
+               learning_rate=1.0)
+
+    x.reportOutputPref = True
+    composition = x
+
+    # x.show_graph()
+    # x.show_graph(show_learning=True)
+
+    # from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.GatingMechanisms.GatingMechanism \
+    #     import _add_gating_mechanism_to_system
+    # _add_gating_mechanism_to_system(Gating_Mechanism)
+    #
+    results = x.run(
+        num_executions=10,
+        # inputs=stim_list,
+        # inputs=[[-1, 30],[2, 10]],
+        # targets=[[0, 0, 1],[0, 0, 1]],
+        inputs=stim_list,
+        targets=target_list,
+        call_before_trial=print_header,
+        call_after_trial=show_target,
+        termination_processing={TimeScale.TRIAL: AfterNCalls(Output_Layer, 1)}
+    )
+
+else:
+    print ("Multilayer Learning Network NOT RUN")
diff --git a/Scripts/DEBUGGING SCRIPTS/EVC Gratton Script_Bug1.py b/Scripts/DEBUGGING SCRIPTS/OLD/EVC Gratton Script_Bug1.py
similarity index 97%
rename from Scripts/DEBUGGING SCRIPTS/EVC Gratton Script_Bug1.py
rename to Scripts/DEBUGGING SCRIPTS/OLD/EVC Gratton Script_Bug1.py
index e65d4bda44e..7826aa81942 100644
--- a/Scripts/DEBUGGING SCRIPTS/EVC Gratton Script_Bug1.py	
+++ b/Scripts/DEBUGGING SCRIPTS/OLD/EVC Gratton Script_Bug1.py	
@@ -6,7 +6,7 @@
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import *
 from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection
+from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
 from PsyNeuLink.Components.System import system
 from PsyNeuLink.Globals.Keywords import *
 
@@ -149,7 +149,7 @@ def show_results():
     print('\nRESULTS (time step {}): '.format(CentralClock.time_step))
     print ('\tDrift rate control signal (from EVC): {}'.
            # format(re.sub('[\[,\],\n]','',str(float(Decision.parameterStates[DRIFT_RATE].value)))))
-           format(re.sub('[\[,\],\n]','',str("{:0.3}".format(float(Decision.parameterStates[DRIFT_RATE].value))))))
+           format(re.sub('[\[,\],\n]','',str("{:0.3}".format(float(Decision._parameter_states[DRIFT_RATE].value))))))
     # print ('\tThreshold control signal (from EVC): {}'.
     #        format(re.sub('[\[,\],\n]','',str(float(Decision.parameterStates[THRESHOLD].value)))))
     for result in results:
diff --git a/Scripts/DEBUGGING SCRIPTS/EVC Gratton Script_Bug2.py b/Scripts/DEBUGGING SCRIPTS/OLD/EVC Gratton Script_Bug2.py
similarity index 97%
rename from Scripts/DEBUGGING SCRIPTS/EVC Gratton Script_Bug2.py
rename to Scripts/DEBUGGING SCRIPTS/OLD/EVC Gratton Script_Bug2.py
index 969d5cda2dc..6c37c50fa3e 100644
--- a/Scripts/DEBUGGING SCRIPTS/EVC Gratton Script_Bug2.py	
+++ b/Scripts/DEBUGGING SCRIPTS/OLD/EVC Gratton Script_Bug2.py	
@@ -6,7 +6,7 @@
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import *
 from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection
+from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
 from PsyNeuLink.Components.System import system
 from PsyNeuLink.Globals.Keywords import *
 
@@ -149,7 +149,7 @@ def show_results():
     print('\nRESULTS (time step {}): '.format(CentralClock.time_step))
     print ('\tDrift rate control signal (from EVC): {}'.
            # format(re.sub('[\[,\],\n]','',str(float(Decision.parameterStates[DRIFT_RATE].value)))))
-           format(re.sub('[\[,\],\n]','',str("{:0.3}".format(float(Decision.parameterStates[DRIFT_RATE].value))))))
+           format(re.sub('[\[,\],\n]','',str("{:0.3}".format(float(Decision._parameter_states[DRIFT_RATE].value))))))
     # print ('\tThreshold control signal (from EVC): {}'.
     #        format(re.sub('[\[,\],\n]','',str(float(Decision.parameterStates[THRESHOLD].value)))))
     for result in results:
diff --git a/Scripts/DEBUGGING SCRIPTS/EVC Gratton Script_Bug3.py b/Scripts/DEBUGGING SCRIPTS/OLD/EVC Gratton Script_Bug3.py
similarity index 97%
rename from Scripts/DEBUGGING SCRIPTS/EVC Gratton Script_Bug3.py
rename to Scripts/DEBUGGING SCRIPTS/OLD/EVC Gratton Script_Bug3.py
index a87dc5e35c4..69e24881f00 100644
--- a/Scripts/DEBUGGING SCRIPTS/EVC Gratton Script_Bug3.py	
+++ b/Scripts/DEBUGGING SCRIPTS/OLD/EVC Gratton Script_Bug3.py	
@@ -6,7 +6,7 @@
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import *
 from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection
+from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
 from PsyNeuLink.Components.System import system
 from PsyNeuLink.Globals.Keywords import *
 
@@ -150,7 +150,7 @@ def show_results():
     print('\nRESULTS (time step {}): '.format(CentralClock.time_step))
     print ('\tDrift rate control signal (from EVC): {}'.
            # format(re.sub('[\[,\],\n]','',str(float(Decision.parameterStates[DRIFT_RATE].value)))))
-           format(re.sub('[\[,\],\n]','',str("{:0.3}".format(float(Decision.parameterStates[DRIFT_RATE].value))))))
+           format(re.sub('[\[,\],\n]','',str("{:0.3}".format(float(Decision._parameter_states[DRIFT_RATE].value))))))
     # print ('\tThreshold control signal (from EVC): {}'.
     #        format(re.sub('[\[,\],\n]','',str(float(Decision.parameterStates[THRESHOLD].value)))))
     for result in results:
diff --git a/Scripts/DEBUGGING SCRIPTS/EVC Gratton Script_Bug4.py b/Scripts/DEBUGGING SCRIPTS/OLD/EVC Gratton Script_Bug4.py
similarity index 97%
rename from Scripts/DEBUGGING SCRIPTS/EVC Gratton Script_Bug4.py
rename to Scripts/DEBUGGING SCRIPTS/OLD/EVC Gratton Script_Bug4.py
index e91fafa0562..f18e6215d3f 100644
--- a/Scripts/DEBUGGING SCRIPTS/EVC Gratton Script_Bug4.py	
+++ b/Scripts/DEBUGGING SCRIPTS/OLD/EVC Gratton Script_Bug4.py	
@@ -6,7 +6,7 @@
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import *
 from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection
+from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
 from PsyNeuLink.Components.System import system
 from PsyNeuLink.Globals.Keywords import *
 
@@ -163,7 +163,7 @@ def show_results():
     print('\nRESULTS (time step {}): '.format(CentralClock.time_step))
     print ('\tDrift rate control signal (from EVC): {}'.
            # format(re.sub('[\[,\],\n]','',str(float(Decision.parameterStates[DRIFT_RATE].value)))))
-           format(re.sub('[\[,\],\n]','',str("{:0.3}".format(float(Decision.parameterStates[DRIFT_RATE].value))))))
+           format(re.sub('[\[,\],\n]','',str("{:0.3}".format(float(Decision._parameter_states[DRIFT_RATE].value))))))
     # print ('\tThreshold control signal (from EVC): {}'.
     #        format(re.sub('[\[,\],\n]','',str(float(Decision.parameterStates[THRESHOLD].value)))))
     for result in results:
diff --git a/Scripts/DEBUGGING SCRIPTS/EVC Gratton Script_Bug5.py b/Scripts/DEBUGGING SCRIPTS/OLD/EVC Gratton Script_Bug5.py
similarity index 91%
rename from Scripts/DEBUGGING SCRIPTS/EVC Gratton Script_Bug5.py
rename to Scripts/DEBUGGING SCRIPTS/OLD/EVC Gratton Script_Bug5.py
index 44e51918b52..23c4acd22b0 100644
--- a/Scripts/DEBUGGING SCRIPTS/EVC Gratton Script_Bug5.py	
+++ b/Scripts/DEBUGGING SCRIPTS/OLD/EVC Gratton Script_Bug5.py	
@@ -6,7 +6,7 @@
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import *
 from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection
+from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
 from PsyNeuLink.Components.System import system
 from PsyNeuLink.Globals.Keywords import *
 
@@ -124,13 +124,13 @@ def test_outcome_function(**kwargs):
 mySystem.controller.show()
 
 # configure EVC components
-# mySystem.controller.controlSignals[0].intensity_cost_function = Linear(slope = 0)
-# mySystem.controller.controlSignals[1].intensity_cost_function = Linear(slope = 0)
-# mySystem.controller.controlSignals[2].intensity_cost_function = Linear(slope = 0)
+# mySystem.controller.control_signals[0].intensity_cost_function = Linear(slope = 0)
+# mySystem.controller.control_signals[1].intensity_cost_function = Linear(slope = 0)
+# mySystem.controller.control_signals[2].intensity_cost_function = Linear(slope = 0)
 #
-mySystem.controller.controlSignals[0].assign_params(request_set={'intensity_cost_function':Linear(slope=0)})
-mySystem.controller.controlSignals[0].assign_params(request_set={'intensity_cost_function':Linear})
-mySystem.controller.controlSignals[2].intensity_cost_function = Linear(slope = 0).function
+mySystem.controller.control_signals[0].assign_params(request_set={'intensity_cost_function':Linear(slope=0)})
+mySystem.controller.control_signals[0].assign_params(request_set={'intensity_cost_function':Linear})
+mySystem.controller.control_signals[2].intensity_cost_function = Linear(slope = 0).function
 
 
 # list(param.name for param in mySystem.mechanismsDict["Flanker 1 Stimulus_PredictionMechanism"].params)
@@ -166,7 +166,7 @@ def show_results():
     print('\nRESULTS (time step {}): '.format(CentralClock.time_step))
     print ('\tDrift rate control signal (from EVC): {}'.
            # format(re.sub('[\[,\],\n]','',str(float(Decision.parameterStates[DRIFT_RATE].value)))))
-           format(re.sub('[\[,\],\n]','',str("{:0.3}".format(float(Decision.parameterStates[DRIFT_RATE].value))))))
+           format(re.sub('[\[,\],\n]','',str("{:0.3}".format(float(Decision._parameter_states[DRIFT_RATE].value))))))
     # print ('\tThreshold control signal (from EVC): {}'.
     #        format(re.sub('[\[,\],\n]','',str(float(Decision.parameterStates[THRESHOLD].value)))))
     for result in results:
diff --git a/Scripts/DEBUGGING SCRIPTS/EVC System Laming Validation Test Script DEBUGGING.py b/Scripts/DEBUGGING SCRIPTS/OLD/EVC System Laming Validation Test Script DEBUGGING.py
similarity index 91%
rename from Scripts/DEBUGGING SCRIPTS/EVC System Laming Validation Test Script DEBUGGING.py
rename to Scripts/DEBUGGING SCRIPTS/OLD/EVC System Laming Validation Test Script DEBUGGING.py
index 439936509fa..e21619b13a5 100644
--- a/Scripts/DEBUGGING SCRIPTS/EVC System Laming Validation Test Script DEBUGGING.py	
+++ b/Scripts/DEBUGGING SCRIPTS/OLD/EVC System Laming Validation Test Script DEBUGGING.py	
@@ -3,7 +3,7 @@
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import *
 from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection
+from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
 from PsyNeuLink.Components.System import system
 from PsyNeuLink.Globals.Keywords import *
 
@@ -109,17 +109,17 @@ def show_results():
            '\n\t\tControlSignal: {}'
            '\n\t\tControlProjection: {}'.
            # format(re.sub('[\[,\],\n]','',str(float(Decision.parameterStates[DRIFT_RATE].value)))))
-           format(re.sub('[\[,\],\n]','',str("{:0.3}".format(float(Decision.parameterStates[DRIFT_RATE].value)))),
-                  mySystem.controller.outputStates['drift_rate_ControlSignal'].value,
-                  Decision.parameterStates[DRIFT_RATE].receivesFromProjections[0].value
+           format(re.sub('[\[,\],\n]','',str("{:0.3}".format(float(Decision._parameter_states[DRIFT_RATE].value)))),
+                  mySystem.controller.output_states['drift_rate_ControlSignal'].value,
+                  Decision._parameter_states[DRIFT_RATE].path_afferents[0].value
                   ))
     print ('\tThreshold control signal (from EVC):'
            '\n\t\tDecision.parameterState: {}'
            '\n\t\tControlSignal: {}'
            '\n\t\tControlProjection: {}'.
-           format(re.sub('[\[,\],\n]','',str(float(Decision.parameterStates[THRESHOLD].value))),
-                  mySystem.controller.outputStates['threshold_ControlSignal'].value,
-                  Decision.parameterStates[THRESHOLD].receivesFromProjections[0].value
+           format(re.sub('[\[,\],\n]','',str(float(Decision._parameter_states[THRESHOLD].value))),
+                  mySystem.controller.output_states['threshold_ControlSignal'].value,
+                  Decision._parameter_states[THRESHOLD].path_afferents[0].value
                   ))
     for result in results:
         print("\t{}: {}".format(result[0],
diff --git a/Scripts/DEBUGGING SCRIPTS/EVC System Laming Validation Test Script VARIATIONS.py b/Scripts/DEBUGGING SCRIPTS/OLD/EVC System Laming Validation Test Script VARIATIONS.py
similarity index 95%
rename from Scripts/DEBUGGING SCRIPTS/EVC System Laming Validation Test Script VARIATIONS.py
rename to Scripts/DEBUGGING SCRIPTS/OLD/EVC System Laming Validation Test Script VARIATIONS.py
index fe5cf05924e..bd55008b6c8 100644
--- a/Scripts/DEBUGGING SCRIPTS/EVC System Laming Validation Test Script VARIATIONS.py	
+++ b/Scripts/DEBUGGING SCRIPTS/OLD/EVC System Laming Validation Test Script VARIATIONS.py	
@@ -3,7 +3,7 @@
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import *
 from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection
+from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
 from PsyNeuLink.Components.System import system
 from PsyNeuLink.Globals.Keywords import *
 
@@ -101,9 +101,9 @@ def show_results():
     print('\nRESULTS (time step {}): '.format(CentralClock.time_step))
     print ('\tDrift rate control signal (from EVC): {}'.
            # format(re.sub('[\[,\],\n]','',str(float(Decision.parameterStates[DRIFT_RATE].value)))))
-           format(re.sub('[\[,\],\n]','',str("{:0.3}".format(float(Decision.parameterStates[DRIFT_RATE].value))))))
+           format(re.sub('[\[,\],\n]','',str("{:0.3}".format(float(Decision._parameter_states[DRIFT_RATE].value))))))
     print ('\tThreshold control signal (from EVC): {}'.
-           format(re.sub('[\[,\],\n]','',str(float(Decision.parameterStates[THRESHOLD].value)))))
+           format(re.sub('[\[,\],\n]','',str(float(Decision._parameter_states[THRESHOLD].value)))))
     for result in results:
         print("\t{}: {}".format(result[0],
                                 re.sub('[\[,\],\n]','',str("{:0.3}".format(float(result[1]))))))
diff --git a/Scripts/DEBUGGING SCRIPTS/EVC-Gratton-System.pdf b/Scripts/DEBUGGING SCRIPTS/OLD/EVC-Gratton-System.pdf
similarity index 100%
rename from Scripts/DEBUGGING SCRIPTS/EVC-Gratton-System.pdf
rename to Scripts/DEBUGGING SCRIPTS/OLD/EVC-Gratton-System.pdf
diff --git a/Scripts/DEBUGGING SCRIPTS/OLD/EVC_Gratton_Script_Validation.py b/Scripts/DEBUGGING SCRIPTS/OLD/EVC_Gratton_Script_Validation.py
new file mode 100644
index 00000000000..dbda7202084
--- /dev/null
+++ b/Scripts/DEBUGGING SCRIPTS/OLD/EVC_Gratton_Script_Validation.py	
@@ -0,0 +1,189 @@
+# from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.Deprecated.LinearMechanism import *
+from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.EVCMechanism import EVCMechanism
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import *
+from PsyNeuLink.Components.Process import process
+from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection
+from PsyNeuLink.Components.System import system
+from PsyNeuLink.Globals.Keywords import *
+import numpy as np
+import random as rand
+
+def test_search_function(controller=None, **kwargs):
+    result = np.array(controller.allocationPolicy).reshape(len(controller.allocationPolicy), -1)
+    return result
+
+def test_outcome_function(**kwargs):
+    result = np.array([0])
+    return result
+
+
+# Preferences:
+mechanism_prefs = ComponentPreferenceSet(
+                prefs = {
+                    kpVerbosePref: PreferenceEntry(False,PreferenceLevel.INSTANCE),
+                    kpReportOutputPref: PreferenceEntry(True,PreferenceLevel.INSTANCE)})
+
+process_prefs = ComponentPreferenceSet(reportOutput_pref=PreferenceEntry(False,PreferenceLevel.INSTANCE),
+                                      verbose_pref=PreferenceEntry(True,PreferenceLevel.INSTANCE))
+
+# Control Parameters
+signalSearchRange = np.arange(1.0, 2.0, 0.2)
+
+# Stimulus Mechanisms
+Target_Stim = TransferMechanism(name='Target Stimulus', function=Linear(slope = 0.3324))
+Flanker_Stim = TransferMechanism(name='Flanker Stimulus', function=Linear(slope = 0.3545221843))
+
+# Processing Mechanisms (Control)
+Target_Rep = TransferMechanism(name='Target Representation',
+                               function=Linear(slope=(1.0,
+                                                     ControlProjection(function=Linear,
+                                                                       control_signal={ALLOCATION_SAMPLES:signalSearchRange}
+                                                                       ))),
+                               prefs=mechanism_prefs)
+Flanker_Rep = TransferMechanism(name='Flanker Representation',
+                               function=Linear(slope=(1.0,
+                                                     ControlProjection(function=Linear,
+                                                                       control_signal={ALLOCATION_SAMPLES:signalSearchRange}
+                                                                       ))),
+                               prefs=mechanism_prefs)
+
+# Processing Mechanism (Automatic)
+Automatic_Component = TransferMechanism(name='Automatic Component',
+                               function=Linear(slope=(1.0)),
+                               prefs=mechanism_prefs)
+
+# Decision Mechanisms
+Decision = DDM(function=BogaczEtAl(drift_rate=(1.0),
+                                   threshold=(0.2645),
+                                   noise=(0.5),
+                                   starting_point=(0),
+                                   t0=0.15),
+               prefs = mechanism_prefs,
+               name='Decision')
+
+# Outcome Mechanisms:
+Reward = TransferMechanism(name='Reward')
+
+
+# Processes:
+TargetControlProcess = process(
+    default_input_value=[0],
+    pathway=[Target_Stim, Target_Rep, Decision],
+    prefs = process_prefs,
+    name = 'Target Control Process')
+
+FlankerControlProcess = process(
+    default_input_value=[0],
+    pathway=[Flanker_Stim, Flanker_Rep, Decision],
+    prefs = process_prefs,
+    name = 'Flanker Control Process')
+
+TargetAutomaticProcess = process(
+    default_input_value=[0],
+    pathway=[Target_Stim, Automatic_Component, Decision],
+    prefs = process_prefs,
+    name = 'Target Automatic Process')
+
+FlankerAutomaticProcess = process(
+    default_input_value=[0],
+    pathway=[Flanker_Stim, Automatic_Component, Decision],
+    prefs = process_prefs,
+    name = 'Flanker1 Automatic Process')
+
+
+RewardProcess = process(
+    default_input_value=[0],
+    pathway=[(Reward, 1)],
+    prefs = process_prefs,
+    name = 'RewardProcess')
+
+# System:
+mySystem = system(processes=[TargetControlProcess, FlankerControlProcess,
+                             TargetAutomaticProcess, FlankerAutomaticProcess,
+                             RewardProcess],
+                  controller=EVCMechanism,
+                  enable_controller=True,
+                  monitor_for_control=[Reward, DDM_PROBABILITY_UPPER_THRESHOLD],
+                  # monitor_for_control=[Reward, DDM_PROBABILITY_UPPER_THRESHOLD, (DDM_RESPONSE_TIME, -1, 1)],
+                  name='EVC Gratton System')
+
+
+# Show characteristics of system:
+mySystem.show()
+mySystem.controller.show()
+
+# configure EVC components
+mySystem.controller.controlSignals[0].intensity_cost_function = Exponential(rate =  0.8046).function
+mySystem.controller.controlSignals[1].intensity_cost_function = Exponential(rate =  0.8046).function
+
+
+# Loop over the KEYS in this dict
+# for mech in mySystem.controller.prediction_mechanisms.keys():
+#
+#     # mySystem.controller.prediction_mechanisms is dictionary organized into key-value pairs where the key is a
+#     # (transfer) mechanism, and the value is the corresponding prediction (integrator) mechanism
+#
+#     # For example: the key which is a transfer mechanism with the name 'Flanker Stimulus'
+#     # acceses an integrator mechanism with the name 'Flanker Stimulus_PredictionMechanism'
+#
+#     if mech.name is 'Flanker Stimulus' or mech.name is 'Target Stimulus':
+#
+#         # when you find a key mechanism (transfer mechanism) with the correct name, print its name
+#         print(mech.name)
+#
+#         # then use that key to access its *value* in the dictionary, which will be an integrator mechanism
+#         # that integrator mechanism is the one whose rate we want to change ( I think!)
+#         # mySystem.controller.prediction_mechanisms[mech].function_object.rate = 0.3481
+#         # mySystem.controller.prediction_mechanisms[mech].parameterStates['rate'].baseValue = 0.3481
+#         # mech.parameterStates['rate'].baseValue = 0.3481
+#         # mySystem.controller.prediction_mechanisms[mech].function_object.rate = 1.0 # 0.3481
+#         mySystem.controller.prediction_mechanisms[mech].parameterStates['rate'].baseValue = 1 # 0.3481
+#
+#     if mech.name is 'Reward':
+#         print(mech.name)
+#         # mySystem.controller.prediction_mechanisms[mech].function_object.rate = 1.0
+#         mySystem.controller.prediction_mechanisms[mech].parameterStates['rate'].baseValue = 1.0
+#
+
+for mech in mySystem.controller.predictionMechanisms.mechanisms:
+    if 'Reward' in mech.name:
+        mech.parameterStates['rate'].baseValue = 1.0
+    if 'Flanker' in mech.name or 'Target' in mech.name:
+        mech.parameterStates['rate'].baseValue = 1.0
+
+
+# print('new rate of integration mechanisms before system execution:')
+# for mech in mySystem.controller.prediction_mechanisms.keys():
+#     print( mySystem.controller.prediction_mechanisms[mech].name)
+#     print( mySystem.controller.prediction_mechanisms[mech].function_object.rate)
+#     print('----')
+
+# generate stimulus environment
+
+nTrials = 3
+targetFeatures = [1, 1, 1]
+flankerFeatures = [1, -1, 1] # for full simulation: flankerFeatures = [-1,1]
+reward = [100, 100, 100]
+
+targetInputList = targetFeatures
+flankerInputList = flankerFeatures
+rewardList = reward
+
+# targetInputList = np.random.choice(targetFeatures, nTrials).tolist()
+# flankerInputList = np.random.choice(flankerFeatures, nTrials).tolist()
+# rewardList = (np.ones(nTrials) * reward).tolist() #np.random.choice(reward, nTrials).tolist()
+
+stim_list_dict = {Target_Stim:targetInputList,
+                  Flanker_Stim:flankerInputList,
+                  Reward:rewardList}
+
+
+mySystem.controller.reportOutputPref = True
+
+# mySystem.show_graph()
+
+mySystem.run(num_executions=nTrials,
+             inputs=stim_list_dict,
+             )
+
diff --git a/Scripts/DEBUGGING SCRIPTS/Multilayer Learning Test Script VARIATIONS.py b/Scripts/DEBUGGING SCRIPTS/OLD/Multilayer Learning Test Script VARIATIONS.py
similarity index 91%
rename from Scripts/DEBUGGING SCRIPTS/Multilayer Learning Test Script VARIATIONS.py
rename to Scripts/DEBUGGING SCRIPTS/OLD/Multilayer Learning Test Script VARIATIONS.py
index 07c574f9b0e..7b3ac98892b 100644
--- a/Scripts/DEBUGGING SCRIPTS/Multilayer Learning Test Script VARIATIONS.py	
+++ b/Scripts/DEBUGGING SCRIPTS/OLD/Multilayer Learning Test Script VARIATIONS.py	
@@ -1,16 +1,18 @@
+# from PsyNeuLink.Components.Functions.Function import Logistic, random_matrix
+from PsyNeuLink.Components.Functions.Function import Logistic
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
 from PsyNeuLink.Components.Process import process
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
 from PsyNeuLink.Components.System import system
-from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection
-from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
-# from PsyNeuLink.Components.Functions.Function import Logistic, random_matrix
-from PsyNeuLink.Components.Functions.Function import Logistic
-# from PsyNeuLink.Globals.Run import run, construct_inputs
+from PsyNeuLink.Globals.TimeScale import TimeScale
 
 Input_Layer = TransferMechanism(name='Input Layer',
                        function=Logistic,
-                       default_input_value = np.zeros((2,)))
+                                default_input_value = np.zeros(2),
+                                # params={STATE_PARAMS:{PARAMETER_STATE_PARAMS:{
+                                #     PARAMETER_MODULATION_OPERATION:Modulation.ADD}}}
+                                )
 
 Hidden_Layer_1 = TransferMechanism(name='Hidden Layer_1',
                           function=Logistic(),
@@ -108,7 +110,7 @@ def show_target():
     print ('\nInput Weights: \n', Input_Weights.matrix)
     print ('Middle Weights: \n', Middle_Weights.matrix)
     print ('Output Weights: \n', Output_Weights.matrix)
-    # print ('MSE: \n', Output_Layer.outputValue[])
+    # print ('MSE: \n', Output_Layer.output_values[])
 
 stim_list = {Input_Layer:[[-1, 30],[2, 10]]}
 target_list = {Output_Layer:[[0, 0, 1],[0, 0, 1]]}
@@ -137,4 +139,6 @@ def show_target():
       inputs=stim_list,
       targets=target_list,
       call_before_trial=print_header,
-      call_after_trial=show_target)
+      call_after_trial=show_target,
+      termination_processing={TimeScale.TRIAL: AfterNCalls(Output_Layer, 1)}
+)
diff --git a/Scripts/DEBUGGING SCRIPTS/OLD/Scripts_DEBUGGING_SCRIPTS_EVC_Gratton_Script_-_SEBASTIAN_py.py b/Scripts/DEBUGGING SCRIPTS/OLD/Scripts_DEBUGGING_SCRIPTS_EVC_Gratton_Script_-_SEBASTIAN_py.py
new file mode 100644
index 00000000000..9b8750a5b29
--- /dev/null
+++ b/Scripts/DEBUGGING SCRIPTS/OLD/Scripts_DEBUGGING_SCRIPTS_EVC_Gratton_Script_-_SEBASTIAN_py.py	
@@ -0,0 +1,188 @@
+# from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.Deprecated.LinearMechanism import *
+from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.EVCMechanism import EVCMechanism
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import *
+from PsyNeuLink.Components.Process import process
+from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
+from PsyNeuLink.Components.System import system
+from PsyNeuLink.Globals.Keywords import *
+import numpy as np
+import random as rand
+
+def test_search_function(controller=None, **kwargs):
+    result = np.array(controller.allocationPolicy).reshape(len(controller.allocationPolicy), -1)
+    return result
+
+def test_outcome_function(**kwargs):
+    result = np.array([0])
+    return result
+
+
+# Preferences:
+mechanism_prefs = ComponentPreferenceSet(
+                prefs = {
+                    kpVerbosePref: PreferenceEntry(False,PreferenceLevel.INSTANCE),
+                    kpReportOutputPref: PreferenceEntry(True,PreferenceLevel.INSTANCE)})
+
+process_prefs = ComponentPreferenceSet(reportOutput_pref=PreferenceEntry(False,PreferenceLevel.INSTANCE),
+                                      verbose_pref=PreferenceEntry(True,PreferenceLevel.INSTANCE))
+
+# Control Parameters
+signalSearchRange = np.arange(0, 10, 2)
+
+# Stimulus Mechanisms
+Target_Stim = TransferMechanism(name='Target Stimulus', function=Linear(slope = 0.3324))
+Flanker_Stim = TransferMechanism(name='Flanker Stimulus', function=Linear(slope = 0.3545221843))
+
+# Processing Mechanisms (Control)
+Target_Rep = TransferMechanism(name='Target Representation',
+                               function=Linear(slope=(1.0,
+                                                     ControlProjection(function=Linear,
+                                                                       control_signal_params={
+                                                                           ALLOCATION_SAMPLES:signalSearchRange}
+                                                                       ))),
+                               prefs=mechanism_prefs)
+Flanker_Rep = TransferMechanism(name='Flanker Representation',
+                               function=Linear(slope=(1.0,
+                                                     ControlProjection(function=Linear,
+                                                                       control_signal_params={
+                                                                           ALLOCATION_SAMPLES:signalSearchRange}
+                                                                       ))),
+                               prefs=mechanism_prefs)
+
+# Processing Mechanism (Automatic)
+Automatic_Component = TransferMechanism(name='Automatic Component',
+                               function=Linear(slope=(1.0)),
+                               prefs=mechanism_prefs)
+
+# Decision Mechanisms
+Decision = DDM(
+    function=BogaczEtAl(
+        drift_rate=(1.0),
+        threshold=(0.2645),
+        noise=(0.5),
+        starting_point=(0),
+        t0=0.15
+    ),
+    prefs = mechanism_prefs,
+    name='Decision',
+    output_states=[
+        DECISION_VARIABLE,
+        RESPONSE_TIME,
+        PROBABILITY_UPPER_THRESHOLD
+    ],
+)
+
+# Outcome Mechanisms:
+Reward = TransferMechanism(name='Reward')
+
+
+# Processes:
+TargetControlProcess = process(
+    default_input_value=[0],
+    pathway=[Target_Stim, Target_Rep, Decision],
+    prefs = process_prefs,
+    name = 'Target Control Process')
+
+FlankerControlProcess = process(
+    default_input_value=[0],
+    pathway=[Flanker_Stim, Flanker_Rep, Decision],
+    prefs = process_prefs,
+    name = 'Flanker Control Process')
+
+TargetAutomaticProcess = process(
+    default_input_value=[0],
+    pathway=[Target_Stim, Automatic_Component, Decision],
+    prefs = process_prefs,
+    name = 'Target Automatic Process')
+
+FlankerAutomaticProcess = process(
+    default_input_value=[0],
+    pathway=[Flanker_Stim, Automatic_Component, Decision],
+    prefs = process_prefs,
+    name = 'Flanker1 Automatic Process')
+
+
+RewardProcess = process(
+    default_input_value=[0],
+    pathway=[Reward],
+    prefs = process_prefs,
+    name = 'RewardProcess')
+
+# System:
+mySystem = system(processes=[TargetControlProcess, FlankerControlProcess,
+                             TargetAutomaticProcess, FlankerAutomaticProcess,
+                             RewardProcess],
+                  controller=EVCMechanism,
+                  enable_controller=True,
+                  monitor_for_control=[Reward, PROBABILITY_UPPER_THRESHOLD],
+                  # monitor_for_control=[Reward, DDM_PROBABILITY_UPPER_THRESHOLD, (DDM_RESPONSE_TIME, -1, 1)],
+                  name='EVC Gratton System')
+
+
+# Show characteristics of system:
+mySystem.show()
+mySystem.controller.show()
+
+# configure EVC components
+mySystem.controller.control_signals[0].intensity_cost_function = Exponential(rate =  0.8046).function
+mySystem.controller.control_signals[1].intensity_cost_function = Exponential(rate =  0.8046).function
+
+
+# Loop over the KEYS in this dict
+for mech in mySystem.controller.prediction_mechs:
+
+    # mySystem.controller.prediction_mechs is dictionary organized into key-value pairs where the key is a
+    # (transfer) mechanism, and the value is the corresponding prediction (integrator) mechanism
+
+    # For example: the key which is a transfer mechanism with the name 'Flanker Stimulus'
+    # acceses an integrator mechanism with the name 'Flanker Stimulus_PredictionMechanism'
+
+    if mech.name is 'Flanker Stimulus' or mech.name is 'Target Stimulus':
+
+        # when you find a key mechanism (transfer mechanism) with the correct name, print its name
+        print(mech.name)
+
+        # then use that key to access its *value* in the dictionary, which will be an integrator mechanism
+        # that integrator mechanism is the one whose rate we want to change ( I think!)
+        # mech.function_object.rate = 0.3481
+        # mech.parameterStates['rate'].base_value = 0.3481
+        # mech.parameterStates['rate'].base_value = 0.3481
+        mech.function_object.rate = 0.3481
+
+    if mech.name is 'Reward':
+        print(mech.name)
+        mech.function_object.rate = 1.0
+        # mech.parameterStates['rate'].base_value = 1.0
+
+
+
+
+print('new rate of integration mechanisms before system execution:')
+for mech in mySystem.controller.prediction_mechs:
+    print( mech.name)
+    print( mech.function_object.rate)
+    print('----')
+
+# generate stimulus environment
+
+nTrials = 20
+targetFeatures = [1]
+flankerFeatures = [-1] # for full simulation: flankerFeatures = [-1,1]
+reward = 100
+
+targetInputList = np.random.choice(targetFeatures, nTrials).tolist()
+flankerInputList = np.random.choice(flankerFeatures, nTrials).tolist()
+rewardList = (np.ones(nTrials) * reward).tolist() #np.random.choice(reward, nTrials).tolist()
+
+stim_list_dict = {Target_Stim:targetInputList,
+                  Flanker_Stim:flankerInputList,
+                  Reward:rewardList}
+
+
+mySystem.controller.reportOutputPref = True
+
+mySystem.run(num_executions=nTrials,
+             inputs=stim_list_dict,
+             )
+
diff --git a/Scripts/DEBUGGING SCRIPTS/Sebastian's Multitasking Model.py b/Scripts/DEBUGGING SCRIPTS/OLD/Sebastian's Multitasking Model.py
similarity index 95%
rename from Scripts/DEBUGGING SCRIPTS/Sebastian's Multitasking Model.py
rename to Scripts/DEBUGGING SCRIPTS/OLD/Sebastian's Multitasking Model.py
index 8872315595d..52f39ec0ad6 100644
--- a/Scripts/DEBUGGING SCRIPTS/Sebastian's Multitasking Model.py	
+++ b/Scripts/DEBUGGING SCRIPTS/OLD/Sebastian's Multitasking Model.py	
@@ -1,13 +1,10 @@
-from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
-from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
-from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection
-from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
 # from PsyNeuLink.Components.Functions.Function import Logistic, random_matrix
 from PsyNeuLink.Components.Functions.Function import Logistic
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
 from PsyNeuLink.Components.System import *
 
-
 # specification of task environment
 NFeatures = 2
 NOutputDimensions = 2
diff --git a/Scripts/DEBUGGING SCRIPTS/Stroop Model Learning Test Script - EQUAL.py b/Scripts/DEBUGGING SCRIPTS/OLD/Stroop Model Learning Test Script - EQUAL.py
similarity index 93%
rename from Scripts/DEBUGGING SCRIPTS/Stroop Model Learning Test Script - EQUAL.py
rename to Scripts/DEBUGGING SCRIPTS/OLD/Stroop Model Learning Test Script - EQUAL.py
index 75f0602f648..362a74bdcf2 100644
--- a/Scripts/DEBUGGING SCRIPTS/Stroop Model Learning Test Script - EQUAL.py	
+++ b/Scripts/DEBUGGING SCRIPTS/OLD/Stroop Model Learning Test Script - EQUAL.py	
@@ -1,9 +1,10 @@
+import numpy as np
+
 from PsyNeuLink.Components.Functions.Function import Linear, Logistic
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
 from PsyNeuLink.Components.System import *
 from PsyNeuLink.Globals.Keywords import *
-from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
-import numpy as np
 
 process_prefs = {REPORT_OUTPUT_PREF: True,
                  VERBOSE_PREF: False}
@@ -77,11 +78,11 @@ def print_header():
 def show_target():
     print ('\nColor Naming\n\tInput: {}\n\tTarget: {}'.
            # format(color_naming_process.input, color_naming_process.target))
-           format(colors.inputValue, mySystem.targets))
+           format(colors.input_values, mySystem.targets))
     print ('Wording Reading:\n\tInput: {}\n\tTarget: {}\n'.
            # format(word_reading_process.input, word_reading_process.target))
-           format(words.inputValue, mySystem.targets))
-    print ('Response: \n', response.outputValue[0])
+           format(words.input_values, mySystem.targets))
+    print ('Response: \n', response.output_values[0])
     print ('Hidden-Output:')
     print (HO_Weights.matrix)
     print ('Color-Hidden:')
diff --git a/Scripts/DEBUGGING SCRIPTS/Stroop Model Learning and Control Test Script.py b/Scripts/DEBUGGING SCRIPTS/OLD/Stroop Model Learning and Control Test Script.py
similarity index 92%
rename from Scripts/DEBUGGING SCRIPTS/Stroop Model Learning and Control Test Script.py
rename to Scripts/DEBUGGING SCRIPTS/OLD/Stroop Model Learning and Control Test Script.py
index 7b512505110..46487de34c8 100644
--- a/Scripts/DEBUGGING SCRIPTS/Stroop Model Learning and Control Test Script.py	
+++ b/Scripts/DEBUGGING SCRIPTS/OLD/Stroop Model Learning and Control Test Script.py	
@@ -1,10 +1,11 @@
+import numpy as np
+
 from PsyNeuLink.Components.Functions.Function import Linear, Logistic
-from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
 from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.EVCMechanism import EVCMechanism
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
 from PsyNeuLink.Components.System import *
 from PsyNeuLink.Globals.Keywords import *
-from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
-import numpy as np
 
 process_prefs = {REPORT_OUTPUT_PREF: True,
                  VERBOSE_PREF: False}
@@ -81,11 +82,11 @@ def print_header():
 def show_target():
     print ('\nColor Naming\n\tInput: {}\n\tTarget: {}'.
            # format(color_naming_process.input, color_naming_process.target))
-           format(colors.inputValue, mySystem.targets))
+           format(colors.input_values, mySystem.targets))
     print ('Wording Reading:\n\tInput: {}\n\tTarget: {}\n'.
            # format(word_reading_process.input, word_reading_process.target))
-           format(words.inputValue, mySystem.targets))
-    print ('Response: \n', response.outputValue[0])
+           format(words.input_values, mySystem.targets))
+    print ('Response: \n', response.output_values[0])
     print ('Hidden-Output:')
     print (HO_Weights.matrix)
     print ('Color-Hidden:')
diff --git a/Scripts/DEBUGGING SCRIPTS/Stroop Model Test Script VARIATIONS.py b/Scripts/DEBUGGING SCRIPTS/OLD/Stroop Model Test Script VARIATIONS.py
similarity index 90%
rename from Scripts/DEBUGGING SCRIPTS/Stroop Model Test Script VARIATIONS.py
rename to Scripts/DEBUGGING SCRIPTS/OLD/Stroop Model Test Script VARIATIONS.py
index 6437359804d..b7f2db4e5b2 100644
--- a/Scripts/DEBUGGING SCRIPTS/Stroop Model Test Script VARIATIONS.py	
+++ b/Scripts/DEBUGGING SCRIPTS/OLD/Stroop Model Test Script VARIATIONS.py	
@@ -59,12 +59,12 @@
 # # Execute processes:
 # for i in range(10):
 #     color_naming_process.execute(input=[1, 1],target=[0,1])
-#     print(response.inputState.receivesFromProjections[0].matrix)
-#     print(response.inputState.receivesFromProjections[1].matrix)
+#     print(response.input_state.path_afferents[0].matrix)
+#     print(response.input_state.path_afferents[1].matrix)
 #
 #     word_reading_process.execute(input=[1, 1], target=[1,0])
-#     print(response.inputState.receivesFromProjections[0].matrix)
-#     print(response.inputState.receivesFromProjections[1].matrix)
+#     print(response.input_state.path_afferents[0].matrix)
+#     print(response.input_state.path_afferents[1].matrix)
 
 # Execute system:
 # mySystem.execute(input=[[1,1],[1,1]])
diff --git a/Scripts/DEBUGGING SCRIPTS/StroopEVCforDST.py b/Scripts/DEBUGGING SCRIPTS/OLD/StroopEVCforDST.py
similarity index 94%
rename from Scripts/DEBUGGING SCRIPTS/StroopEVCforDST.py
rename to Scripts/DEBUGGING SCRIPTS/OLD/StroopEVCforDST.py
index 4773dce4f95..23565ae1376 100644
--- a/Scripts/DEBUGGING SCRIPTS/StroopEVCforDST.py	
+++ b/Scripts/DEBUGGING SCRIPTS/OLD/StroopEVCforDST.py	
@@ -5,14 +5,11 @@
 @author: lauraab
 """
 
-from PsyNeuLink.Components.Functions.Function import Linear, Logistic
 from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.EVCMechanism import EVCMechanism
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
-from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection
-from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
+from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
 from PsyNeuLink.Components.System import *
-from PsyNeuLink.Globals.Keywords import *
 
 #helpful commands! .show()   .excute()
 color_word_combine = TransferMechanism(name='color_word_combine', function = Linear()) #default_input_value = [1], #JON HOW TO MAKE THIS A SUBTRACTION? 
@@ -36,7 +33,7 @@
 Reward = TransferMechanism(name='Reward')
 RewardProcess = process(default_input_value=[1], pathway=[(Reward, 1)], name = 'RewardProcess')
 #mySystem = system(processes = [color_process, word_process])
-mySystem = system(processes = [color_process, word_process, RewardProcess], controller = EVCMechanism, enable_controller = True, monitor_for_control=[Reward, (DDM_DECISION_VARIABLE,1,1), (DDM_RESPONSE_TIME, -1, 1)], name='Stroop Model') #output, difference between color / word unit activation  #outputValue attribute?
+mySystem = system(processes = [color_process, word_process, RewardProcess], controller = EVCMechanism, enable_controller = True, monitor_for_control=[Reward, (DDM_DECISION_VARIABLE,1,1), (DDM_RESPONSE_TIME, -1, 1)], name='Stroop Model') #output, difference between color / word unit activation  #output_values attribute?
 mySystem.execute(input=[[1],[-1],[1]])
 
 mySystem.controller.show()
@@ -47,7 +44,7 @@
 # mySystem.execute(input=[[.1],[-0.1]])[0]
 
 
-#outputValue for color, separate for word process, and then combine?
+#output_values for color, separate for word process, and then combine?
 # decision_DDM = DDM(function=BogaczEtAl(drift_rate= difference_output*, threshold=(1, ControlProjection)) #or could use params = {DRIFT_RATE:(0.2, ControlProjection), STARTING_POINT:-0.5}
 #DDM, drift rate = output, EVC regulates threshold 
 # DDM_PROBABILITY_UPPER_THRESHOLD      # what does this mean?: if time_scale is TimeScale.TIME_STEP, this is `None;
@@ -84,12 +81,12 @@
 #color transfer mechanism, EVC regulates bias   ##TransferMechanism
     #e.g. color_logistic_transfer_mechanism = TransferMechanism(function=Logistic(gain=1.0, bias=-4) #?bias = control_signal_bias
     #range 0 to 1? 
-    #outputValue for color, separate for word process, and then combine?
+    #output_values for color, separate for word process, and then combine?
 
 #word process 
 #word input        ## ?Mapping Projection    ORIGIN 
 #word transfer mechanism, EVC regulates bias    ##TransferMechanism
-#output, difference between color / word unit activation  #outputValue attribute?
+#output, difference between color / word unit activation  #output_values attribute?
  
 #DDM, drift rate = output, EVC regulates threshold 
 # decision_DDM = DDM(function=BogaczEtAl(drift_rate= difference_output*, threshold=(1, ControlProjection)) #or could use params = {DRIFT_RATE:(0.2, ControlProjection), STARTING_POINT:-0.5}
diff --git a/Scripts/DEBUGGING SCRIPTS/Stroop_model_markus.py b/Scripts/DEBUGGING SCRIPTS/OLD/Stroop_model_markus.py
similarity index 94%
rename from Scripts/DEBUGGING SCRIPTS/Stroop_model_markus.py
rename to Scripts/DEBUGGING SCRIPTS/OLD/Stroop_model_markus.py
index 06c3fcc034a..b6b2b31e840 100644
--- a/Scripts/DEBUGGING SCRIPTS/Stroop_model_markus.py	
+++ b/Scripts/DEBUGGING SCRIPTS/OLD/Stroop_model_markus.py	
@@ -1,13 +1,10 @@
-from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
-from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
-from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection
-from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
 # from PsyNeuLink.Components.Functions.Function import Logistic, random_matrix
 from PsyNeuLink.Components.Functions.Function import Logistic
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
 from PsyNeuLink.Components.System import *
 
-
 # specification of task environment
 NFeatures = 1
 NOutputDimensions = 2
diff --git a/Scripts/DEMO SCRIPTS/DDM_scheduled_simple.py b/Scripts/DEMO SCRIPTS/DDM_scheduled_simple.py
new file mode 100644
index 00000000000..a658796e720
--- /dev/null
+++ b/Scripts/DEMO SCRIPTS/DDM_scheduled_simple.py	
@@ -0,0 +1,65 @@
+import logging
+
+from PsyNeuLink.Components.System import *
+from PsyNeuLink.Components.Process import process
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import DDM
+from PsyNeuLink.Components.Functions.Function import Linear, Integrator
+from PsyNeuLink.scheduling.Scheduler import Scheduler
+from PsyNeuLink.scheduling.condition import AfterNCalls, AtPass, WhenFinished
+from PsyNeuLink.Globals.Keywords import DIFFUSION
+from PsyNeuLink.Globals.TimeScale import TimeScale
+
+logger = logging.getLogger(__name__)
+
+o = TransferMechanism(
+    name='origin',
+    default_input_value = [0],
+    function=Linear(slope=.5),
+    prefs={REPORT_OUTPUT_PREF: PreferenceEntry(True,PreferenceLevel.INSTANCE)}
+)
+
+ddm = DDM(
+    function=Integrator(
+        integration_type = DIFFUSION,
+        noise=0.5
+    ),
+    name='ddm',
+    time_scale=TimeScale.TIME_STEP,
+    thresh=10
+)
+
+term = TransferMechanism(
+    name='terminal',
+    default_input_value = [0],
+    function=Linear(slope=2.0),
+    prefs={REPORT_OUTPUT_PREF: PreferenceEntry(True,PreferenceLevel.INSTANCE)}
+)
+
+p = process(
+    default_input_value = [0],
+    pathway = [o, ddm, term],
+    name = 'p',
+)
+
+# origin → DDM → terminal
+s = system(
+    processes=[p],
+    name='s',
+)
+
+stim_list = {o: [[1]]}
+
+s.scheduler_processing = Scheduler(system=s)
+s.scheduler_processing.add_condition(o, AtPass(0))
+# ddm has default condition of Always - run at every chance
+s.scheduler_processing.add_condition(term, WhenFinished(ddm))
+
+term_conds = {TimeScale.TRIAL: AfterNCalls(term, 1)}
+
+s.show_graph()
+results = s.run(
+    inputs=stim_list,
+    termination_processing=term_conds
+)
+logger.info('System result: {0}'.format(results))
diff --git a/Scripts/DEMO SCRIPTS/DDM_scheduled_simple_limited_calls.py b/Scripts/DEMO SCRIPTS/DDM_scheduled_simple_limited_calls.py
new file mode 100644
index 00000000000..032b4843336
--- /dev/null
+++ b/Scripts/DEMO SCRIPTS/DDM_scheduled_simple_limited_calls.py	
@@ -0,0 +1,65 @@
+import logging
+
+from PsyNeuLink.Components.System import *
+from PsyNeuLink.Components.Process import process
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import DDM
+from PsyNeuLink.Components.Functions.Function import Linear, Integrator
+from PsyNeuLink.scheduling.Scheduler import Scheduler
+from PsyNeuLink.scheduling.condition import Any, AfterNCalls, AtPass, WhenFinished
+from PsyNeuLink.Globals.Keywords import DIFFUSION
+from PsyNeuLink.Globals.TimeScale import TimeScale
+
+logger = logging.getLogger(__name__)
+
+o = TransferMechanism(
+    name='origin',
+    default_input_value = [0],
+    function=Linear(slope=.5),
+    prefs={REPORT_OUTPUT_PREF: PreferenceEntry(True,PreferenceLevel.INSTANCE)}
+)
+
+ddm = DDM(
+    function=Integrator(
+        integration_type = DIFFUSION,
+        noise=0.5
+    ),
+    name='ddm',
+    time_scale=TimeScale.TIME_STEP,
+    thresh=10
+)
+
+term = TransferMechanism(
+    name='terminal',
+    default_input_value = [0],
+    function=Linear(slope=2.0),
+    prefs={REPORT_OUTPUT_PREF: PreferenceEntry(True,PreferenceLevel.INSTANCE)}
+)
+
+p = process(
+    default_input_value = [0],
+    pathway = [o, ddm, term],
+    name = 'p',
+)
+
+# origin → DDM → terminal
+s = system(
+    processes=[p],
+    name='s',
+)
+
+stim_list = {o: [[1]]}
+
+s.scheduler_processing = Scheduler(system=s)
+s.scheduler_processing.add_condition(o, AtPass(0))
+# ddm has default condition of Always - run at every chance
+s.scheduler_processing.add_condition(term, Any(WhenFinished(ddm), AfterNCalls(ddm, 10)))
+
+term_conds = {TimeScale.TRIAL: AfterNCalls(term, 1)}
+
+s.show_graph()
+results = s.run(
+    inputs=stim_list,
+    termination_processing=term_conds
+)
+logger.info('System result: {0}'.format(results))
diff --git a/Scripts/DEMO SCRIPTS/EVC System DEMO Script.py b/Scripts/DEMO SCRIPTS/EVC System DEMO Script.py
index 5b47af2918e..aaa33a56c58 100644
--- a/Scripts/DEMO SCRIPTS/EVC System DEMO Script.py	
+++ b/Scripts/DEMO SCRIPTS/EVC System DEMO Script.py	
@@ -3,7 +3,7 @@
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import *
 from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection
+from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
 from PsyNeuLink.Components.System import system
 from PsyNeuLink.Globals.Keywords import *
 
@@ -85,11 +85,11 @@ def show_results():
     print('\nRESULTS (time step {}): '.format(CentralClock.time_step))
     print ('\tDrift rate control signal (from EVC): {}'.
            # format(re.sub('[\[,\],\n]','',str(float(Decision.parameterStates[DRIFT_RATE].value)))))
-           format(re.sub('[\[,\],\n]','',str("{:0.3}".format(float(Decision.parameterStates[DRIFT_RATE].value))))))
+           format(re.sub('[\[,\],\n]','',str("{:0.3}".format(float(Decision._parameter_states[DRIFT_RATE].value))))))
     print ('\tThreshold control signal (from EVC): {}'.
-           format(re.sub('[\[,\],\n]','',str(float(Decision.parameterStates[THRESHOLD].value))),
-                  mySystem.controller.outputStates['threshold_ControlSignal'].value,
-                  Decision.parameterStates[THRESHOLD].receivesFromProjections[0].value
+           format(re.sub('[\[,\],\n]','',str(float(Decision._parameter_states[THRESHOLD].value))),
+                  mySystem.controller.output_states['threshold_ControlSignal'].value,
+                  Decision._parameter_states[THRESHOLD].path_afferents[0].value
                   ))
     for result in results:
         print("\t{}: {}".format(result[0],
diff --git a/Scripts/DEMO SCRIPTS/Integrator_scheduled_timescales.py b/Scripts/DEMO SCRIPTS/Integrator_scheduled_timescales.py
new file mode 100644
index 00000000000..40081a34b24
--- /dev/null
+++ b/Scripts/DEMO SCRIPTS/Integrator_scheduled_timescales.py	
@@ -0,0 +1,86 @@
+import logging
+
+from PsyNeuLink.Components.System import *
+from PsyNeuLink.Components.Process import process
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.IntegratorMechanism import IntegratorMechanism
+from PsyNeuLink.Components.Functions.Function import Linear, Integrator
+from PsyNeuLink.scheduling.Scheduler import Scheduler
+from PsyNeuLink.scheduling.condition import AfterNCalls, Any, AtPass, EveryNCalls
+
+logger = logging.getLogger(__name__)
+
+process_prefs = {
+    REPORT_OUTPUT_PREF: False,
+    VERBOSE_PREF: False
+}
+
+A = TransferMechanism(
+    name='A',
+    default_input_value = [0],
+    function=Linear(slope=2.0),
+    prefs={REPORT_OUTPUT_PREF: PreferenceEntry(False,PreferenceLevel.INSTANCE)}
+)
+
+B = IntegratorMechanism(
+    name='B',
+    default_input_value = [0],
+    function=Integrator(
+        rate=.5,
+        integration_type=SIMPLE
+    ),
+    prefs={REPORT_OUTPUT_PREF: PreferenceEntry(False,PreferenceLevel.INSTANCE)}
+)
+
+C = IntegratorMechanism(
+    name='C',
+    default_input_value = [0],
+    function=Integrator(
+        rate=.5,
+        integration_type=SIMPLE
+    ),
+    prefs={REPORT_OUTPUT_PREF: PreferenceEntry(False,PreferenceLevel.INSTANCE)}
+)
+
+D = TransferMechanism(
+    name='D',
+    default_input_value = [0],
+    function=Linear(slope=1.0),
+    prefs={REPORT_OUTPUT_PREF: PreferenceEntry(False,PreferenceLevel.INSTANCE)}
+)
+
+p = process(
+    default_input_value = [0],
+    pathway = [A, B, D],
+    name = 'p'
+)
+
+q = process(
+    default_input_value = [0],
+    pathway = [A, C, D],
+    name = 'q',
+    prefs=process_prefs
+)
+
+s = system(
+    processes=[p, q],
+    name = 's'
+)
+
+stim_list = {A: [[1]]}
+
+s.scheduler_processing = Scheduler(system=s)
+s.scheduler_processing.add_condition(A, Any(AtPass(0), EveryNCalls(D, 1)))
+# B has default condition of Always - run at every chance
+s.scheduler_processing.add_condition(C, EveryNCalls(B, 5))
+s.scheduler_processing.add_condition(D, EveryNCalls(C, 1))
+
+term_conds = {TimeScale.TRIAL: AfterNCalls(D, 1)}
+
+s.show_graph()
+results = s.run(
+    inputs=stim_list,
+    termination_processing=term_conds
+)
+logger.info('Executed in order: {0}'.format(s.scheduler_processing.execution_list))
+logger.info('System result: {0}'.format(results))
diff --git a/Scripts/DEMO SCRIPTS/Multilayer Learning Test Script DEMO.py b/Scripts/DEMO SCRIPTS/Multilayer Learning Test Script DEMO.py
index db0f9d51904..cd59d45cb82 100644
--- a/Scripts/DEMO SCRIPTS/Multilayer Learning Test Script DEMO.py	
+++ b/Scripts/DEMO SCRIPTS/Multilayer Learning Test Script DEMO.py	
@@ -3,8 +3,8 @@
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
 from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection
-from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
+from PsyNeuLink.Components.Projections.ModulatoryProjections.LearningProjection import LearningProjection
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
 from PsyNeuLink.Components.System import system
 import matplotlib
 matplotlib.use('TkAgg')
@@ -82,7 +82,7 @@
 #     print ('- Middle 1: \n', Hidden_Layer_1.value)
 #     print ('- Middle 2: \n', Hidden_Layer_2.value)
 #     print ('- Output:\n', Output_Layer.value)
-#     # print ('MSE: \n', Output_Layer.outputValue[0])
+#     # print ('MSE: \n', Output_Layer.output_values[0])
 #
 # # **************************************************
 #endregion
diff --git a/Scripts/DEMO SCRIPTS/NEW_Learning_Markus.py b/Scripts/DEMO SCRIPTS/NEW_Learning_Markus.py
index bc53ab47830..d65383e2a91 100644
--- a/Scripts/DEMO SCRIPTS/NEW_Learning_Markus.py	
+++ b/Scripts/DEMO SCRIPTS/NEW_Learning_Markus.py	
@@ -1,12 +1,13 @@
-from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
-from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection
-from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
+rom PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
 #from PsyNeuLink.Components.Functions.Function import Logistic , random_matrix
 from PsyNeuLink.Components.Functions.Function import Logistic, Linear
 from PsyNeuLink.Components.System import *
-from PsyNeuLink.Components.Functions.Function import SoftMax, Reinforcement
-
+# from PsyNeuLink.Components.Functions.Function import Logistic , random_matrix
+from PsyNeuLink.Components.Functions.Function import Logistic, Linear
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
+from PsyNeuLink.Components.System import *
 
 # specification of task environment
 NFeatures = 1
diff --git a/Scripts/DEMO SCRIPTS/Stroop Demo Script with Learning.py b/Scripts/DEMO SCRIPTS/Stroop Demo Script with Learning.py
index 8767ef6c0fb..ad330a2ac69 100644
--- a/Scripts/DEMO SCRIPTS/Stroop Demo Script with Learning.py	
+++ b/Scripts/DEMO SCRIPTS/Stroop Demo Script with Learning.py	
@@ -6,7 +6,7 @@
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import *
 from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection
+from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
 from PsyNeuLink.Components.System import system
 from PsyNeuLink.Globals.Keywords import *
 
diff --git a/Scripts/DEMO SCRIPTS/Stroop Demo Script.py b/Scripts/DEMO SCRIPTS/Stroop Demo Script.py
index 2e087f68345..2e4d826c2df 100644
--- a/Scripts/DEMO SCRIPTS/Stroop Demo Script.py	
+++ b/Scripts/DEMO SCRIPTS/Stroop Demo Script.py	
@@ -6,7 +6,7 @@
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import *
 from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection
+from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
 from PsyNeuLink.Components.System import system
 from PsyNeuLink.Globals.Keywords import *
 
diff --git a/Scripts/DEMO SCRIPTS/Xor_Script_2_processes.py b/Scripts/DEMO SCRIPTS/Xor_Script_2_processes.py
index 634f0605013..a290d5d2157 100644
--- a/Scripts/DEMO SCRIPTS/Xor_Script_2_processes.py	
+++ b/Scripts/DEMO SCRIPTS/Xor_Script_2_processes.py	
@@ -3,14 +3,10 @@
 
 # In[ ]:
 
-import numpy as np
-from PsyNeuLink.Components.Functions.Function import Logistic, Linear
-from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.EVCMechanism import EVCMechanism
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
 from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection
-from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
 from PsyNeuLink.Components.System import system
 
 
diff --git a/Scripts/DEMO SCRIPTS/Xor_Script_NotYetWorking.py b/Scripts/DEMO SCRIPTS/Xor_Script_NotYetWorking.py
index 7d4424f8c78..060da3762fe 100644
--- a/Scripts/DEMO SCRIPTS/Xor_Script_NotYetWorking.py	
+++ b/Scripts/DEMO SCRIPTS/Xor_Script_NotYetWorking.py	
@@ -3,14 +3,10 @@
 
 # In[ ]:
 
-import numpy as np
-from PsyNeuLink.Components.Functions.Function import Logistic, Linear
-from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.EVCMechanism import EVCMechanism
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
 from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection
-from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
 from PsyNeuLink.Components.System import system
 
 
diff --git a/Scripts/DEMO SCRIPTS/Xor_Script_Working.py b/Scripts/DEMO SCRIPTS/Xor_Script_Working.py
index 17f0f019a9f..ef8d08ad27a 100644
--- a/Scripts/DEMO SCRIPTS/Xor_Script_Working.py	
+++ b/Scripts/DEMO SCRIPTS/Xor_Script_Working.py	
@@ -3,14 +3,10 @@
 
 # In[ ]:
 
-import numpy as np
-from PsyNeuLink.Components.Functions.Function import Logistic, Linear
-from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.EVCMechanism import EVCMechanism
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
 from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection
-from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
 from PsyNeuLink.Components.System import system
 
 
diff --git a/Scripts/MISCELLANEOUS SCRIPTS/DDM Learning Test Script.py b/Scripts/MISCELLANEOUS SCRIPTS/DDM Learning Test Script.py
index 6500a2858dc..24813810527 100644
--- a/Scripts/MISCELLANEOUS SCRIPTS/DDM Learning Test Script.py	
+++ b/Scripts/MISCELLANEOUS SCRIPTS/DDM Learning Test Script.py	
@@ -1,8 +1,8 @@
+from PsyNeuLink.Components.Functions.Function import Logistic
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
 from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
-from PsyNeuLink.Components.Functions.Function import Logistic
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
 
 Input_Layer = TransferMechanism(name='Input Layer',
                        function=Logistic(),
diff --git a/Scripts/MISCELLANEOUS SCRIPTS/DDM Test Script (simple).py b/Scripts/MISCELLANEOUS SCRIPTS/DDM Test Script (simple).py
index 456a3c49dd3..19a28e5d3ef 100644
--- a/Scripts/MISCELLANEOUS SCRIPTS/DDM Test Script (simple).py	
+++ b/Scripts/MISCELLANEOUS SCRIPTS/DDM Test Script (simple).py	
@@ -23,7 +23,7 @@
                              prefs={REPORT_OUTPUT_PREF: True})
 simple_ddm_process.execute(1.0)
 
-print ('Decision variable: ', myMechanism.outputValue[0])
-print ('RT mean: ', myMechanism.outputValue[1])
-print ('ER mean: ', myMechanism.outputValue[2])
+print ('Decision variable: ', myMechanism.output_values[0])
+print ('RT mean: ', myMechanism.output_values[1])
+print ('ER mean: ', myMechanism.output_values[2])
 
diff --git a/Scripts/MISCELLANEOUS SCRIPTS/EVC System Laming Validation Test Script COPY.py b/Scripts/MISCELLANEOUS SCRIPTS/EVC System Laming Validation Test Script COPY.py
index fe36cb6d978..2257bad00f1 100644
--- a/Scripts/MISCELLANEOUS SCRIPTS/EVC System Laming Validation Test Script COPY.py	
+++ b/Scripts/MISCELLANEOUS SCRIPTS/EVC System Laming Validation Test Script COPY.py	
@@ -3,7 +3,7 @@
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import *
 from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection
+from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
 from PsyNeuLink.Components.System import system
 from PsyNeuLink.Globals.Keywords import *
 
@@ -87,8 +87,8 @@ def show_results():
 
     results = sorted(zip(mySystem.terminalMechanisms.outputStateNames, mySystem.terminalMechanisms.outputStateValues))
     print('\nRESULTS (time step {}): '.format(CentralClock.time_step))
-    print ('\tDrift rate control signal (from EVC): {}'.format(Decision.parameterStates[DRIFT_RATE].value))
-    print ('\tThreshold control signal (from EVC): {}'.format(Decision.parameterStates[THRESHOLD].value))
+    print ('\tDrift rate control signal (from EVC): {}'.format(Decision._parameter_states[DRIFT_RATE].value))
+    print ('\tThreshold control signal (from EVC): {}'.format(Decision._parameter_states[THRESHOLD].value))
     import re
     for result in results:
         result_0 = re.sub('[\[,\],\n]','',str(result[0]))
diff --git a/Scripts/MISCELLANEOUS SCRIPTS/EVC System Laming Validation Test Script with Custom Fn.py b/Scripts/MISCELLANEOUS SCRIPTS/EVC System Laming Validation Test Script with Custom Fn.py
index 1feae34c766..d92ac55aad2 100644
--- a/Scripts/MISCELLANEOUS SCRIPTS/EVC System Laming Validation Test Script with Custom Fn.py	
+++ b/Scripts/MISCELLANEOUS SCRIPTS/EVC System Laming Validation Test Script with Custom Fn.py	
@@ -3,7 +3,7 @@
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import *
 from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection
+from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
 from PsyNeuLink.Components.System import system
 from PsyNeuLink.Globals.Keywords import *
 
@@ -111,9 +111,9 @@ def show_results():
     print('\nRESULTS (time step {}): '.format(CentralClock.time_step))
     print ('\tDrift rate control signal (from EVC): {}'.
            # format(re.sub('[\[,\],\n]','',str(float(Decision.parameterStates[DRIFT_RATE].value)))))
-           format(re.sub('[\[,\],\n]','',str("{:0.3}".format(float(Decision.parameterStates[DRIFT_RATE].value))))))
+           format(re.sub('[\[,\],\n]','',str("{:0.3}".format(float(Decision._parameter_states[DRIFT_RATE].value))))))
     print ('\tThreshold control signal (from EVC): {}'.
-           format(re.sub('[\[,\],\n]','',str(float(Decision.parameterStates[THRESHOLD].value)))))
+           format(re.sub('[\[,\],\n]','',str(float(Decision._parameter_states[THRESHOLD].value)))))
     for result in results:
         print("\t{}: {}".format(result[0],
                                 re.sub('[\[,\],\n]','',str("{:0.3}".format(float(result[1]))))))
diff --git a/Scripts/MISCELLANEOUS SCRIPTS/EVC System Test Script (with MPI).py b/Scripts/MISCELLANEOUS SCRIPTS/EVC System Test Script (with MPI).py
index d3d157ced42..ae8520f73d2 100644
--- a/Scripts/MISCELLANEOUS SCRIPTS/EVC System Test Script (with MPI).py	
+++ b/Scripts/MISCELLANEOUS SCRIPTS/EVC System Test Script (with MPI).py	
@@ -1,7 +1,7 @@
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.Deprecated.LinearMechanism import *
 from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection
+from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
 from PsyNeuLink.Components.System import System_Base
 from PsyNeuLink.Components.Functions.Function import Exponential, Linear
 from PsyNeuLink.Globals.Keywords import *
diff --git a/Scripts/MISCELLANEOUS SCRIPTS/Integrator-test-script.py b/Scripts/MISCELLANEOUS SCRIPTS/Integrator-test-script.py
index 1247d7bda51..e5b1874d1f2 100644
--- a/Scripts/MISCELLANEOUS SCRIPTS/Integrator-test-script.py	
+++ b/Scripts/MISCELLANEOUS SCRIPTS/Integrator-test-script.py	
@@ -338,7 +338,7 @@
     my_Transfer2 = TransferMechanism(name='my_Transfer2',
                            default_input_value = [0,0],
                            function=Logistic(gain=0.1, bias=0.2),
-                           noise=GammaDist(scale = 1.0, shape = 1.0).function,
+                           noise=GammaDist(scale = 1.0, dist_shape = 1.0).function,
                            time_constant = 0.1,
                            time_scale=TimeScale.TIME_STEP
                            )
diff --git a/Scripts/MISCELLANEOUS SCRIPTS/Learning Signal Test Script.py b/Scripts/MISCELLANEOUS SCRIPTS/Learning Signal Test Script.py
index 04d37dc6205..3969336af73 100644
--- a/Scripts/MISCELLANEOUS SCRIPTS/Learning Signal Test Script.py	
+++ b/Scripts/MISCELLANEOUS SCRIPTS/Learning Signal Test Script.py	
@@ -1,9 +1,9 @@
+# from PsyNeuLink.Components.Functions.Function import Logistic, random_matrix
+from PsyNeuLink.Components.Functions.Function import Logistic
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
 from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
-# from PsyNeuLink.Components.Functions.Function import Logistic, random_matrix
-from PsyNeuLink.Components.Functions.Function import Logistic
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
 
 random_weight_matrix = lambda sender, receiver : random_matrix(sender, receiver, .2, -.1)
 
diff --git a/Scripts/MISCELLANEOUS SCRIPTS/Multilayer Learning Test Script (CLEAN).py b/Scripts/MISCELLANEOUS SCRIPTS/Multilayer Learning Test Script (CLEAN).py
index dacf349d71f..965ee890c50 100644
--- a/Scripts/MISCELLANEOUS SCRIPTS/Multilayer Learning Test Script (CLEAN).py	
+++ b/Scripts/MISCELLANEOUS SCRIPTS/Multilayer Learning Test Script (CLEAN).py	
@@ -1,10 +1,9 @@
-from PsyNeuLink.Components.Process import process
+from PsyNeuLink.Components.Functions.Function import Logistic
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
-from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection
-from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
-from PsyNeuLink.Components.Functions.Function import Logistic
-
+from PsyNeuLink.Components.Process import process
+from PsyNeuLink.Components.Projections.ModulatoryProjections.LearningProjection import LearningProjection
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
 
 Input_Layer = TransferMechanism(name='Input Layer',
                        function=Logistic(),
@@ -66,6 +65,6 @@
     print ('Middle Weights: \n', Middle_Weights.matrix)
     print ('Output Weights: \n', Output_Weights.matrix)
 
-    # print ('MSE: \n', Output_Layer.outputValue[])
+    # print ('MSE: \n', Output_Layer.output_values[])
 
 print(results)
diff --git a/Scripts/MISCELLANEOUS SCRIPTS/Reinforcement Learning Test Script [SIMPLE].py b/Scripts/MISCELLANEOUS SCRIPTS/Reinforcement Learning Test Script [SIMPLE].py
index 3eb4f643633..b77e1e761f3 100644
--- a/Scripts/MISCELLANEOUS SCRIPTS/Reinforcement Learning Test Script [SIMPLE].py	
+++ b/Scripts/MISCELLANEOUS SCRIPTS/Reinforcement Learning Test Script [SIMPLE].py	
@@ -3,7 +3,7 @@
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
 from PsyNeuLink.Components.System import System, system
 from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection
+from PsyNeuLink.Components.Projections.ModulatoryProjections.LearningProjection import LearningProjection
 from PsyNeuLink.Components.Functions.Function import PROB
 from PsyNeuLink.Components.Functions.Function import SoftMax, Reinforcement
 from PsyNeuLink.Globals.TimeScale import CentralClock
@@ -25,24 +25,24 @@
             learning=LearningProjection(learning_function=Reinforcement(learning_rate=.05)),
             target=0)
 
-print ('reward prediction weights: \n', action_selection.inputState.receivesFromProjections[0].matrix)
-print ('targetMechanism weights: \n', action_selection.outputState.sendsToProjections[0].matrix)
+print ('reward prediction weights: \n', action_selection.input_state.path_afferents[0].matrix)
+print ('targetMechanism weights: \n', action_selection.output_state.efferents[0].matrix)
 
 actions = ['left', 'middle', 'right']
 reward_values = [15, 7, 13]
 first_reward = 0
 
 # Must initialize reward (won't be used, but needed for declaration of lambda function)
-action_selection.outputState.value = [0, 0, 1]
+action_selection.output_state.value = [0, 0, 1]
 # Get reward value for selected action)
-reward = lambda : [reward_values[int(np.nonzero(action_selection.outputState.value)[0])]]
+reward = lambda : [reward_values[int(np.nonzero(action_selection.output_state.value)[0])]]
 
 def print_header():
     print("\n\n**** TRIAL: ", CentralClock.trial)
 
 def show_weights():
-    print ('\nreward prediction weights: \n', action_selection.inputState.receivesFromProjections[0].matrix)
-    print ('action selected: ', action_selection.outputState.value)
+    print ('\nreward prediction weights: \n', action_selection.input_state.path_afferents[0].matrix)
+    print ('action selected: ', action_selection.output_state.value)
 
 p.run(num_executions=10,
       # inputs=[[[1, 1, 1]]],
diff --git a/Scripts/MISCELLANEOUS SCRIPTS/SEBASTIAN SCRIPT.py b/Scripts/MISCELLANEOUS SCRIPTS/SEBASTIAN SCRIPT.py
index 473f3dfc037..f1bf1f216b8 100644
--- a/Scripts/MISCELLANEOUS SCRIPTS/SEBASTIAN SCRIPT.py	
+++ b/Scripts/MISCELLANEOUS SCRIPTS/SEBASTIAN SCRIPT.py	
@@ -1,13 +1,12 @@
+# from PsyNeuLink.Components.Functions.Function import Logistic, random_matrix
+from PsyNeuLink.Components.Functions.Function import Logistic
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
 from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection
-from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
-# from PsyNeuLink.Components.Functions.Function import Logistic, random_matrix
-from PsyNeuLink.Components.Functions.Function import Logistic
+from PsyNeuLink.Components.Projections.ModulatoryProjections.LearningProjection import LearningProjection
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
 from PsyNeuLink.Components.System import system
 
-
 # set up layers
 
 Input_Layer = TransferMechanism(name='Input Layer',
diff --git a/Scripts/MISCELLANEOUS SCRIPTS/System Learning Test Script.py b/Scripts/MISCELLANEOUS SCRIPTS/System Learning Test Script.py
index e920e8d5312..c9f042f9534 100644
--- a/Scripts/MISCELLANEOUS SCRIPTS/System Learning Test Script.py	
+++ b/Scripts/MISCELLANEOUS SCRIPTS/System Learning Test Script.py	
@@ -1,11 +1,8 @@
-from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
-from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection
-from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
-# from PsyNeuLink.Components.Functions.Function import Logistic, random_matrix
-from PsyNeuLink.Components.Functions.Function import Logistic
+from PsyNeuLink.Components.Projections.ModulatoryProjections.LearningProjection import LearningProjection
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
 from PsyNeuLink.Components.System import *
+
 # from PsyNeuLink.Globals.Run import run, construct_inputs
 
 Input_Layer = TransferMechanism(name='Input Layer',
@@ -97,7 +94,7 @@ def show_target():
     print ('\nInput Weights: \n', Input_Weights.matrix)
     print ('Middle Weights: \n', Middle_Weights.matrix)
     print ('Output Weights: \n', Output_Weights.matrix)
-    # print ('MSE: \n', Output_Layer.outputValue[])
+    # print ('MSE: \n', Output_Layer.output_values[])
 
 stim_list = {Input_Layer:[[-1, 30],[2, 10]]}
 
diff --git a/Scripts/MISCELLANEOUS SCRIPTS/Test Script.py b/Scripts/MISCELLANEOUS SCRIPTS/Test Script.py
index 436f6218c19..eb2ac8462cc 100644
--- a/Scripts/MISCELLANEOUS SCRIPTS/Test Script.py	
+++ b/Scripts/MISCELLANEOUS SCRIPTS/Test Script.py	
@@ -10,7 +10,7 @@
 #                 owner=DDM,
 #                 verbose_pref=PreferenceEntry(True,PreferenceLevel.INSTANCE),
 #                 reportOutput_pref=PreferenceEntry(True,PreferenceLevel.SYSTEM),
-#                 functionRuntimeParams_pref=PreferenceEntry(ModulationOperation.OVERRIDE,PreferenceLevel.CATEGORY),
+#                 functionRuntimeParams_pref=PreferenceEntry(Modulation.OVERRIDE,PreferenceLevel.CATEGORY),
 #                 name='Reassigned'
 
 DDM_prefs = ComponentPreferenceSet(
@@ -19,7 +19,7 @@
                 prefs = {
                     kpVerbosePref: PreferenceEntry(True,PreferenceLevel.INSTANCE),
                     kpReportOutputPref: PreferenceEntry(False,PreferenceLevel.SYSTEM),
-                    kpRuntimeParamModulationPref: PreferenceEntry(ModulationOperation.OVERRIDE,PreferenceLevel.CATEGORY)})
+                    kpRuntimeParamModulationPref: PreferenceEntry(Modulation.OVERRIDE,PreferenceLevel.CATEGORY)})
 
 
 DDM_prefs.verbosePref = PreferenceEntry(True,PreferenceLevel.INSTANCE)
@@ -36,7 +36,7 @@
                 prefs = {
                     kpVerbosePref: PreferenceEntry(True,PreferenceLevel.INSTANCE),
                     kpReportOutputPref: PreferenceEntry(True,PreferenceLevel.SYSTEM),
-                    kpRuntimeParamModulationPref: PreferenceEntry(ModulationOperation.OVERRIDE,PreferenceLevel.INSTANCE)})
+                    kpRuntimeParamModulationPref: PreferenceEntry(Modulation.OVERRIDE,PreferenceLevel.INSTANCE)})
 
 my_DDM = DDM(name='my_DDM')
 my_DDM.prefs = DDM_prefs
@@ -111,9 +111,9 @@
 #                                            {
 #                                                # INPUT_STATE_PARAMS:{},
 #                                                PARAMETER_STATE_PARAMS:
-#                                                    {PARAMETER_MODULATION_OPERATION: ModulationOperation.MULTIPLY, # B
+#                                                    {PARAMETER_MODULATION_OPERATION: Modulation.MULTIPLY, # B
 #                                                     DRIFT_RATE:(30.0,
-#                                                                      ModulationOperation.MULTIPLY), # C
+#                                                                      Modulation.MULTIPLY), # C
 #                                                     THRESHOLD:20.0,
 #                                                     FUNCTION_PARAMS:
 #                                                        {LinearCombination.OFFSET: 100}, # A
@@ -125,9 +125,9 @@
 #                                            {
 #                                                # INPUT_STATE_PARAMS:{},
 #                                                PARAMETER_STATE_PARAMS:
-#                                                    {PARAMETER_MODULATION_OPERATION: ModulationOperation.MULTIPLY, # B
+#                                                    {PARAMETER_MODULATION_OPERATION: Modulation.MULTIPLY, # B
 #                                                     DRIFT_RATE:(30.0,
-#                                                                      ModulationOperation.MULTIPLY), # C
+#                                                                      Modulation.MULTIPLY), # C
 #                                                     THRESHOLD:20.0,
 #                                                     FUNCTION_PARAMS:
 #                                                        {LinearCombination.OFFSET: 100}, # A
diff --git a/Scripts/MISCELLANEOUS SCRIPTS/Two Layer Test Script.py b/Scripts/MISCELLANEOUS SCRIPTS/Two Layer Test Script.py
index 505531c8746..500fdbd3e35 100644
--- a/Scripts/MISCELLANEOUS SCRIPTS/Two Layer Test Script.py	
+++ b/Scripts/MISCELLANEOUS SCRIPTS/Two Layer Test Script.py	
@@ -1,10 +1,7 @@
-from PsyNeuLink.Components.Functions.Function import Logistic
-from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.EVCMechanism import EVCMechanism
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
 from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection
-from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
 from PsyNeuLink.Components.System import system
 
 # from PsyNeuLink.Globals.Run import run, construct_inputs
@@ -94,5 +91,3 @@ def show_target():
 
 else:
     print ("Multilayer Learning Network NOT RUN")
-
-TEST = True
\ No newline at end of file
diff --git a/Scripts/MISCELLANEOUS SCRIPTS/ddm-bug.py b/Scripts/MISCELLANEOUS SCRIPTS/ddm-bug.py
new file mode 100644
index 00000000000..568a31696cb
--- /dev/null
+++ b/Scripts/MISCELLANEOUS SCRIPTS/ddm-bug.py	
@@ -0,0 +1,59 @@
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import *
+from PsyNeuLink.Globals.Keywords import *
+from PsyNeuLink.Components.Process import process
+
+
+
+import time
+print("========TIME STEP MODE========")
+print()
+print("MECHANISM ")
+my_DDM = DDM(function=Integrator(integration_type=DIFFUSION, noise=0.5),
+             name='My_DDM',
+             time_scale=TimeScale.TIME_STEP
+             )
+print("result  = ", my_DDM.execute(500))
+print("--------------")
+time.sleep(0.3)
+print("PROCESS ")
+my_Transfer_Test = TransferMechanism(name='my_Transfer_Test',
+                        function=Linear()
+                        )
+my_DDM_proc = DDM(function=Integrator(integration_type=DIFFUSION, noise=0.5),
+             name='My_DDM_Proc',
+             time_scale=TimeScale.TIME_STEP
+             )
+
+MyProcess = process(
+    pathway=[my_Transfer_Test, my_DDM_proc],
+    name='MyProcess')
+print("RESULT = ", MyProcess.execute(500))
+print("--------------")
+print()
+print()
+time.sleep(0.3)
+
+
+print("========TRIAL MODE========")
+
+
+print()
+print("MECHANISM ")
+my_DDM_Trial = DDM()
+print("RESULT  = ", my_DDM_Trial.execute(500))
+print("--------------")
+time.sleep(0.3)
+print("PROCESS ")
+my_Transfer_Test_Trial = TransferMechanism(name='my_Transfer_Test_Trial',
+                        function=Linear()
+                        )
+my_DDM_proc_trial = DDM()
+
+MyProcess2 = process(
+    pathway=[my_Transfer_Test_Trial, my_DDM_proc_trial],
+    name='MyProcess')
+
+print("RESULT = ", MyProcess2.execute([500]))
+print("--------------")
+time.sleep(0.3)
\ No newline at end of file
diff --git a/Scripts/MISCELLANEOUS SCRIPTS/documentation_examples.py b/Scripts/MISCELLANEOUS SCRIPTS/documentation_examples.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/Scripts/MISCELLANEOUS SCRIPTS/mechanism-generator-test.py b/Scripts/MISCELLANEOUS SCRIPTS/mechanism-generator-test.py
new file mode 100644
index 00000000000..fca2c93847a
--- /dev/null
+++ b/Scripts/MISCELLANEOUS SCRIPTS/mechanism-generator-test.py	
@@ -0,0 +1,67 @@
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import *
+from PsyNeuLink.Components.Functions.Function import Logistic, Linear
+from PsyNeuLink.Components.Process import process
+from PsyNeuLink.Components.System import system
+from PsyNeuLink.mechanismGenerator import mechanismGenerator
+from PsyNeuLink.Globals.Keywords import *
+import graphviz
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
+
+mechanism1 = TransferMechanism(name='my_Transfer1',
+                       default_input_value = [0],
+                       function=Linear(),
+                       time_constant =0.0,
+                       params= {NOISE: 25.0}
+
+                       )
+# print(mechanism1.execute(100)," = value of executing mechanism1 independently with input of 100")
+
+mechanism2 = TransferMechanism(name='my_Transfer2',
+                               default_input_value=[0],
+                               function=Linear(slope=2.0),
+                               time_constant=0.0
+
+                               )
+
+# print(mechanism2.execute(125)," = value of executing mechanism2 independently with input of 100")
+
+mechanism3 = TransferMechanism(name='my_Transfer3',
+                               default_input_value=[0],
+                               function=Linear(slope=2.0),
+                               time_constant=0.0
+
+                               )
+# print(mechanism3.execute(250)," = value of executing mechanism3 independently with input of 200")
+
+mechanism4 = TransferMechanism(name='my_Transfer4',
+                               default_input_value=[0],
+                               function=Linear(slope=2.0),
+                               time_constant=0.0
+
+                               )
+# print(mechanism4.execute(500)," = value of executing mechanism4 independently with input of 400")
+
+
+mechanism5 = TransferMechanism(name='my_Transfer5',
+                               default_input_value=[0],
+                               function=Linear(),
+                               time_constant=0.0
+
+                               )
+
+# print(mechanism5.execute(1000)," = value of executing mechanism5 independently with input of 800")
+
+mech1tuple = mechanism1, {PARAMETER_STATE_PARAMS:{FUNCTION_PARAMS:{SLOPE:10.0, INTERCEPT: 15.0}}}
+mech2tuple = mechanism2, {PARAMETER_STATE_PARAMS:{FUNCTION_PARAMS:{SLOPE:50.0, INTERCEPT: 45.0}}}
+
+path = [mech1tuple, mechanism2, mechanism3, mechanism4, mechanism5]
+process1 = process(default_input_value=[100],
+                 params={PATHWAY:path},
+                 prefs={kpVerbosePref: PreferenceEntry(False, PreferenceLevel.INSTANCE),
+                        kpReportOutputPref: PreferenceEntry(True, PreferenceLevel.INSTANCE)})
+print(process1.runtime_params_dict)
+print(process1.pathway)
+
+system1 = system(processes=[process1], scheduler= mechanismGenerator(path))
+
+print(system1.execute([[100]]), " = value of executing system with input of 100")
\ No newline at end of file
diff --git a/Scripts/MISCELLANEOUS SCRIPTS/rate_bug.py b/Scripts/MISCELLANEOUS SCRIPTS/rate_bug.py
new file mode 100644
index 00000000000..195b01eb9fa
--- /dev/null
+++ b/Scripts/MISCELLANEOUS SCRIPTS/rate_bug.py	
@@ -0,0 +1,93 @@
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import DDM
+from PsyNeuLink.Components.Process import process
+from PsyNeuLink.Components.Functions.Function import Integrator,BogaczEtAl
+from PsyNeuLink.Globals.Utilities import *
+from pprint import pprint
+
+
+def test_DDM_rate_int():
+    stim = 10
+    D = DDM(
+            name='DDM',
+            function = Integrator(
+                                    integration_type= DIFFUSION,
+                                    noise = 0.0,
+                                    rate = 5,
+                                    time_step_size=1.0
+                                  ),
+            time_scale=TimeScale.TIME_STEP
+           )
+    P = process(pathway=[D])
+    # val = float(D.execute(stim)[0])
+    print('BEFORE EXECUTION ')
+    pprint(D.function_object.__dict__)
+    val = P.execute(stim)
+    print("AFTER EXECUTION ")
+    pprint(D.function_object.__dict__)
+    print("=======================================================================================")
+
+    return val
+
+def test_DDM_rate_list_len_1():
+    stim = 10
+    D_2 = DDM(
+        name='DDM',
+        function=Integrator(
+            integration_type=DIFFUSION,
+            noise=0.0,
+            rate=[5],
+            time_step_size=1.0
+        ),
+        time_scale=TimeScale.TIME_STEP
+    )
+    P_2 = process(pathway=[D_2])
+    # val = float(D_2.execute(stim)[0])
+    print('BEFORE EXECUTION ')
+    pprint(D_2.function_object.__dict__)
+    val = P_2.execute(stim)
+    print("AFTER EXECUTION ")
+    pprint(D_2.function_object.__dict__)
+    print("=======================================================================================")
+
+
+def test_DDM_rate_int_bog():
+    stim = 10
+    D_bog = DDM(
+            name='DDM',
+            function = BogaczEtAl(drift_rate=5)
+           )
+    P_bog = process(pathway=[D_bog])
+    # val = float(D.execute(stim)[0])
+    print('BEFORE EXECUTION ')
+    pprint(D_bog.function_object.__dict__)
+    val = P_bog.execute(stim)
+    print("AFTER EXECUTION ")
+    pprint(D_bog.function_object.__dict__)
+    print("=======================================================================================")
+
+    return val
+
+def test_DDM_rate_list_len_1_bog():
+    stim = 10
+    D_2_bog = DDM(
+        name='DDM',
+        function=BogaczEtAl(drift_rate=[5, 5])
+    )
+    P_2_bog = process(pathway=[D_2_bog])
+    # val = float(D_2_bog.execute(stim)[0])
+    print('BEFORE EXECUTION ')
+    pprint(D_2_bog.function_object.__dict__)
+    val = P_2_bog.execute(stim)
+    print("AFTER EXECUTION ")
+    pprint(D_2_bog.function_object.__dict__)
+    print("=======================================================================================")
+
+    return val
+#
+# print("Bog 1 = ", test_DDM_rate_int_bog())
+# print("Bog 2 = ",test_DDM_rate_list_len_1_bog())
+# print("Bog 3 = ",test_DDM_rate_int_bog())
+
+print("Integrator 1 = ",test_DDM_rate_int())
+print("Integrator 2 = ", test_DDM_rate_list_len_1())
+print("Integrator 3 = ", test_DDM_rate_int())
\ No newline at end of file
diff --git a/Scripts/MISCELLANEOUS SCRIPTS/testing_mechanisms.py b/Scripts/MISCELLANEOUS SCRIPTS/testing_mechanisms.py
new file mode 100644
index 00000000000..fe353377830
--- /dev/null
+++ b/Scripts/MISCELLANEOUS SCRIPTS/testing_mechanisms.py	
@@ -0,0 +1,93 @@
+import numpy as np
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.IntegratorMechanism import IntegratorMechanism
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
+from PsyNeuLink.Globals.Keywords import EXECUTING
+from PsyNeuLink.Components.Process import process
+from PsyNeuLink.Components.Functions.Function import Linear, SimpleIntegrator, ConstantIntegrator, AccumulatorIntegrator
+from PsyNeuLink.Components.Functions.Function import AdaptiveIntegrator, DriftDiffusionIntegrator, OrnsteinUhlenbeckIntegrator
+from PsyNeuLink.Components.Functions.Function import FunctionError
+from PsyNeuLink.Globals.Keywords import ADAPTIVE, CONSTANT, DIFFUSION, SIMPLE
+from PsyNeuLink.Globals.TimeScale import TimeScale
+
+
+I = IntegratorMechanism(
+        name='IntegratorMechanism',
+        function=SimpleIntegrator(
+        ),
+        context="EXECUTING",
+        time_scale=TimeScale.TIME_STEP
+    )
+# P = process(pathway=[I])
+
+#  returns previous_value + rate*variable + noise
+# so in this case, returns 10.0
+# print("print1, ", I.context)
+val = float(I.execute(10, ignore_execution_id=True))
+# testing initializer
+I.function_object.reset_initializer = 5.0
+val2 = float(I.execute(0,  ignore_execution_id=True))
+print([val, val2])
+print([val, val2] == [10.0, 5.0])
+
+#
+# I2 = IntegratorMechanism(
+#         name='IntegratorMechanism',
+#         function=AdaptiveIntegrator(
+#             rate=0.5),
+#         context = "EXECUTING",
+#         time_scale=TimeScale.TIME_STEP
+#     )
+# # val = float(I.execute(10)[0])
+# P = process(pathway=[I2])
+# val = float(P.execute(10))
+# I2.context = None
+# print("after setting context to None [1/2] ", I2.context)
+# # returns (rate)*variable + (1-rate*previous_value) + noise
+# # rate = 1, noise = 0, so in this case, returns 10.0
+# # testing initializer
+# I2.function_object.reset_initializer = 1.0
+# val2 = float(P.execute(1))
+# print("after setting context to None [2/2] ", I2.context)
+# print(val2)
+# assert [val, val2] == [5.0, 1.0]
+# print([val, val2] == [5.0, 1.0])
+#
+#
+# T = TransferMechanism(
+#         name='TransferMechanism',
+#         context = "EXECUTING",
+#         time_scale=TimeScale.TIME_STEP
+#     )
+# # val = float(I.execute(10)[0])
+# # P = process(pathway=[T])
+# val = float(T.execute(10))
+# # returns (rate)*variable + (1-rate*previous_value) + noise
+# # rate = 1, noise = 0, so in this case, returns 10.0
+# T.context = "EXECUTING"
+# print(T.context)
+# # testing initializer
+# T.function_object.reset_initializer = 1.0
+# val2 = float(T.execute(1))
+# print(val2)
+# assert [val, val2] == [5.0, 1.0]
+# print([val, val2] == [5.0, 1.0])
+
+def test_mechanisms_without_system_or_process_no_input():
+    I = IntegratorMechanism(
+            name='IntegratorMechanism',
+            default_input_value= 10,
+            function=SimpleIntegrator(
+            ),
+            time_scale=TimeScale.TIME_STEP
+        )
+    T = TransferMechanism(function=Linear(slope=2.0, intercept=5.0))
+    M = MappingProjection(sender=I, receiver=T)
+
+    print(I.variable, " = I.var")
+    print(T.variable, " = T.var")
+    res1 = float(I.execute(context=EXECUTING + " MECHANISM"))
+
+    res2 = float(T.execute(context=EXECUTING))
+    assert res1, res2 == (10, 25)
+test_mechanisms_without_system_or_process_no_input()
\ No newline at end of file
diff --git a/Scripts/Scratch Pad.py b/Scripts/Scratch Pad.py
index 1b02d16d116..ab09b1d68a4 100644
--- a/Scripts/Scratch Pad.py	
+++ b/Scripts/Scratch Pad.py	
@@ -1,33 +1,25 @@
-import numpy as np
-
 # GLOBALS:
 from PsyNeuLink.Globals.Keywords import *
-# FUNCTIONS:
-from PsyNeuLink.Components.Functions.Function import Logistic
-from PsyNeuLink.Components.Functions.Function import Linear
-
-# STATES:
-# from PsyNeuLink.Components.States.OutputState import OutputState
-# from PsyNeuLink.Globals.Keywords import PARAMETER_STATE_PARAMS
-
-# PROJECTIONS:
-from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
-# from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection
-# from PsyNeuLink.Components.Projections.ControlProjection import ControldProjection
-# from PsyNeuLink.Components.States.ParameterState import ParameterState, PARAMETER_STATE_PARAMS
 
 # MECHANISMS:
-from PsyNeuLink.Components.Mechanisms.Mechanism import Mechanism_Base, Mechanism, mechanism
 # from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.IntegratorMechanism import IntegratorMechanism
-from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
-# from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TRANSFER_MEAN
-from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import DDM
+# from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
 # from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
-from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.IntegratorMechanism import IntegratorMechanism
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.LCA import LCA, LCA_OUTPUT
 
 # COMPOSITIONS:
-from PsyNeuLink.Components.Process import Process, Process_Base, process
-from PsyNeuLink.Components.System import system, System, System_Base
+from PsyNeuLink.Components.Process import process
+from PsyNeuLink.Components.System import system
+
+
+# FUNCTIONS:
+# STATES:
+# from PsyNeuLink.Components.States.OutputState import OutputState
+# from PsyNeuLink.Globals.Keywords import PARAMETER_STATE_PARAMS
+# PROJECTIONS:
+# from PsyNeuLink.Components.Projections.ModulatoryProjections.LearningProjection import LearningProjection
+# from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControldProjection
+# from PsyNeuLink.Components.States.ParameterState import ParameterState, PARAMETER_STATE_PARAMS
 
 class ScratchPadError(Exception):
     def __init__(self, error_value):
@@ -112,6 +104,15 @@ def __init__(self, error_value):
 
 #endregion
 
+#region TEST arg vs. paramClassDefault @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+
+# myTransfer = TransferMechanism(output_states=[*TransferMechanism.my_mean, {NAME: 'NEW_STATE'}],
+#                                # input_states={NAME: 'MY INPUT'}
+#                                )
+# TEST_CONDITION = True
+
+#endregion
+
 # region TEST ReadOnlyOrderedDict
 
 # from collections import UserDict, OrderedDict
@@ -284,9 +285,75 @@ def __init__(self, error_value):
 #region TEST Linear FUNCTION WITH MATRIX @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
 #
 # print = Linear(variable=[[1,1],[2,2]])
+#endregion
+
+
+#region TEST 2 Mechanisms and a Projection @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
 
-# #region TEST ASSIGNMENT OF PROJECTION TO PARAMETER @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+# from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
+# from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.IntegratorMechanism import IntegratorMechanism
+# from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
+#
+# my_mech_A = IntegratorMechanism()
+# my_mech_B = TransferMechanism()
+# proj = MappingProjection(sender=my_mech_A,
+#                          receiver=my_mech_B)
+# my_mech_B.execute(context=EXECUTING)
+#
+#endregion
+
+
+#region TEST Modulation @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+
+# from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import *
+# from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.ControlMechanism import ControlMechanism_Base
+# from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.EVCMechanism import EVCMechanism
+# from PsyNeuLink.Components.States.ModulatorySignals.ControlSignal import ControlSignal
+# from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
+# from PsyNeuLink.Components.Functions.Function import *
+#
+# My_Transfer_Mech_A = TransferMechanism(
+#                            function=Logistic(
+#                                         gain=(1.0, ControlSignal(modulation=ModulationParam.ADDITIVE))))
+#
+#
+# My_Mech_A = TransferMechanism(function=Logistic)
+# My_Mech_B = TransferMechanism(function=Linear,
+#                              output_states=[RESULT, MEAN])
+#
+# Process_A = process(pathway=[My_Mech_A])
+# Process_B = process(pathway=[My_Mech_B])
+# My_System = system(processes=[Process_A, Process_B])
 #
+# My_EVC_Mechanism = EVCMechanism(system=My_System,
+#                                 monitor_for_control=[My_Mech_A.output_states[RESULT],
+#                                                      My_Mech_B.output_states[MEAN]],
+#                                 control_signals=[(GAIN, My_Mech_A),
+#                                                  {NAME: INTERCEPT,
+#                                                   MECHANISM: My_Mech_B,
+#                                                   MODULATION:ModulationParam.ADDITIVE}],
+#                                 name='My EVC Mechanism')
+#
+#
+# My_Mech_A = TransferMechanism(function=Logistic)
+# My_Mech_B = TransferMechanism(function=Linear,
+#                              output_states=[RESULT, MEAN])
+# Process_A = process(pathway=[My_Mech_A])
+# Process_B = process(pathway=[My_Mech_B])
+#
+# My_System = system(processes=[Process_A, Process_B],
+#                                 monitor_for_control=[My_Mech_A.output_states[RESULT],
+#                                                      My_Mech_B.output_states[MEAN]],
+#                                 control_signals=[(GAIN, My_Mech_A),
+#                                                  {NAME: INTERCEPT,
+#                                                   MECHANISM: My_Mech_B,
+#                                                   MODULATION:ModulationParam.ADDITIVE}],
+#                    name='My Test System')
+
+#endregion
+
+# region TEST ASSIGNMENT OF PROJECTION TO PARAMETER @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+
 # Decision = DDM(function=BogaczEtAl(drift_rate=ControlProjection),
 #                name='Decision')
 #
@@ -308,14 +375,14 @@ def __init__(self, error_value):
 # #
 # # # my_process = process(pathway=[transfer_mechanism_1,
 # # #                               (transfer_mechanism_2,{PARAMETER_STATE_PARAMS:{SLOPE:(1.0,
-# # #                                                                                     ModulationOperation.OVERRIDE)}}),
+# # #                                                                                     Modulation.OVERRIDE)}}),
 # # #                               transfer_mechanism_2])
 # # # my_process.run(inputs=[[[0]]])
 # #
 # # # mapping_1 = MappingProjection(sender=transfer_mechanism_1, receiver=transfer_mechanism_3)
 # # # mapping_2 = MappingProjection(sender=transfer_mechanism_2, receiver=transfer_mechanism_3)
 # # print(transfer_mechanism_3.execute(input=1.0,
-# #                                    runtime_params={PARAMETER_STATE_PARAMS:{SLOPE:(2.0, ModulationOperation.OVERRIDE)}}))
+# #                                    runtime_params={PARAMETER_STATE_PARAMS:{SLOPE:(2.0, Modulation.OVERRIDE)}}))
 # #
 #
 # my_control = ControlProjection(name='MY CONTROL')
@@ -361,7 +428,7 @@ def __init__(self, error_value):
 # transfer_mechanism_1.execute()
 # # my_process = process(pathway=[transfer_mechanism_1,
 # #                               (transfer_mechanism_2,{PARAMETER_STATE_PARAMS:{SLOPE:(1.0,
-# #                                                                                     ModulationOperation.OVERRIDE)}}),
+# #                                                                                     Modulation.OVERRIDE)}}),
 # #                               transfer_mechanism_2])
 # # my_process.run(inputs=[[[0]]])
 #
@@ -369,13 +436,13 @@ def __init__(self, error_value):
 # # mapping_2 = MappingProjection(sender=transfer_mechanism_2, receiver=transfer_mechanism_3)
 # transfer_mechanism_3.function_object.runtimeParamStickyAssignmentPref = False
 # print(transfer_mechanism_3.execute(input=1.0,
-#                                    runtime_params={PARAMETER_STATE_PARAMS:{SLOPE:(6.0, ModulationOperation.OVERRIDE)}}))
+#                                    runtime_params={PARAMETER_STATE_PARAMS:{SLOPE:(6.0, Modulation.OVERRIDE)}}))
 # # print(transfer_mechanism_3.execute(input=1.0))
 # print(transfer_mechanism_3.execute(input=1.0,
 #                                    runtime_params={PARAMETER_STATE_PARAMS:{INTERCEPT:(100.0,
-#                                                                                    ModulationOperation.OVERRIDE),
+#                                                                                    Modulation.OVERRIDE),
 #                                                                             # SLOPE:(6.0,
-#                                                                             #        ModulationOperation.OVERRIDE
+#                                                                             #        Modulation.OVERRIDE
 #                                                                                       }}))
 # # print(transfer_mechanism_3.run(inputs=[1.0],
 # #                                num_executions=3))
@@ -432,7 +499,6 @@ def __init__(self, error_value):
 # # print(my_adaptive_integrator.execute([3]))
 # # print(my_adaptive_integrator.execute([3]))
 # # print(my_adaptive_integrator.execute([3]))
-
 #endregion
 
 #region TEST INSTANTATION OF System() @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
@@ -459,7 +525,7 @@ def __init__(self, error_value):
 #
 # from PsyNeuLink.Components.System import System_Base
 # from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import DDM
-# from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection
+# from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
 #
 # mech = DDM()
 #
@@ -588,10 +654,16 @@ def __init__(self, error_value):
 
 #region TEST LinearCombination FUNCTION @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
 
-# from Components.Function import *
-# #
+# from PsyNeuLink.Components.Functions.Function import LinearCombination
+
 # x = LinearCombination()
-# print (x.execute(([1, 1],[2, 2])))
+# # print (x.execute(([1, 1],[2, 2])))
+#
+# print (x.execute(([[1, 1],[2, 2]],
+#                   [[3, 3],[4, 4]])))
+
+# print (x.execute(([[[[1, 1],[2, 2]]]])))
+
 
 #endregion
 
@@ -605,7 +677,7 @@ def __init__(self, error_value):
 
 #endregion
 
-#region TEST BackProp FUNCTION @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+#region TEST Backprop FUNCTION @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
 
 # from Components.Function import *
 #
@@ -618,45 +690,65 @@ def __init__(self, error_value):
 #
 #endregion
 
-#region TEST AutoAssociator
-
-my_auto = TransferMechanism(default_input_value=[0,0,0],
-                            # function=Logistic
-                            )
-
-my_auto_matrix = MappingProjection(sender=my_auto,
-                                   receiver=my_auto,
-                                   matrix=FULL_CONNECTIVITY_MATRIX)
-# THIS DOESN'T WORK, AS Process._instantiate_pathway() EXITS AFTER PROCESSING THE LONE MECHANISM
-#                    SO NEVER HAS A CHANCE TO SEE THE PROJECTION AND THEREBY ASSIGN IT A LearningProjection
-my_process = process(pathway=[my_auto],
-
-# THIS DOESN'T WORK, AS Process._instantiate_pathway() ONLY CHECKS PROJECTIONS AFTER ENCOUNTERING ANOTHER MECHANISM
-# my_process = process(pathway=[my_auto, my_auto_matrix],
-                     learning=LEARNING
-                     )
-
-# my_process = process(pathway=[my_auto, FULL_CONNECTIVITY_MATRIX, my_auto],
-#                      learning=LEARNING,
-#                      target=[0,0,0])
-
-# print(my_process.execute([1,1,1]))
-# print(my_process.execute([1,1,1]))
-# print(my_process.execute([1,1,1]))
-# print(my_process.execute([1,1,1]))
+# region TEST RecurrentTransferMechanism / LCA @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+#
+# # print("TEST RecurrentTransferMechanism / LCA")
+# # my_auto = LCA(
+# #         size=3,
+# #         output_states=[LCA_OUTPUT.RESULT,
+# #                        LCA_OUTPUT.ENTROPY,
+# #                        LCA_OUTPUT.ENERGY,
+# #                        LCA_OUTPUT.MAX_VS_AVG]
+# #         # inhibition
+# # )
+#
+# # my_auto = RecurrentTransferMechanism(
+# #         default_input_value=[0,0,0],
+# #         size=3,
+# #         function=Logistic,
+# #         # matrix=RANDOM_CONNECTIVITY_MATRIX,
+# #         matrix=np.array([[1,1,1],[1,1,1],[1,1,1]])
+# #         # matrix=[[1,1,1],[1,1,1],[1,1,1]]
+# # )
+#
+# # my_auto = TransferMechanism(default_input_value=[0,0,0],
+# #                             # function=Logistic
+# #                             )
+# #
+# # my_auto_matrix = MappingProjection(sender=my_auto,
+# #                                    receiver=my_auto,
+# #                                    matrix=FULL_CONNECTIVITY_MATRIX)
+#
+# # THIS DOESN'T WORK, AS Process._instantiate_pathway() EXITS AFTER PROCESSING THE LONE MECHANISM
+# #                    SO NEVER HAS A CHANCE TO SEE THE PROJECTION AND THEREBY ASSIGN IT A LearningProjection
+# my_process = process(pathway=[my_auto],
+#
+# # THIS DOESN'T WORK, AS Process._instantiate_pathway() ONLY CHECKS PROJECTIONS AFTER ENCOUNTERING ANOTHER MECHANISM
+# # my_process = process(pathway=[my_auto, my_auto_matrix],
+#                      target=[0,0,0],
+#                      learning=LEARNING
+#                      )
+#
+# # my_process = process(pathway=[my_auto, FULL_CONNECTIVITY_MATRIX, my_auto],
+# #                      learning=LEARNING,
+# #                      target=[0,0,0])
+#
+# # print(my_process.execute([1,1,1]))
+# # print(my_process.execute([1,1,1]))
+# # print(my_process.execute([1,1,1]))
+# # print(my_process.execute([1,1,1]))
+# #
+# input_list = {my_auto:[1,1,1]}
+# target_list = {my_auto:[0,0,0]}
 #
-input_list = {my_auto:[1,1,1]}
-target_list = {my_auto:[0,0,0]}
-
-# print(my_process.run(inputs=input_list, targets=target_list, num_executions=5))
-
-my_system = system(processes=[my_process],
-                   targets=[0,0,0])
-
-print(my_system.run(inputs=input_list,
-                    targets=target_list,
-                    num_executions=5))
-
+# # print(my_process.run(inputs=input_list, targets=target_list, num_executions=5))
+#
+# my_system = system(processes=[my_process],
+#                    targets=[0,0,0])
+#
+# print(my_system.run(inputs=input_list,
+#                     targets=target_list,
+#                     num_executions=5))
 
 #endregion
 
@@ -709,13 +801,12 @@ def __init__(self, error_value):
 
 #endregion
 
-
 #region TEST Matrix Assignment to MappingProjection @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
 
 # from PsyNeuLink.Components.Process import *
 # from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
 # from PsyNeuLink.Components.Functions.Function import Linear
-# from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
+# from PsyNeuLink.Components.Projections.TransmissiveProjections.MappingProjection import MappingProjection
 #
 # my_mech = TransferMechanism(function=Linear())
 # my_mech2 = TransferMechanism(function=Linear())
@@ -730,7 +821,7 @@ def __init__(self, error_value):
 
 #endregion
 
-#region TEST matrix
+#region TEST matrix @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
 
 # Input_Weights_matrix = (np.arange(2*5).reshape((2, 5)) + 1)/(2*5)
 # Middle_Weights_matrix = (np.arange(5*4).reshape((5, 4)) + 1)/(5*4)
@@ -746,6 +837,28 @@ def __init__(self, error_value):
 # c = np.dot(b, a, )
 # print(c)
 
+#endregion  ********
+
+#region TEST Stability and Distance @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+
+# matrix = [[0,-1],[-1,0]]
+# normalize = False
+# activity = [100,0]
+#
+#
+# eng = Stability(variable_default=activity,
+#              matrix=matrix,
+#              normalize=normalize
+#              )
+#
+# dist = Distance(variable_default=[activity,activity],
+#                 metric=CROSS_ENTROPY,
+#                 # normalize=normalize
+#                 )
+#
+# print("Stability: ",eng.function(activity))
+# print("Distance: ", dist.function(activity))
+
 #endregion
 
 #region TEST Matrix Assignment to MappingProjection @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
@@ -753,7 +866,7 @@ def __init__(self, error_value):
 # from PsyNeuLink.Components.Process import *
 # from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
 # from PsyNeuLink.Components.Functions.Function import Linear, Logistic
-# from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
+# from PsyNeuLink.Components.Projections.TransmissiveProjections.MappingProjection import MappingProjection
 #
 # color_naming = TransferMechanism(default_input_value=[0,0],
 #                         function=Linear,
@@ -785,13 +898,10 @@ def __init__(self, error_value):
 #
 # endregion
 
-
 # ----------------------------------------------- UTILITIES ------------------------------------------------------------
 
 #region TEST typecheck: @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
 
-import typecheck as tc
-
 # @tc.typecheck
 # def foo2(record:(int,int,bool), rgb:tc.re("^[rgb]$")) -> tc.any(int,float) :
 #     # don't expect the following to make much sense:
@@ -854,6 +964,26 @@ def __init__(self, error_value):
 
 #endregion
 
+#region TEST get_user_attributes @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+
+# import inspect
+#
+# def get_class_attributes(cls):
+#     boring = dir(type('dummy', (object,), {}))
+#     return [item
+#             for item in inspect.getmembers(cls)
+#             if item[0] not in boring]
+#
+# class my_class():
+#     attrib1 = 0
+#     attrib2 = 1
+#     # def __init__(self):
+#         # self.attrib1 = 0
+#         # self.attrib2 = 1
+#
+# print(get_class_attributes(my_class))
+
+#endregion
 
 #region TEST Function definition in class: @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
 
@@ -1548,9 +1678,8 @@ def __init__(self, error_value):
 #         return self._prefs
 #
 #endregion
-#region @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-#
-# # - TEST: Preferences:
+
+#region TEST: Preferences @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
 #
 # # x = DDM()
 # # x.prefs.show()
@@ -1558,7 +1687,7 @@ def __init__(self, error_value):
 # DDM_prefs = ComponentPreferenceSet(
 #                 reportOutput_pref=PreferenceEntry(True,PreferenceLevel.SYSTEM),
 #                 verbose_pref=PreferenceEntry(True,PreferenceLevel.SYSTEM),
-#                 kpFunctionRuntimeParams_pref=PreferenceEntry(ModulationOperation.MULTIPLY,PreferenceLevel.TYPE)
+#                 kpFunctionRuntimeParams_pref=PreferenceEntry(Modulation.MULTIPLY,PreferenceLevel.TYPE)
 #                 )
 # DDM_prefs.show()
 # # DDM.classPreferences = DDM_prefs
@@ -1568,9 +1697,8 @@ def __init__(self, error_value):
 #
 
 #endregion
-#region @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
 
-# # - TEST:  GET ATTRIBUTE LIST
+#region TEST:  GET ATTRIBUTE LIST @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
 #
 # class x:
 #     def __init__(self):
@@ -1590,10 +1718,9 @@ def __init__(self, error_value):
 #         print (value)
 
 #endregion
-#region @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-
-# - TEST:  PROPERTY GETTER AND SETTER
 
+#region TEST:  PROPERTY GETTER AND SETTER @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+#
 # ************
 # 
 # EXAMPLE:
@@ -1833,7 +1960,6 @@ def __init__(self, error_value):
 #
 #
 #
-
 #endregion
 
 #region TEST:  DICTIONARY MERGE @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
@@ -1858,7 +1984,7 @@ def __init__(self, error_value):
 #
 #endregion
 
-# #region TEST: SEQUENTIAL ERROR HANDLING @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+# region TEST: SEQUENTIAL ERROR HANDLING @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
 # # state_params = None
 # state_params = {}
 # # state_params = {'Already there': 0}
@@ -1883,8 +2009,6 @@ def __init__(self, error_value):
 #     pass
 # #endregion
 
-# print(state_params)
-
 #region TEST:  ORDERED DICTIONARY ORDERING @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
 # from collections import OrderedDict
 #
@@ -2048,6 +2172,333 @@ def __init__(self, error_value):
 #
 # #endregion
 
+#region TEST OF List indexed by string @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+
+
+# # a = MyClass()
+# # b = MyClass()
+# # # a.name = 'hello'
+# # a.name.append('hello')
+# # print(a.name)
+# # print(b.name)
+#
+#
+# # from collections import UserList
+# #
+# # class ClassListTest(UserList):
+# #     """Implements dict-like list, that can be indexed by the names of the States in its entries.
+# #
+# #     Supports getting and setting entries in the list using string (in addition to numeric) indices.
+# #     For getting an entry:
+# #         the string must match the name of a State in the list; otherwise an excpetion is raised.
+# #     For setting an entry:
+# #         the string must match the name of the State being assigned;
+# #         if there is already a State in the list the name of which matches the string, it is replaced;
+# #         if there is no State in the list the name of which matches the string, the State is appended to the list.
+# #
+# #     IMPLEMENTATION NOTE:
+# #         This class allows the states of a mechanism to be maintained in lists, while providing the convenience
+# #         (to the user) of access and assignment by name (e.g., akin to a dict).
+# #         Lists are used (instead of a dict or OrderedDict) since:
+# #             - ordering is in many instances convenient, and in some critical (e.g., for consistent mapping from
+# #                 collections of states to other variables, such as lists of their values);
+# #             - they are most commonly accessed either exhaustively (e.g., in looping through them during execution),
+# #                 or by index (e.g., to get the first, "primary" one), which makes the efficiencies of a dict for
+# #                 accessing by key/name less critical;
+# #             - the number of states in a collection for a given mechanism is likely to be small so that, even when
+# #                 accessed by key/name, the inefficiencies of searching a list are likely to be inconsequential.
+# #     """
+# #
+# #     def __init__(self, list=None, name=None, **kwargs):
+# #         self.name = name or self.__class__.__name__
+# #         UserList.__init__(self, list, **kwargs)
+# #         # self._ordered_keys = []
+# #
+# #     def __getitem__(self, index):
+# #         try:
+# #             return self.data[index]
+# #         except TypeError:
+# #             index = self._get_index_for_item(index)
+# #             return self.data[index]
+# #
+# #     def __setitem__(self, index, value):
+# #         try:
+# #             self.data[index] = value
+# #         except TypeError:
+# #             if not index is value.name:
+# #                 raise ScratchPadError("Name of entry for {} ({}) must match the name of its State ({})".
+# #                                       format(self.name, index, value.name))
+# #             index_num = self._get_index_for_item(index)
+# #             if index_num is not None:
+# #                 self.data[index_num] = value
+# #             else:
+# #                 self.data.append(value)
+# #
+# #     def _get_index_for_item(self, index):
+# #         if isinstance(index, str):
+# #             # return self.data.index(next(obj for obj in self.data if obj.name is index))
+# #             obj = next((obj for obj in self.data if obj.name is index), None)
+# #             if obj is None:
+# #                 return None
+# #             else:
+# #                 return self.data.index(obj)
+# #
+# #         elif isinstance(index, MyClass):
+# #             return self.data.index(index)
+# #         else:
+# #             raise ScratchPadError("{} is not a legal index for {} (must be number, string or State".
+# #                                   format(index, self.name))
+# #
+# #     def __delitem__(self, index):
+# #         del self.data[index]
+# #
+# #     def clear(self):
+# #         super().clear(self)
+# #
+# #     # def pop(self, index, *args):
+# #     #     raise UtilitiesError("{} is read-only".format(self.name))
+# #     # def popitem(self):
+# #     #     raise UtilitiesError("{} is read-only".format(self.name))
+# #
+# #     def __additem__(self, index, value):
+# #         if index >= len(self.data):
+# #             self.data.append(value)
+# #         else:
+# #             self.data[index] = value
+# #
+# #
+# #     def __contains__(self, item):
+# #         if super().__contains__(item):
+# #             return True
+# #         else:
+# #             return any(item is obj.name for obj in self.data)
+# #
+# #     def copy(self):
+# #         return self.data.copy()
+#
+# class MyClass():
+#     name = None
+#     def __init__(self, name=None):
+#         self.name = name
+#         # self.name = name
+#
+# my_obj = MyClass(name='hello')
+# my_obj_2 = MyClass(name='goodbye')
+# my_obj_3 = MyClass(name='goodbye')
+#
+# from PsyNeuLink.Globals.Utilities import ContentAddressableList
+#
+# my_list = ContentAddressableList(component_type=MyClass)
+# # my_list.append(my_state)
+# my_list['hello'] = my_obj
+# my_list['goodbye'] = my_obj_2
+# print(my_list, len(my_list))
+# print(my_list[0])
+# print(my_list['hello'])
+# print(my_list['goodbye'])
+# my_list['goodbye'] = my_obj_3
+# print(my_list['goodbye'])
+# print('hello' in my_list)
+
+#endregion
+
+#region TEST parse_state_spec @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+
+# print("TEST parse_gated_state_spec")
+#
+# from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.GatingMechanisms.GatingMechanism import _parse_gating_signal_spec
+# from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.GatingMechanisms.GatingSignal import GatingSignal
+# from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.GatingMechanisms.GatingMechanism import GatingMechanism
+# from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import DDM
+# from PsyNeuLink.Components.Functions.Function import ModulationParam
+# from PsyNeuLink.Components.States.OutputState import OutputState
+#
+# gating_mech = GatingMechanism()
+# mech_1 = DDM()
+# mech_2 = DDM()
+#
+# single_dict = {NAME:'DECISION_VARIABLE', MECHANISM:mech_1}
+# a = _parse_gating_signal_spec(owner=gating_mech, state_spec=single_dict)
+# print('\nsingle_dict:', a)
+#
+# # THESE ARE A HACK TO ASSIGN A PRE-EXISTING GATING SIGNAL TO gating_mech WITHOUTH HAVING TO CONSTRUCT ONE
+# x = DDM()
+# x.name ='Default_input_state_GatingSignal'
+# x.efferents = []
+# gating_mech._gating_signals = [x]
+# # --------------------------------------------------------------
+#
+# single_tuple = ('DECISION_VARIABLE', mech_1)
+# b = _parse_gating_signal_spec(gating_mech, state_spec=single_tuple)
+# print('\nsingle_tuple:', b)
+#
+# multi_states_dicts = {'MY_SIGNAL':[{NAME:'DECISION_VARIABLE',
+#                                    MECHANISM:mech_1},
+#                                    {NAME:'RESPONSE_TIME',
+#                                    MECHANISM:mech_1}],
+#                       MODULATION: ModulationParam.ADDITIVE}
+# c = _parse_gating_signal_spec(gating_mech, state_spec=multi_states_dicts)
+# print('\nmulti_states_dicts:', c)
+#
+# multi_states_tuples = {'MY_SIGNAL':[('Default_input_state',mech_1),
+#                                    ('Default_input_state',mech_2)]}
+# d = _parse_gating_signal_spec(gating_mech, state_spec=multi_states_tuples)
+# print('\nmulti_states_tuples:', d)
+#
+# multi_states_combo = {'MY_SIGNAL':[{NAME:'Default_input_state',
+#                                    MECHANISM:mech_1},
+#                                    ('Default_input_state',mech_2)]}
+# e = _parse_gating_signal_spec(gating_mech, state_spec=multi_states_combo)
+# print('\nmulti_states_combo:', e)
+
+#endregion
+
+# region TEST parse_monitored_value @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+
+# from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ObjectiveMechanism import *
+# from PsyNeuLink.Components.States.OutputState import OutputState
+#
+# print("TEST parse_monitored_value")
+#
+# def _parse_monitored_value(owner, monitored_values):
+#     """Parse specifications contained in monitored_values list or dict,
+#
+#     Can take either a list or dict of specifications.
+#     If it is a list, each item must be one of the following:
+#         - OuptutState
+#         - Mechanism
+#         - string
+#         - value
+#         - dict
+#
+#     If it is a dict, each item must be an entry, the key of which must be a string that is used as a name
+#         specification, and the value of which can be any of the above.
+#
+#     Return a list of specification dicts, one for each item of monitored_values
+#     """
+#
+#
+#     def parse_spec(spec):
+#
+#         # OutputState:
+#         if isinstance(spec, OutputState):
+#             name = spec.owner.name + MONITORED_VALUE_NAME_SUFFIX
+#             value = spec.value
+#             call_for_projection = True
+#
+#         # Mechanism:
+#         elif isinstance(spec, Mechanism_Base):
+#             name = spec.name + MONITORED_VALUE_NAME_SUFFIX
+#             value = spec.output_state.value
+#             call_for_projection = True
+#
+#         # # If spec is a MonitoredOutputStatesOption:
+#         # # FIX: NOT SURE WHAT TO DO HERE YET
+#         # elif isinstance(montiored_value, MonitoredOutputStateOption):
+#         #     value = ???
+#         #     call_for_projection = True
+#
+#         # If spec is a string:
+#         # - use as name of inputState
+#         # - instantiate InputState with defalut value (1d array with single scalar item??)
+#
+#         # str:
+#         elif isinstance(spec, str):
+#             name = spec
+#             value = DEFAULT_MONITORED_VALUE
+#             call_for_projection = False
+#
+#         # value:
+#         elif is_value_spec(spec):
+#             name = owner.name + MONITORED_VALUE_NAME_SUFFIX
+#             value = spec
+#             call_for_projection = False
+#
+#         elif isinstance(spec, tuple):
+#             # FIX: REPLACE CALL TO parse_spec WITH CALL TO _parse_state_spec
+#             name = owner.name + MONITORED_VALUE_NAME_SUFFIX
+#             value = spec[0]
+#             call_for_projection = spec[1]
+#
+#         # dict:
+#         elif isinstance(spec, dict):
+#
+#             name = None
+#             for k, v in spec.items():
+#                 # Key is not a spec keyword, so dict must be of the following form: STATE_NAME_ASSIGNMENT:STATE_SPEC
+#                 #
+#                 if not k in {NAME, VALUE, STATE_PROJECTIONS}:
+#                     name = k
+#                     value = v
+#
+#             if NAME in spec:
+#                 name = spec[NAME]
+#
+#             call_for_projection = False
+#             if STATE_PROJECTIONS in spec:
+#                 call_for_projection = spec[STATE_PROJECTIONS]
+#
+#             if isinstance(spec[VALUE], (dict, tuple)):
+#                 # FIX: REPLACE CALL TO parse_spec WITH CALL TO _parse_state_spec
+#                 entry_name, value, call_for_projection = parse_spec(spec[VALUE])
+#
+#             else:
+#                 value = spec[VALUE]
+#
+#         else:
+#             raise ObjectiveMechanismError("Specification for {} arg of {} ({}) must be an "
+#                                           "OutputState, Mechanism, value or string".
+#                                           format(MONITORED_VALUES, owner.name, spec))
+#
+#         return name, value, call_for_projection
+#
+#     # If it is a dict, convert to list by:
+#     #    - assigning the key of each entry to a NAME entry of the dict
+#     #    - placing the value in a VALUE entry of the dict
+#     if isinstance(monitored_values, dict):
+#         monitored_values_list = []
+#         for name, spec in monitored_values.items():
+#             monitored_values_list.append({NAME: name, VALUE: spec})
+#         monitored_values = monitored_values_list
+#
+#     if isinstance(monitored_values, list):
+#
+#         for i, monitored_value in enumerate(monitored_values):
+#             name, value, call_for_projection = parse_spec(monitored_value)
+#             monitored_values[i] = {NAME: name,
+#                                    VALUE: value,
+#                                    PROJECTION: call_for_projection}
+#
+#     else:
+#         raise ObjectiveMechanismError("{} arg for {} ({} )must be a list or dict".
+#                                       format(MONITORED_VALUES, owner.name, monitored_values))
+#
+#     return monitored_values
+#
+#
+#
+#     # def add_monitored_values(self, states_spec, context=None):
+#     #     """Validate specification and then add inputState to ObjectiveFunction + MappingProjection to it from state
+#     #
+#     #     Use by other objects to add a state or list of states to be monitored by EVC
+#     #     states_spec can be a Mechanism, OutputState or list of either or both
+#     #     If item is a Mechanism, each of its outputStates will be used
+#     #
+#     #     Args:
+#     #         states_spec (Mechanism, MechanimsOutputState or list of either or both:
+#     #         context:
+#     #     """
+#     #     states_spec = list(states_spec)
+#     #     validate_monitored_value(self, states_spec, context=context)
+#     #     self._instantiate_monitored_output_states(states_spec, context=context)
+#
+# class SCRATCH_PAD():
+#     name = 'SCRATCH_PAD'
+#
+# print(_parse_monitored_value(SCRATCH_PAD, {'TEST_STATE_NAME':{VALUE: (32, 'Projection')}}))
+
+#endregion
+
 #region PREFERENCE TESTS @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
 
 #
@@ -2339,7 +2790,7 @@ def __init__(self, error_value):
 
 # from Components.Projections.ControlProjection import *
 #
-# # Initialize controlSignal with some settings
+# # Initialize control_signal with some settings
 # settings = ControlSignalSettings.DEFAULTS | \
 #            ControlSignalSettings.DURATION_COST | \
 #            ControlSignalSettings.LOG
@@ -2356,7 +2807,7 @@ def __init__(self, error_value):
 # # Can also change settings on the fly (note:  ControlProjection.OFF is just an enum defined in the ControlProjection module)
 # x.set_adjustment_cost(OFF)
 #
-# # Display some values in controlSignal (just to be sure it is set up OK)
+# # Display some values in control_signal (just to be sure it is set up OK)
 # print("Intensity Function: ", x.functions[kwControlSignalIntensityFunction].name)
 # print("Initial Intensity: ", x.intensity)
 #
diff --git a/Scripts/TEST SCRIPTS/Documentation Examples Script.py b/Scripts/TEST SCRIPTS/Documentation Examples Script.py
index de5fa79ad27..a223324e0b0 100644
--- a/Scripts/TEST SCRIPTS/Documentation Examples Script.py	
+++ b/Scripts/TEST SCRIPTS/Documentation Examples Script.py	
@@ -2,17 +2,13 @@
 # Runs examples in the PsyNeuLink Documentation
 #
 
-from PsyNeuLink.Globals.Keywords import *
-from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
+from PsyNeuLink.Components.Functions.Function import *
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import DDM
-from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
-from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection
-# from PsyNeuLink.Components.Functions.Function import Logistic, random_matrix
-from PsyNeuLink.Components.Functions.Function import Logistic
-from PsyNeuLink.Globals.Run import run
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
+from PsyNeuLink.Components.Process import process
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
 
-from PsyNeuLink.Components.Functions.Function import *
+# from PsyNeuLink.Components.Functions.Function import Logistic, random_matrix
 
 #region PROCESS EXAMPLES ********************************************************************
 
@@ -111,7 +107,7 @@
 mechanism_1 = TransferMechanism()
 mechanism_2 = DDM()
 some_params = {PARAMETER_STATE_PARAMS:{THRESHOLD:2,NOISE:0.1}}
-my_process = process(pathway=[mechanism_1, TransferMechanism, (mechanism_2, some_params, 0)])
+my_process = process(pathway=[mechanism_1, TransferMechanism, (mechanism_2, some_params)])
 print(my_process.execute())
 
 # Default projection specification
diff --git a/Scripts/TEST SCRIPTS/EVC System Laming Validation Test Script.py b/Scripts/TEST SCRIPTS/EVC System Laming Validation Test Script.py
index 480976320e5..5964271fb91 100644
--- a/Scripts/TEST SCRIPTS/EVC System Laming Validation Test Script.py	
+++ b/Scripts/TEST SCRIPTS/EVC System Laming Validation Test Script.py	
@@ -3,9 +3,11 @@
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import *
 from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.ControlProjection import ControlProjection
+from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
 from PsyNeuLink.Components.System import system
+from PsyNeuLink.Components.States.ModulatorySignals.ControlSignal import ControlSignal
 from PsyNeuLink.Globals.Keywords import *
+from PsyNeuLink.scheduling.condition import AfterNCalls
 
 import random
 random.seed(0)
@@ -20,38 +22,70 @@
 process_prefs = ComponentPreferenceSet(reportOutput_pref=PreferenceEntry(False,PreferenceLevel.INSTANCE),
                                       verbose_pref=PreferenceEntry(True,PreferenceLevel.INSTANCE))
 
+
 # Mechanisms:
-Input = TransferMechanism(name='Input',
-                 # params={MONITOR_FOR_CONTROL:[MonitoredOutputStatesOption.PRIMARY_OUTPUT_STATES]}
+Input = TransferMechanism(name='Input')
 
-                 )
 Reward = TransferMechanism(name='Reward',
                  # params={MONITOR_FOR_CONTROL:[PROBABILITY_UPPER_THRESHOLD,(RESPONSE_TIME, -1, 1)]}
+                           output_states=[RESULT, MEAN, VARIANCE]
                   )
+
 Decision = DDM(function=BogaczEtAl(drift_rate=(1.0, ControlProjection(function=Linear,
-                                                                      control_signal={
-                                                                          ALLOCATION_SAMPLES:np.arange(0.1, 1.01, 0.3)}
+                                                                      control_signal_params={
+                                                                          ALLOCATION_SAMPLES:np.arange(0.1, 1.01, 0.3)},
                                                                       )),
+
                                    threshold=(1.0, ControlProjection(function=Linear,
-                                                                     control_signal={
+                                                                     control_signal_params={
                                                                          ALLOCATION_SAMPLES:np.arange(0.1, 1.01, 0.3)}
                                                                      )),
+
+                                   # threshold=(1.0, ControlProjection(function=Linear,
+                                   #                                   control_signal_params={
+                                   #                                       ALLOCATION_SAMPLES:np.arange(0.1, 1.01, 0.3),
+                                   #                                       MODULATION:ModulationParam.ADDITIVE}
+                                   #                                   )),
+
+                                   # threshold=(1.0, ControlSignal(function=Linear,
+                                   #                               allocation_samples=np.arange(0.1, 1.01, 0.3))),
+
+                                   # threshold={VALUE: 1.0,
+                                   #            CONTROL_PROJECTION: ControlProjection(function=Linear,
+                                   #                                                  control_signal_params={
+                                   #                                                      ALLOCATION_SAMPLES:
+                                   #                                                          np.arange(0.1, 1.01, 0.3)}
+                                   #                                                  ),
+                                   #            FUNCTION:LinearCombination()},
+
                                    noise=(0.5),
                                    starting_point=(0),
                                    t0=0.45),
+               output_states=[DECISION_VARIABLE,
+                              RESPONSE_TIME,
+                              PROBABILITY_UPPER_THRESHOLD],
                prefs = DDM_prefs,
                name='Decision')
 
+# TEST = Decision._parameter_states[DRIFT_RATE].execute()
+# Decision._parameter_states[DRIFT_RATE].value = 10
+# TEST = Decision._parameter_states[DRIFT_RATE]._execute(function_params={SCALE: 3}, context=None)
+# Decision.function_object.drift_rate = 20
+# TEST = Decision._parameter_states[DRIFT_RATE]._execute(function_params={SCALE: 2}, context=None)
+# Decision.function_object._drift_rate = 30
+# TEST = Decision._parameter_states[DRIFT_RATE]._execute(function_params=None, context=None)
+# TEST
+
 # Processes:
 TaskExecutionProcess = process(
     default_input_value=[0],
-    pathway=[(Input, 0), IDENTITY_MATRIX, (Decision, 0)],
+    pathway=[Input, IDENTITY_MATRIX, Decision],
     prefs = process_prefs,
     name = 'TaskExecutionProcess')
 
 RewardProcess = process(
     default_input_value=[0],
-    pathway=[(Reward, 1)],
+    pathway=[Reward],
     prefs = process_prefs,
     name = 'RewardProcess')
 
@@ -59,11 +93,13 @@
 mySystem = system(processes=[TaskExecutionProcess, RewardProcess],
                   controller=EVCMechanism,
                   # controller=EVCMechanism(monitor_for_control=[Reward,
-                  #                                              DDM_PROBABILITY_UPPER_THRESHOLD,
-                  #                                              DDM_RESPONSE_TIME],
+                  #                                              Decision.PROBABILITY_UPPER_THRESHOLD,
+                  #                                              Decision.RESPONSE_TIME],
                   #                         outcome_function=LinearCombination(exponents=[1, 1, -1])),
                   enable_controller=True,
-                  monitor_for_control=[Reward, DDM_PROBABILITY_UPPER_THRESHOLD, (DDM_RESPONSE_TIME, -1, 1)],
+                  monitor_for_control=[Reward,
+                                       Decision.PROBABILITY_UPPER_THRESHOLD,
+                                       (Decision.RESPONSE_TIME, 1, -1)],
                   # monitor_for_control=[Input, PROBABILITY_UPPER_THRESHOLD,(RESPONSE_TIME, -1, 1)],
                   # monitor_for_control=[MonitoredOutputStatesOption.ALL_OUTPUT_STATES],
                   name='EVC Test System')
@@ -100,26 +136,25 @@ def show_trial_header():
 def show_results():
     import re
     print('\nRESULTS (time step {}): '.format(CentralClock.time_step))
-    results_for_decision = \
-        [result for result in sorted(zip(mySystem.terminalMechanisms.outputStateNames,
-                                           mySystem.terminalMechanisms.outputStateValues)) if 'DDM' in result[0]]
+    results_for_decision = [(state.name, state.value) for state in Decision.output_states]
+
     print("\tDecision:")
     print('\t\tControlSignal values:')
-    print ('\t\t\tDrift rate control signal (from EVC): {}'.
+    print ('\t\t\tDrift rate control signal (from EVC): {} / parameter value: {}'.
            # format(re.sub('[\[,\],\n]','',str(float(Decision.parameterStates[DRIFT_RATE].value)))))
-           format(re.sub('[\[,\],\n]','',str("{:0.3}".format(float(Decision.parameterStates[DRIFT_RATE].value))))))
-    print ('\t\t\tThreshold control signal (from EVC): {}'.
-           format(re.sub('[\[,\],\n]','',str(float(Decision.parameterStates[THRESHOLD].value))),
-                  mySystem.controller.outputStates['threshold_ControlSignal'].value,
-                  Decision.parameterStates[THRESHOLD].receivesFromProjections[0].value
+           format(
+            mySystem.controller.output_states['drift_rate_ControlSignal'].value,
+            re.sub('[\[,\],\n]','',str("{:0.3}".format(float(Decision._parameter_states[DRIFT_RATE].value))))))
+    print ('\t\t\tThreshold control signal (from EVC): {} / parameter value: {}'.
+           format(mySystem.controller.output_states['threshold_ControlSignal'].value,
+                  re.sub('[\[,\],\n]','',str(float(Decision._parameter_states[THRESHOLD].value)))
+                  # Decision._parameter_states[THRESHOLD].mod_afferents[0].value
                   ))
     print('\t\tOutput:')
     for result in results_for_decision:
         print("\t\t\t{}: {}".format(result[0],
                                 re.sub('[\[,\],\n]','',str("{:0.3}".format(float(result[1]))))))
-    results_for_reward = \
-        [result for result in sorted(zip(mySystem.terminalMechanisms.outputStateNames,
-                                           mySystem.terminalMechanisms.outputStateValues)) if 'transfer' in result[0]]
+    results_for_reward = [(state.name, state.value) for state in Reward.output_states]
     print("\tReward:\n\t\tOutput:")
     for result in results_for_reward:
         print("\t\t\t{}: {}".format(result[0],
@@ -131,12 +166,13 @@ def show_results():
 mySystem.controller.reportOutputPref = False
 
 # mySystem.show_graph(direction='LR')
-# mySystem.show_graph_with_control()
+# mySystem.show_graph(show_control=True)
 
 # mySystem.run(inputs=trial_list,
 # # mySystem.run(inputs=reversed_trial_list,
-mySystem.run(inputs=stim_list_dict,
-             call_before_trial=show_trial_header,
-             # call_after_time_step=show_results
-             call_after_trial=show_results
-             )
+mySystem.run(
+    inputs=stim_list_dict,
+    call_before_trial=show_trial_header,
+    # call_after_time_step=show_results
+    call_after_trial=show_results,
+)
diff --git a/Scripts/TEST SCRIPTS/EVC-Test-System.pdf b/Scripts/TEST SCRIPTS/EVC-Test-System.pdf
new file mode 100644
index 00000000000..0ec3a640069
Binary files /dev/null and b/Scripts/TEST SCRIPTS/EVC-Test-System.pdf differ
diff --git a/Scripts/TEST SCRIPTS/META Test Script.py b/Scripts/TEST SCRIPTS/META Test Script.py
index a0f98923727..36eaec27a93 100644
--- a/Scripts/TEST SCRIPTS/META Test Script.py	
+++ b/Scripts/TEST SCRIPTS/META Test Script.py	
@@ -4,8 +4,6 @@
 import sys
 
 
-
-
 # Insures that runs are deterministic for use in Jenkins testing
 random.seed(0)
 np.random.seed(0)
diff --git a/Scripts/TEST SCRIPTS/Mixed NN & DDM script.py b/Scripts/TEST SCRIPTS/Mixed NN & DDM script.py
index 797afcf7f3e..5c65c5ba3b8 100644
--- a/Scripts/TEST SCRIPTS/Mixed NN & DDM script.py	
+++ b/Scripts/TEST SCRIPTS/Mixed NN & DDM script.py	
@@ -8,6 +8,7 @@
 # random.seed(0)
 # np.random.seed(0)
 
+random_matrix = get_matrix(RANDOM_CONNECTIVITY_MATRIX, 2, 5)
 
 myInputLayer = TransferMechanism(name='Input Layer',
                         function=Linear(),
@@ -25,11 +26,12 @@
 myProcess = process(name='Neural Network DDM Process',
                     default_input_value=[0, 0],
                     pathway=[myInputLayer,
-                                   RANDOM_CONNECTIVITY_MATRIX,
-                                   # FULL_CONNECTIVITY_MATRIX,
-                                   myHiddenLayer,
-                                   FULL_CONNECTIVITY_MATRIX,
-                                   myDDM])
+                             random_matrix,
+                             # RANDOM_CONNECTIVITY_MATRIX,
+                             # FULL_CONNECTIVITY_MATRIX,
+                             myHiddenLayer,
+                             FULL_CONNECTIVITY_MATRIX,
+                             myDDM])
 
 myProcess.reportOutputPref = True
 myInputLayer.reportOutputPref = True
diff --git a/Scripts/TEST SCRIPTS/Multilayer Learning Test Script.py b/Scripts/TEST SCRIPTS/Multilayer Learning Test Script.py
index a61378433e2..ad019f30a4b 100644
--- a/Scripts/TEST SCRIPTS/Multilayer Learning Test Script.py	
+++ b/Scripts/TEST SCRIPTS/Multilayer Learning Test Script.py	
@@ -1,17 +1,19 @@
 from PsyNeuLink.Components.Functions.Function import Logistic
-from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.EVCMechanism import EVCMechanism
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import *
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
 from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection
-from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
 from PsyNeuLink.Components.System import system
+from PsyNeuLink.Globals.TimeScale import TimeScale
+from PsyNeuLink.scheduling.condition import AfterNCalls
+from PsyNeuLink.Components.States.OutputState import *
+from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
 
 # from PsyNeuLink.Globals.Run import run, construct_inputs
 
 Input_Layer = TransferMechanism(name='Input Layer',
-                       function=Logistic,
-                       default_input_value = np.zeros((2,)))
+                                function=Logistic,
+                                default_input_value = np.zeros((2,)))
 
 Hidden_Layer_1 = TransferMechanism(name='Hidden Layer_1',
                           function=Logistic(),
@@ -132,12 +134,15 @@ def show_target():
     print ('- Input Weights: \n', Input_Weights.matrix)
     print ('- Middle Weights: \n', Middle_Weights.matrix)
     print ('- Output Weights: \n', Output_Weights.matrix)
+    # print ('- Input Weights: \n', Input_Weights._parameter_states[MATRIX].function_object.previous_value)
+    # print ('- Middle Weights: \n', Input_Weights._parameter_states[MATRIX].function_object.previous_value)
+    # print ('- Output Weights: \n', Input_Weights._parameter_states[MATRIX].function_object.previous_value)
     print ('\nSTIMULI:\n\n- Input: {}\n- Target: {}\n'.format(i, t))
     print ('ACTIVITY FROM OLD WEIGHTS: \n')
     print ('- Middle 1: \n', Hidden_Layer_1.value)
     print ('- Middle 2: \n', Hidden_Layer_2.value)
     print ('- Output:\n', Output_Layer.value)
-    # print ('MSE: \n', Output_Layer.outputValue[0])
+    # print ('MSE: \n', Output_Layer.output_values[0])
 
 if COMPOSITION is PROCESS:
     # z.execute()
@@ -162,15 +167,19 @@ def show_target():
     x.reportOutputPref = True
     composition = x
 
-    # x.show_graph_with_learning()
-    results = x.run(num_executions=10,
-                    # inputs=stim_list,
-                    # inputs=[[-1, 30],[2, 10]],
-                    # targets=[[0, 0, 1],[0, 0, 1]],
-                    inputs=stim_list,
-                    targets=target_list,
-                    call_before_trial=print_header,
-                    call_after_trial=show_target)
+    # x.show_graph(show_learning=True)
+    # x.show_graph()
+    results = x.run(
+        num_executions=10,
+        # inputs=stim_list,
+        # inputs=[[-1, 30],[2, 10]],
+        # targets=[[0, 0, 1],[0, 0, 1]],
+        inputs=stim_list,
+        targets=target_list,
+        call_before_trial=print_header,
+        call_after_trial=show_target,
+        termination_processing={TimeScale.TRIAL: AfterNCalls(Output_Layer, 1)}
+    )
 
 else:
     print ("Multilayer Learning Network NOT RUN")
diff --git a/Scripts/TEST SCRIPTS/Reinforcement Learning Test Script.py b/Scripts/TEST SCRIPTS/Reinforcement Learning Test Script.py
index 40faa3764f1..914ad2cfd44 100644
--- a/Scripts/TEST SCRIPTS/Reinforcement Learning Test Script.py	
+++ b/Scripts/TEST SCRIPTS/Reinforcement Learning Test Script.py	
@@ -2,7 +2,7 @@
 
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
 from PsyNeuLink.Components.Process import process
-from PsyNeuLink.Components.Projections.LearningProjection import LearningProjection
+from PsyNeuLink.Components.Projections.ModulatoryProjections.LearningProjection import LearningProjection
 from PsyNeuLink.Components.Functions.Function import PROB
 from PsyNeuLink.Components.Functions.Function import SoftMax, Reinforcement
 from PsyNeuLink.Components.System import System_Base, system
@@ -28,8 +28,8 @@
             learning=LearningProjection(learning_function=Reinforcement(learning_rate=0.05)),
             target=0)
 
-print ('reward prediction weights: \n', action_selection.inputState.receivesFromProjections[0].matrix)
-print ('targetMechanism weights: \n', action_selection.outputState.sendsToProjections[0].matrix)
+print ('reward prediction weights: \n', action_selection.input_state.path_afferents[0].matrix)
+print ('targetMechanism weights: \n', action_selection.output_state.efferents[0].matrix)
 
 actions = ['left', 'middle', 'right']
 # reward_values = [15, 7, 13]
@@ -38,20 +38,20 @@
 first_reward = 0
 
 # Must initialize reward (won't be used, but needed for declaration of lambda function)
-action_selection.outputState.value = [0, 0, 1]
+action_selection.output_state.value = [0, 0, 1]
 # Get reward value for selected action)
-reward = lambda : [reward_values[int(np.nonzero(action_selection.outputState.value)[0])]]
+reward = lambda : [reward_values[int(np.nonzero(action_selection.output_state.value)[0])]]
 
 def print_header():
     print("\n\n**** TRIAL: ", CentralClock.trial)
 
 def show_weights():
-    # print ('\nreward prediction weights: \n', action_selection.inputState.receivesFromProjections[0].matrix)
+    # print ('\nreward prediction weights: \n', action_selection.input_state.path_afferents[0].matrix)
     # print ('action selected: ', action_selection.outputState.value)
-    print ('Reward prediction weights: \n', action_selection.inputState.receivesFromProjections[0].matrix)
+    print ('Reward prediction weights: \n', action_selection.input_state.path_afferents[0].matrix)
     print ('\nAction selected:  {}; predicted reward: {}'.
-           format(np.nonzero(action_selection.outputState.value)[0][0],
-           action_selection.outputState.value[np.nonzero(action_selection.outputState.value)][0]))
+           format(np.nonzero(action_selection.output_state.value)[0][0],
+           action_selection.output_state.value[np.nonzero(action_selection.output_state.value)][0]))
 
 p.run(num_executions=10,
       inputs=[[[1, 1, 1]]],
diff --git a/Scripts/TEST SCRIPTS/Stroop Model Learning Test Script.py b/Scripts/TEST SCRIPTS/Stroop Model Learning Test Script.py
index 0b53d69cdca..5225be529a1 100644
--- a/Scripts/TEST SCRIPTS/Stroop Model Learning Test Script.py	
+++ b/Scripts/TEST SCRIPTS/Stroop Model Learning Test Script.py	
@@ -1,9 +1,10 @@
+import numpy as np
+
 from PsyNeuLink.Components.Functions.Function import Linear, Logistic
 from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
 from PsyNeuLink.Components.System import *
 from PsyNeuLink.Globals.Keywords import *
-from PsyNeuLink.Components.Projections.MappingProjection import MappingProjection
-import numpy as np
 
 process_prefs = {REPORT_OUTPUT_PREF: True,
                  VERBOSE_PREF: False}
@@ -76,12 +77,11 @@ def print_header():
 
 def show_target():
     print ('\nColor Naming\n\tInput: {}\n\tTarget: {}'.
-           # format(color_naming_process.input, color_naming_process.target))
-           format(colors.inputValue, mySystem.targets))
+           format(colors.input_states.values_as_lists, mySystem.targets))
     print ('Wording Reading:\n\tInput: {}\n\tTarget: {}\n'.
            # format(word_reading_process.input, word_reading_process.target))
-           format(words.inputValue, mySystem.targets))
-    print ('Response: \n', response.outputValue[0])
+           format(words.input_states.values_as_lists, mySystem.targets))
+    print ('Response: \n', response.output_values[0])
     print ('Hidden-Output:')
     print (HO_Weights.matrix)
     print ('Color-Hidden:')
diff --git a/Scripts/TEST SCRIPTS/Stroop Model Test Script.py b/Scripts/TEST SCRIPTS/Stroop Model Test Script.py
index 284fc84537c..80a6040bb9e 100644
--- a/Scripts/TEST SCRIPTS/Stroop Model Test Script.py	
+++ b/Scripts/TEST SCRIPTS/Stroop Model Test Script.py	
@@ -61,21 +61,21 @@
 # Execute processes:
 for i in range(10):
     color_naming_process.execute(input=[1, 1],target=[0,1])
-    print(response.inputState.receivesFromProjections[0].matrix)
-    print(response.inputState.receivesFromProjections[1].matrix)
+    print(response.input_state.path_afferents[0].matrix)
+    print(response.input_state.path_afferents[1].matrix)
 
     word_reading_process.execute(input=[1, 1], target=[1,0])
-    print(response.inputState.receivesFromProjections[0].matrix)
-    print(response.inputState.receivesFromProjections[1].matrix)
+    print(response.input_state.path_afferents[0].matrix)
+    print(response.input_state.path_afferents[1].matrix)
 
 # # Execute system:
 # for i in range(10):
 #     mySystem.execute(input=[[1,1],[1,1]],
 #                      target=[[0,1],[1,0]])
-#     print(response.inputState.receivesFromProjections[0].matrix)
-#     print(response.inputState.receivesFromProjections[1].matrix)
-#     print(response.inputState.receivesFromProjections[0].matrix)
-#     print(response.inputState.receivesFromProjections[1].matrix)
+#     print(response.input_state.path_afferents[0].matrix)
+#     print(response.input_state.path_afferents[1].matrix)
+#     print(response.input_state.path_afferents[0].matrix)
+#     print(response.input_state.path_afferents[1].matrix)
 
 # mySystem.show_graph()
 #
diff --git a/TODO List.py b/TODO List.py
index 4cfe9b40a98..583df552658 100644
--- a/TODO List.py	
+++ b/TODO List.py	
@@ -2,7 +2,7 @@
 # **************************************************  ToDo *************************************************************
 #region CURRENT: -------------------------------------------------------------------------------------------------------
 
-# Bryn:
+ # Bryn:
 #  1) Programmatic creation of @property
 #  2) Plug & play
 
@@ -30,53 +30,62 @@
 #   8) • DOCUMENTATION: SHOULD WE INCLUDE COMPONENT ATTRIBUTES IN DOCSTRING FOR SUBCLASSES (E.G., params, prefs, etc.)
 #   9) • DOCUMENTATION: SHOULD EXAMPLES BE GENERIC (SEE COMPONENT FUNCTION) OR SPECIFIC (USING ACTUAL PNL OBJECTS)?
 #  10) DOCUMENTATION: INCLUDE EXAMPLES "INLINE" OR IN THEIR OWN SECTION AT THE END?
-#  11) • SHOULD COMPONENT NAMES HAVE SPACES OF UNDERSCORES?
-#  12) • SHOULD learning_rate FOR LearningProjection SET SLOPE OF function OR DIRECTLY MULTIPLY WEIGHTS?
+#  11) DOCUMENTATION: INTERNALLY-MADE ATTRIBUTE ASSIGNEMENTS SHOULD GENERALLY BE TO THE BACKFIELD (_<attrib>)
+#                       TO AVOID INVOCATION OF assign_params BY PROPERTY
+#                       (WHICH IS INTENDED FOR VALIDATION OF EXTERNAL ASSIGNMENTS)
+#  12) • SHOULD COMPONENT NAMES HAVE SPACES OF UNDERSCORES?
+#  13) • SHOULD learning_rate FOR LearningProjection SET SLOPE OF function OR DIRECTLY MULTIPLY WEIGHTS?
+#  14) HOW TO HANDLE RECURRENT PROJECTIONS RE: LEARNING?
+#  15) EVER ALLOW INSTANTIATION OF SENDER BY PROJECTION (OR ALWAYS RELY ON deferred_init())?
+#  16) HOW TO HANDLE GATING:
+#      • SPECIFICATION (WHERE AND HOW)
+#      • MODULATION (WHO'S RESPONSIBLE FOR THE PARAMETER_MODULATION_OPERATION:  THE STATE OR THE PROJECTION?
+#      • EXECUTION:  "INLINE" (WILL INTRODUCE CYCLES IN GRAPH) OR LIKE CONTROL (PRECLUDES W/IN TRIAL GATING);
+#                    EITHER WAY, HOW TO IMPLEMENT W/IN ROUND (I.E., W/IN TRIAL) GATING OF A STATE?
+#                    E.G., CLASSIC CASES IN WHICH EITHER A STIMULUS GATES ITSELF INTO WM (DA)
+#                          OR A DECISION INDUCES LC GATING OF RESPONSE?
+#      PROPOSAL:  ALL ADAPTIVE PROJECTIONS MODULATE A PARAMETER OF THE FUNCTION OF THE STATE TO WHICH THEY PROJECT:
+#                      LEARNING:  MATRIX PARAMETER STATE OF MAPPING PROJECTION (ADD / INTERCEPT)
+#                      GATING:    INPUT AND OUTPUT STATE OF MECHANISM (MULTIPLY / SLOPE/GAIN)
+#                      CONTROL:   PARAMETER STATE STATE OF MECHANISM (MULTIPLY / SLOPE/GAIN)
+#                 NEED SOME WAY TO SPECIFY WHICH PARAMETER OF THE STATE'S FUNCTION THE MODULATORY PROJECTION MODULATES
+#      ADD SUBCLASSING OF PROJECTIONS:
+#                 TRANSMISSIVE (MappingProjection):  FROM PROCESSING MECHANISM
+#                 ADAPTIVE (LearningProjection, GatingProjection, ControlProjection): FROM ADAPTIVE MECHANISM
+#  17) State:  put integration_method as args or in params (i.e., paramClassDefaults, only on State?)
 
 # TASKS:
+#  0) IMPLEMENT: Composition
+#       Assign projections for ObjectiveMechanisms, LearningMechanisms, ControlMechanisms, etc.
+#       Validate that all items are in the same Composition
+#       add_projection_to and add_projection_from methods
+#       methods in LearningAuxilliary
+#       search for "Composition" in LearningProjection, LearningMechanism and LearningAuxlliary
+#       specification of ProcessingMechanism that should be associated with TARGET ObjectiveMechanism:
+#             one that either has either no outgoing projections, or none that receive LearningProjections
+#       got rid of special cases for Objective function altogether
+#              (since comparator is just special case of derivative = 0)
+#       added attribute to Projections:  has_learning_projection
 #  1) BREAK UP FUNCTION INTO SEPARATE MODULES
 #  2) IMPLEMENT CLASSES FOR ALL KEYWORDS (SIMILAR TO MatrixKeywords)
-#  3) IMPLEMENT gating projections
 #  4) IMPLEMENT ABC
 # 4a) IMPLEMENT Swap execute (on Mechanism) and _execute (on its subclasses)
 #  5) IMPLEMENT Compilation / paralleliztion of execution
 #  6) IMPLEMENT Recurrent layer / KWTA??
+# 13) IMPLEMENT MSPRT / KWTA
 #  7) IMPLEMENT TD learning
 #  8) IMPLEMENT CLS / NMPH
-#  9) IMPLEMENT Conflict (energy / entropy)
+#  9) IMPLEMENT Conflict (stability / distance)
 # 10) IMPLEMENT TensorFLow wrapper
 # 11) IMPLEMENT Production System model (using scheduler??)
 # 12) IMPLEMENT LEABRA
-# 13) IMPLEMENT Model fitting
+# 14) IMPLEMENT Model fitting
 
+# VALIDATE: (then add to META TEST)
+#  RecurrentTransferMechanism
+#  LCA (Test from ScratchPad (search for: my_auto)
+#  XOR 2 PROCESS
 
-# COMPOSITION IMPLEMENTATION NOTE:
-#   add_projection_to and add_projection_from methods
-#   methods in LearningAuxilliary
-#   search for "Composition" in LearningProjection, LearningMechanism and LearningAuxlliary
-#   specification of ProcessingMechanism that should be associated with TARGET ObjectiveMechanism:
-#         one that either has either no outgoing projections, or none that receive LearningProjections
-#   got rid of special cases for Objective function altogether (since comparator is just special case of derivative = 0)
-#   added attribute to Projections:  has_learning_projection
-
-# IMPLEMENT:  BogcazEtAl:
-#                 add Diti, Dpenalty, RR calculation, and add RR to return value
-#                 modify variable to accept drift_rate??
-#
-# IMPLEMENT:  Deferred init for control.
-
-# IMPLEMENT General creation of INPUT_STATES for all mechanisms as ObjectiveMechanism does it
-#           Use that to generalize creation of inputStates for PredictionMechanism by EVCMechanism
-
-# FIX:  Stroop Model Learning Test Script:
-#            Why is error_matrix being assigned as parameterState on TERMINAL mechanism?
-#                   getting done in LearningAux LC?
-# FIX: WHY IS EVCMechanism GETTING NAMED "EVCMechanism-1"?  IS IT GETTING INSTANTIATED TWICE?
-# FIX: ADD TO Run THE ABILITY TO CONVERT CHARACTERS OR HASHES OF WORDS TO NUMERIC VALUES
-# FIX: printout of intial_value (see devel_upstream on Quill)
-# FIX fix _update_parameter_state in Projection as in Mechanism
-
-# FIX: Flip names of Xxxx() and Xxxx_Base()
 
 # FIX / TEST: runtime_params:
 # COMMENT:
@@ -103,18 +112,21 @@
 #             + MAPPING_PROJECTION_PARAMS:<dict>:
 #                  entry will be passed to all of the State's MappingProjections,
 #                  along with any in a PROJECTION_PARAMS dict, and override paramInstanceDefaults
+#             + LEARNING_PROJECTION_PARAMS:<dict>:
+#                  entry will be passed to all of the State's LearningProjections,
+#                  along with any in a PROJECTION_PARAMS dict, and override paramInstanceDefaults
 #             + CONTROL_PROJECTION_PARAMS:<dict>:
 #                  entry will be passed to all of the State's ControlProjections,
 #                  along with any in a PROJECTION_PARAMS dict, and override paramInstanceDefaults
+#             + GATING_PROJECTION_PARAMS:<dict>:
+#                  entry will be passed to all of the State's GatingProjections,
+#                  along with any in a PROJECTION_PARAMS dict, and override paramInstanceDefaults
 #             + <projectionName>:<dict>:
 #                  entry will be passed to the State's projection with the key's name,
 #                  along with any in the PROJECTION_PARAMS and MappingProjection or ControlProjection dicts
 # COMMENT
 
 
-# IMPLEMENT: NAME FOR FUNCTIONS (INCLUDING REGISTRY?)
-# IMPLEMENT: function{} and owner_name IN exception messages (as for SoftMax derivative exception)
-
 # DOCUMENTATION: items in paramClassDefaults are created as attributes (not properties), and are thus not validated
 #                    when assignments are made to them;  items in user_params are created as properties, and are
 #                    validated (via assign_params) when assignments are made to them.
@@ -137,6 +149,422 @@
 
 # DOCUMENTATION: ?? MOVE `parameter specification dictionary <Mechanism_Creation>` TO Component??
 
+# DOCUMENTATION: FOR TransferMechanism AND ALL OF ITS SUBCLASSES:
+#                       document that it uses only the first item of its variable arg/attrib (variable[0])
+
+# DOCUMENTATION: size AND shape ATTRIBUTES in: IntegratorMechanism,
+#                                              TransferMechanism,
+#                                              RecurrentTransferMechansm
+#                                              DDM
+#                                              "developer's notes" in Components
+
+# DOCUMENTATION:  doscstring for EVCMechainms._instantiate_prediction_mechanism
+
+# DOCUMENTATION: add heading for Primary OutputState in States
+
+# DOCUMENTATION: input_states, parameter_states and output_states as ContentAddressableList
+
+# DOCUMENTATION:rst pages for Gating components
+
+# DOCUMENTATION: can suppress variable that is an arg for a construcutor in a parent class from
+#                   having to be an arg in the constructor of a subclass by putting it in paramClassDefaults
+#                   for the subclass (e.g.:  MONITORED_VALUES for ComparatorMechanism)
+
+# DOCUMENTATION: TABLE SOMEWHERE OF ALL SPECIFICATION DICIONARIES AND THEIR ENTRIES
+
+# DOCUMENTATION: constructor args are all stored in user_params:
+#                       properties are created for them on their classes
+#                       they are validated when assigned from the command line
+#                       validation can be avoided (caveat emptor) by assigning to the backing_field (_param)
+#                paramClassDefaults are NOT in user_params:
+#                       no properties are created for them
+#                       they are not validated when assigned
+
+# IMPLEMENT:  BogcazEtAl:
+#                 add D_iti, D_penalty, RR calculation, and add RR to return value
+#                 modify variable to accept drift_rate??
+#
+# IMPLEMENT:  Deferred init for control.
+
+# IMPLEMENT General creation of INPUT_STATES for all mechanisms as ObjectiveMechanism does it
+#           Use that to generalize creation of input_states for PredictionMechanism by EVCMechanism
+#
+# IMPLEMENT: ADD TO Run THE ABILITY TO CONVERT CHARACTERS OR HASHES OF WORDS TO NUMERIC VALUES
+
+# IMPLEMENT: LCA (Leaky Competitive Accumulator):  SET DEFAULT PARAMS THAT ~ DDM
+
+
+# SEARCH & REPLACE: allocation_policy -> control_policy (AND ALL VARIANTS THEREOF)
+# SEARCH & REPLACE: base_value -> base_value
+# SEARCH & REPLACE: Modulation.ADD -> ADDITIVE, and MULTIPLY -> MULTIPLICATIVE
+# SEARCH & REPLACE: monitored_values -> monitor_values
+
+# FIX State:
+#         Consolidate _instantiate_state_lists into _instantiate_state,
+#             and have other methods loop on their own through calls to _instantiate_state
+#
+#         LINE 1543:
+#                     # FIX 5/21/17: CHECK FOR DICT HERE AND USE NAME PARAM AS NAME IF IT IS IN THE DICT
+#           ALTERNATIVE: DON'T BOTHER WITH NAMES IN _instantiate_state_list(), AND REMAND TO _instantiate_state()
+#
+#         State._parse_state_spec:  if state_spec is a value, set both [VARIABLE] and [VALUE] to it??
+#         State._instantiate_state:  set constraint to constraint_dict[VALUE] rather than VARIABLE
+
+
+# FIX: FINISH input/output refactoring: ----------------------------------------------------------------------------
+#
+# IMPLEMENT: Override input_states and output_states properties with assignments to:
+#                monitored_values (for input_states of ObjectiveMechanism)
+#                control_signals (for output_states of ControlMechanism)
+#                gating_signals (for output_states of GatingMechanism)
+# IMPLEMENT:  **gating** arg for GatingMechanism (comparable to **control** arg for ControlMechanism)
+# IMPLEMENT:  **train** arg for LearningMechanism (comparable to **control** arg for ControlMechanism)
+# FIX: check for duplicate names (using state Registry) and/or states (as at end of _parse_gating_signal_spec)
+# FIX: base_value assignment problem: ??WHY IS ASSIGNMENT TO <component>.function_object.<param>
+# FIX:      NOT UPDATING <component>._parameter_states[PARAM].base_value?
+#             Mechanism._instantiate_function (~LINE 1355):
+#             MODIFIED 5/22/17 NEW:
+#             FIX: THIS SHOULDN'T BE NECESSARY ??WHY ISN'T ParameterState base_value GETTING UPDATED WITH ASSIGNMENT:
+#             ParameterState:
+#                 # @property
+#                 # def base_value(self):
+
+# IMPLEMENT:  ASSIGN ParameterState.variable TO BACKING FIELD OF PARAMETER (ParameterState.owner._<param_name>)
+# IMPLEMENT:  FUNCTION ENTRIES OF STATE SPECIFICATION DICTIONARY - SHOULD NOW BE FUNCTIONAL (6/13/17)
+#             TEST BY ASSIGNING INTEGRATION FUNCTION TO InputState and/or OutputState
+# IMPLEMENT:  USE STATE_SPECIFICATION_DICT TO OVERRIDE MODULATION META PARAMS (BY REASSIGNING THEM TO OTHER FCT PARAMS)
+# IMPLEMENT:  MODULATION_PROJECTION ENTRIES OF STATE SPECIFICATION DICTIONARY -
+#                TO ASSIGN MULTIPLE PRJOJECTIONS TO A SINGLE OutputState (e.g, MULTIPLE PROJECTIONS FOR A GatingSignal)
+# IMPLEMENT: standard_output_states for ObjectiveMechanism, LCA, RecurrentTransfer, and Intregrator
+#              (use ones at top of OutputState)
+# IMPLEMENT: Move RecurrentTransfer and LCA calculate functions to standard_output_states
+# IMPLEMENT: Subclasses of ObjectiveMechanism (Comparator and Monitor)
+# IMPLEMENT: standard_input_states (e.g., SAMPLE & TARGET as defaults for the Comparator subclass)
+# IMPLEMENT: Add WEIGHTS and EXPONENTS entry (or single one with tuple value) to inputState specification dicts
+#              and use that in ObjectiveMechanism instead of args or tuple specs
+# IMPLEMENT: Add GATING entry to both input and otuput state dicts
+# IMPLEMENT: modulation_function to specification dict
+
+#  DOCUMENTATION: State Specification Dictionaries:
+#                            - general notion, schema and keys common to all states (in State)
+#                                  STATE_PROJECTIONS
+#                                  MODULATORY_PROJECTIONS
+#                                  MODULATION_FUNCTION
+#                            - specific ones for each subclass (in subclass pages)
+#                                  InputState:
+#                                      ??SOURCES [=STATE PROJECTIONS] can be string, mech, or outputState
+#                                      GATING [=MODULATORY PROJECTIONS]
+#                                  ParameterState:
+#                                      CONTROL [=MODULATORY_PROJECTIONS] can be(string, mech) or ParameterState
+#                                      LEARNING [=MODULATORY PROJECTIONS]
+#                                  OutputState:
+#                                      DESTINATIONS [=STATE_PROJECTIONS] can be string, mech, or inputStae
+#                                      GATING [=MODULATORY PROJECTIONS]
+#  DOCUMENTATION: weights and exponents args/attributes
+#  DOCUMENTATION: Add mention of `standard_input_states` and `standard_output_states` to Mechanism
+#  DOCUMENTATION: Add mention of specification dictionary format for **control_signals** arg for ControlMechanism
+#  DOCUMENTATION: Matrix specification as 2nd item of in 2-item tuple in list for input_states arg of Mechanism
+#  FIX: `error_signal` as default primary outputState
+#  FIX: MAKE SURE THAT System AND/OR EVCMechanism ASSIGN OutputStates TO MONITOR ONLY
+#  FIX:      THOSE MECHANISMS FOR WHICH THE OUTPUTSTATES WERE SPECIFIED (UNLESS GIVEN A GENERIC NAME)
+#      PROTOCOL:
+#          a) Mechanism implements empty input_state and output_state in paramClassDefaults,
+#                to insure that they are included in user_params - STILL TRUE, OR SET TO None??
+#          b) If any are specified in constructor args (input_states or output_states), they totally override
+#                paramClassDefaults [??CURRENTLY DEALT WITH IN Mechanism._filter_params??]
+#          c) Subclass implements default set in module, and uses those as defaults for constructor args
+#                (??standard way of overriding paramClassDefault or need to implement in assign_args_to_dicts)?
+
+#          See ObjectiveMechanism._instantiate_input_state_for_monitored_value for parse of input_state specification
+
+# DECIDE WHETHER OR NOT input_states AND output_states SHOULD BE properties (at bottom of assign_args_to_params):
+#          IF SO, make sure paramClassDefaults ones get done if input_states/output_states aren't args or are None
+#                   and/or enforce as args in inits of all mechanisms?
+#          IF NOT, make sure they are removed from user_params
+
+#     input_states and output_states can be specificed by a dict, in which the key is the name and value is the
+#           value to use as its variable, or a dict of params
+#     Mechanism implements PRIMARY input_state and output_state in paramClassDefaults for base_class;
+#        it can then be relabeled by subclasses if desired.
+#      Replace output_states construtor arg with control_signals, gating_signals, learning_signals
+#               subclasss __init__ should intercept and rename as output_states,
+#                  and a property that accesses output_states
+#      Implement list of possible states, named, and usable in the arg
+#      Do same for input_states ObjectiveMechanism that gets subclassed (Comparator for learning, Monitor for control),
+#               that replaces input_states with SAMPLE and TARGET (Comparator), or monitored_states (Monitor)
+#     TEST USING assign_params ON COMMAND LINE TO ADD AN inputState or outputState
+#     DOCUMENTATION for input_state(s) and output_state(s)
+#                   finish "NEW DOCUMENTATION" in ObjectiveMechanism
+#                   variable and value: 2d np.arrays
+#                   input_values and output_values:  lists of values, but each value may be an np.ndarray
+#                   [in/out]put_states.values_as_lists: lists of values, each of which has been converted to a list
+#                   NO LONGER TRUE (since they are in paramClassDefaults??):
+#                                 Only appear in user_params if specified in arg of constructor
+#
+# FIX: Duplicated MappingProjections:  State, Line 423 vs. ObjectiveMechanism Line 617
+# FIX:   In LearningAuxiliary:
+#                  # input_states=[{NAME:SAMPLE,
+#                  #                VARIABLE:sample_input},
+#                  #               {NAME:TARGET,
+#                  #                VARIABLE:target_input}],
+#                  input_states=[{SAMPLE:sample_input},
+#                                {TARGET:target_input}],
+
+# ------------------------------------------------------------------------------------------------------------------
+
+# MODULATORY COMPONENTS ----------------------------------------------------------
+
+# DOCUMENTATION: GENERAL PRINCIPLES:
+#                     InputState:
+#                           TransmissiveProjections -> variable
+#                           ModulatoryProjections -> state.function_param
+#                     ParameterStates:
+#                           mech.param -> variable
+#                           ModulatoryProjections -> state.function_param
+#                     OutputState:
+#                           owner's value -> variable
+#                           ModulatoryProjections -> state.function_param
+#                     Modulatory OutputStates:
+#                           they have modulation as an additional attribute,
+#                               that specifies how they influence receiver parameterState
+#                           LearningSignal:  modulation is additive
+#                           ControlSignal, GatingSignal:  modulation is
+#                     Modulatory Projections:
+#                           LearningProjections:  ParameterStates of Transmissive Projections
+#                               (usually MATRIX of MappingProjection)
+#                           ControlProjections:  ParameterStates of Mechanisms
+#                               (usually corresponding to its function)
+#                           GatingProjections:  InputStates and OutputStates of Mechanisms
+#                               (usually praimry inputState)
+#                     Types of modulation:
+#                           "state-based" (usually Control and Gating):
+#                               receiver ParameterStates use TransferFunction;
+#                               changes stored in OutputState of signal
+#                           "weight-based" (usually Learning):
+#                               receiver ParameterStates use IntegratorFunction;
+#                               changes stored in receiver ParameterState (i.e., MappingProjection)
+#
+# DOCUMENTATION:
+#   *** PUT IN InputState AND OutputState DOCUMENTATION
+#
+#   Gating can be also be specified for an `InputState` or `OutputState` when it is created
+#     in any of the following ways:
+#
+#     * in a 2-item tuple, in which the first item is a `state specification <LINK>`,
+#       and the second item is a `gating specification <>`
+#
+#     * keywords GATE (==GATE_PRIMARY) GATE_ALL, GATE_PRIMARY
+#         or an entry in the state specification dictionary with the key "GATING", and a value that is the
+#         keyword TRUE/FALSE, ON/OFF, GATE, a ModulationOperation value, GatingProjection, or its constructor
+#
+# DOCUMENTATION: parameters always return the value of their parameterState if they have one
+#                (tested in getter for property)
+#                   in order to insure that any modulation (control/learning) has beeen applied
+#                Do this in definition of property (using backfield as "base_vale")
+# DOCUMENTATION: modulation argument/attribute in ControlSignal and GatingSignal
+# DOCUMENTATION: ModulatoryMechanism: describe how they work, i.e., that they assign the value of their
+#                  outputState to the paraemter of the state's function specified in their modulation param
+# DOCUMENTATION: State functions: must be TransferFunction or CombinationFunction, and implement meta mod params
+# DOCUMENTATION: State: use of integration function to implement persistence
+# DOCUMENTATION: GatingSignal (per ControlSignal) -- describe modulation in both
+# DOCUMENTATION: Various forms of specification;  if Mechainsm: assume primary InputState
+# DOCUMENTATION: STATE FUNCTIONS MUST ALWAYS BE A TransferFunction
+# DOCUMENTATION: UPDATE ParameterState_Parameter_Modulation_Operation WITH REFACTORING OF modulation arg/param
+# DOCUMENTATION: UPDATE ControlSignal to describe modulation attribute (as in GatingSignal)
+# DOCUMENTATION: ADD ENTRIES FOR variable, function, vaue, params, name and prefs to GatingMechanism and ControlMechaism
+# DOCUMENTATION: GatingMechanism:  ADD TO docstring [AND DO SAME IN ControlMechanism and LearningMechanism]
+#             this means that the GatingMechanism's function must return as many items as it has GatingSignals,
+#             with each item of the function's value used by a corresponding GatingSignal.
+#             Note: multiple GatingProjections can be assigned to the same GatingSignal to achieve "divergent gating"
+#                   (that is, gating of many states with a single value -- e.g., LC)
+# DOCUMENTATION: revise LearningMechanism docstring to include output_state attribute, and describe situation with
+#                    (multiple possible) LearningSignal entries, their relatioship to learing_signal attribute, and
+#                    the ERROR_SIGNAL OutputState.
+# DOCUMENTATION: add output_states to attribute in docstring for ControlMechanism and GatingMechanism
+# DOCUMENTATION: add section on LearningSignals to LearningMechanism docstring:
+#                   note that default (and most common case) is for a single LearningSignal
+#                   that projections to the parmaeterState for the MATRIX parameter of a MappingProjection,
+# DOCUMENTATION: upgrade LearningSignal's learning_rate description (mention how it affect ParameterState's function)
+# DOCUMENTATION: can instantiate LearningSignal w/o LearningProjections:
+#                    creates a default LearningSignal, along with ERROR output_state
+
+# IMPLEMENT: control_signal property for ControlProjection (assign to self.variable, as for LearningProjection)
+#            gating_signal property for GatingProjection (assign to self.variable, as for LearningProjection)
+# IMPLEMENT / FIX:  CHANGE LearningProjection BACK TO TRANSMISSIVE PROJECTION (OR A NEW CATEGORY?)
+#                   - Reassign class specification
+#                   - Check for anyplace it is assigned to mod_afferents and move to trans_afferents 
+# IMPLEMENT: OVERRIDE FUNCTIONALITY FOR ModulationParam
+# IMPLEMENT / TEST: FUNCTION to specification dict (state's function)
+# IMPLEMENT: Hadamard modulation for LinearCombination:
+#                hadamard_offset and hadamard_scale params
+#                applied to result of standard LinearCombination function
+#                (use function recursively to perform the hadamard modulation)
+#                if hadamard params are specified, use them as template to determine shape of items in variable
+#                   (so that a single item can be passed to be modulate, as per LearningProjection)
+#                alternative: use variable arg in constructor as template to determine shape/size of each item
+
+# IMPLEMENT: Some way to specify function for parameterState for a param (e.g., MATRIX for MappingProjection)
+#                PARAMETER_STATE_FUNCTION entry in parameter specification dict?
+#                If implemented, remove _instantiate_parameter_state override in MappingProjection that assigns function
+#                      and assign function in paramClassDefaults
+# IMPLEMENT: learning_rate param for LearningSignal
+# IMPLEMENT: LearningProjection as full Modulatory projection:
+#               add learning_signal_params attribute to LearningProjection that conveys them to learningSignal
+# IMPLEMENT: _instantiate_modulated_param that assigns actual function_param to modulated_param property
+#            State.update should use modulated_param rather than modulation
+#            modulation as @property that validates value and then calls _instantiate_modulated_param
+
+# IMPLEMENT: all_efferents as @property = efferents + mod_efferents
+
+# IMPLEMENT: abstract _parse_state_specs:
+#                 integrate _parse_gating_signal_spec (in GatingSignal) into it
+#                 use in _validate_params for ControlMechanism and LearningMechanism
+
+# FIX: WHEN ControlMechanism IS ASSIGNED TO SYSTEM,
+#      VALIDATE THAT ALL ITEMS LISTED IN monitor_for_control ATTTRIB ARE IN THE SAME SYSTEM AS THE ControlMechanism.
+#               (SEE ControlMechanism._validate_params LINE 316)
+#               AND THAT ITEMS LISTED IN control_signals ATTRIB ARE THE SAME SYSTEM AS THE ControlMechanism
+#               (SEE ControlMechanism._validate_params LINE 419)
+#      (SINCE COULDN'T DO IT IN _validate_params AS EVC WAS NOT YET IN SYSTEM)
+
+# FIX: UPDATE WITH MODULATION_MODS
+           # ParameterState LINE 708:
+           #  WHAT ABOUT GatingProjection??
+# FIX: Line 2018 of Component:
+#     why does this function_param_specs = self.paramsCurrent[FUNCTION_PARAMS].copy() have rate and noise?
+# FIX: NEED SOME WAY OF INCLUDING GATING MECHANISM IN SYSTEM AND ASSIGNING IT execution_id
+# FIX: ALLOW ModulationParam TO BE NAME OF AN ACTUAL PARAMETER OF A FUNCTION
+#        MODIFY _get_modulated_param TO LOOK FOR REAL NAME FIRST
+#       THEN ADD HADAMARD OFFSET AND HADAMARD SCALE PARAMS TO LinearCombination
+# FIX: PROBLEM: LearningMechanism.output_value MAY NEED TO BE A 3d array
+# FIX:                      (TO ACCOMDOATE 2d array (MATRICES) AS ENTRIES)
+
+# FIX: LearningMechanism: learned_projection attribute -> learned_projections list
+# FIX: Make modulation an attribute of AdaptiveMechanism that enforces presence of attribute in sublasses
+# FIX: Integrate learning_rate into rate param of Integrator function of ParameterState
+# FIX: Implement same for LearningSignal and ControlSignal:
+#           GatingMechanism, LINE 290:
+#           # Create registry for GatingSignals (to manage names)
+#             from PsyNeuLink.Globals.Registry import register_category
+#             register_category(entry=GatingSignal,
+#                               base_class=State_Base,
+#                               registry=self._stateRegistry,
+#                               context=context)
+# SEARCH & REPLACE: '_'+PARAM_NAME -> get_backingfield_name
+# FIX: ParameterState function specification (LINE 558): Why can't Linear class (vs. Linear() constructor) be used?
+# FIX: Recheck that weights are gettin properly set in Mechanism_instantiate_function
+# FIX: NAMES OF ControlProjections AND GatingProjections
+# FIX: GET RID OF ControlSignal LINE 434: self.reference_value = reference_value
+# FIX: UPDATE WITH MODULATION_MODS
+# FIX: MODIFY ControlProjections and LearningProjections TO FUNCTION LIKE GatingProjections:
+# FIX:        - CHANGE control_signal -> modulatory_signal_params (AS for GatingProjection)
+# FIX:                    (AND WHERE REFERENCED (??AS control_signal_specs??) IN ControlSignal AND ControlMechanism)
+# FIX:                    DO THE SAME FOR LearningProjection
+# FIX:        - ControlMechanism AND LearningMechanism SHOULD IMPLEMENT modulation PARAM,
+# FIX:                    THAT TAKES VALUE OF ModulationParam AS VALUE
+# FIX:        - State._instantiate_projections_to_state:
+# FIX:                    Assign ControlProjections and LearningProjections to mod_afferents (like GatingProjections)
+# FIX:        - State.update:
+# FIX:                    ControlProjection LearningProjection values SHOULD MODIFY PARAMETER OF STATE'S FUNCTION
+# FIX:                          BASED ON THEIR modulation PARAMETER
+# FIX: CHANGE modulatory_signal_params FOR GatingSignal back to gating_signal_params
+#                  (in GatingProjection and GatingMechanism)
+# FIX: Specification of modulation for GatingSignal:
+#             - modualtion_operation -> modulation
+#             - add attribute to GatingMechanism, used as default for all GatingSignals unless individually specified
+#             - DOCUMENT that to specify for individual GatingSignals, need to use specification dictionary format,
+#                  and specify it in a MODULATION entry of the PARAMS dict
+# FIX: ADD "GATING" KEYWORD and GatingSignal CLASS TO INPUT_STATE TUPLE SPECIFICATION,
+#     WHICH SHOULD INSTANTIATE A DEFERRED_INIT GatingProjection (JUST LIKE A ControlProjection)
+# FIX: MAKE ControlSignal stateful (since its last_allocation attribute pertains to a prior state)
+# FIX: DEAL WITH DEPENDENCY OF costFunctionNames: referenced in ControlSignal but defined in EVCMechanism
+#
+# IMPLEMENT: Support for multiple GatingProjections from a single GatingSignal
+# IMPLEMENT: Abstract modulatory projection in AdaptiveMechanism
+#                - using _instantiate_output_states and _instantiate_projections
+#                - should parallel implementation of input_states and monitored_values in ObjectiveMechanism
+# SEARCH & REPLACE: monitored_values -> monitor_values
+# IMPLEMENT: modulation FOR ModulatoryProjections
+#            function_type or method_type SPECIFICATION IN ADDITION TO Modulation FOR modulation
+#                 parameter of ModulatoryFunctions
+# IMPLEMENT: modualtion_operation For ControlSignal, that assigns its value to its ControlProjection
+# IMPLEMENT: CONSTRAINT ON STATE FUNCTIONS TO BE A TransferFunction
+# FIX: State.update(): THERE *MUST* BE A MORE EFFICIENT WAY OF DOING ALL OF THIS (INCLUDING DEALING WITH stateParams)
+# FIX:           MODIFY ParameterState and LearningProjection so that latter projects to mod_afferents (vs. afferents)
+# FIX: is_param_spec to allow tuple with projection specification in it TO PASS TYPECHECK
+# FIX: PARSING OF input_state AND output_state ARGS IN Mechanism CONSTRUCTORS:
+# FIX:    * input_states arg crashes
+# FIX:    * doesn't recognize reference to existing specification dict for outputStates in constructor arg
+#            getting packaged by Python interpreter as a tuple in the arg: either look for tuples or use *ref notation?
+#               (see my_mean example in TransferMechanism)
+# IMPLEMENT: Move `calculate` attribute of outputState to function, while preserving index attribute
+# IMPLEMENT: MEANS TO SPECIFY GATING FOR A InputState AND OutputState OF A MECHANISM:
+#         * implement default (RESULTS) for primary outputState
+#               (??and change reference to it in DOCS from "primary" to "results" outputState
+#         * create sets of input_states and outputStates either at top of module or in class (below paramClassDefaults)
+#         * parse input_state and output_state specs for duplicate names or entries, and update dicts accordingly
+#              (so that default versions can be modified, not just added to -- see Mechanism._filter_params
+#         * implement GatingProjection spec:  can be only value for input_state or output_state arg,
+#              with keywords GATE (==GATE_PRIMARY) GATE_ALL, GATE_PRIMARY
+#              or an entry in the state specification dictionary with the key "GATING", and a value that is the
+#              keyword TRUE/FALSE, ON/OFF, GATE, a ModulationOpearation value, GatingProjection, or its constructor
+# -----------------------------------------------------------------------------------
+
+# FIX: Recursion loop problem for setters / assign_params
+#         init():  instantiate_output_states (context = INITIALIZING):
+#             setter (context => SET_ATTRIBUTE)
+#             assign_params (context = SET_ATTRIBUTE)
+#             instantiate_output_state (context = None or SET_ATTRIBUTE)
+#             setter (context => SET_ATTRIBUTE)…
+#             assign_params (context = SET_ATTRIBUTE)
+#
+#         COMMAND LINE:
+#             init():  instantiate_output_states (context = COMMAND_LINE):
+#             setter (context => SET_ATTRIBUTE)
+#             assign_params (context = SET_ATTRIBUTE)
+#             instantiate_output_state (context = None or SET_ATTRIBUTE)
+#             setter (context => SET_ATTRIBUTE)…
+#             assign_params (context = SET_ATTRIBUTE)
+
+# IMPLEMENT: Consolidate State._parse_state_spec and GatingSignal._parse_gating_signal_spec;
+#              include arg that specifies keywords to look for in dicts
+
+# FIX: REPLACE: kwConstants must == arg names
+# IMPLEMENT: NAME FOR FUNCTIONS (INCLUDING REGISTRY?)
+# IMPLEMENT: function{} and owner_name IN exception messages (as for SoftMax derivative exception)
+# FIX: Stability and Distance:  name is getting set to TYPE rather than to SUBTYPE name
+#                               DOCUMENT args and attribs
+
+# FIX: matrix problem for LCA in assign_args_to_dict (MODIFIED 5/2/17)
+
+# FIX: Refactor to use self.size rather than len(variable[0]) ??or len(variable)??
+#        (see Components._instantiate_defaults, ~Line 1202)
+
+# FIX/IMPLEMENT: TransferMechanism:
+#                      - rename outputStates (get rid of "TRANSFER_";  do same for RecurrentTransferMechanism
+#                      - allow OutputStates to be specifiable using a list in an **outputs** arg
+
+# FIX: Backpropagation AND Reinforcement:
+#        ADD TYPECHECKING FOR error_matrix AND THEN CLEAN UP _validate_params
+#        ADD _instantiate_attributes_before_function AND IF MappingProjection IS SPECIFIED FOR error_matrix,
+#                  convert to ParameterState (SEE Stability FOR EXAMPLE)
+
+# FIX: WHY IS EVCMechanism GETTING NAMED "EVCMechanism-1"?  IS IT GETTING INSTANTIATED TWICE?
+# FIX: printout of intitial_value with brackets (see devel_upstream on Quill)
+# FIX fix _update_parameter_state in Projection as in Mechanism
+
+# FIX: Flip names of Xxxx() and Xxxx_Base()
+
+# FIX: ALLOW Projection PARAM SPECIFICATION TO MAKE IT THROUGH Component._validate_params
+#         (e.g., FOR Stability AND Backpropagation FUNCTIONS)
+
+# FIX: SEARCH FOR "` argument" and replace with "**<arg>** argument"
+# FIX: SEARCH FOR ALL MATRIX KEYWORDS AND REPLACE WITH `MATRIX KEYWORD`
+# FIX: DOCUMENTATION OF RESULTS OF TransferMechanism EXECUTION (AND OTHERS), REGARDING WHAT GOES IN THE value ATTRIBUTE
+
+# FIX: Component._validate_params: if param is a string, check for keywords before raising excpetion
+#                                  (will require consoliation of all keywords?)
+
 # TEST: Autoassociative SOFT_CLAMP
 # TEST: Autoassociative learning:  fix [auto_mech] version
 # FIX: ADD PredictionMechanisms TO system.graph AND ASK NATE TO RENDER THEM IN BLUE
@@ -145,7 +573,6 @@
 # IMPLEMENT: Simple Hebbian learning
 # TEST: learning_rate is assignable and "sticks" at function, mech, process and system levels
 
-# FIX: ADD XOR 2 PROCESS TO META TEST SCRIPT (ONCE VALIDATED)
 # FIX: FUNCTION DOCUMENTATION: variable VS. variable_default
 # FIX: OUTPUT TEMPLATE SPECIFICATION FOR LinearMatrix FUNCTION
 
@@ -213,7 +640,7 @@
 #                                f) parse MonitoredOUtputStates specification for monitored_values arg
 #                                g) Fix EVC use of ObjectiveMechanism (needs to now call for Mapping Projection
 #     4.5): LearningMechanism:
-#              Name inputStates using input_state_names (or create them explicitly using the values of variable?)
+#              Name input_states using input_state_names (or create them explicitly using the values of variable?)
 #              Instantiate the MappingProjection from its ObjectiveMechanism
 #                            (as ObjectiveMechanism does, by calling module function if not None)
 #              Need to change how learning function is specified, since no longer belongs in LearningProjection
@@ -343,12 +770,12 @@
 #             controller.predicted_inputs: ndarray of current value of outputState
 #                                          for each predictionMechanism in self.system.prediction_mechanisms
 #             controller.monitored_states: list of the mechanism outputStates being monitored for outcomes
-#             controller.inputValue: list of current outcome values for monitored_states
-#             controller.controlSignals: list of controlSignal objects
-#             controlSignal.allocation_samples: set of samples specified for that controlSignal
-#             [TBI:] controlSignal.allocation_range: range that the controlSignal value can take
+#             controller.input_value: list of current outcome values for monitored_states
+#             controller.control_signals: list of control_signal objects
+#             control_signal.allocation_samples: set of samples specified for that control_signal
+#             [TBI:] control_signal.allocation_range: range that the control_signal value can take
 #             controller.allocation_policy: current allocation_policy
-#             controller.outputValue: list of current controlSignal values
+#             controller.output_values: list of current control_signal values
 #             controller.value_function: calls the three following functions (done explicitly, so each can be specified)
 #             controller.outcome_aggregation function: aggregates outcomes (using specified weights and exponentiation)
 #             controller.cost_function:  aggregate costs of control signals
@@ -385,24 +812,24 @@
 #                which are simply convenience string constants that are the same as the name of the argument
 #                for the parameter in the component's constructor. (see :ref:`EVCMechanism_Creation` for text)
 
-# DOCUMENT: inputValue and outputValue are lists for convenience of user access, whereas
+# DOCUMENT: input_value and output_values are lists for convenience of user access, whereas
 #           variable and value are 2d np.arrays that are used as internal datastructures
 #
 
 
-# DOCUMENT:  Explain better the relationship of an inputStates variable to its value, and of thes to the
+# DOCUMENT:  Explain better the relationship of an input_states variable to its value, and of thes to the
 #            to the potential for multiple items of a mechanism's variable (with an example:  ComparatorMechanism)
 
 
 # DOCUMENT:  FIGURES FOR:
 #                   inputState: inputValue vs. variable
-#                   OutputState: function vs. calculate, value vs. outputState.value vs. outputValue
+#                   OutputState: function vs. calculate, value vs. outputState.value vs. output_values
 
 # DOCUMENT:  IN MECHANISM, CLARIFY:
 #                DISTINCTION BETWEEN THE RESULT OF ITS FUNCTION,
 #                                     AND ITS ``value`` ATTRIBUTE, WHICH IS WHAT IS RETURNED BY ITS EXECUTE METHOD
 #                DISTINCTION BETWEEN ``value``, WHICH IS THE RESULT OF THE MECHANISM'S EXECUTION,
-#                                AND ``outputValue``, WHICH IS A SUMMARY OF THE ``value`` OF EACH OF ITS outputStates
+#                                AND ``output_values``, WHICH IS A SUMMARY OF THE ``value`` OF EACH OF ITS outputStates
 #
 # DOCUMENTATION: add show to Systsem and Process
 #
@@ -423,7 +850,7 @@
 #                 + State object: it will be validated
 #                 + State subclass: a default state will be implemented using a default value
 #                 + dict with specifications for a State object to create:
-#                 + ParamValueProjection tuple: a state will be implemented using the value and assigned the projection
+#                 + 2-item tuple: a state will be implemented using the first (value) item and assigned the projection
 #                 + projection object or class: a default state will be implemented and assigned the projection
 #                 + value: a default state will be implemented using the value
 
@@ -448,7 +875,7 @@
 #     - exclude if it is:
 #        assigned a non-numeric value (including None, NotImplemented, False or True)
 #           unless it is:
-#               a tuple (could be on specifying ControlProjection, LearningProjection or ModulationOperation)
+#               a tuple (could be on specifying ControlProjection, LearningProjection or Modulation)
 #               a dict with the name FUNCTION_PARAMS (otherwise exclude)
 #        a function
 #            IMPLEMENTATION NOTE: FUNCTION_RUNTIME_PARAM_NOT_SUPPORTED
@@ -457,7 +884,7 @@
 
 # IMPLEMENT / DOCUMENT:
 #             IMPLEMENTATION NOTE:  Process._execute_learning - ~line 1909
-#                This implementation restricts learning to parameterStates of projections to inputStates
+#                This implementation restricts learning to parameterStates of projections to input_states
 #                That means that other parameters (e.g. object or function parameters) are not currenlty learnable
 #             ADD LEARNING TO OF OTHER PARAMETER STATES (E.G., OBJECT ITSELF AND/OR ITS FUNCTION)
 
@@ -813,7 +1240,7 @@
 #   CHANGE ALL VARIABLES FROM THEIR LOCAL NAMES (E.G., Allocation_Source, Input_value, etc) to variable
 #   Projections: sendsTo and sendsFrom
 #   "or isinstance(" -> use tuple
-#   Change "baseValue" -> "instanceValue" for prefs
+#   Change "base_value" -> "instanceValue" for prefs
 #   super(<class name>, self) -> super() [CHECK FUNCTIONALITY IN EACH CASE]
 #   NotImplemented -> None (and adjust tests accordingly)
 #
@@ -1130,7 +1557,7 @@
 #           If the MappingProjection is defined outside the Process pathway and not explicitly listed in it,
 #               it will not be included in the Process;  this is because deferring intialization means that
 #               even if the sender or the receiver is specified, the projection will not be assigned to the
-#               specified mechanism's projection list (sendsToProjections, receivesFromProjections), and thus not
+#               specified mechanism's projection list (efferents, afferents), and thus not
 #               identified in _instantiate_pathway.  Could allow sender to be left unspecified and still
 #               proceed with initialization that thus be recognized by the Process;  however, can't do the reverse
 #               (specify sender but not receiver) since receiver *must* be specified to initialize a projection
@@ -1196,12 +1623,13 @@
 #                    - this is so Mechanism base class can do housekeeping before and after subclass._execute)
 #                    - if a subclass does not implement _execute, calling it will call .function directly
 #                    -  if INITIALIZING is in context for call to execute, initMethod is checked to determine whether:
-#                        only subclass._execute is run (initMethod = INIT__EXECUTE__METHOD_ONLY)
-#                        only subclass.function is run (initMethod = INIT_FUNCTION_METHOD_ONLY)
+#                        subclass._execute (but NOT Mechanism.execute) is run (initMethod = INIT__EXECUTE__METHOD_ONLY)
+#                        ONLY subclass.function is run (initMethod = INIT_FUNCTION_METHOD_ONLY)
+#                              (EXAMPLE: EVCMechanism -- don't want to run full simulation during init)
 #                        full subclass._execute and Mechanism.execute method are run
 #                States use .execute only to call .function (during init);  they are updated using <state>.update()
 #            .function is the "business end" of the object:
-#                - generally it is a Function Function
+#                - generally it is a Function object
 #                - but can be anything that adheres to the Function API
 
 # DOCUMENT: Construction/Initialization Implementation:
@@ -1216,7 +1644,7 @@
 #         self.init_args['name'] = name
 #         del self.init_args['self']
 #     - set self.value = DEFERRED_INITIALIZATION
-# 3) Where projections are ordinarily instantiated, assign instantiated stub" to sendsToProjections,
+# 3) Where projections are ordinarily instantiated, assign instantiated stub" to efferents,
 # 4) When a process is instantiated, the last thing it does is call _deferred_init
 #    for all of the projections associated with the mechanism in its pathway,
 #    beginning with the last and moving backward though the pathway
@@ -1269,8 +1697,8 @@
 # DOCUMENT: System.mechanisms:  DICT:
 #                KEY FOR EACH ENTRY IS A MECHANIMS IN THE SYSTEM
 #                VALUE IS A LIST OF THE PROCESSES TO WHICH THE MECHANISM BELONGS
-# DOCUMENT: MEANING OF / DIFFERENCES BETWEEN self.variable, self.inputValue, self.value and self.outputValue
-# DOCUMENT: DIFFERENCES BETWEEN EVCMechanism.inputStates (that receive projections from monitored States) and
+# DOCUMENT: MEANING OF / DIFFERENCES BETWEEN self.variable, self.inputValue, self.value and self.output_values
+# DOCUMENT: DIFFERENCES BETWEEN EVCMechanism.input_states (that receive projections from monitored States) and
 #                               EVCMechanism.MonitoredOutputStates (the terminal states themselves)
 # DOCUMENT: CURRENTLY, PREDICTION MECHANISMS USE OUTPUT OF CORRESPONDING ORIGIN MECHANISM
 #           (RATHER THAN THEIR INPUT, WHICH == INPUT TO THE PROCESS)
@@ -1285,7 +1713,7 @@
 #           All other Function objects: value is always 1D np.array
 #    - Mechanism.value is always an indexible object of which the first item is a 1D np.array
 #               (corresponding to the value of Mechanism.outputState and Mechanism.outputStates.items()[0]
-#     Mechanism.variable[i] <-> Mechanism.inputState.items()e[i]
+#     Mechanism.variable[i] <-> Mechanism.input_state.items()e[i]
 #     Mechanism.value[i] <-> Mechanism.ouptputState.items()e[i]
 #    - variable = input, value = output
 #    - Function.variable and Function.value are always converted to 2D np.ndarray
@@ -1312,14 +1740,14 @@
 #                 - what gets called
 #
 # DOCUMENT: ControlSignals are now NEVER specified for params by default;
-#           they must be explicitly specified using ParamValueProjection tuple: (paramValue, CONTROL_PROJECTION)
+#           they must be explicitly specified using 2-item tuple: (paramValue, CONTROL_PROJECTION)
 #     - Clean up ControlProjection InstanceAttributes
 # DOCUMENT _instantiate_state_list() in Mechanism
 # DOCUMENT: change comment in DDM re: FUNCTION_RUN_TIME_PARAM
 # DOCUMENT: Change to InputState, OutputState re: owner vs. ownerValue
 # DOCUMENT: use of runtime params, including:
-#                  - specification of value (exposed or as tuple with ModulationOperation
-#                  - role of  RuntimeParamModulationPref / ModulationOperation
+#                  - specification of value (exposed or as tuple with Modulation
+#                  - role of  RuntimeParamModulationPref / Modulation
 # DOCUMENT: INSTANTIATION OF EACH DEFAULT ControlProjection CREATES A NEW outputState FOR DefaultController
 #                                AND A NEW inputState TO GO WITH IT
 #                                UPDATES VARIABLE OF owner TO BE CORRECT LENGTH (FOR #IN/OUT STATES)
@@ -1342,7 +1770,9 @@
 #              OUTPUT_STATE_PARAMS
 #              PROJECTION_PARAMS
 #              MAPPING_PROJECTION_PARAMS
+#              LEARNING_PROJECTION_PARAMS
 #              CONTROL_PROJECTION_PARAMS
+#              GATING_PROJECTION_PARAMS
 #              <projection name-specific> params
     # SORT OUT RUNTIME PARAMS PASSED IN BY MECHANISM:
     #    A - ONES FOR EXECUTE METHOD (AGGREGATION FUNCTION) OF inputState
@@ -1378,7 +1808,7 @@
 #                     prefs = {
 #                         kpVerbosePref: PreferenceEntry(False,PreferenceLevel.INSTANCE),
 #                         kpReportOutputPref: PreferenceEntry(True,PreferenceLevel.INSTANCE),
-#                         kpRuntimeParamModulationPref: PreferenceEntry(ModulationOperation.OVERRIDE,PreferenceLevel.CATEGORY)})
+#                         kpRuntimeParamModulationPref: PreferenceEntry(Modulation.OVERRIDE,PreferenceLevel.CATEGORY)})
 #
 # FIX: SOLUTION TO ALL OF THE ABOVE:  CHANGE LOG PREF TO LIST OF KW ENTRIES RATHER THAN BOOL COMBOS (SEE LOG)
 # FIX: Problems validating LogEntry / Enums:
@@ -1466,7 +1896,7 @@
 #      In projection() and Function:
 #          DefaultProjection
 #      In Process_Base:
-#          kwProcessDefaultProjection: Components.Projections.MappingProjection
+#          kwProcessDefaultProjection: Components.Projections.TransmissiveProjections.MappingProjection
 #          kwProcessDefaultProjectionFunction: Components.Function.LinearMatrix
 #  DefaultMechanism is now being assigned in Process;
 #  -  need to re-instate some form of set_default_mechanism() in Mechanism
@@ -1563,16 +1993,16 @@
 # FIX!!: Get straight ComponentName vs. ComponentType (e.g.,. EVCMechanism, ComparatorMechanism)
 #
 # FIX!!: VARIABLE VS. VALUE BUSINESS:
-#     QUESTION: is mechanism.value always == mechanism.outputValue (if not, document example)
+#     QUESTION: is mechanism.value always == mechanism.output_values (if not, document example)
 #     QUESTION: is self.value re-initialized prior to every system execution? process execution?
-#     FIX: Mechanism.inputValue and outputValue should both be lists (not np.arrays)
+#     FIX: Mechanism.inputValue and output_values should both be lists (not np.arrays)
 #     FIX: WHY BOTHER WITH inputValue ATTRIBUTE?  IF IT IS WORTH KEEPING, ADD TO DOCUMENTATION OF MECHANISM AND INPUTSTATE
-#     IMPLEMENT: 7/3/16 inputValue (== self.variable) WHICH IS 2D NP.ARRAY OF inputState.value FOR ALL inputStates
+#     IMPLEMENT: 7/3/16 inputValue (== self.variable) WHICH IS 2D NP.ARRAY OF inputState.value FOR ALL input_states
 #     DOCUMENTATION:  inputValue is a list, variable is a 2d np.nparray
-#     FIX:  RECONCILE DOCUMENTATION WITH ACTUALITY:  value == outputValue or just 1st item of outputValue
-#           CURRENTLY:  value = outputValue (DDM doesn't even have an outputValue
-#     FIX:  MAKE IT SO THAT value = output of function, and outputValue is what is returned by execute
-#     FIX:                  check that outputValue is concatenation of outputState values
+#     FIX:  RECONCILE DOCUMENTATION WITH ACTUALITY:  value == output_values or just 1st item of output_values
+#           CURRENTLY:  value = output_values (DDM doesn't even have an output_values
+#     FIX:  MAKE IT SO THAT value = output of function, and output_values is what is returned by execute
+#     FIX:                  check that output_values is concatenation of outputState values
 #     IMPLEMENT .input FOR Component:  == ndarray of all inputState.variables
 #     IMPLEMENT .output FOR Component:  == ndarray of all outputState.variables
 #     FIX: GET STRAIGHT system.input vs. system.inputValue
@@ -1598,7 +2028,7 @@
 
 # FIX:  ALLOW `name` TO BE A PARAM IN A PARAMS DICT (WHICH OVERRIDES name ARGUMENT OF CONSTRUCTOR)
 #
-# FIX: MAKE ALL OBJECT PARAMS @PRPOERTY SO THEY ARE READ-ONLY (I.E., ACCESSSIBLE ONLY VIA ASSIGN_PARAMS)
+# FIX: MAKE ALL OBJECT PARAMS @PRPOERTY SO THEY ARE READ-ONLY (I.E., ACCESSSIBLE ONLY VIA COMMAND_LINE)
 #      AND USE SETTER TO ISSUE WARNING ON ATTEMPTS TO ASSIGN;  INTERNALLY USE _PARAM TO DO DIRECT ASSIGNMENTS
 #
 # FIX:  IN _validate_params, ARE FUNCTION_PARAMS CHECKED AGAINST FUNCTION?  SHOULD BE.
@@ -1824,11 +2254,11 @@
 #    "SEQUENTIAL"/"ANALYTIC" MODE:
 #    1) Call every Process on each cycle
 #        a) Each Process calls the Mechanisms in its Pathway list in the sequence in which they appear;
-#            the next one is called when Mechanism.receivesFromProjections.frequency modulo CurrentTime() = 0
+#            the next one is called when Mechanism.path_afferents.frequency modulo CurrentTime() = 0
 #
 # VS:
 #        a) Each Process polls all the Mechanisms in its Pathway list on each cycle
-#            each one is called when Mechanism.receivesFromProjections.frequency modulo CurrentTime() = 0
+#            each one is called when Mechanism.path_afferents.frequency modulo CurrentTime() = 0
 #
 # SEQUENTIAL MODE:
 #     COMPUTE LCD (??GCF??)
@@ -1894,7 +2324,7 @@
 #                execute based on that, rather than dedicated line in System.execute
 # IMPLEMENT: *** sort System.executionList (per System.show() and exeucte based on that, rather than checking modulos
 # IMPLEMENT: *** EXAMINE MECHANISMS (OR OUTPUT STATES) IN SYSTEM FOR monitor ATTRIBUTE,
-#                AND ASSIGN THOSE AS MONITORED STATES IN EVC (inputStates)
+#                AND ASSIGN THOSE AS MONITORED STATES IN EVC (input_states)
 # IMPLEMENT: System.execute() should call EVC.update or EVC.execute_system METHOD??? (with input passed to System on command line)
 # IMPLEMENT: Store input passed on command line (i.e., at runtime) in self.input attribute (for access by EVC)??
 # IMPLEMENT: run() function (in Systems module) that runs default System
@@ -2027,10 +2457,10 @@
 # CONFIRM: VALIDATION METHODS CHECK THE FOLLOWING CONSTRAINT: (AND ADD TO CONSTRAINT DOCUMENTATION):
 #          #OF OUTPUTSTATES MUST MATCH #ITEMS IN OUTPUT OF EXECUTE METHOD **
 #
-# TEST: MAKE SURE THAT outputValue IS GETTING SET PROPERLY
+# TEST: MAKE SURE THAT output_values IS GETTING SET PROPERLY
 #                  (IN Mechanism.execute OR Mecchanism._update_output_states)
 #
-# IMPLEMENT: dictionaries for receivesFromProjections and sendsToProjections;
+# IMPLEMENT: dictionaries for afferents and efferents;
 #            each entry is the name of an inputState or outputState;  value is tuple with...??
 # IMPLEMENT: add built-in names for mechanism's InputStates and OutputStates (like ParameterStates)
 # IMPLEMENT: reference to mechanism by name in pathway (look it up in Registry)
@@ -2058,7 +2488,7 @@
 #               (in Mechanism_Base.@status.setter, ~Line 1746)
 #
 # IMPLEMENT: EXAMINE MECHANISMS (OR OUTPUT STATES) IN SYSTEM FOR monitor ATTRIBUTE,
-#                AND ASSIGN THOSE AS MONITORED STATES IN EVC (inputStates)
+#                AND ASSIGN THOSE AS MONITORED STATES IN EVC (input_states)
 #
 # - IMPLEMENT: CLEAN UP ORGANIZATION OF STATES AND PARAMS
 # Mechanism components:                Params:
@@ -2072,13 +2502,13 @@
 # - IMPLEMENTATION OF MULTIPLE INPUT AND OUTPUT STATES:
 # - IMPLEMENT:  ABSTRACT HANDLING OF MULTIPLE STATES (AT LEAST FOR INPUT AND OUTPUT STATES, AND POSSIBLE PARAMETER??
 # - Implement: Add StateSpec tuple specificaton in list for  INPUT_STATE and OutputStates
-#        - akin to ParamValueProjection
+#        - akin to 2-item tuple
 #        - this is because OrderedDict is a specialty class so don't want to impose their use on user specification
 #        - adjust _validate_params and instantiate_output_state accordingly
 # - Implement: allow list of names, that will be used to instantiate states using self.value
 # - Implement: allow dict entry values to be types (that should be checked against self.value)
 #
-# - NEED TO INITIALIZE:            kwStateValue: NotImplemented,
+# - NEED TO INITIALIZE:            STATE_VALUE: NotImplemented,
 # - IMPLEMENTATION NOTE: move defaultMechanism to a preference (in Mechanism.__init__() or Process.__init())
 # - IMPLEMENTATION NOTE: *** SHOULD THIS UPDATE AFFECTED PARAM(S) BY CALLING RELEVANT PROJECTIONS?
 # -    ASSGIGN  *** HANDLE SAME AS MECHANISM STATE AND PROJECTION STATE DEFAULTS:
@@ -2134,7 +2564,7 @@
 #                                             (that is checked when ControlMechanism is implemented
 #            DOCUMENT: if it appears in a tuple with a Mechanism, or in the Mechamism's params list,
 #                          it is applied to just that mechanism
-#     DOCUMENT: DIFFERENCES BETWEEN EVCMechanism.inputStates (that receive projections from monitored States) and
+#     DOCUMENT: DIFFERENCES BETWEEN EVCMechanism.input_states (that receive projections from monitored States) and
 #                                   EVCMechanism.MonitoredOutputStates (the terminal states themselves)
 #
 # FIX/DOCUMENT:  WHY SYSTEM: None FOR EVCMechanism AND DefaultControlMechanism [TRY REMOVING FROM BOTH]
@@ -2182,17 +2612,17 @@
 #
 # IMPLEMENT      MOVE ASSIGNMENT OF monitor_for_control_factors TO THERE
 #
-# - IMPLEMENT: controlSignals attribute:  list of control signals for mechanism
-#                                        (get from outputStates.sendsToProjections)
-# - IMPLEMENT: controlSignalSearchSpace argument in constructor, that can be:
-#                   - 2d array (each item of which is validated for length = len(self.controlSignals
+# - IMPLEMENT: control_signals attribute:  list of control signals for mechanism
+#                                        (get from outputStates.efferents)
+# - IMPLEMENT: control_signal_search_space argument in constructor, that can be:
+#                   - 2d array (each item of which is validated for length = len(self.control_signals
 #                   - function that returns a 2d array, validate per above.
 #
 # - IMPLEMENT: EXAMINE MECHANISMS (OR OUTPUT STATES) IN SYSTEM FOR monitor ATTRIBUTE,
-#                AND ASSIGN THOSE AS MONITORED STATES IN EVC (inputStates)
+#                AND ASSIGN THOSE AS MONITORED STATES IN EVC (input_states)
 #
 # - IMPLEMENT: .add_projection(Mechanism or State) method:
-#                   - add controlSignal projection from EVC to specified Mechanism/State
+#                   - add control_signal projection from EVC to specified Mechanism/State
 #                   - validate that Mechanism / State.owner is in self.system
 #                   ?? use Mechanism.add_projection method
 # - IMPLEMENT: FUNCTION_PARAMS for cost:  operation (additive or multiplicative), weight?
@@ -2201,12 +2631,12 @@
 #               ONCE THAT IS DONE, THEN FIX: IN System._instantiate_attributes_before_function:
 #                                                         self.controller = EVCMechanism(params={SYSTEM: self})#
 # - IMPLEMENT: ??execute_system method, that calls execute.update with input pass to System at run time?
-# ? IMPLEMENT .add_projection(Mechanism or State) method that adds controlSignal projection
+# ? IMPLEMENT .add_projection(Mechanism or State) method that adds control_signal projection
 #                   validate that Mechanism / State.owner is in self.system
 #                   ? use Mechanism.add_projection method
 
 # IMPLEMENT: call ControlMechanism should call ControlProjection._instantiate_sender()
-#                to instantaite new outputStates and Projections in _take_over_as_default_controller()
+#                to instantaite new outputStates and Projections in _assign_as_controller()
 #
 # IMPLEMENT: kwPredictionInputTarget option to specify which mechanism the EVC should use to receive, as input,
 #                the output of a specified prediction mechanims:  tuple(PredictionMechanism, TargetInputMechanism)
@@ -2225,32 +2655,32 @@
 # DOCUMENT:  protocol for assigning DefaultControlMechanism
 #           Initial assignment is to SystemDefaultCcontroller
 #           When any other ControlMechanism is instantiated, if params[MAKE_DEFAULT_CONTROLLER] = True
-#                then the class's _take_over_as_default_controller() method
+#                then the class's _assign_as_controller() method
 #                     is called in _instantiate_attributes_after_function
 # it moves all ControlProjections from DefaultController to itself
 #
 # FIX: IN ControlProjection._instantiate_sender:
 # FIX 6/28/16:  IF CLASS IS ControlMechanism SHOULD ONLY IMPLEMENT ONCE;  THEREAFTER, SHOULD USE EXISTING ONE
 #
-# FIX: ControlMechanism._take_over_as_default_controller() IS NOT FULLY DELETING DefaultController.outputStates
+# FIX: ControlMechanism._assign_as_controller() IS NOT FULLY DELETING DefaultController.outputStates
 #
-# FIX: PROBLEM - ControlMechanism._take_over_as_default_controller()
-# FIX:           NOT SETTING sendsToProjections IN NEW CONTROLLER (e.g., EVC)
+# FIX: PROBLEM - ControlMechanism._assign_as_controller()
+# FIX:           NOT SETTING efferents IN NEW CONTROLLER (e.g., EVC)
 #
 # SOLUTIONS:
 # 1) CLEANER: use _instantiate_sender on ControlProjection to instantiate both outputState and projection
-# 2) EASIER: add self.sendsToProjections.append() statement in _take_over_as_default_controller()
+# 2) EASIER: add self.efferents.append() statement in _assign_as_controller()
 #
 #
 # BACKGROUND INFO:
 # _instantiate_sender normally called from Projection in _instantiate_attributes_before_function
 #      calls sendsToProjection.append
-# _instantiate_control_projection normally called from ControlProjection in _instantiate_sender
+# _instantiate_control_signal normally called from ControlProjection in _instantiate_sender
 #
 # Instantiate EVC:  __init__ / _instantiate_attributes_after_function:
 #     take_over_as_default(): [ControlMechanism]
 #         iterate through old controller’s outputStates
-#             _instantiate_control_projection() for current controller
+#             _instantiate_control_signal() for current controller
 #                 _instantiate_state() [Mechanism]
 #                     state_type() [OutputState]
 #
@@ -2300,7 +2730,7 @@
 #
 # IMPLEMENT:  ?? ADD OPTION TO OVERRIDE "LAZY UPDATING" OF PARAMETER STATES, SO THAT ANY CHANGES CAN BE SEEN IN A PRINT
 #                STATEMENT AS SOON AS THEY HAVE OCCURRED)
-# IMPLEMENT: ADD OPTION TO SPECIFY WHICH OUTPUT STATES (self.outputValue) TO INCLUDE IN REPORT_OUTPUT
+# IMPLEMENT: ADD OPTION TO SPECIFY WHICH OUTPUT STATES (self.output_values) TO INCLUDE IN REPORT_OUTPUT
 #            (e.g., DDM)
 # IMPLEMENT:  ParamsDict - > .<param>:
 #             In update parameter states, assign self.param.value == parameterState[<param>].value
@@ -2348,7 +2778,7 @@
     # value (variable) == owner's functionOutputValue since that is where it gets it's value
     #    -- ?? should also do this in Mechanism, as per inputState:
                 # See:
-                # Validate self.inputState.value against variable for FUNCTION
+                # Validate self.input_state.value against variable for FUNCTION
                 # Note:  this is done when inputState is first assigned,
                 #        but needs to be done here in case FUNCTION is changed
     # uses MappingProjetion as default projection
@@ -2378,7 +2808,7 @@
 # QUESTION:  WHERE DOES THIS BELONG (WHERE IS InputState USED AS VARIABLE OR ASSIGNMENT SPECIFICATION)??
 #            (WAS IN Initialization arguments: UNDER __init_ FOR Mechanism_Base)
 #             - variable : value, InputState or specification dict for one
-#                       if value, it will be used as variable (template of self.inputState.value)
+#                       if value, it will be used as variable (template of self.input_state.value)
 #                       if State or specification dict, it's value attribute will be used
 
 # QUESTION:  WHERE DOES THIS BELONG (WHERE ARE ParameterStates SPECIFIED FOR ASSIGNMENT)??
@@ -2449,7 +2879,7 @@
 #            Coordinate this with system assignement to ControlMechanism
 #
 # FIX ************************************************
-# FIX: controlSignal prefs not getting assigned
+# FIX: control_signal prefs not getting assigned
 
 # Fix: rewrite this all with @property:
 #
@@ -2467,7 +2897,7 @@
 #
 #
 # IMPLEMENTATION NOTE:  ADD DESCRIPTION OF ControlProjection CHANNELS:  ADDED TO ANY SENDER OF A ControlProjection:
-    # USED, AT A MININUM, FOR ALIGNING VALIDATION OF inputStates WITH ITEMS IN variable
+    # USED, AT A MININUM, FOR ALIGNING VALIDATION OF input_states WITH ITEMS IN variable
     #                      ?? AND SAME FOR FOR outputStates WITH value
     # SHOULD BE INCLUDED IN INSTANTIATION OF CONTROL MECHANISM (per SYSTEM DEFAULT CONTROL MECHANISM)
     #     IN OVERRIDES OF _validate_variable AND
@@ -2510,7 +2940,7 @@
 #             - assign LearningProjection to all MappingProjections
 # IMPLEMENT: learning argument and attribute for System (that assigns learning to all of its processes and,
 #            and raises and exception if they can't handle it
-# IMPLEMENT: LEARNING_PROJECTION_PARAMS to parallel CONTROL_PROJECTION_PARAMS
+# IMPLEMENT: LEARNING_PROJECTION_PARAMS and GATING_PROJECTION_PARAMS to parallel CONTROL_PROJECTION_PARAMS
 # IMPLEMENT: *** IF LEARNING IS SPECIFIED FOR PROCESS, REMOVE THE NEED TO SPECIFY TARGET:
 #                  AUTOMATICALLY ASSIGN IT TO BE SAME FORMAT AS OUTPUT OF TERMINAL MECHANISM:
 #                IN Process: WARN BUT SET TARGET TO self.terminal.outputState
@@ -2573,10 +3003,10 @@
 #                            preceding processing mechanism's output projection (pop)
 #                                pop = ppm.outputState.projections[0]
 #                            preceding processing mechanism's output projection learning signal (popls):
-#                                popls = pop.parameterState.receivesFromProjections[0]
+#                                popls = pop.parameterState.path_afferents[0]
 #                            preceding ErrorMonitoringMechanism (pem):
 #                                pem = popls.sender.owner
-#                            assign MappingProjection from pem.outputState to self.inputState
+#                            assign MappingProjection from pem.outputState to self.input_state
 #                        - Get weight matrix for pop (pwm):
 #                                pwm = pop.parameterState.params[MATRIX]
 #    - update: compute weight changes based on errorSignal received rom ErrorMonitor Mechanism and pwm
@@ -2604,13 +3034,13 @@
 #
 # Two object types:
 # 1) ComparatorMechanism (MonioringMechanism):
-#     - has two inputStates:  i) system output;  ii) training input
+#     - has two input_states:  i) system output;  ii) training input
 #     - computes some objective function on them (default:  Hadamard difference)
 #     - default ComparatorMechanism that is associated with default LearningProjection
 #
 # 2) LearnningSignal (Projection):
 #     - sender:  output of Monitoring Mechanism
-#         default: receiver.owner.outputState.sendsToProjections.<MonitoringMechanism> if specified,
+#         default: receiver.owner.outputState.efferents.<MonitoringMechanism> if specified,
 #                  else default ComparatorMechanism
 #     - receiver: MappingProjection parameterState (or some equivalent thereof)
 #
@@ -2621,7 +3051,7 @@
 #        ParameterState and Training Projection both call that object
 # MappingProjection should have LEARNING_PARAM which:
 #    - specifies LearningProjection
-#    - uses self.outputStates.sendsToProjections.<MonitoringMechanism> if specified
+#    - uses self.outputStates.efferents.<MonitoringMechanism> if specified
 #    - otherwise defaults to LinearCompartor (which it instantiates for itself) and LearningProjection with BP
 #
 # Projection mechanism:
@@ -2752,7 +3182,7 @@
 #     CYCLE WARNING:  if verbose, on run() warn about any un-initialized recurrent projections
 #     Equivalent of run() for initialize()
 #
-# IMPLEMENT: show function for results of system.execute (integrate with system.outputValues) and process.execute
+# IMPLEMENT: show function for results of system.execute (integrate with system.output_values) and process.execute
 # IMPLEMENT: show function for system and process that shows inputs
 # IMPLEMENT: ??change specification of inputs in construct_inputs to name of process rather than mechanism
 # IMPLEMENT: help function for process.run and system.run that explains required structure of inputs
@@ -2812,7 +3242,7 @@
 #
 # FIX: NEED TO BE ABLE TO SPECIFY phaseSpec FOR EVC;  EITHER:
 #       ALLOW EVC TO BE IN A PROCESS, AND RECEIVE PROCESS-SPECIFIED PROJECTIONS,
-#       WHICH SHOULD AUTOMATICALLY INSTANTIATE CORRESPONDING MONITORED STATES (EVC.inputStates):
+#       WHICH SHOULD AUTOMATICALLY INSTANTIATE CORRESPONDING MONITORED STATES (EVC.input_states):
 #       THAT IS:
 #           WHEN A PROCESS INSTANTIATES AN PROJECTION TO AN EVC MECHANISM,
 #           IT SHOULD NOT JUST ADD THE PROJECTION TO THE PRIMARY INPUT STATE
@@ -2881,10 +3311,10 @@
 #               either in their _validate_params method, or in class function
 #
 #     Make sure add_monitored_value works
-#     Allow inputStates to be named (so they can be used as ComparatorMechanism)
+#     Allow input_states to be named (so they can be used as ComparatorMechanism)
 #     Move it to ProcessingMechanism
 #  Replace ComparatorMechanmism with ObjectiveMechanism
-#   using a particular function and named inputStates
+#   using a particular function and named input_states
 #   FIX: typechecking
 #   FIX: rename `monitor` and `names` args
 #   - IMPLEMENT call to _instantiate_input_states (not plural) once that is implemented (see State above):
@@ -2903,10 +3333,10 @@
 #endregion
 
 #region EVCMechanism -----------------------------------------------------------------------------------
-#     Validate that EVCMechanism.inputState matches outputState from EVCMechanism.monitoring_mechanism
+#     Validate that EVCMechanism.input_state matches outputState from EVCMechanism.monitoring_mechanism
 #     Allow it to take monitoring_mechanism as an argument
 #           (in which case it must be validated, but then don't bother to instantiate ObjectiveMechanism)
 #     Make sure add_monitored_value works:
 #           Needs to call ObjectiveMechanism.add_monitored_value
 #           Needs to update self.system.graph to include ObjectiveMechanism:
-#endregion
\ No newline at end of file
+#endregion
diff --git a/dev_requirements.txt b/dev_requirements.txt
new file mode 100644
index 00000000000..d1f3ac72c7d
--- /dev/null
+++ b/dev_requirements.txt
@@ -0,0 +1,3 @@
+pytest
+pytest-profiling
+pytest-helpers-namespace
\ No newline at end of file
diff --git a/docs/bin/activate b/docs/bin/activate
new file mode 120000
index 00000000000..a045e459039
--- /dev/null
+++ b/docs/bin/activate
@@ -0,0 +1 @@
+//anaconda/bin/activate
\ No newline at end of file
diff --git a/docs/bin/conda b/docs/bin/conda
new file mode 120000
index 00000000000..6f1d2227d2f
--- /dev/null
+++ b/docs/bin/conda
@@ -0,0 +1 @@
+//anaconda/bin/conda
\ No newline at end of file
diff --git a/docs/bin/deactivate b/docs/bin/deactivate
new file mode 120000
index 00000000000..5c79faf520b
--- /dev/null
+++ b/docs/bin/deactivate
@@ -0,0 +1 @@
+//anaconda/bin/deactivate
\ No newline at end of file
diff --git a/docs/build/html/ControlMechanism.html b/docs/build/html/ControlMechanism.html
index 8dfbdf5b3af..3c6ab5c75e8 100644
--- a/docs/build/html/ControlMechanism.html
+++ b/docs/build/html/ControlMechanism.html
@@ -36,7 +36,7 @@
     <link rel="top" title="PsyNeuLink 0.1 documentation" href="index.html"/>
         <link rel="up" title="Adaptive Mechanism" href="AdaptiveMechanism.html"/>
         <link rel="next" title="DefaultControlMechanism" href="DefaultControlMechanism.html"/>
-        <link rel="prev" title="LearningProjection" href="LearningProjection.html"/> 
+        <link rel="prev" title="Learning Mechanism" href="LearningMechanism.html"/> 
 
   
   <script src="_static/js/modernizr.min.js"></script>
@@ -114,7 +114,7 @@
 <li class="toctree-l2"><a class="reference internal" href="Mechanism.html#class-reference">Class Reference</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="State.html">States</a></li>
+<li class="toctree-l1"><a class="reference internal" href="State.html">State</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Projection.html">Projection</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Run.html">Run</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Component.html">Component</a></li>
@@ -188,85 +188,139 @@ <h1>Control Mechanism<a class="headerlink" href="#control-mechanism" title="Perm
 <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">¶</a></h2>
 <p>A ControlMechanism is an <a class="reference internal" href="AdaptiveMechanism.html"><span class="doc">Adaptive Mechanism</span></a> that modifies the parameter(s) of one or more <code class="xref any docutils literal"><span class="pre">ProcessingMechanisms</span></code>.
 It&#8217;s function takes a value (usually the output of an <a class="reference internal" href="ObjectiveMechanism.html"><span class="doc">ObjectiveMechanism</span></a>) and uses that to calculate an
-<a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>:  a list of <a class="reference internal" href="ControlProjection.html#ControlProjection.ControlProjection.allocation" title="ControlProjection.ControlProjection.allocation"><code class="xref any py py-attr docutils literal"><span class="pre">allocation</span></code></a> values for each of its ControlSignals that specify the value to assign
-to each parameter of a ProcessingMechanism (or its function) that it controls.  Each of these values is conveyed by
-a <a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjection</span></a> to the <a class="reference internal" href="ParameterState.html"><span class="doc">parameterState</span></a> of the corresopnding ProcessingMechanism.  A
-ControlMechanism can regulate only the parameters of mechanism in the system for which it is the
+<a class="reference internal" href="EVCMechanism.html#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>:  a list of <a class="reference internal" href="ControlProjection.html#ModulatoryProjections.ControlProjection.ControlProjection.allocation" title="ModulatoryProjections.ControlProjection.ControlProjection.allocation"><code class="xref any py py-attr docutils literal"><span class="pre">allocation</span></code></a> values for each of its ControlSignals that specify the value to assign
+to each parameter of a ProcessingMechanism (or its function) that it controls.  Each of these values is assigned as
+the value of a corresponding <a class="reference internal" href="ControlSignal.html"><span class="doc">ControlSignal</span></a> (a subclass of <a class="reference internal" href="OutputState.html"><span class="doc">OutputState</span></a> used by ControlMechanisms), and conveyed by
+the associated <a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjection</span></a> to the <a class="reference internal" href="ParameterState.html"><span class="doc">parameterState</span></a> of the relevant ProcessingMechanism.
+A ControlMechanism can regulate only the parameters of mechanisms in the system for which it is the
 <a class="reference internal" href="System.html#system-execution-control"><span class="std std-ref">controller</span></a>.  The control components of a system can be displayed using the system&#8217;s
-<a class="reference internal" href="System.html#System.System_Base.show_graph" title="System.System_Base.show_graph"><code class="xref any py py-meth docutils literal"><span class="pre">show_graph</span></code></a> method with its <strong>show_control</strong> argument assigned :keyword:<a href="#id1"><span class="problematic" id="id2">``</span></a>True`.  The control components of a
+<a class="reference internal" href="System.html#System.System_Base.show_graph" title="System.System_Base.show_graph"><code class="xref any py py-meth docutils literal"><span class="pre">show_graph</span></code></a> method with its <strong>show_control</strong> argument assigned :keyword:<a href="#id1"><span class="problematic" id="id2">``</span></a>True`.</p>
+<p>The control components of a
 system are executed after all ProcessingMechanisms and <a class="reference internal" href="LearningMechanism.html"><span class="doc">learning components</span></a> in that system have
 been executed.</p>
 </div>
 <div class="section" id="creating-a-controlmechanism">
 <span id="controlmechanism-creation"></span><h2>Creating A ControlMechanism<a class="headerlink" href="#creating-a-controlmechanism" title="Permalink to this headline">¶</a></h2>
-<p>ControlMechanisms can be created by using the standard Python method of calling the constructor for the desired type.
-A ControlMechanism is also created automatically whenever a <a class="reference internal" href="System.html#system-creation"><span class="std std-ref">system is created</span></a>, and assigned as
-the <a class="reference internal" href="System.html#system-execution-control"><span class="std std-ref">controller</span></a> for that system. The <a class="reference internal" href="OutputState.html"><span class="doc">outputStates</span></a> to be monitored by a
-ControlMechanism are specified in its <a class="reference internal" href="EVCMechanism.html#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitored_output_states" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitored_output_states"><code class="xref any py py-attr docutils literal"><span class="pre">monitored_output_states</span></code></a> argument, which can take  a number of
-<a class="reference internal" href="ObjectiveMechanism.html#objectivemechanism-monitored-outputstates"><span class="std std-ref">forms</span></a>.  When the ControlMechanism is created, it automatically creates
-an ObjectiveMechanism that is used to monitor and evaluate the mechanisms and/or outputStates specified in its
-<code class="xref any docutils literal"><span class="pre">monitor_for_control</span></code> attribute.  The result of the evaluation is used to
-specify the value of the ControlMechanism&#8217;s <a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjections</span></a>. How a ControlMechanism creates its
-ControlProjections and determines their value based on the outcome of its evaluation  depends on the
-<a class="reference internal" href="#"><span class="doc">subclass</span></a>.</p>
-<div class="section" id="specifying-control-for-a-parameter">
-<span id="controlmechanism-specifying-control"></span><h3>Specifying control for a parameter<a class="headerlink" href="#specifying-control-for-a-parameter" title="Permalink to this headline">¶</a></h3>
+<p>ControlMechanisms can be created using the standard Python method of calling the constructor for the desired type.
+A ControlMechanism is also created automatically whenever a <a class="reference internal" href="System.html#system-creation"><span class="std std-ref">system is created</span></a>, and it is assigned as
+the <a class="reference internal" href="System.html#system-execution-control"><span class="std std-ref">controller</span></a> for that system. The values to be monitored by the ControlMechanism are
+specified in the <strong>monitor_for_control</strong> argument of its constructor, and the parameters it controls are specified in
+the <strong>control_signals</strong> argument.  When the ControlMechanism is created, it automatically creates
+an ObjectiveMechanism (used to monitor and evaluate the values specified in <strong>monitor_for_control</strong>)
+as well as <a class="reference internal" href="ControlSignal.html"><span class="doc">ControlSignals</span></a> and <a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjections</span></a> used to control the parameters
+specified in <strong>control_signals</strong>, as described below. The kind of ObjectiveMechanism created by a ControlMechanism,
+and how it evalutes the values it monitors, depends on the <a class="reference internal" href="#"><span class="doc">subclass</span></a> of ControlMechanism.</p>
+<div class="section" id="specifying-parameters-to-control">
+<span id="controlmechanism-control-signals"></span><h3>Specifying Parameters to Control<a class="headerlink" href="#specifying-parameters-to-control" title="Permalink to this headline">¶</a></h3>
 <p>ControlMechanisms are used to control the parameter values of mechanisms and/or their functions.  A parameter can be
-specified for control by assigning a <a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjection</span></a> as part of its value when creating the mechanism or function
-to which the parameter belongs (see <a class="reference internal" href="Mechanism.html#mechanism-parameters"><span class="std std-ref">Mechanism Parameters</span></a>).</p>
+specified for control by assigning it a <a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjection</span></a> (along with the parameter&#8217;s value) when creating the
+mechanism or function to which the parameter belongs (see <a class="reference internal" href="Mechanism.html#mechanism-parameters"><span class="std std-ref">Mechanism Parameters</span></a>).  The parameters to be controlled by
+a ControlMechanism can also be specified in the <strong>control_signals</strong>  argument of the constructor for a ControlMechanism.
+The <strong>control_signals</strong> argument must be a list, each item of which can use any of the forms used for
+<a class="reference internal" href="ControlSignal.html#controlsignal-specification"><span class="std std-ref">specifying a ControlSignal</span></a>.</p>
+<p>A <a class="reference internal" href="ControlSignal.html"><span class="doc">ControlSignal</span></a> is created for each item listed in <strong>control_signals</strong>, and all of the ControlSignals for a
+ControlMechanism are listed in its <code class="xref any docutils literal"><span class="pre">control_signals</span></code> attribute.  Each ControlSignal
+is assigned a <a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjection</span></a> to the parameterState of the mechanisms associated with the specified parameter of the
+mechanism or its function, that is used to control the parameter&#8217;s value. ControlSignals are a type of <a class="reference internal" href="OutputState.html"><span class="doc">OutputState</span></a>,
+and so they are also listed in the ControlMechanism&#8217;s <code class="xref any docutils literal"><span class="pre">output_states</span></code> attribute.</p>
 </div>
-<div class="section" id="monitored-outputstates">
-<span id="controlmechanism-monitored-outputstates"></span><h3>Monitored OutputStates<a class="headerlink" href="#monitored-outputstates" title="Permalink to this headline">¶</a></h3>
-<p>When an ControlMechanism is constructed automatically, it creates an <a class="reference internal" href="ObjectiveMechanism.html"><span class="doc">ObjectiveMechanism</span></a> (specified in its
-<code class="xref any docutils literal"><span class="pre">montioring_mechanism</span></code> attribute) that is used to monitor and evaluate the system&#8217;s performance.  The
-ObjectiveMechanism monitors each mechanism and/or outputState listed in the ControlMechanism&#8217;s
-&#8216;monitor_for_control &lt;ControlMechanism.monitor_for_control&gt;` attribute, and evaluates them using the its <a class="reference internal" href="Function.html#Function.BogaczEtAl.function" title="Function.BogaczEtAl.function"><code class="xref any py py-meth docutils literal"><span class="pre">function</span></code></a>.
-This information is used to set the value of the ControlMechanism&#8217;s ControlProjections.</p>
+<div class="section" id="specifying-values-to-monitor-for-control">
+<span id="controlmechanism-monitored-values"></span><h3>Specifying Values to Monitor for Control<a class="headerlink" href="#specifying-values-to-monitor-for-control" title="Permalink to this headline">¶</a></h3>
+<p>When an ControlMechanism is created, it automatically creates an <a class="reference internal" href="ObjectiveMechanism.html"><span class="doc">ObjectiveMechanism</span></a> that is used to monitor and
+evaluate the values specified in the <strong>monitor_for_control</strong> argument of the ControlMechanism&#8217;s constructor.
+The <strong>monitor_for_control</strong> argument must be a list, each item of which must refer to a mechanism or the outputState
+of one.  These are assigned to the ObjectiveMechanism&#8217;s <a class="reference internal" href="ObjectiveMechanism.html"><span class="doc">monitored_values</span></a> attribute, and the
+ObjectiveMechanism is referenced by the ControlMechanism&#8217;s
+<code class="xref any docutils literal"><span class="pre">monitoring_mechanism</span></code> attribute.</p>
+<p>ObjectiveMechanism monitors each mechanism and/or outputState listed in the ControlMechanism&#8217;s
+&#8216;monitor_for_control &lt;ControlMechanism.monitor_for_control&gt;` attribute, and evaluates them using the its <a class="reference internal" href="Function.html#Function.Reduce.function" title="Function.Reduce.function"><code class="xref any py py-meth docutils literal"><span class="pre">function</span></code></a>.
+This information is used to set the value of the ControlMechanism&#8217;s ControlSignals.</p>
+<p class="rubric" id="controlmechanism-examples">Examples</p>
+<p>EXAMPLES HERE</p>
 </div>
 </div>
 <div class="section" id="execution">
 <span id="controlmechanism-execution"></span><h2>Execution<a class="headerlink" href="#execution" title="Permalink to this headline">¶</a></h2>
-<p>A ControlMechanism that is a system&#8217;s <a class="reference internal" href="System.html#System.System_Base.controller" title="System.System_Base.controller"><code class="xref any py py-attr docutils literal"><span class="pre">controller</span></code></a> is always the last mechanism to be executed (see <a class="reference internal" href="System.html#system-execution-control"><span class="std std-ref">System Control</span></a>).  Its <code class="xref any docutils literal"><span class="pre">function</span></code> takes as its input the values of the
-outputStates in its <a class="reference internal" href="EVCMechanism.html#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitored_output_states" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitored_output_states"><code class="xref any py py-attr docutils literal"><span class="pre">monitored_output_states</span></code></a> attribute, and uses those to determine the value of its
-<a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjections</span></a>. In the subsequent round of execution, each ControlProjection&#8217;s value is
-used by the <a class="reference internal" href="ParameterState.html"><span class="doc">ParameterState</span></a> to which it projects to update the parameter being controlled.</p>
+<p>A ControlMechanism that is a system&#8217;s <a class="reference internal" href="System.html#System.System_Base.controller" title="System.System_Base.controller"><code class="xref any py py-attr docutils literal"><span class="pre">controller</span></code></a> is always the last mechanism to be executed (see <a class="reference internal" href="System.html#system-execution-control"><span class="std std-ref">System Control</span></a>).  Its <code class="xref any docutils literal"><span class="pre">function</span></code> takes as its input the value in its
+<em>ERROR_SIGNAL</em> <code class="xref any docutils literal"><span class="pre">input_state</span></code>, and use that to determine its
+<code class="xref any docutils literal"><span class="pre">allocation_policy</span></code> that specifies the value assigned to each of its
+<a class="reference internal" href="ControlSignal.html"><span class="doc">ControlSignals</span></a>.  Each of those is used by its associated <a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjection</span></a> to set the
+value of the ParameterState for the parameter it controls.  In the subsequent round of execution,
+those parameter values are used by the mechanism when it executes.</p>
 <div class="admonition note">
 <p class="first admonition-title">Note</p>
 <p class="last">A <a class="reference internal" href="ParameterState.html"><span class="doc">ParameterState</span></a> that receives a <a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjection</span></a> does not update its value until its owner mechanism
 executes (see <code class="xref any docutils literal"><span class="pre">Lazy</span> <span class="pre">Evaluation</span></code> for an explanation of &#8220;lazy&#8221; updating).  This means that even if a
-ControlMechanism has executed, a parameter that it controls will not assume its new value until the corresponding
-receiver mechanism has executed.</p>
+ControlMechanism has executed, a parameter that it controls will not assume its new value until the mechanism
+to which it belongs has executed.</p>
 </div>
 </div>
 <div class="section" id="class-reference">
 <span id="controlmechanism-class-reference"></span><h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalink to this headline">¶</a></h2>
 <dl class="class">
 <dt id="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base">
-<em class="property">class </em><code class="descclassname">AdaptiveMechanisms.ControlMechanisms.ControlMechanism.</code><code class="descname">ControlMechanism_Base</code><span class="sig-paren">(</span><em>default_input_value=None</em>, <em>monitor_for_control=None</em>, <em>function=Linear</em>, <em>params=None</em>, <em>name=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">AdaptiveMechanisms.ControlMechanisms.ControlMechanism.</code><code class="descname">ControlMechanism_Base</code><span class="sig-paren">(</span><em>monitor_for_control=None</em>, <em>control_signals=None</em>, <em>modulation=ModulationParam.MULTIPLICATIVE          function=Linear</em>, <em>params=None</em>, <em>name=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base" title="Permalink to this definition">¶</a></dt>
 <dd><p>Abstract class for ControlMechanism.</p>
 <div class="admonition note">
 <p class="first admonition-title">Note</p>
 <p class="last">ControlMechanisms should NEVER be instantiated by a direct call to the base class.
 They should be instantiated using the constructor for a <a class="reference internal" href="#"><span class="doc">subclass</span></a>.</p>
 </div>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>monitor_for_control</strong> (<em>List[OutputState specification] : default None</em>) &#8211; specifies set of outputStates to monitor (see <span class="xref std std-ref">ControlMechanism_Monitored_OutputStates</span> for
+specification options).</li>
+<li><strong>control_signals</strong> (<em>List[parameter of Mechanism or its function, </em>) &#8211; ParameterState, Mechanism tuple[str, Mechanism] or dict]
+specifies the parameters to be controlled by the ControlMechanism
+(see <code class="xref any docutils literal"><span class="pre">control_signals</span></code> for details).</li>
+<li><strong>modulation</strong> (<em>ModulationParam : ModulationParam.MULTIPLICATIVE</em>) &#8211; specifies the default form of modulation to be used by the ControlMechanism&#8217;s ControlSignals,
+unless they are <a class="reference internal" href="ControlSignal.html#controlsignal-specification"><span class="std std-ref">individually specified</span></a>.</li>
+<li><strong>function</strong> (<em>TransferFunction : default Linear(slope=1, intercept=0)</em>) &#8211; specifies function used to combine values of monitored output states.</li>
+<li><strong>params</strong> (<em>Optional[Dict[param keyword, param value]]</em>) &#8211; a <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> that can be used to specify the parameters
+for the mechanism, parameters for its function, and/or a custom function and its parameters. Values
+specified for parameters in the dictionary override any assigned to those parameters in arguments of the
+constructor.</li>
+<li><strong>name</strong> (<em>str : default ControlMechanism-&lt;index&gt;</em>) &#8211; a string used for the name of the mechanism.
+If not is specified, a default is assigned by <code class="xref any docutils literal"><span class="pre">MechanismRegistry</span></code>
+(see <span class="xref doc">Registry</span> for conventions used in naming, including for default and duplicate names).</li>
+<li><strong>prefs</strong> (<em>Optional[PreferenceSet or specification dict : Mechanism.classPreferences]</em>) &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for the mechanism.
+If it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
+(see <span class="xref doc">PreferenceSet</span> for details).</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+<dl class="attribute">
+<dt id="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.monitoring_mechanism">
+<code class="descname">monitoring_mechanism</code><a class="headerlink" href="#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.monitoring_mechanism" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>ObjectiveMechanism</em> &#8211; mechanism that monitors and evalutes the values specified in the ControlMechanism&#8217;s <strong>monitor_for_control</strong>
+argument, and transmits the result to the ControlMechanism&#8217;s <em>ERROR_SIGNAL</em>
+<code class="xref any docutils literal"><span class="pre">input_state</span></code>.</p>
+</dd></dl>
+
 <dl class="attribute">
-<dt id="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.controlProjections">
-<code class="descname">controlProjections</code><a class="headerlink" href="#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.controlProjections" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>List[ControlProjection]</em> &#8211; list of <a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjections</span></a> managed by the ControlMechanism.
-There is one for each ouputState in the <a class="reference internal" href="TransferMechanism.html#TransferMechanism.TransferMechanism.outputStates" title="TransferMechanism.TransferMechanism.outputStates"><code class="xref any py py-attr docutils literal"><span class="pre">outputStates</span></code></a> dictionary.</p>
+<dt id="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.control_signals">
+<code class="descname">control_signals</code><a class="headerlink" href="#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.control_signals" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>List[ControlSignal]</em> &#8211; list of <code class="xref any docutils literal"><span class="pre">ControlSignals</span></code> for the ControlMechanism, each of which sends a <a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjection</span></a>
+to the <a class="reference internal" href="ParameterState.html"><span class="doc">parameterState</span></a> for the parameter it controls (same as ControlMechanism&#8217;s
+<code class="xref any docutils literal"><span class="pre">output_states</span></code> attribute).</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.controlProjectionCosts">
-<code class="descname">controlProjectionCosts</code><a class="headerlink" href="#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.controlProjectionCosts" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>2d np.array</em> &#8211; array of costs associated with each of the control signals in the <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.controlProjections" title="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.controlProjections"><code class="xref any py py-attr docutils literal"><span class="pre">controlProjections</span></code></a> attribute.</p>
+<dt id="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.control_projections">
+<code class="descname">control_projections</code><a class="headerlink" href="#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.control_projections" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>List[ControlProjection]</em> &#8211; list of <a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjections</span></a>, one for each <a class="reference internal" href="ControlSignal.html"><span class="doc">ControlSignal</span></a> in <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.control_signals" title="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.control_signals"><code class="xref any py py-attr docutils literal"><span class="pre">control_signals</span></code></a>.</p>
 </dd></dl>
 
 <dl class="attribute">
 <dt id="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy">
 <code class="descname">allocation_policy</code><a class="headerlink" href="#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>2d np.array</em> &#8211; array of values assigned to each control signal in the <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.controlProjections" title="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.controlProjections"><code class="xref any py py-attr docutils literal"><span class="pre">controlProjections</span></code></a> attribute.
-This is the same as the ControlMechanism&#8217;s <code class="xref any docutils literal"><span class="pre">value</span></code> attribute.</p>
+<dd><p><em>2d np.array</em> &#8211; each item is the value assigned to the corresponding ControlSignal in <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.control_signals" title="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.control_signals"><code class="xref any py py-attr docutils literal"><span class="pre">control_signals</span></code></a>
+(same as the ControlMechanism&#8217;s <code class="xref any docutils literal"><span class="pre">value</span></code> attribute).</p>
 </dd></dl>
 
 </dd></dl>
@@ -284,7 +338,7 @@ <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this head
         <a href="DefaultControlMechanism.html" class="btn btn-neutral float-right" title="DefaultControlMechanism" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
       
       
-        <a href="LearningProjection.html" class="btn btn-neutral" title="LearningProjection" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
+        <a href="LearningMechanism.html" class="btn btn-neutral" title="Learning Mechanism" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
       
     </div>
   
diff --git a/docs/build/html/ControlProjection.html b/docs/build/html/ControlProjection.html
index 0c3b08cf52e..f5aa9f7e4f8 100644
--- a/docs/build/html/ControlProjection.html
+++ b/docs/build/html/ControlProjection.html
@@ -35,7 +35,7 @@
         <link rel="search" title="Search" href="search.html"/>
     <link rel="top" title="PsyNeuLink 0.1 documentation" href="index.html"/>
         <link rel="up" title="EVCMechanism" href="EVCMechanism.html"/>
-        <link rel="next" title="States" href="State.html"/>
+        <link rel="next" title="State" href="State.html"/>
         <link rel="prev" title="ControlSignal" href="ControlSignal.html"/> 
 
   
@@ -89,16 +89,25 @@
 <li class="toctree-l1"><a class="reference internal" href="System.html">System</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Process.html">Process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Mechanism.html">Mechanism</a></li>
-<li class="toctree-l1"><a class="reference internal" href="State.html">States</a></li>
+<li class="toctree-l1"><a class="reference internal" href="State.html">State</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="Projection.html">Projection</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="MappingProjection.html">MappingProjection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="LearningProjection.html">LearningProjection</a></li>
-<li class="toctree-l2 current"><a class="current reference internal" href="#">ControlProjection</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#overview">Overview</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#creating-a-controlprojection">Creating a ControlProjection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#structure">Structure</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#execution">Execution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#class-reference">Class Reference</a></li>
+<li class="toctree-l2"><a class="reference internal" href="PathwayProjection.html">PathwayProjection</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="ModulatoryProjection.html">ModulatoryProjection</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="LearningProjection.html">LearningProjection</a></li>
+<li class="toctree-l3 current"><a class="current reference internal" href="#">ControlProjection</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#overview">Overview</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#creating-a-controlprojection">Creating a ControlProjection</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#structure">Structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#execution">Execution</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#class-reference">Class Reference</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="GatingProjection.html">GatingProjection</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ModulatoryProjection.html#overview">Overview</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ModulatoryProjection.html#creating-a-modulatoryprojection">Creating a ModulatoryProjection</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ModulatoryProjection.html#structure">Structure</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ModulatoryProjection.html#execution">Execution</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ModulatoryProjection.html#class-reference">Class Reference</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="Projection.html#overview">Overview</a></li>
@@ -162,56 +171,68 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
             
-  <div class="section" id="controlprojection">
-<h1>ControlProjection<a class="headerlink" href="#controlprojection" title="Permalink to this headline">¶</a></h1>
-<div class="toctree-wrapper compound">
-</div>
-<span class="target" id="module-ControlProjection"></span><div class="section" id="overview">
+  <div class="section" id="module-ModulatoryProjections.ControlProjection">
+<span id="controlprojection"></span><h1>ControlProjection<a class="headerlink" href="#module-ModulatoryProjections.ControlProjection" title="Permalink to this headline">¶</a></h1>
+<div class="section" id="overview">
 <span id="controlprojection-overview"></span><h2>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">¶</a></h2>
-<p>A ControlProjection is a subclass of <a class="reference internal" href="Projection.html"><span class="doc">Projection</span></a> that projects to the <a class="reference internal" href="ParameterState.html"><span class="doc">parameterState</span></a>
-of a <a class="reference internal" href="ProcessingMechanism.html"><span class="doc">Processing Mechanism</span></a>. It takes the value of an <code class="xref any docutils literal"><span class="pre">outputState</span></code>), and uses it to  modify the value of the parameter associated with the
-parameterState to which it projects.  All of the ControlProjections in a system, along with its other
-<a class="reference internal" href="ControlMechanism.html"><span class="doc">control components</span></a>, can be displayed using the system&#8217;s <a class="reference internal" href="System.html#System.System_Base.show_graph" title="System.System_Base.show_graph"><code class="xref any py py-meth docutils literal"><span class="pre">show_graph</span></code></a> method with its
-<strong>show_control</strong> argument assigned <code class="xref std std-keyword docutils literal"><span class="pre">True</span></code>.</p>
+<p>A ControlProjection is a subclass of <a class="reference internal" href="ModulatoryProjection.html"><span class="doc">ModulatoryProjection</span></a> that projects to the <a class="reference internal" href="ParameterState.html"><span class="doc">parameterState</span></a> of
+a <a class="reference internal" href="ProcessingMechanism.html"><span class="doc">Processing Mechanism</span></a>. It takes the value of a <a class="reference internal" href="ControlSignal.html"><span class="doc">ControlSignal</span></a> of a <a class="reference internal" href="ControlMechanism.html"><span class="doc">Control Mechanism</span></a>, and uses it to  modify the
+value of the parameter associated with the ParameterState to which it projects.  All of the ControlProjections in a
+system, along with its other <a class="reference internal" href="ControlMechanism.html"><span class="doc">control components</span></a>, can be displayed using the system&#8217;s <a class="reference internal" href="System.html#System.System_Base.show_graph" title="System.System_Base.show_graph"><code class="xref any py py-meth docutils literal"><span class="pre">show_graph</span></code></a>
+method with its <strong>show_control</strong> argument assigned as <code class="xref std std-keyword docutils literal"><span class="pre">True</span></code>.</p>
 </div>
 <div class="section" id="creating-a-controlprojection">
 <span id="controlprojection-creation"></span><h2>Creating a ControlProjection<a class="headerlink" href="#creating-a-controlprojection" title="Permalink to this headline">¶</a></h2>
 <p>A ControlProjection can be created using any of the standard ways to <a class="reference internal" href="Projection.html#projection-creation"><span class="std std-ref">create a projection</span></a>,
-or by including it in the <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">specification of a parameter</span></a> for a mechanism,
-MappingProjection, or the <code class="xref std std-keyword docutils literal"><span class="pre">function</span></code> of either of these.  If a ControlProjection is created using its
-constructor on its own, the <a class="reference internal" href="#ControlProjection.ControlProjection.receiver" title="ControlProjection.ControlProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a> argument must be specified.  If it is included in a
-parameter specification, the parameterState for the parameter being specified will be assigned as the
-ControlProjection&#8217;s <a class="reference internal" href="#ControlProjection.ControlProjection.receiver" title="ControlProjection.ControlProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a>.  If its <a class="reference internal" href="#ControlProjection.ControlProjection.sender" title="ControlProjection.ControlProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a> is not
-specified, its assignment depends on the <a class="reference internal" href="#ControlProjection.ControlProjection.receiver" title="ControlProjection.ControlProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a>.  If the receiver belongs to a
-mechanism that is part of a system, then the ControlProjection&#8217;s <a class="reference internal" href="#ControlProjection.ControlProjection.sender" title="ControlProjection.ControlProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a> is assigned to an
-outputState of the system&#8217;s <a class="reference internal" href="System.html#System.System_Base.controller" title="System.System_Base.controller"><code class="xref any py py-attr docutils literal"><span class="pre">controller</span></code></a>. Otherwise, the <a class="reference internal" href="#ControlProjection.ControlProjection.sender" title="ControlProjection.ControlProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a> is assigned to
-the outputState of a <a class="reference internal" href="DefaultControlMechanism.html"><span class="doc">DefaultControlMechanism</span></a>.</p>
+or by including it in a tuple that <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">specifies a parameter</span></a> for a <a class="reference internal" href="Mechanism.html"><span class="doc">Mechanism</span></a>,
+<a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a>, or the <code class="xref any docutils literal"><span class="pre">function</span></code> of either of these.  If a ControlProjection is created
+using its constructor on its own, the <a class="reference internal" href="#ModulatoryProjections.ControlProjection.ControlProjection.receiver" title="ModulatoryProjections.ControlProjection.ControlProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a> argument must be specified.  If it is
+included in a parameter specification, the ParameterState for the parameter being specified will be assigned as the
+ControlProjection&#8217;s <a class="reference internal" href="#ModulatoryProjections.ControlProjection.ControlProjection.receiver" title="ModulatoryProjections.ControlProjection.ControlProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a>.  If its <a class="reference internal" href="#ModulatoryProjections.ControlProjection.ControlProjection.sender" title="ModulatoryProjections.ControlProjection.ControlProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a> is not
+specified, its assignment depends on the <a class="reference internal" href="#ModulatoryProjections.ControlProjection.ControlProjection.receiver" title="ModulatoryProjections.ControlProjection.ControlProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a>.  If the receiver belongs to a
+Mechanism that is part of a System, then the ControlProjection&#8217;s <a class="reference internal" href="#ModulatoryProjections.ControlProjection.ControlProjection.sender" title="ModulatoryProjections.ControlProjection.ControlProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a> is assigned to a
+<a class="reference internal" href="ControlSignal.html"><span class="doc">ControlSignal</span></a> of the System&#8217;s <a class="reference internal" href="System.html#System.System_Base.controller" title="System.System_Base.controller"><code class="xref any py py-attr docutils literal"><span class="pre">controller</span></code></a>.</p>
+<div class="section" id="deferred-initialization">
+<span id="controlprojection-deferred-initialization"></span><h3>Deferred Initialization<a class="headerlink" href="#deferred-initialization" title="Permalink to this headline">¶</a></h3>
+<p>When a ControlProjection is created, its full initialization is <span class="xref std std-ref">deferred</span> until its
+<a class="reference internal" href="#ModulatoryProjections.ControlProjection.ControlProjection.sender" title="ModulatoryProjections.ControlProjection.ControlProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a> and <a class="reference internal" href="#ModulatoryProjections.ControlProjection.ControlProjection.receiver" title="ModulatoryProjections.ControlProjection.ControlProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a> have been fully specified.  This allows
+a ControlProjection to be created before its <a class="reference internal" href="LearningProjection.html#ModulatoryProjections.LearningProjection.LearningProjection.sender" title="ModulatoryProjections.LearningProjection.LearningProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a> (and/or <a class="reference internal" href="Function.html#Function.Stability.MappingProjection.receiver" title="Function.Stability.MappingProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a>) have been created (e.g., before them in a
+script), by calling its constructor without specifying its <strong>sender</strong> or <strong>receiver</strong> arguments. However, for the
+ControlProjection to be operational, initialization must be completed by calling its <code class="xref any docutils literal"><span class="pre">deferred_init</span></code> method.  This is
+not necessary if the parameter(s) to be controlled are specified in the <strong>control_signals</strong> argument of a <a class="reference internal" href="System.html"><span class="doc">System</span></a>, or
+in a tuple specifying the parameter of a <a class="reference internal" href="Mechanism.html"><span class="doc">Mechanism</span></a> (or its <code class="xref any docutils literal"><span class="pre">function</span> <span class="pre">&lt;Mechanism.function</span></code>) that is part of a System
+&#8211; in those cases, deferred initialization is completed automatically.</p>
+</div>
 </div>
 <div class="section" id="structure">
 <span id="controlprojection-structure"></span><h2>Structure<a class="headerlink" href="#structure" title="Permalink to this headline">¶</a></h2>
-<p>A ControlProjection has the same structure as a <a class="reference internal" href="Projection.html"><span class="doc">Projection</span></a>.  Its <a class="reference internal" href="#ControlProjection.ControlProjection.sender" title="ControlProjection.ControlProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a>
-can be the outputState of any mechanism, but is generally a <a class="reference internal" href="ControlMechanism.html"><span class="doc">ControlMechanism</span></a> and commonly the
-<a class="reference internal" href="ControlSignal.html"><span class="doc">ControlSignal</span></a> of an <a class="reference internal" href="EVCMechanism.html"><span class="doc">EVCMechanism</span></a>.  Its <a class="reference internal" href="#ControlProjection.ControlProjection.receiver" title="ControlProjection.ControlProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a> is always the <a class="reference internal" href="ParameterState.html"><span class="doc">parameterState</span></a> of a mechanism or <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a>, that is associated with a parameter of either the
-parameterState&#8217;s owner or it owner&#8217;s <code class="xref std std-keyword docutils literal"><span class="pre">function</span></code>.  The <code class="xref any docutils literal"><span class="pre">function</span></code> of a
-ControlProjection is, by default, the identity function;  that is, it uses the <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code> of its <a class="reference internal" href="#ControlProjection.ControlProjection.sender" title="ControlProjection.ControlProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a> to modify the value of the parameter that it controls.</p>
+<p>The <a class="reference internal" href="#ModulatoryProjections.ControlProjection.ControlProjection.sender" title="ModulatoryProjections.ControlProjection.ControlProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a> of a ControlProjection is a <a class="reference internal" href="ControlSignal.html"><span class="doc">ControlSignal</span></a> of a <a class="reference internal" href="ControlMechanism.html"><span class="doc">Control Mechanism</span></a>.  Its
+<a class="reference internal" href="#ModulatoryProjections.ControlProjection.ControlProjection.receiver" title="ModulatoryProjections.ControlProjection.ControlProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a> is the <a class="reference internal" href="ParameterState.html"><span class="doc">ParameterState</span></a> of a <a class="reference internal" href="Mechanism.html"><span class="doc">Mechanism</span></a> or <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a>, that is
+associated with a parameter of the ParameterState&#8217;s owner or its owner&#8217;s <code class="xref any docutils literal"><span class="pre">function</span></code>.  The
+<code class="xref any docutils literal"><span class="pre">function</span></code> of a ControlProjection is, by default, the identity function;  that is,
+it conveys the <code class="xref any docutils literal"><span class="pre">value</span></code> of its <a class="reference internal" href="#ModulatoryProjections.ControlProjection.ControlProjection.sender" title="ModulatoryProjections.ControlProjection.ControlProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a> to
+its <a class="reference internal" href="#ModulatoryProjections.ControlProjection.ControlProjection.receiver" title="ModulatoryProjections.ControlProjection.ControlProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a>, for use in modifying the value of the parameter that it controls.</p>
 </div>
 <div class="section" id="execution">
 <span id="controlprojection-execution"></span><h2>Execution<a class="headerlink" href="#execution" title="Permalink to this headline">¶</a></h2>
-<p>A ControlProjection uses its <code class="xref any docutils literal"><span class="pre">function</span></code> to assign its <a class="reference internal" href="#ControlProjection.ControlProjection.value" title="ControlProjection.ControlProjection.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>
-from the one received  from its <a class="reference internal" href="#ControlProjection.ControlProjection.sender" title="ControlProjection.ControlProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a>.  This is used by the paramaterState to which the
-ControlProjection projects to modify the corresponding parameter of its owner or owner&#8217;s <code class="xref std std-keyword docutils literal"><span class="pre">function</span></code>.</p>
+<p>A ControlProjection cannot be executed directly.  It is executed when the <a class="reference internal" href="ParameterState.html"><span class="doc">ParameterState</span></a> to which it projects is
+updated.  Note that this only occurs when the ProcessingMechanism to which the <a class="reference internal" href="ParameterState.html"><span class="doc">ParameterState</span></a> belongs is executed
+(see <span class="xref std std-ref">Lazy Evaluation</span> for an explanation of &#8220;lazy&#8221; updating). When a ControlProjection is executed, its
+<code class="xref any docutils literal"><span class="pre">function</span></code> assigns the value of the <a class="reference internal" href="ControlSignal.html"><span class="doc">ControlSignal</span></a> from which it projects as its own
+<a class="reference internal" href="#ModulatoryProjections.ControlProjection.ControlProjection.value" title="ModulatoryProjections.ControlProjection.ControlProjection.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>. This is used by the <a class="reference internal" href="ParameterState.html"><span class="doc">ParameterState</span></a> to which the ControlProjection projects to
+modify the parameter for which it is responsible.</p>
 <div class="admonition note">
 <p class="first admonition-title">Note</p>
 <p class="last">The changes in a parameter in response to the execution of a ControlProjection are not applied until the
-mechanism that receives the projection are next executed; see <span class="xref std std-ref">Lazy Evaluation</span> for an explanation of &#8220;lazy&#8221;
+Mechanism that receives the projection are next executed; see <span class="xref std std-ref">Lazy Evaluation</span> for an explanation of &#8220;lazy&#8221;
 updating).</p>
 </div>
 </div>
 <div class="section" id="class-reference">
 <span id="controlprojection-class-reference"></span><h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalink to this headline">¶</a></h2>
 <dl class="class">
-<dt id="ControlProjection.ControlProjection">
-<em class="property">class </em><code class="descclassname">ControlProjection.</code><code class="descname">ControlProjection</code><span class="sig-paren">(</span><em>sender=None</em>, <em>receiver=None</em>, <em>function=Linear        params=None</em>, <em>name=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#ControlProjection.ControlProjection" title="Permalink to this definition">¶</a></dt>
+<dt id="ModulatoryProjections.ControlProjection.ControlProjection">
+<em class="property">class </em><code class="descclassname">ModulatoryProjections.ControlProjection.</code><code class="descname">ControlProjection</code><span class="sig-paren">(</span><em>sender=None</em>, <em>receiver=None</em>, <em>function=Linear            control_signal_params      params=None</em>, <em>name=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#ModulatoryProjections.ControlProjection.ControlProjection" title="Permalink to this definition">¶</a></dt>
 <dd><blockquote>
 <div>Implements a projection that controls the parameter of a mechanism or its <code class="xref std std-keyword docutils literal"><span class="pre">function</span></code>.</div></blockquote>
 <table class="docutils field-list" frame="void" rules="none">
@@ -221,12 +242,15 @@ <h1>ControlProjection<a class="headerlink" href="#controlprojection" title="Perm
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
 <li><strong>sender</strong> (<em>Optional[Mechanism or OutputState]</em>) &#8211; specifies the source of the input for the ControlProjection;  usually an <a class="reference internal" href="OutputState.html"><span class="doc">outputState</span></a> of a
 <a class="reference internal" href="ControlMechanism.html"><span class="doc">ControlMechanism</span></a>, and commonly the <a class="reference internal" href="ControlSignal.html"><span class="doc">ControlSignal</span></a> of an <a class="reference internal" href="EVCMechanism.html"><span class="doc">EVCMechanism</span></a>.  If it is not
-specified, an outputState of the <a class="reference internal" href="DefaultControlMechanism.html"><span class="doc">DefaultControlMechanism</span></a> for the system to which the receiver belongs will
-be assigned.</li>
+specified, the ControlProjection will
+be ignored during execution.</li>
 <li><strong>receiver</strong> (<em>Optional[Mechanism or ParameterState]</em>) &#8211; specifies the parameterState associated with the parameter to be controlled.  This must be specified,
 or be able to be determined by the context in which the ControlProjection is created or assigned.</li>
 <li><strong>function</strong> (<em>TransferFunction : default Linear</em>) &#8211; specifies the function used to convert the <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code> of the ControlProjection&#8217;s
-<a class="reference internal" href="#ControlProjection.ControlProjection.sender" title="ControlProjection.ControlProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a>  to its own <a class="reference internal" href="#ControlProjection.ControlProjection.value" title="ControlProjection.ControlProjection.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>.</li>
+<a class="reference internal" href="#ModulatoryProjections.ControlProjection.ControlProjection.sender" title="ModulatoryProjections.ControlProjection.ControlProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a>  to its own <a class="reference internal" href="#ModulatoryProjections.ControlProjection.ControlProjection.value" title="ModulatoryProjections.ControlProjection.ControlProjection.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>.</li>
+<li><strong>control_signal_params</strong> (<em>Dict[param keyword, param value]</em>) &#8211; a <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> that can be used to specify the parameters for
+the <a class="reference internal" href="ControlSignal.html"><span class="doc">ControlSignal</span></a> that is the sender of the projection (see <a class="reference internal" href="ControlSignal.html#controlsignal-structure"><span class="std std-ref">Structure</span></a> for a description
+of ControlSignal parameters).</li>
 <li><strong>params</strong> (<em>Optional[Dict[param keyword, param value]]</em>) &#8211; a <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> that can be used to specify the parameters for
 the projection, its <code class="xref any docutils literal"><span class="pre">function</span></code>, and/or a custom function and its parameters.
 Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the
@@ -243,51 +267,51 @@ <h1>ControlProjection<a class="headerlink" href="#controlprojection" title="Perm
 </tbody>
 </table>
 <dl class="attribute">
-<dt id="ControlProjection.ControlProjection.componentType">
-<code class="descname">componentType</code><a class="headerlink" href="#ControlProjection.ControlProjection.componentType" title="Permalink to this definition">¶</a></dt>
+<dt id="ModulatoryProjections.ControlProjection.ControlProjection.componentType">
+<code class="descname">componentType</code><a class="headerlink" href="#ModulatoryProjections.ControlProjection.ControlProjection.componentType" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>CONTROL_PROJECTION</em></p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="ControlProjection.ControlProjection.sender">
-<code class="descname">sender</code><a class="headerlink" href="#ControlProjection.ControlProjection.sender" title="Permalink to this definition">¶</a></dt>
+<dt id="ModulatoryProjections.ControlProjection.ControlProjection.sender">
+<code class="descname">sender</code><a class="headerlink" href="#ModulatoryProjections.ControlProjection.ControlProjection.sender" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>OutputState of ControlMechanism</em> &#8211; mechanism that provides the current input for the ControlProjection (usually a
 <a class="reference internal" href="ControlMechanism.html"><span class="doc">ControlMechanism</span></a>).</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="ControlProjection.ControlProjection.receiver">
-<code class="descname">receiver</code><a class="headerlink" href="#ControlProjection.ControlProjection.receiver" title="Permalink to this definition">¶</a></dt>
+<dt id="ModulatoryProjections.ControlProjection.ControlProjection.receiver">
+<code class="descname">receiver</code><a class="headerlink" href="#ModulatoryProjections.ControlProjection.ControlProjection.receiver" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>ParameterState of Mechanism</em> &#8211; <a class="reference internal" href="ParameterState.html"><span class="doc">parameterState</span></a> for the parameter to be modified by the ControlProjection.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="ControlProjection.ControlProjection.allocation">
-<code class="descname">allocation</code><a class="headerlink" href="#ControlProjection.ControlProjection.allocation" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>1d np.array</em> &#8211; the input to the ControlProjection; same as the <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code> of the <a class="reference internal" href="#ControlProjection.ControlProjection.sender" title="ControlProjection.ControlProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a>.</p>
+<dt id="ModulatoryProjections.ControlProjection.ControlProjection.allocation">
+<code class="descname">allocation</code><a class="headerlink" href="#ModulatoryProjections.ControlProjection.ControlProjection.allocation" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>1d np.array</em> &#8211; the input to the ControlProjection; same as the <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code> of the <a class="reference internal" href="#ModulatoryProjections.ControlProjection.ControlProjection.sender" title="ModulatoryProjections.ControlProjection.ControlProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a>.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="ControlProjection.ControlProjection.value">
-<code class="descname">value</code><a class="headerlink" href="#ControlProjection.ControlProjection.value" title="Permalink to this definition">¶</a></dt>
+<dt id="ModulatoryProjections.ControlProjection.ControlProjection.value">
+<code class="descname">value</code><a class="headerlink" href="#ModulatoryProjections.ControlProjection.ControlProjection.value" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>float</em> &#8211; during initialization, assigned a keyword string (either <code class="xref any docutils literal"><span class="pre">INITIALIZING</span></code> or <code class="xref any docutils literal"><span class="pre">DEFERRED_INITIALIZATION</span></code>);
 during execution, is assigned the current value of the ControlProjection.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="ControlProjection.ControlProjection.name">
-<code class="descname">name</code><a class="headerlink" href="#ControlProjection.ControlProjection.name" title="Permalink to this definition">¶</a></dt>
+<dt id="ModulatoryProjections.ControlProjection.ControlProjection.name">
+<code class="descname">name</code><a class="headerlink" href="#ModulatoryProjections.ControlProjection.ControlProjection.name" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>str : default ControlProjection-&lt;index&gt;</em> &#8211; the name of the ControlProjection.
-Specified in the <a class="reference internal" href="#ControlProjection.ControlProjection.name" title="ControlProjection.ControlProjection.name"><code class="xref any py py-attr docutils literal"><span class="pre">name</span></code></a> argument of the constructor for the projection;
+Specified in the <strong>name</strong> argument of the constructor for the projection;
 if not is specified, a default is assigned by ProjectionRegistry
 (see <span class="xref doc">Registry</span> for conventions used in naming, including for default and duplicate names).</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="ControlProjection.ControlProjection.prefs">
-<code class="descname">prefs</code><a class="headerlink" href="#ControlProjection.ControlProjection.prefs" title="Permalink to this definition">¶</a></dt>
+<dt id="ModulatoryProjections.ControlProjection.ControlProjection.prefs">
+<code class="descname">prefs</code><a class="headerlink" href="#ModulatoryProjections.ControlProjection.ControlProjection.prefs" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for projection.
-Specified in the <a class="reference internal" href="#ControlProjection.ControlProjection.prefs" title="ControlProjection.ControlProjection.prefs"><code class="xref any py py-attr docutils literal"><span class="pre">prefs</span></code></a> argument of the constructor for the projection;
+Specified in the <strong>prefs</strong> argument of the constructor for the projection;
 if it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
 (see <span class="xref doc">PreferenceSet</span> for details).</p>
 </dd></dl>
@@ -304,7 +328,7 @@ <h1>ControlProjection<a class="headerlink" href="#controlprojection" title="Perm
   
     <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
       
-        <a href="State.html" class="btn btn-neutral float-right" title="States" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
+        <a href="State.html" class="btn btn-neutral float-right" title="State" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
       
       
         <a href="ControlSignal.html" class="btn btn-neutral" title="ControlSignal" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
diff --git a/docs/build/html/DDM.html b/docs/build/html/DDM.html
index fc677d12149..634d19ee25a 100644
--- a/docs/build/html/DDM.html
+++ b/docs/build/html/DDM.html
@@ -35,7 +35,7 @@
         <link rel="search" title="Search" href="search.html"/>
     <link rel="top" title="PsyNeuLink 0.1 documentation" href="index.html"/>
         <link rel="up" title="Processing Mechanism" href="ProcessingMechanism.html"/>
-        <link rel="next" title="ObjectiveMechanism" href="ObjectiveMechanism.html"/>
+        <link rel="next" title="TransferMechanism" href="TransferMechanism.html"/>
         <link rel="prev" title="IntegratorMechanism" href="IntegratorMechanism.html"/> 
 
   
@@ -90,7 +90,6 @@
 <li class="toctree-l1"><a class="reference internal" href="Process.html">Process</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="Mechanism.html">Mechanism</a><ul class="current">
 <li class="toctree-l2 current"><a class="reference internal" href="ProcessingMechanism.html">Processing Mechanism</a><ul class="current">
-<li class="toctree-l3"><a class="reference internal" href="TransferMechanism.html">TransferMechanism</a></li>
 <li class="toctree-l3"><a class="reference internal" href="IntegratorMechanism.html">IntegratorMechanism</a></li>
 <li class="toctree-l3 current"><a class="current reference internal" href="#">DDM</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="#overview">Overview</a></li>
@@ -100,11 +99,10 @@
 <li class="toctree-l4"><a class="reference internal" href="#class-reference">Class Reference</a></li>
 </ul>
 </li>
+<li class="toctree-l3"><a class="reference internal" href="TransferMechanism.html">TransferMechanism</a></li>
+<li class="toctree-l3"><a class="reference internal" href="RecurrentTransferMechanism.html">RecurrentTransferMechanism</a></li>
+<li class="toctree-l3"><a class="reference internal" href="LCA.html">LCA</a></li>
 <li class="toctree-l3"><a class="reference internal" href="ObjectiveMechanism.html">ObjectiveMechanism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ProcessingMechanism.html#overview">Overview</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ProcessingMechanism.html#creating-a-processingmechanism">Creating a ProcessingMechanism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ProcessingMechanism.html#structure">Structure</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ProcessingMechanism.html#execution">Execution</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="AdaptiveMechanism.html">Adaptive Mechanism</a></li>
@@ -115,7 +113,7 @@
 <li class="toctree-l2"><a class="reference internal" href="Mechanism.html#class-reference">Class Reference</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="State.html">States</a></li>
+<li class="toctree-l1"><a class="reference internal" href="State.html">State</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Projection.html">Projection</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Run.html">Run</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Component.html">Component</a></li>
@@ -197,7 +195,7 @@ <h1>DDM<a class="headerlink" href="#ddm" title="Permalink to this headline">¶</
 <div class="section" id="creating-a-ddm-mechanism">
 <span id="ddm-creation"></span><h2>Creating a DDM Mechanism<a class="headerlink" href="#creating-a-ddm-mechanism" title="Permalink to this headline">¶</a></h2>
 <p>A DDM Mechanism can be instantiated directly by calling its constructor, or by using the <a class="reference internal" href="Mechanism.html#Mechanism.mechanism" title="Mechanism.mechanism"><code class="xref any py py-func docutils literal"><span class="pre">mechanism</span></code></a> function
-and specifying DDM as its <code class="xref any docutils literal"><span class="pre">mech_spec</span></code> argument.  The analytic solution used in <code class="xref any docutils literal"><span class="pre">TRIAL</span></code> mode is selected
+and specifying DDM as its <strong>mech_spec</strong> argument.  The analytic solution used in <code class="xref any docutils literal"><span class="pre">TRIAL</span></code> mode is selected
 using the <a class="reference internal" href="#DDM.DDM.function" title="DDM.DDM.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> argument, which can be simply the name of a DDM function (first example below),
 or a call to the function with arguments specifying its parameters (see <a class="reference internal" href="#ddm-execution"><span class="std std-ref">Execution</span></a> below for a description of
 DDM function parameters) and, optionally, a <a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjection</span></a> (second example):</p>
@@ -227,7 +225,7 @@ <h1>DDM<a class="headerlink" href="#ddm" title="Permalink to this headline">¶</
 expected mean response time and accuracy, while <a class="reference internal" href="Function.html#Function.NavarroAndFuss" title="Function.NavarroAndFuss"><code class="xref any py py-class docutils literal"><span class="pre">NavarroAndFuss</span></code></a> also returns an expected mean correct response time
 and accuracy.</p>
 <p>When <a class="reference internal" href="#DDM.DDM.time_scale" title="DDM.DDM.time_scale"><code class="xref any py py-attr docutils literal"><span class="pre">time_scale</span></code></a> is set to <code class="xref any docutils literal"><span class="pre">TimeScale.TIME_STEP</span></code>, the <a class="reference internal" href="#DDM.DDM.function" title="DDM.DDM.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> paramter must be set
-to &#8216;Integrator&#8217; with an <a class="reference internal" href="IntegratorMechanism.html#IntegratorMechanism.IntegratorMechanism.Integrator.integration_type" title="IntegratorMechanism.IntegratorMechanism.Integrator.integration_type"><code class="xref any py py-attr docutils literal"><span class="pre">integration_type</span></code></a> of DIFFUSION.</p>
+to &#8216;Integrator&#8217; with an <code class="xref any docutils literal"><span class="pre">integration_type</span></code> of DIFFUSION.</p>
 </div>
 <div class="section" id="ddm-parameters">
 <span id="id2"></span><h3>DDM Parameters<a class="headerlink" href="#ddm-parameters" title="Permalink to this headline">¶</a></h3>
@@ -255,7 +253,7 @@ <h1>DDM<a class="headerlink" href="#ddm" title="Permalink to this headline">¶</
               <span class="p">)</span>
 </pre></div>
 </div>
-<p><a class="reference internal" href="IntegratorMechanism.html#integrator"><span class="std std-ref">Integrator</span></a></p>
+<p><code class="xref any docutils literal"><span class="pre">Integrator</span></code></p>
 <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">my_DDM_TimeStep</span> <span class="o">=</span> <span class="n">DDM</span><span class="p">(</span><span class="n">function</span><span class="o">=</span><span class="n">Integrator</span><span class="p">(</span> <span class="n">integration_type</span> <span class="o">=</span> <span class="n">DIFFUSION</span><span class="p">,</span>
                                   <span class="n">noise</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
                                   <span class="n">time_step_size</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span>
@@ -289,12 +287,6 @@ <h1>DDM<a class="headerlink" href="#ddm" title="Permalink to this headline">¶</
 </div>
 <div class="section" id="class-reference">
 <span id="ddm-class-reference"></span><h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalink to this headline">¶</a></h2>
-<dl class="class">
-<dt id="DDM.DDM_Output">
-<em class="property">class </em><code class="descclassname">DDM.</code><code class="descname">DDM_Output</code><a class="headerlink" href="#DDM.DDM_Output" title="Permalink to this definition">¶</a></dt>
-<dd><p>An enumeration.</p>
-</dd></dl>
-
 <dl class="class">
 <dt id="DDM.DDM">
 <em class="property">class </em><code class="descclassname">DDM.</code><code class="descname">DDM</code><span class="sig-paren">(</span><em>default_input_value=None</em>, <em>function=BogaczEtAl</em>, <em>params=None</em>, <em>name=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#DDM.DDM" title="Permalink to this definition">¶</a></dt>
@@ -315,7 +307,7 @@ <h1>DDM<a class="headerlink" href="#ddm" title="Permalink to this headline">¶</
 <li><strong>function</strong> (<em>IntegratorFunction : default BogaczEtAl</em>) &#8211; specifies the analytic solution to use for the decision process if <a class="reference internal" href="#DDM.DDM.time_scale" title="DDM.DDM.time_scale"><code class="xref any py py-attr docutils literal"><span class="pre">time_scale</span></code></a> is set to
 <code class="xref any docutils literal"><span class="pre">TimeScale.TRIAL</span></code>; can be <a class="reference internal" href="Function.html#Function.BogaczEtAl" title="Function.BogaczEtAl"><code class="xref any py py-class docutils literal"><span class="pre">BogaczEtAl</span></code></a> or <a class="reference internal" href="Function.html#Function.NavarroAndFuss" title="Function.NavarroAndFuss"><code class="xref any py py-class docutils literal"><span class="pre">NavarroAndFuss</span></code></a> (note:  the latter requires that the MatLab engine
 is installed). If <a class="reference internal" href="#DDM.DDM.time_scale" title="DDM.DDM.time_scale"><code class="xref any py py-attr docutils literal"><span class="pre">time_scale</span></code></a> is set to <code class="xref any docutils literal"><span class="pre">TimeScale.TIME_STEP</span></code>, <a class="reference internal" href="#DDM.DDM.function" title="DDM.DDM.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> must
-be <a class="reference internal" href="IntegratorMechanism.html#IntegratorMechanism.IntegratorMechanism.Integrator" title="IntegratorMechanism.IntegratorMechanism.Integrator"><code class="xref any py py-class docutils literal"><span class="pre">Integrator</span></code></a> with an integration_type of DIFFUSION, and the mechanism
+be <code class="xref any docutils literal"><span class="pre">Integrator</span></code> with an integration_type of DIFFUSION, and the mechanism
 will return the result of one time step.</li>
 <li><strong>time_scale</strong> (<em>TimeScale : default TimeScale.TRIAL</em>) &#8211; specifies whether the mechanism is executed on the time_step or trial time scale.
 This must be set to <code class="xref any docutils literal"><span class="pre">TimeScale.TRIAL</span></code> to use one of the analytic solutions specified by
@@ -345,7 +337,7 @@ <h1>DDM<a class="headerlink" href="#ddm" title="Permalink to this headline">¶</
 <code class="descname">function</code><a class="headerlink" href="#DDM.DDM.function" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>IntegratorFunction : default BogaczEtAl</em> &#8211; the function used to compute the outcome of the decision process when <a class="reference internal" href="#DDM.DDM.time_scale" title="DDM.DDM.time_scale"><code class="xref any py py-attr docutils literal"><span class="pre">time_scale</span></code></a> is
 <code class="xref any docutils literal"><span class="pre">TimeScale.TRIAL</span></code>.  If <a class="reference internal" href="#DDM.DDM.time_scale" title="DDM.DDM.time_scale"><code class="xref any py py-attr docutils literal"><span class="pre">time_scale</span></code></a> is set to <code class="xref any docutils literal"><span class="pre">TimeScale.TIME_STEP</span></code>, <a class="reference internal" href="#DDM.DDM.function" title="DDM.DDM.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>
-must be <a class="reference internal" href="IntegratorMechanism.html#IntegratorMechanism.IntegratorMechanism.Integrator" title="IntegratorMechanism.IntegratorMechanism.Integrator"><code class="xref any py py-class docutils literal"><span class="pre">Integrator</span></code></a> with an &#8216;integration_type &lt;Integrator.integration_type&gt;&#8217; of DIFFUSION, and the mechanism
+must be <code class="xref any docutils literal"><span class="pre">Integrator</span></code> with an &#8216;integration_type &lt;Integrator.integration_type&gt;&#8217; of DIFFUSION, and the mechanism
 will return the result of one time step.</p>
 </dd></dl>
 
@@ -359,12 +351,12 @@ <h1>DDM<a class="headerlink" href="#ddm" title="Permalink to this headline">¶</
 <dl class="attribute">
 <dt id="DDM.DDM.value">
 <code class="descname">value</code><a class="headerlink" href="#DDM.DDM.value" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>2d np.array[array(float64),array(float64),array(float64),array(float64)]</em> &#8211; result of executing DDM <a class="reference internal" href="#DDM.DDM.function" title="DDM.DDM.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>; same items as <a class="reference internal" href="#DDM.DDM.outputValue" title="DDM.DDM.outputValue"><code class="xref any py py-attr docutils literal"><span class="pre">outputValue</span></code></a>.</p>
+<dd><p><em>2d np.array[array(float64),array(float64),array(float64),array(float64)]</em> &#8211; result of executing DDM <a class="reference internal" href="#DDM.DDM.function" title="DDM.DDM.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>; same items as <a class="reference internal" href="#DDM.DDM.output_values" title="DDM.DDM.output_values"><code class="xref any py py-attr docutils literal"><span class="pre">output_values</span></code></a>.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="DDM.DDM.outputValue">
-<code class="descname">outputValue</code><a class="headerlink" href="#DDM.DDM.outputValue" title="Permalink to this definition">¶</a></dt>
+<dt id="DDM.DDM.output_values">
+<code class="descname">output_values</code><a class="headerlink" href="#DDM.DDM.output_values" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>List[array(float64),array(float64),array(float64),array(float64)]</em> &#8211; a list with the following items:
 * <strong>decision variable</strong> (value of <code class="xref any docutils literal"><span class="pre">DDM_DECISION_VARIABLE</span></code> outputState);
 * <strong>response time</strong> (value of <code class="xref any docutils literal"><span class="pre">DDM_RESPONSE_TIME</span></code> outputState);
@@ -439,7 +431,7 @@ <h1>DDM<a class="headerlink" href="#ddm" title="Permalink to this headline">¶</
   
     <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
       
-        <a href="ObjectiveMechanism.html" class="btn btn-neutral float-right" title="ObjectiveMechanism" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
+        <a href="TransferMechanism.html" class="btn btn-neutral float-right" title="TransferMechanism" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
       
       
         <a href="IntegratorMechanism.html" class="btn btn-neutral" title="IntegratorMechanism" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
diff --git a/docs/build/html/DefaultControlMechanism.html b/docs/build/html/DefaultControlMechanism.html
index 52081271c95..6091bfb53df 100644
--- a/docs/build/html/DefaultControlMechanism.html
+++ b/docs/build/html/DefaultControlMechanism.html
@@ -114,7 +114,7 @@
 <li class="toctree-l2"><a class="reference internal" href="Mechanism.html#class-reference">Class Reference</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="State.html">States</a></li>
+<li class="toctree-l1"><a class="reference internal" href="State.html">State</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Projection.html">Projection</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Run.html">Run</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Component.html">Component</a></li>
@@ -171,14 +171,11 @@
             
   <div class="section" id="module-AdaptiveMechanisms.ControlMechanisms.DefaultControlMechanism">
 <span id="defaultcontrolmechanism"></span><h1>DefaultControlMechanism<a class="headerlink" href="#module-AdaptiveMechanisms.ControlMechanisms.DefaultControlMechanism" title="Permalink to this headline">¶</a></h1>
-<p>The DefaultControlMechanism is created whenever PsyNeuLink is run.  It is assigned as the ControlMechanism for any
-<a class="reference internal" href="System.html"><span class="doc">System</span></a> that is created for which no ControlMechanism is specified.  The DefaultControlMechanism creates
-an inputState for each ControlProjection it is assigned, and uses <code class="xref py py-data docutils literal"><span class="pre">defaultControlAllocation</span></code> as the value for
-the control signal.  By default,  <code class="xref py py-data docutils literal"><span class="pre">defaultControlAllocation</span></code> = 1, so that ControlProjections from the
-DefaultControlMechanism have no effect on their parameters.  Thus, for the most part, the DefaultControlMechanism
-serves as a place marker for ControlProjections (having on effect on the parameters it &#8220;controls&#8221;), until the system
-is assigned a more useful ControlMechanism (such as the <a class="reference internal" href="EVCMechanism.html"><span class="doc">EVCMechanism</span></a>).  However, it can be used to uniformly
-control the parameters that receive ControlProjections from it, by manually changing the value of
+<p>The DefaultControlMechanism is created for a <a class="reference internal" href="System.html"><span class="doc">System</span></a> if no other controller type is specified. The
+DefaultControlMechanism createsan inputState for each ControlProjection it is assigned, and uses
+<code class="xref any docutils literal"><span class="pre">defaultControlAllocation</span></code> as the value for the control signal.  By default,  <code class="xref py py-data docutils literal"><span class="pre">defaultControlAllocation</span></code> =  1,
+so that ControlProjections from the DefaultControlMechanism have no effect on their parameters.  However, it can be
+used to uniformly control the parameters that receive ControlProjections from it, by manually changing the value of
 <code class="xref any docutils literal"><span class="pre">defaultControlAllocation</span></code>.  See <a class="reference internal" href="ControlMechanism.html"><span class="doc">Control Mechanism</span></a> for additional details of how ControlMechanisms are
 created, executed and their attributes.</p>
 </div>
diff --git a/docs/build/html/Defaults.html b/docs/build/html/Defaults.html
index e997bd5c224..31e1e8a66c5 100644
--- a/docs/build/html/Defaults.html
+++ b/docs/build/html/Defaults.html
@@ -86,7 +86,7 @@
 <li class="toctree-l1"><a class="reference internal" href="System.html">System</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Process.html">Process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Mechanism.html">Mechanism</a></li>
-<li class="toctree-l1"><a class="reference internal" href="State.html">States</a></li>
+<li class="toctree-l1"><a class="reference internal" href="State.html">State</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Projection.html">Projection</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Run.html">Run</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Component.html">Component</a></li>
diff --git a/docs/build/html/EVCMechanism.html b/docs/build/html/EVCMechanism.html
index ceaa20716f5..74c63dea7e3 100644
--- a/docs/build/html/EVCMechanism.html
+++ b/docs/build/html/EVCMechanism.html
@@ -114,7 +114,7 @@
 <li class="toctree-l2"><a class="reference internal" href="Mechanism.html#class-reference">Class Reference</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="State.html">States</a></li>
+<li class="toctree-l1"><a class="reference internal" href="State.html">State</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Projection.html">Projection</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Run.html">Run</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Component.html">Component</a></li>
@@ -187,16 +187,16 @@ <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this head
 the purpose and structure of the EVCMechanism.</p>
 <p>An EVCMechanism has one <a class="reference internal" href="ControlSignal.html"><span class="doc">ControlSignal</span></a> for each parameter of the mechanism or function that it controls.  Each
 ControlSignal is associated with a <a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjection</span></a>.  The ControlProjection regulates the value of the parameter
-it controls, with the magnitude of that regulation determined by the ControlSignal&#8217;s <a class="reference internal" href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.intensity" title="AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.intensity"><code class="xref any py py-attr docutils literal"><span class="pre">intensity</span></code></a>. A
-particular combination of ControlSignal intensities is called an <a class="reference internal" href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>. When a system is executed,
-it concludes by executing the EVCMechanism, which determines the <a class="reference internal" href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> (i.e., the ControlSignal
+it controls, with the magnitude of that regulation determined by the ControlSignal&#8217;s <code class="xref any docutils literal"><span class="pre">intensity</span></code>. A
+particular combination of ControlSignal intensities is called an <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>. When a system is executed,
+it concludes by executing the EVCMechanism, which determines the <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> (i.e., the ControlSignal
 intensities, and thereby the values of the parameters being controlled) in the next round of execution.</p>
-<p id="evcmechanism-evc">The procedure by which the EVCMechanism selects an <a class="reference internal" href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> when it is executed is determined by its
+<p id="evcmechanism-evc">The procedure by which the EVCMechanism selects an <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> when it is executed is determined by its
 <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> attribute. By default, this evaluates the performance of the system under every
-possible <a class="reference internal" href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>, and chooses the best one. It does this by simulating the system under each
-<a class="reference internal" href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>, and evaluating the expected value of control (EVC): a cost-benefit analysis that weighs
+possible <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>, and chooses the best one. It does this by simulating the system under each
+<a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>, and evaluating the expected value of control (EVC): a cost-benefit analysis that weighs
 the cost of the ControlSignals against the outcome of performance for the given policy.  The EVCMechanism then
-selects the <a class="reference internal" href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> that generates the maximum EVC, and that allocation_policy is implemented for the
+selects the <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> that generates the maximum EVC, and that allocation_policy is implemented for the
 next round of execution. Each step of this procedure can be modified, or it can be replaced entirely, by assigning
 custom functions to corresponding parameters of the EVCMechanism, as described under <a class="reference internal" href="#evc-calculation"><span class="std std-ref">EVC Calculation</span></a> below.</p>
 </div>
@@ -210,7 +210,7 @@ <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this head
 <code class="xref any docutils literal"><span class="pre">montioring_mechanism</span></code> attribute) that is used to monitor and evaluate the system&#8217;s performance.  The
 ObjectiveMechanism monitors each mechanism and/or outputState listed in the EVCMechanism&#8217;s
 &#8216;monitor_for_control &lt;EVCMechanism.monitor_for_control&gt;` attribute, and evaluates them using the function specified in
-the EVCMechanism&#8217;s <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.outcome_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.outcome_function"><code class="xref any py py-attr docutils literal"><span class="pre">outcome_function</span></code></a> attribute. This information is used to set the <a class="reference internal" href="ControlProjection.html#ControlProjection.ControlProjection.allocation" title="ControlProjection.ControlProjection.allocation"><code class="xref any py py-attr docutils literal"><span class="pre">allocation</span></code></a> values for the
+the EVCMechanism&#8217;s <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.outcome_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.outcome_function"><code class="xref any py py-attr docutils literal"><span class="pre">outcome_function</span></code></a> attribute. This information is used to set the <a class="reference internal" href="ControlProjection.html#ModulatoryProjections.ControlProjection.ControlProjection.allocation" title="ModulatoryProjections.ControlProjection.ControlProjection.allocation"><code class="xref any py py-attr docutils literal"><span class="pre">allocation</span></code></a> values for the
 EVCMechanism&#8217;s  <a class="reference internal" href="ControlSignal.html"><span class="doc">ControlSignals</span></a>.  Each ControlSignal is implemented as an <a class="reference internal" href="OutputState.html"><span class="doc">outputState</span></a> of the EVCMechanism, that is assigned a  <a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjections</span></a> which projects to the
 <a class="reference internal" href="ParameterState.html"><span class="doc">parameterStates</span></a> for the parameters of the mechanisms and/or functions controlled by that
 ControlSignal.  In addition, a set of prediction mechanisms  are created that are used to keep a running average of
@@ -234,23 +234,23 @@ <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this head
 </div>
 <div class="section" id="function">
 <h3>Function<a class="headerlink" href="#function" title="Permalink to this headline">¶</a></h3>
-<p>The <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> of an EVCMechanism returns an <a class="reference internal" href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> &#8211; that is, the <a class="reference internal" href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.intensity" title="AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.intensity"><code class="xref any py py-attr docutils literal"><span class="pre">intensity</span></code></a> of
+<p>The <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> of an EVCMechanism returns an <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> &#8211; that is, the <code class="xref any docutils literal"><span class="pre">intensity</span></code> of
 each of its <a class="reference internal" href="ControlSignal.html"><span class="doc">ControlSignals</span></a> &#8211; that will be used in the next round of the system&#8217;s execution.  Any
-function can be used that returns an appropriate value (i.e., that specifies an <a class="reference internal" href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> for the exact
-number of ControlSignals in the EVCMechanism&#8217;s <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignals" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignals"><code class="xref any py py-attr docutils literal"><span class="pre">controlSignals</span></code></a> attribute, using the correct format for the <a class="reference internal" href="ControlProjection.html#ControlProjection.ControlProjection.allocation" title="ControlProjection.ControlProjection.allocation"><code class="xref any py py-attr docutils literal"><span class="pre">allocation</span></code></a>
+function can be used that returns an appropriate value (i.e., that specifies an <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> for the exact
+number of ControlSignals in the EVCMechanism&#8217;s <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signals" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signals"><code class="xref any py py-attr docutils literal"><span class="pre">control_signals</span></code></a> attribute, using the correct format for the <a class="reference internal" href="ControlProjection.html#ModulatoryProjections.ControlProjection.ControlProjection.allocation" title="ModulatoryProjections.ControlProjection.ControlProjection.allocation"><code class="xref any py py-attr docutils literal"><span class="pre">allocation</span></code></a>
 value of each ControlSignal). The default function is <code class="xref any docutils literal"><span class="pre">ControlSignalGridSearch</span></code>, which evaluates the performance of the
-system under a range of specified allocationPolicies, and returns the <a class="reference internal" href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> that generates the best
+system under a range of specified allocationPolicies, and returns the <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> that generates the best
 performance (the greatest EVC). This evaluation and selection procedure, including the four evaluation functions that it
 uses (all of which are customizable), is described below.</p>
 <div class="section" id="evc-calculation">
 <span id="id1"></span><h4>EVC Calculation<a class="headerlink" href="#evc-calculation" title="Permalink to this headline">¶</a></h4>
 <p>The default EVC <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> calculates the expected value of control (EVC) by a conducting a
-grid search over every possible <a class="reference internal" href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>.  The set of allocationPolicies sampled is determined by the
-<a class="reference internal" href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.allocation_samples" title="AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.allocation_samples"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_samples</span></code></a> attribute of each <a class="reference internal" href="ControlSignal.html"><span class="doc">ControlSignal</span></a>. Each policy is constructed by drawing one value from the
-<a class="reference internal" href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.allocation_samples" title="AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.allocation_samples"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_samples</span></code></a> attribute of each of the EVCMechanism&#8217;s ControlSignals.  An <a class="reference internal" href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> is constructed
-for every possible combination of values, and stored in the EVCMechanism&#8217;s <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignalSearchSpace" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignalSearchSpace"><code class="xref any py py-attr docutils literal"><span class="pre">controlSignalSearchSpace</span></code></a> attribute.  The
-EVCMechanism&#8217;s <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.run_simulation" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.run_simulation"><code class="xref any py py-meth docutils literal"><span class="pre">run_simulation</span></code></a> method is then used to simulate the system under each <a class="reference internal" href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> in
-<a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignalSearchSpace" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignalSearchSpace"><code class="xref any py py-attr docutils literal"><span class="pre">controlSignalSearchSpace</span></code></a>, calculate the EVC for each of those policies, and return the policy with the greatest EVC.
+grid search over every possible <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>.  The set of allocationPolicies sampled is determined by the
+<code class="xref any docutils literal"><span class="pre">allocation_samples</span></code> attribute of each <a class="reference internal" href="ControlSignal.html"><span class="doc">ControlSignal</span></a>. Each policy is constructed by drawing one value from the
+<code class="xref any docutils literal"><span class="pre">allocation_samples</span></code> attribute of each of the EVCMechanism&#8217;s ControlSignals.  An <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> is constructed
+for every possible combination of values, and stored in the EVCMechanism&#8217;s <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signal_search_space" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signal_search_space"><code class="xref any py py-attr docutils literal"><span class="pre">control_signal_search_space</span></code></a> attribute.  The
+EVCMechanism&#8217;s <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.run_simulation" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.run_simulation"><code class="xref any py py-meth docutils literal"><span class="pre">run_simulation</span></code></a> method is then used to simulate the system under each <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> in
+<a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signal_search_space" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signal_search_space"><code class="xref any py py-attr docutils literal"><span class="pre">control_signal_search_space</span></code></a>, calculate the EVC for each of those policies, and return the policy with the greatest EVC.
 By default, only the maximum EVC is saved and returned.  However, by setting the <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.save_all_values_and_policies" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.save_all_values_and_policies"><code class="xref any py py-attr docutils literal"><span class="pre">save_all_values_and_policies</span></code></a>
 attribute to <code class="xref any docutils literal"><span class="pre">True</span></code>, each policy and its EVC can be saved for each simulation run (in <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.EVC_policies" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.EVC_policies"><code class="xref any py py-attr docutils literal"><span class="pre">EVC_policies</span></code></a> and <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.EVC_values" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.EVC_values"><code class="xref any py py-attr docutils literal"><span class="pre">EVC_values</span></code></a>,
 respectively). The EVC is calculated for each policy using the following four functions, each of which can be
@@ -259,14 +259,14 @@ <h3>Function<a class="headerlink" href="#function" title="Permalink to this head
 below):</p>
 <ul class="simple" id="evc-auxiliary-functions">
 <li><a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.value_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.value_function"><code class="xref any py py-attr docutils literal"><span class="pre">value_function</span></code></a> - this is an &#8220;orchestrating&#8221; function that calls the three subordinate functions described below,
-which do the actual work of evaluating the performance of the system and the <a class="reference internal" href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.cost" title="AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.cost"><code class="xref any py py-attr docutils literal"><span class="pre">cost</span></code></a> of the ControlSignals under the
-current <a class="reference internal" href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>, and combining these to calculate the EVC.  This function can be replaced with a
+which do the actual work of evaluating the performance of the system and the <code class="xref any docutils literal"><span class="pre">cost</span></code> of the ControlSignals under the
+current <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>, and combining these to calculate the EVC.  This function can be replaced with a
 user-defined function to fully customize the calculation of the EVC, by assigning a custom function to the
 <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.value_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.value_function"><code class="xref any py py-attr docutils literal"><span class="pre">value_function</span></code></a> attribute of the EVCMechanism (see <a class="reference internal" href="#evcmechanism-calling-and-assigning-functions"><span class="std std-ref">note</span></a> below).</li>
 </ul>
 <ul class="simple">
 <li><a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.outcome_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.outcome_function"><code class="xref any py py-attr docutils literal"><span class="pre">outcome_function</span></code></a> - this combines the values of the outputStates in the EVCMechanism&#8217;s <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitored_output_states" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitored_output_states"><code class="xref any py py-attr docutils literal"><span class="pre">monitored_output_states</span></code></a>
-attribute to generate an aggregated outcome value for the current <a class="reference internal" href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>. The default is the
+attribute to generate an aggregated outcome value for the current <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>. The default is the
 <a class="reference internal" href="Function.html#Function.LinearCombination" title="Function.LinearCombination"><code class="xref any py py-class docutils literal"><span class="pre">LinearCombination</span></code></a> function, which computes an elementwise (Hadamard) product of the outputState values, using any
 <code class="xref any docutils literal"><span class="pre">weights</span> <span class="pre">and/or</span> <span class="pre">exponents</span> <span class="pre">specified</span> <span class="pre">for</span> <span class="pre">the</span> <span class="pre">outputStates</span></code> to scale and/or
 exponentiate the contribution that each makes to the aggregated outcome (see <a class="reference internal" href="#evcmechanism-examples"><span class="std std-ref">examples</span></a>).
@@ -274,13 +274,13 @@ <h3>Function<a class="headerlink" href="#function" title="Permalink to this head
 EVCMechanism&#8217;s <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.outcome_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.outcome_function"><code class="xref any py py-attr docutils literal"><span class="pre">outcome_function</span></code></a> attribute (see <a class="reference internal" href="#evcmechanism-calling-and-assigning-functions"><span class="std std-ref">note</span></a> below).</li>
 </ul>
 <ul class="simple">
-<li><a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.cost_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.cost_function"><code class="xref any py py-attr docutils literal"><span class="pre">cost_function</span></code></a> - this combines the <a class="reference internal" href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.cost" title="AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.cost"><code class="xref any py py-attr docutils literal"><span class="pre">cost</span></code></a> of the EVCMechanism&#8217;s ControlSignals to generate an aggregated cost for
-the current <a class="reference internal" href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>.  The default is the <a class="reference internal" href="Function.html#Function.LinearCombination" title="Function.LinearCombination"><code class="xref any py py-class docutils literal"><span class="pre">LinearCombination</span></code></a> function, which sums the costs.  The
+<li><a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.cost_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.cost_function"><code class="xref any py py-attr docutils literal"><span class="pre">cost_function</span></code></a> - this combines the <code class="xref any docutils literal"><span class="pre">cost</span></code> of the EVCMechanism&#8217;s ControlSignals to generate an aggregated cost for
+the current <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>.  The default is the <a class="reference internal" href="Function.html#Function.LinearCombination" title="Function.LinearCombination"><code class="xref any py py-class docutils literal"><span class="pre">LinearCombination</span></code></a> function, which sums the costs.  The
 evaluation of cost can be further customized by specifying a custom function for the <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.cost_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.cost_function"><code class="xref any py py-attr docutils literal"><span class="pre">cost_function</span></code></a> attribute.</li>
 </ul>
 <ul class="simple">
-<li><a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.combine_outcome_and_cost_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.combine_outcome_and_cost_function"><code class="xref any py py-attr docutils literal"><span class="pre">combine_outcome_and_cost_function</span></code></a> - this combines the aggregated outcome and aggregated <a class="reference internal" href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.cost" title="AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.cost"><code class="xref any py py-attr docutils literal"><span class="pre">cost</span></code></a> values for the
-current <a class="reference internal" href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>, to determine the EVC for that policy.  The default is the <a class="reference internal" href="Function.html#Function.LinearCombination" title="Function.LinearCombination"><code class="xref any py py-class docutils literal"><span class="pre">LinearCombination</span></code></a>
+<li><a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.combine_outcome_and_cost_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.combine_outcome_and_cost_function"><code class="xref any py py-attr docutils literal"><span class="pre">combine_outcome_and_cost_function</span></code></a> - this combines the aggregated outcome and aggregated <code class="xref any docutils literal"><span class="pre">cost</span></code> values for the
+current <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>, to determine the EVC for that policy.  The default is the <a class="reference internal" href="Function.html#Function.LinearCombination" title="Function.LinearCombination"><code class="xref any py py-class docutils literal"><span class="pre">LinearCombination</span></code></a>
 function, which subtracts the aggregated cost from the aggregated outcome. The way in which the outcome and cost
 are combined to determine the EVC can be customized by specifying a custom function for the
 <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.combine_outcome_and_cost_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.combine_outcome_and_cost_function"><code class="xref any py py-attr docutils literal"><span class="pre">combine_outcome_and_cost_function</span></code></a> attribute (see <a class="reference internal" href="#evcmechanism-calling-and-assigning-functions"><span class="std std-ref">note</span></a> below).</li>
@@ -288,7 +288,7 @@ <h3>Function<a class="headerlink" href="#function" title="Permalink to this head
 <div class="admonition note" id="evcmechanism-calling-and-assigning-functions">
 <p class="first admonition-title">Note</p>
 <p class="last">The EVCMechanism function attributes described above are all implemented as PsyNeuLink <a class="reference internal" href="Function.html"><span class="doc">Function</span></a> objects
-(so that, among other reasons, they can be parameterized using a <a class="reference internal" href="Function.html#Function.NormalDist.params" title="Function.NormalDist.params"><code class="xref any py py-attr docutils literal"><span class="pre">params</span></code></a> dictionary).  Therefore, to call the
+(so that, among other reasons, they can be parameterized using a <a class="reference internal" href="Function.html#Function.Distance.params" title="Function.Distance.params"><code class="xref any py py-attr docutils literal"><span class="pre">params</span></code></a> dictionary).  Therefore, to call the
 function itself, it must be referenced as <code class="docutils literal"><span class="pre">&lt;EVCMechanism&gt;.&lt;function_attribute&gt;.function</span></code>.  A custom function
 assigned to one of the function attributes can be either a PsyNeuLink <a class="reference internal" href="Function.html"><span class="doc">Function</span></a>, or a generic python
 function or method (including a lambda function).  However, if it is one of the latter, it is automatically
@@ -303,20 +303,20 @@ <h3>Function<a class="headerlink" href="#function" title="Permalink to this head
 <span id="evcmechanism-controlsignal"></span><h3>ControlSignals<a class="headerlink" href="#controlsignals" title="Permalink to this headline">¶</a></h3>
 <p>A <a class="reference internal" href="ControlSignal.html"><span class="doc">ControlSignal</span></a> is used to regulate the parameter of a mechanism or its function. An EVCMechanism has one
 ControlSignal for each parameter that it controls.  One <a class="reference internal" href="OutputState.html"><span class="doc">outputState</span></a> of the EVCMechanism is dedicated to
-each of its ControlSignals, and the value of that outputState is the ControlSignal&#8217;s <a class="reference internal" href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.intensity" title="AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.intensity"><code class="xref any py py-attr docutils literal"><span class="pre">intensity</span></code></a>.  When an EVCMechanism
+each of its ControlSignals, and the value of that outputState is the ControlSignal&#8217;s <code class="xref any docutils literal"><span class="pre">intensity</span></code>.  When an EVCMechanism
 is <a class="reference internal" href="#evcmechanism-creation"><span class="std std-ref">created automatically</span></a>, it creates a ControlSignal for each parameter that has been
 specified for control in the system (a parameter is specified  for control by assigning it a ControlProjection;
-see <a class="reference internal" href="Mechanism.html#mechanism-parameters"><span class="std std-ref">Mechanism Parameters</span></a>).  The ControlSignals of an EVCMechanism are listed in it <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignals" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignals"><code class="xref any py py-attr docutils literal"><span class="pre">controlSignals</span></code></a>
+see <a class="reference internal" href="Mechanism.html#mechanism-parameters"><span class="std std-ref">Mechanism Parameters</span></a>).  The ControlSignals of an EVCMechanism are listed in it <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signals" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signals"><code class="xref any py py-attr docutils literal"><span class="pre">control_signals</span></code></a>
 attribute. Each ControlSignal is associated with a <a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjection</span></a> that projects to the
 <a class="reference internal" href="ParameterState.html"><span class="doc">parameterState</span></a> for the parameter controlled by that ControlSignal. The EVCMechanism&#8217;s
-<a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> assigns an <a class="reference internal" href="ControlProjection.html#ControlProjection.ControlProjection.allocation" title="ControlProjection.ControlProjection.allocation"><code class="xref any py py-attr docutils literal"><span class="pre">allocation</span></code></a> value to each of its ControlSignals. The
-<a class="reference internal" href="ControlProjection.html#ControlProjection.ControlProjection.allocation" title="ControlProjection.ControlProjection.allocation"><code class="xref any py py-attr docutils literal"><span class="pre">allocation</span></code></a> for a given ControlSignal determines that ControlSignal&#8217;s <a class="reference internal" href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.intensity" title="AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.intensity"><code class="xref any py py-attr docutils literal"><span class="pre">intensity</span></code></a>, which is then assigned as the
+<a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> assigns an <a class="reference internal" href="ControlProjection.html#ModulatoryProjections.ControlProjection.ControlProjection.allocation" title="ModulatoryProjections.ControlProjection.ControlProjection.allocation"><code class="xref any py py-attr docutils literal"><span class="pre">allocation</span></code></a> value to each of its ControlSignals. The
+<a class="reference internal" href="ControlProjection.html#ModulatoryProjections.ControlProjection.ControlProjection.allocation" title="ModulatoryProjections.ControlProjection.ControlProjection.allocation"><code class="xref any py py-attr docutils literal"><span class="pre">allocation</span></code></a> for a given ControlSignal determines that ControlSignal&#8217;s <code class="xref any docutils literal"><span class="pre">intensity</span></code>, which is then assigned as the
 value of the ControlSignal&#8217;s ControlProjection.  The value of the ControlProjection is then used by the parameterState
 to which it projects to modify the value of the parameter for which it is responsible.  A ControlSignal also
-calculates a <a class="reference internal" href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.cost" title="AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.cost"><code class="xref any py py-attr docutils literal"><span class="pre">cost</span></code></a>, based on its <a class="reference internal" href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.intensity" title="AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.intensity"><code class="xref any py py-attr docutils literal"><span class="pre">intensity</span></code></a> and/or its time course. The <a class="reference internal" href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.cost" title="AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.cost"><code class="xref any py py-attr docutils literal"><span class="pre">cost</span></code></a> is included in the evaluation that the
-EVCMechanism carries out for a given <a class="reference internal" href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>, and that it uses to adapt the ControlSignal&#8217;s <a class="reference internal" href="ControlProjection.html#ControlProjection.ControlProjection.allocation" title="ControlProjection.ControlProjection.allocation"><code class="xref any py py-attr docutils literal"><span class="pre">allocation</span></code></a> in
-the future.  When the EVCMechanism chooses an <a class="reference internal" href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> to evaluate, it selects an allocation value from the
-ControlSignal&#8217;s <a class="reference internal" href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.allocation_samples" title="AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.allocation_samples"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_samples</span></code></a> attribute.</p>
+calculates a <code class="xref any docutils literal"><span class="pre">cost</span></code>, based on its <code class="xref any docutils literal"><span class="pre">intensity</span></code> and/or its time course. The <code class="xref any docutils literal"><span class="pre">cost</span></code> is included in the evaluation that the
+EVCMechanism carries out for a given <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>, and that it uses to adapt the ControlSignal&#8217;s <a class="reference internal" href="ControlProjection.html#ModulatoryProjections.ControlProjection.ControlProjection.allocation" title="ModulatoryProjections.ControlProjection.ControlProjection.allocation"><code class="xref any py py-attr docutils literal"><span class="pre">allocation</span></code></a> in
+the future.  When the EVCMechanism chooses an <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> to evaluate, it selects an allocation value from the
+ControlSignal&#8217;s <code class="xref any docutils literal"><span class="pre">allocation_samples</span></code> attribute.</p>
 </div>
 <div class="section" id="prediction-mechanisms">
 <span id="evcmechanism-prediction-mechanisms"></span><h3>Prediction Mechanisms<a class="headerlink" href="#prediction-mechanisms" title="Permalink to this headline">¶</a></h3>
@@ -338,24 +338,24 @@ <h3>Function<a class="headerlink" href="#function" title="Permalink to this head
 <div class="section" id="execution">
 <span id="evcmechanism-execution"></span><h2>Execution<a class="headerlink" href="#execution" title="Permalink to this headline">¶</a></h2>
 <p>When an EVCMechanism is executed, it updates the value of its <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.predictionMechanisms" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.predictionMechanisms"><code class="xref any py py-attr docutils literal"><span class="pre">predictionMechanisms</span></code></a> and <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitoring_mechanism" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitoring_mechanism"><code class="xref any py py-attr docutils literal"><span class="pre">monitoring_mechanism</span></code></a>,
-and then calls its <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>, which determines and implements the <a class="reference internal" href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> for
+and then calls its <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>, which determines and implements the <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> for
 the next round of the system&#8217;s execution.  By default, the EVCMechanism identifies and implements the
-<a class="reference internal" href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> that maximizes the EVC evaluated for the outputStates it is monitoring, as described below.
+<a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> that maximizes the EVC evaluated for the outputStates it is monitoring, as described below.
 However, this procedure can be modified by specifying a custom function for any or all of the functions described below.</p>
-<p id="evcmechanism-default-function">The default <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> for an EVCMechanism selects an <a class="reference internal" href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> by assessing
-the performance of the system under each of the policies in its <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignalSearchSpace" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignalSearchSpace"><code class="xref any py py-attr docutils literal"><span class="pre">controlSignalSearchSpace</span></code></a>, and selecting the
-one that yields the maximum EVC. The <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignalSearchSpace" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignalSearchSpace"><code class="xref any py py-attr docutils literal"><span class="pre">controlSignalSearchSpace</span></code></a> is constructed by creating a set of
-allocationPolicies that represent all permutations of the <a class="reference internal" href="ControlProjection.html#ControlProjection.ControlProjection.allocation" title="ControlProjection.ControlProjection.allocation"><code class="xref any py py-attr docutils literal"><span class="pre">allocation</span></code></a> values to be sampled for each ControlSignal.
-Each <a class="reference internal" href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> in the set is constructed by drawing one value from the <a class="reference internal" href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.allocation_samples" title="AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.allocation_samples"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_samples</span></code></a> of each
-ControlSignal, and the set contains all combinations of these values.  For each <a class="reference internal" href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>, the default
+<p id="evcmechanism-default-function">The default <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> for an EVCMechanism selects an <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> by assessing
+the performance of the system under each of the policies in its <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signal_search_space" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signal_search_space"><code class="xref any py py-attr docutils literal"><span class="pre">control_signal_search_space</span></code></a>, and selecting the
+one that yields the maximum EVC. The <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signal_search_space" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signal_search_space"><code class="xref any py py-attr docutils literal"><span class="pre">control_signal_search_space</span></code></a> is constructed by creating a set of
+allocationPolicies that represent all permutations of the <a class="reference internal" href="ControlProjection.html#ModulatoryProjections.ControlProjection.ControlProjection.allocation" title="ModulatoryProjections.ControlProjection.ControlProjection.allocation"><code class="xref any py py-attr docutils literal"><span class="pre">allocation</span></code></a> values to be sampled for each ControlSignal.
+Each <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> in the set is constructed by drawing one value from the <code class="xref any docutils literal"><span class="pre">allocation_samples</span></code> of each
+ControlSignal, and the set contains all combinations of these values.  For each <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>, the default
 <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> calls the EVCMechanism&#8217;s <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.value_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.value_function"><code class="xref any py py-attr docutils literal"><span class="pre">value_function</span></code></a> which, in turn, carries out the
 following steps:</p>
 <ul class="simple">
-<li><strong>Implement the allocation_policy.</strong> Assign the <a class="reference internal" href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.allocation" title="AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.allocation"><code class="xref any py py-attr docutils literal"><span class="pre">allocation</span></code></a> value specified
+<li><strong>Implement the allocation_policy.</strong> Assign the <code class="xref any docutils literal"><span class="pre">allocation</span></code> value specified
 for each ControlSignal.</li>
 </ul>
 <ul class="simple">
-<li><strong>Simulate performance.</strong>  Execute the system under the current <a class="reference internal" href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> using the EVCMechanism&#8217;s
+<li><strong>Simulate performance.</strong>  Execute the system under the current <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> using the EVCMechanism&#8217;s
 <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.run_simulation" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.run_simulation"><code class="xref any py py-meth docutils literal"><span class="pre">run_simulation</span></code></a> method and the value of its <code class="xref any docutils literal"><span class="pre">predicted_inputs</span></code> attribute as the input to the system (this uses
 the history of previous trials to generate an average expected input value).</li>
 </ul>
@@ -367,7 +367,7 @@ <h3>Function<a class="headerlink" href="#function" title="Permalink to this head
 outputStates the EVCMechanism monitors (listed in its <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitored_output_states" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitored_output_states"><code class="xref any py py-attr docutils literal"><span class="pre">monitored_output_states</span></code></a> attribute);</li>
 </ul>
 <ul class="simple">
-<li>the <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.cost_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.cost_function"><code class="xref any py py-attr docutils literal"><span class="pre">cost_function</span></code></a> calculates the <strong>cost</strong> of the allocation_policy by aggregating the <a class="reference internal" href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.cost" title="AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.cost"><code class="xref any py py-attr docutils literal"><span class="pre">cost</span></code></a> of the
+<li>the <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.cost_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.cost_function"><code class="xref any py py-attr docutils literal"><span class="pre">cost_function</span></code></a> calculates the <strong>cost</strong> of the allocation_policy by aggregating the <code class="xref any docutils literal"><span class="pre">cost</span></code> of the
 EVCMechanism&#8217;s ControlSignals;</li>
 </ul>
 <ul class="simple">
@@ -380,7 +380,7 @@ <h3>Function<a class="headerlink" href="#function" title="Permalink to this head
 <p>If the <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.save_all_values_and_policies" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.save_all_values_and_policies"><code class="xref any py py-attr docutils literal"><span class="pre">save_all_values_and_policies</span></code></a> attribute is <code class="xref any docutils literal"><span class="pre">True</span></code>, the allocation policy is saved in the
 EVCMechanism&#8217;s <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.EVC_policies" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.EVC_policies"><code class="xref any py py-attr docutils literal"><span class="pre">EVC_policies</span></code></a> attribute, and its value is saved in the <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.EVC_values" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.EVC_values"><code class="xref any py py-attr docutils literal"><span class="pre">EVC_values</span></code></a> attribute. The
 <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> returns the allocation_policy that yielded the maximum EVC. This is then
-implemented by assigning the <a class="reference internal" href="ControlProjection.html#ControlProjection.ControlProjection.allocation" title="ControlProjection.ControlProjection.allocation"><code class="xref any py py-attr docutils literal"><span class="pre">allocation</span></code></a> specified for each ControlSignal by the designated allocation_policy.
+implemented by assigning the <a class="reference internal" href="ControlProjection.html#ModulatoryProjections.ControlProjection.ControlProjection.allocation" title="ModulatoryProjections.ControlProjection.ControlProjection.allocation"><code class="xref any py py-attr docutils literal"><span class="pre">allocation</span></code></a> specified for each ControlSignal by the designated allocation_policy.
 These allocations determine the value of the parameters being controlled in the next round of the system&#8217;s execution.</p>
 <p>This procedure can be modified by assigning custom functions to any or all of the ones described above, including the
 EVCMechanism&#8217;s <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> itself.  The requirements for each are described in the function
@@ -402,15 +402,9 @@ <h3>Function<a class="headerlink" href="#function" title="Permalink to this head
 </div>
 <div class="section" id="class-reference">
 <span id="evcmechanism-class-reference"></span><h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalink to this headline">¶</a></h2>
-<dl class="class">
-<dt id="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.ControlSignalCostOptions">
-<em class="property">class </em><code class="descclassname">AdaptiveMechanisms.ControlMechanisms.EVCMechanism.</code><code class="descname">ControlSignalCostOptions</code><a class="headerlink" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.ControlSignalCostOptions" title="Permalink to this definition">¶</a></dt>
-<dd><p>An enumeration.</p>
-</dd></dl>
-
 <dl class="class">
 <dt id="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism">
-<em class="property">class </em><code class="descclassname">AdaptiveMechanisms.ControlMechanisms.EVCMechanism.</code><code class="descname">EVCMechanism</code><span class="sig-paren">(</span><em>prediction_mechanism_type=IntegratorMechanism</em>, <em>prediction_mechanism_params=None</em>, <em>monitor_for_control=None</em>, <em>function=ControlSignalGridSearch                                       value_function=ValueFunction</em>, <em>outcome_function=LinearCombination(operation=PRODUCT)</em>, <em>cost_function=LinearCombination(operation=SUM)</em>, <em>combine_outcome_and_cost_function=LinearCombination(operation=SUM)     save_all_values_and_policies:bool=:keyword:`False`</em>, <em>params=None</em>, <em>name=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">AdaptiveMechanisms.ControlMechanisms.EVCMechanism.</code><code class="descname">EVCMechanism</code><span class="sig-paren">(</span><em>prediction_mechanism_type=IntegratorMechanism</em>, <em>prediction_mechanism_params=None</em>, <em>monitor_for_control=None</em>, <em>control_signals=None</em>, <em>function=ControlSignalGridSearch                                       value_function=ValueFunction</em>, <em>outcome_function=LinearCombination(operation=PRODUCT)</em>, <em>cost_function=LinearCombination(operation=SUM)</em>, <em>combine_outcome_and_cost_function=LinearCombination(operation=SUM)     save_all_values_and_policies:bool=:keyword:`False`</em>, <em>params=None</em>, <em>name=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism" title="Permalink to this definition">¶</a></dt>
 <dd><p>Optimizes the <a class="reference internal" href="ControlSignal.html"><span class="doc">ControlSignals</span></a> for a sysem &lt;System&gt;`.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
@@ -423,17 +417,19 @@ <h3>Function<a class="headerlink" href="#function" title="Permalink to this head
 <li><strong>prediction_mechanism_params</strong> (<em>Optional[Dict[param keyword, param value]] : default None</em>) &#8211; a <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> passed to the constructor for the
 <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.prediction_mechanism_type" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.prediction_mechanism_type"><code class="xref any py py-attr docutils literal"><span class="pre">prediction_mechanism_type</span></code></a> mechanism. The same one is passed to all
 <a class="reference internal" href="#evcmechanism-prediction-mechanisms"><span class="std std-ref">prediction mechanisms</span></a> created for the EVCMechanism.</li>
-<li><strong>monitor_for_control</strong> (<em>List[OutputState or Tuple[OutputState, list or 1d np.array, list or 1d np.array]] :     default MonitoredOutputStatesOptions.PRIMARY_OUTPUT_STATES</em>) &#8211; specifies set of outputState values to monitor (see <a class="reference internal" href="ControlMechanism.html#controlmechanism-monitored-outputstates"><span class="std std-ref">Monitored OutputStates</span></a> for
+<li><strong>monitor_for_control</strong> (<em>List[OutputState or Tuple[OutputState, list or 1d np.array, list or 1d np.array]] :     default MonitoredOutputStatesOptions.PRIMARY_OUTPUT_STATES</em>) &#8211; specifies set of outputState values to monitor (see <code class="xref any docutils literal"><span class="pre">ControlMechanism_Monitored_OutputStates</span></code> for
 specification options).</li>
-<li><strong>function</strong> (<em>function : ControlSignalGridSearch</em>) &#8211; specifies the function used to determine the <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> for the next execution of the system
+<li><strong>control_signals</strong> (<em>List[Attribute of Mechanism or its function, ParameterState, or tuple[str, Mechanism]</em>) &#8211; specifies the parameters to be controlled by the EVCMechanism
+(see <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signals" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signals"><code class="xref any py py-attr docutils literal"><span class="pre">control_signals</span></code></a> for details).</li>
+<li><strong>function</strong> (<em>function or method : ControlSignalGridSearch</em>) &#8211; specifies the function used to determine the <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> for the next execution of the system
 (see <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> attribute for a description of the default function).</li>
-<li><strong>value_function</strong> (<em>function : value_function</em>) &#8211; specifies the function used to calculate the value of the current <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>
+<li><strong>value_function</strong> (<em>function or method : value_function</em>) &#8211; specifies the function used to calculate the value of the current <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>
 (see <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.value_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.value_function"><code class="xref any py py-attr docutils literal"><span class="pre">value_function</span></code></a> attribute for additional details).</li>
-<li><strong>outcome_function</strong> (<em>function : LinearCombination(operation=PRODUCT)</em>) &#8211; specifies the function used to calculate the outcome associated with the current <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>
+<li><strong>outcome_function</strong> (<em>function or method : LinearCombination(operation=PRODUCT)</em>) &#8211; specifies the function used to calculate the outcome associated with the current <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>
 (see <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.outcome_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.outcome_function"><code class="xref any py py-attr docutils literal"><span class="pre">outcome_function</span></code></a> attribute for additional details).</li>
-<li><strong>cost_function</strong> (<em>function : LinearCombination(operation=SUM)</em>) &#8211; specifies the function used to calculate the cost associated with the current <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>
+<li><strong>cost_function</strong> (<em>function or method : LinearCombination(operation=SUM)</em>) &#8211; specifies the function used to calculate the cost associated with the current <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>
 (see <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.cost_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.cost_function"><code class="xref any py py-attr docutils literal"><span class="pre">cost_function</span></code></a> attribute for additional details).</li>
-<li><strong>combine_outcome_and_cost_function</strong> (<em>function : LinearCombination(operation=SUM)</em>) &#8211; specifies the function used to combine the outcome and cost associated with the current <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>,
+<li><strong>combine_outcome_and_cost_function</strong> (<em>function or method : LinearCombination(operation=SUM)</em>) &#8211; specifies the function used to combine the outcome and cost associated with the current <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>,
 to determine its value (see <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.combine_outcome_and_cost_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.combine_outcome_and_cost_function"><code class="xref any py py-attr docutils literal"><span class="pre">combine_outcome_and_cost_function</span></code></a> attribute for additional details).</li>
 <li><strong>save_all_values_and_policies</strong> (<em>bool : default False</em>) &#8211; when it is <code class="xref std std-keyword docutils literal"><span class="pre">True</span></code>, saves all of the control allocation policies tested in <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.EVC_policies" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.EVC_policies"><code class="xref any py py-attr docutils literal"><span class="pre">EVC_policies</span></code></a> and their
 values in <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.EVC_values" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.EVC_values"><code class="xref any py py-attr docutils literal"><span class="pre">EVC_values</span></code></a>.</li>
@@ -454,7 +450,8 @@ <h3>Function<a class="headerlink" href="#function" title="Permalink to this head
 <dl class="attribute">
 <dt id="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.make_default_controller">
 <code class="descname">make_default_controller</code><a class="headerlink" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.make_default_controller" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>bool : default True</em> &#8211; if <code class="xref any docutils literal"><span class="pre">True</span></code>, assigns EVCMechanism when instantiated as the DefaultController</p>
+<dd><p><em>bool : default True</em> &#8211; if <code class="xref any docutils literal"><span class="pre">True</span></code>, calls deferred_init() for each ControlProjection in its system without a sender,
+creates a ControlSignal for it, and assigns itself as its sender.</p>
 </dd></dl>
 
 <dl class="attribute">
@@ -464,9 +461,9 @@ <h3>Function<a class="headerlink" href="#function" title="Permalink to this head
 </dd></dl>
 
 <dl class="attribute">
-<dt id="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignals">
-<code class="descname">controlSignals</code><a class="headerlink" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignals" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>OrderedDict[str, ControlSignal]</em> &#8211; list of <a class="reference internal" href="OutputState.html"><span class="doc">outputStates</span></a> for the EVCMechanism, each of which corresponds to one of its
+<dt id="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signals">
+<code class="descname">control_signals</code><a class="headerlink" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signals" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>ContentAddressableList[ControlSignal]</em> &#8211; list of <a class="reference internal" href="OutputState.html"><span class="doc">outputStates</span></a> for the EVCMechanism, each of which corresponds to one of its
 ControlSignals.</p>
 </dd></dl>
 
@@ -516,7 +513,7 @@ <h3>Function<a class="headerlink" href="#function" title="Permalink to this head
 <dd><p><em>ObjectiveMechanism</em> &#8211; the &#8216;ObjectiveMechanism&#8217; that monitors the mechanisms and/or outputStates used by the EVCMechanism to evaluate
 the system&#8217;s performance.  Each mechanism and/or outputState listed in the EVCMechanism&#8217;s
 <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitored_output_states" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitored_output_states"><code class="xref any py py-attr docutils literal"><span class="pre">monitored_output_states</span></code></a> attribute projects to an inputState of the <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitoring_mechanism" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitoring_mechanism"><code class="xref any py py-attr docutils literal"><span class="pre">monitoring_mechanism</span></code></a>.  The EVCMechanism&#8217;s
-<a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.outcome_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.outcome_function"><code class="xref any py py-attr docutils literal"><span class="pre">outcome_function</span></code></a> is assiged as the <a class="reference internal" href="ObjectiveMechanism.html#ObjectiveMechanism.ObjectiveMechanism.function" title="ObjectiveMechanism.ObjectiveMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> for <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitoring_mechanism" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitoring_mechanism"><code class="xref any py py-attr docutils literal"><span class="pre">monitoring_mechanism</span></code></a>.
+<a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.outcome_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.outcome_function"><code class="xref any py py-attr docutils literal"><span class="pre">outcome_function</span></code></a> is assiged as the <code class="xref any docutils literal"><span class="pre">function</span></code> for <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitoring_mechanism" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitoring_mechanism"><code class="xref any py py-attr docutils literal"><span class="pre">monitoring_mechanism</span></code></a>.
 Its result is provided by a projection from the <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitoring_mechanism" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitoring_mechanism"><code class="xref any py py-attr docutils literal"><span class="pre">monitoring_mechanism</span></code></a> to the EVCMechanism.</p>
 </dd></dl>
 
@@ -539,11 +536,11 @@ <h3>Function<a class="headerlink" href="#function" title="Permalink to this head
 <code class="descname">function</code><a class="headerlink" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>function : default ControlSignalGridSearch</em> &#8211; determines the <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> to use for the next round of the system&#8217;s
 execution. The default function, <code class="xref any docutils literal"><span class="pre">ControlSignalGridSearch</span></code>, conducts an exhaustive (<em>grid</em>) search of all
-combinations of the <a class="reference internal" href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.allocation_samples" title="AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.allocation_samples"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_samples</span></code></a> of its ControlSignals (and contained in its
-<a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignalSearchSpace" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignalSearchSpace"><code class="xref any py py-attr docutils literal"><span class="pre">controlSignalSearchSpace</span></code></a> attribute), by executing the system (using <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.run_simulation" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.run_simulation"><code class="xref any py py-meth docutils literal"><span class="pre">run_simulation</span></code></a>) for each
+combinations of the <code class="xref any docutils literal"><span class="pre">allocation_samples</span></code> of its ControlSignals (and contained in its
+<a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signal_search_space" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signal_search_space"><code class="xref any py py-attr docutils literal"><span class="pre">control_signal_search_space</span></code></a> attribute), by executing the system (using <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.run_simulation" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.run_simulation"><code class="xref any py py-meth docutils literal"><span class="pre">run_simulation</span></code></a>) for each
 combination, evaluating the result using <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.value_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.value_function"><code class="xref any py py-attr docutils literal"><span class="pre">value_function</span></code></a>, and returning the allocation_policy that generated
 the highest value.  If a custom function is specified, it must accommodate a <code class="xref std std-keyword docutils literal"><span class="pre">controller</span></code> argument that
-specifies an EVCMechanism (and provides access to its attributes, including <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignalSearchSpace" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignalSearchSpace"><code class="xref any py py-attr docutils literal"><span class="pre">controlSignalSearchSpace</span></code></a>),
+specifies an EVCMechanism (and provides access to its attributes, including <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signal_search_space" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signal_search_space"><code class="xref any py py-attr docutils literal"><span class="pre">control_signal_search_space</span></code></a>),
 and must return an array with the same format (number and type of elements) as the EVCMechanism&#8217;s
 <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a> attribute.</p>
 </dd></dl>
@@ -551,10 +548,10 @@ <h3>Function<a class="headerlink" href="#function" title="Permalink to this head
 <dl class="attribute">
 <dt id="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy">
 <code class="descname">allocation_policy</code><a class="headerlink" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>2d np.array : defaultControlAllocation</em> &#8211; determines the value assigned as the <a class="reference internal" href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.variable" title="AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> for each <a class="reference internal" href="ControlSignal.html"><span class="doc">ControlSignal</span></a> and its
+<dd><p><em>2d np.array : defaultControlAllocation</em> &#8211; determines the value assigned as the <code class="xref any docutils literal"><span class="pre">variable</span></code> for each <a class="reference internal" href="ControlSignal.html"><span class="doc">ControlSignal</span></a> and its
 associated <a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjection</span></a>.  Each item of the array must be a 1d array (usually containing a scalar)
-that specifies an <a class="reference internal" href="ControlProjection.html#ControlProjection.ControlProjection.allocation" title="ControlProjection.ControlProjection.allocation"><code class="xref any py py-attr docutils literal"><span class="pre">allocation</span></code></a> for the corresponding ControlSignal, and the number of items must equal the
-number of ControlSignals in the EVCMechanism&#8217;s <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignals" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignals"><code class="xref any py py-attr docutils literal"><span class="pre">controlSignals</span></code></a> attribute.</p>
+that specifies an <a class="reference internal" href="ControlProjection.html#ModulatoryProjections.ControlProjection.ControlProjection.allocation" title="ModulatoryProjections.ControlProjection.ControlProjection.allocation"><code class="xref any py py-attr docutils literal"><span class="pre">allocation</span></code></a> for the corresponding ControlSignal, and the number of items must equal the
+number of ControlSignals in the EVCMechanism&#8217;s <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signals" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signals"><code class="xref any py py-attr docutils literal"><span class="pre">control_signals</span></code></a> attribute.</p>
 </dd></dl>
 
 <dl class="attribute">
@@ -571,8 +568,8 @@ <h3>Function<a class="headerlink" href="#function" title="Permalink to this head
 which it is carrying out the calculation; an <code class="xref std std-keyword docutils literal"><span class="pre">outcome</span></code> argument that is a scalar value that reflects
 the outcome of the function of the ObjectiveMechanism (based on the value of the outputStates being monitored
 (and specified in the EVCMechanism&#8217;s <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitored_output_states" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitored_output_states"><code class="xref any py py-attr docutils literal"><span class="pre">monitored_output_states</span></code></a> attribute;
-and a <code class="xref std std-keyword docutils literal"><span class="pre">costs</span></code> argument that is a 2d array of costs, each item of which is the <a class="reference internal" href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.cost" title="AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.cost"><code class="xref any py py-attr docutils literal"><span class="pre">cost</span></code></a> of a
-ControlSignal in the EVCMechanism&#8217;s <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignals" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignals"><code class="xref any py py-attr docutils literal"><span class="pre">controlSignals</span></code></a> attribute.  A custom function assigned to
+and a <code class="xref std std-keyword docutils literal"><span class="pre">costs</span></code> argument that is a 2d array of costs, each item of which is the <code class="xref any docutils literal"><span class="pre">cost</span></code> of a
+ControlSignal in the EVCMechanism&#8217;s <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signals" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signals"><code class="xref any py py-attr docutils literal"><span class="pre">control_signals</span></code></a> attribute.  A custom function assigned to
 <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.value_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.value_function"><code class="xref any py py-attr docutils literal"><span class="pre">value_function</span></code></a> can also call any of the other EVCMechanism functions described below (however,
 see <a class="reference internal" href="#evcmechanism-calling-and-assigning-functions"><span class="std std-ref">note</span></a> above).</p>
 </dd></dl>
@@ -586,12 +583,12 @@ <h3>Function<a class="headerlink" href="#function" title="Permalink to this head
 examples &lt;EVCMechanism_Examples&gt;`) are used as the <a class="reference internal" href="Function.html#Function.LinearCombination.weights" title="Function.LinearCombination.weights"><code class="xref any py py-attr docutils literal"><span class="pre">weights</span></code></a> and <a class="reference internal" href="Function.html#Function.LinearCombination.exponents" title="Function.LinearCombination.exponents"><code class="xref any py py-attr docutils literal"><span class="pre">exponents</span></code></a> parameters of the
 <a class="reference internal" href="Function.html#Function.LinearCombination" title="Function.LinearCombination"><code class="xref any py py-class docutils literal"><span class="pre">LinearCombination</span></code></a> function, respectively. If the default <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.outcome_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.outcome_function"><code class="xref any py py-attr docutils literal"><span class="pre">outcome_function</span></code></a> is called by a custom
 <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.value_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.value_function"><code class="xref any py py-attr docutils literal"><span class="pre">value_function</span></code></a>, the weights and/or exponents can be specified as 1d arrays in a <code class="xref any docutils literal"><span class="pre">WEIGHTS</span></code> and/or <code class="xref any docutils literal"><span class="pre">EXPONENTS</span></code>
-entry of a <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> specified for the <a class="reference internal" href="Function.html#Function.NormalDist.params" title="Function.NormalDist.params"><code class="xref any py py-attr docutils literal"><span class="pre">params</span></code></a> argument of
+entry of a <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> specified for the <a class="reference internal" href="Function.html#Function.Distance.params" title="Function.Distance.params"><code class="xref any py py-attr docutils literal"><span class="pre">params</span></code></a> argument of
 the <a class="reference internal" href="Function.html#Function.LinearCombination" title="Function.LinearCombination"><code class="xref any py py-class docutils literal"><span class="pre">LinearCombination</span></code></a> function. The length of each array must equal the number of (and values be listed in
 the same order as) the outputStates in the EVCMechanism&#8217;s <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitored_output_states" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitored_output_states"><code class="xref any py py-attr docutils literal"><span class="pre">monitored_output_states</span></code></a> attribute.  These
 specifications will supercede any made for individual outputStates in the <code class="xref any docutils literal"><span class="pre">monitor_for_control</span></code> argument or
 <code class="xref any docutils literal"><span class="pre">MONITOR_FOR_CONTROL</span></code> entry of a parameter specification dictionary for the
-EVCMechanism (see <a class="reference internal" href="ControlMechanism.html#controlmechanism-monitored-outputstates"><span class="std std-ref">Monitored OutputStates</span></a>).  The default function can also be replaced
+EVCMechanism (see <code class="xref any docutils literal"><span class="pre">ControlMechanism_Monitored_OutputStates</span></code>).  The default function can also be replaced
 with any <a class="reference internal" href="#evcmechanism-calling-and-assigning-functions"><span class="std std-ref">custom function</span></a> that returns a scalar value.  If
 used with the EVCMechanism&#8217;s default <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.value_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.value_function"><code class="xref any py py-attr docutils literal"><span class="pre">value_function</span></code></a>, a custom outcome_function must accommodate two
 arguments (passed by name): a <code class="xref std std-keyword docutils literal"><span class="pre">controller</span></code> argument that is the EVCMechanism itself (and can be used
@@ -604,20 +601,20 @@ <h3>Function<a class="headerlink" href="#function" title="Permalink to this head
 <dl class="attribute">
 <dt id="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.cost_function">
 <code class="descname">cost_function</code><a class="headerlink" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.cost_function" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>function : default LinearCombination(operation=SUM)</em> &#8211; calculates the cost for a given <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>.  The default combines the <a class="reference internal" href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.cost" title="AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.cost"><code class="xref any py py-attr docutils literal"><span class="pre">cost</span></code></a> of each ControlSignals in
-<a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignals" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignals"><code class="xref any py py-attr docutils literal"><span class="pre">controlSignals</span></code></a> by summing them using the <a class="reference internal" href="Function.html#Function.LinearCombination" title="Function.LinearCombination"><code class="xref any py py-class docutils literal"><span class="pre">LinearCombination</span></code></a> function. If the default <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.cost_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.cost_function"><code class="xref any py py-attr docutils literal"><span class="pre">cost_function</span></code></a> is
+<dd><p><em>function : default LinearCombination(operation=SUM)</em> &#8211; calculates the cost for a given <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.allocation_policy"><code class="xref any py py-attr docutils literal"><span class="pre">allocation_policy</span></code></a>.  The default combines the <code class="xref any docutils literal"><span class="pre">cost</span></code> of each ControlSignals in
+<a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signals" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signals"><code class="xref any py py-attr docutils literal"><span class="pre">control_signals</span></code></a> by summing them using the <a class="reference internal" href="Function.html#Function.LinearCombination" title="Function.LinearCombination"><code class="xref any py py-class docutils literal"><span class="pre">LinearCombination</span></code></a> function. If the default <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.cost_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.cost_function"><code class="xref any py py-attr docutils literal"><span class="pre">cost_function</span></code></a> is
 called by a custom <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.value_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.value_function"><code class="xref any py py-attr docutils literal"><span class="pre">value_function</span></code></a>, the weights and/or exponents parameters of the function can be used,
 respectively, to scale and/or exponentiate the contribution of each ControlSignal&#8217;s cost to the aggregated
 value.  These must be specified as 1d arrays in a <code class="xref any docutils literal"><span class="pre">WEIGHTS</span></code> and/or <code class="xref any docutils literal"><span class="pre">EXPONENTS</span></code> entry of a
-<a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> specified for the <a class="reference internal" href="Function.html#Function.NormalDist.params" title="Function.NormalDist.params"><code class="xref any py py-attr docutils literal"><span class="pre">params</span></code></a> argument of the
+<a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> specified for the <a class="reference internal" href="Function.html#Function.Distance.params" title="Function.Distance.params"><code class="xref any py py-attr docutils literal"><span class="pre">params</span></code></a> argument of the
 <a class="reference internal" href="Function.html#Function.LinearCombination" title="Function.LinearCombination"><code class="xref any py py-class docutils literal"><span class="pre">LinearCombination</span></code></a> function; the length of each array must equal the number of (and the values listed in the
-same order as) the ControlSignals in the EVCMechanism&#8217;s <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignals" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignals"><code class="xref any py py-attr docutils literal"><span class="pre">controlSignals</span></code></a> attribute, and be in the same order.
+same order as) the ControlSignals in the EVCMechanism&#8217;s <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signals" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signals"><code class="xref any py py-attr docutils literal"><span class="pre">control_signals</span></code></a> attribute, and be in the same order.
 The default function can also be replaced with any
 <a class="reference internal" href="#evcmechanism-calling-and-assigning-functions"><span class="std std-ref">custom function</span></a> that returns a scalar value.  If used with
 the EVCMechanism&#8217;s default <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.value_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.value_function"><code class="xref any py py-attr docutils literal"><span class="pre">value_function</span></code></a>, a custom cost_function must accommodate two arguments (passed by
 name): a <code class="xref std std-keyword docutils literal"><span class="pre">controller</span></code> argument that is the EVCMechanism itself;  and a
-<code class="xref std std-keyword docutils literal"><span class="pre">costs</span></code> argument, that is 1d array of scalar values specifying the <a class="reference internal" href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.cost" title="AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.cost"><code class="xref any py py-attr docutils literal"><span class="pre">cost</span></code></a> for each ControlSignal listed
-in the <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignals" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignals"><code class="xref any py py-attr docutils literal"><span class="pre">controlSignals</span></code></a> attribute of the <code class="xref std std-keyword docutils literal"><span class="pre">controller</span></code> argument.</p>
+<code class="xref std std-keyword docutils literal"><span class="pre">costs</span></code> argument, that is 1d array of scalar values specifying the <code class="xref any docutils literal"><span class="pre">cost</span></code> for each ControlSignal listed
+in the <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signals" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signals"><code class="xref any py py-attr docutils literal"><span class="pre">control_signals</span></code></a> attribute of the <code class="xref std std-keyword docutils literal"><span class="pre">controller</span></code> argument.</p>
 </dd></dl>
 
 <dl class="attribute">
@@ -629,7 +626,7 @@ <h3>Function<a class="headerlink" href="#function" title="Permalink to this head
 exponents parameters of the <a class="reference internal" href="Function.html#Function.LinearCombination" title="Function.LinearCombination"><code class="xref any py py-class docutils literal"><span class="pre">LinearCombination</span></code></a> function can be used, respectively, to scale and/or exponentiate
 the contribution of the outcome and/or cost to the result.  These must be specified as 1d arrays in a <code class="xref any docutils literal"><span class="pre">WEIGHTS</span></code>
 and/or EXPONENTS entry of a  <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter specifiction dictionary</span></a>
-assigned to the function&#8217;s <a class="reference internal" href="Function.html#Function.NormalDist.params" title="Function.NormalDist.params"><code class="xref any py py-attr docutils literal"><span class="pre">params</span></code></a> argument; each array must have two elements, the first for the outcome
+assigned to the function&#8217;s <a class="reference internal" href="Function.html#Function.Distance.params" title="Function.Distance.params"><code class="xref any py py-attr docutils literal"><span class="pre">params</span></code></a> argument; each array must have two elements, the first for the outcome
 and second for the cost. The default function can also be replaced with any
 <a class="reference internal" href="#evcmechanism-calling-and-assigning-functions"><span class="std std-ref">custom function</span></a> that returns a scalar value.  If used with
 the EVCMechanism&#8217;s default <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.value_function" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.value_function"><code class="xref any py py-attr docutils literal"><span class="pre">value_function</span></code></a>, a custom combine_outcome_and_cost_function must accomoudate three
@@ -639,8 +636,8 @@ <h3>Function<a class="headerlink" href="#function" title="Permalink to this head
 </dd></dl>
 
 <dl class="attribute">
-<dt id="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignalSearchSpace">
-<code class="descname">controlSignalSearchSpace</code><a class="headerlink" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignalSearchSpace" title="Permalink to this definition">¶</a></dt>
+<dt id="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signal_search_space">
+<code class="descname">control_signal_search_space</code><a class="headerlink" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signal_search_space" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>2d np.array</em> &#8211; an array that contains arrays of allocation policies.  Each allocation policy contains one value for each of
 the mechanism&#8217;s ControlSignals.  By default, it is assigned a set of all possible allocation policies
 (using np.meshgrid to construct all permutations of ControlSignal values).</p>
@@ -649,7 +646,7 @@ <h3>Function<a class="headerlink" href="#function" title="Permalink to this head
 <dl class="attribute">
 <dt id="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.EVC_max">
 <code class="descname">EVC_max</code><a class="headerlink" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.EVC_max" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>1d np.array with single value</em> &#8211; the maximum EVC value over all allocation policies in <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignalSearchSpace" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignalSearchSpace"><code class="xref any py py-attr docutils literal"><span class="pre">controlSignalSearchSpace</span></code></a>.</p>
+<dd><p><em>1d np.array with single value</em> &#8211; the maximum EVC value over all allocation policies in <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signal_search_space" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signal_search_space"><code class="xref any py py-attr docutils literal"><span class="pre">control_signal_search_space</span></code></a>.</p>
 </dd></dl>
 
 <dl class="attribute">
@@ -669,14 +666,14 @@ <h3>Function<a class="headerlink" href="#function" title="Permalink to this head
 <dt id="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.save_all_values_and_policies">
 <code class="descname">save_all_values_and_policies</code><a class="headerlink" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.save_all_values_and_policies" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>bool : default False</em> &#8211; specifies whether or not to save all allocation policies and associated EVC values (in addition to the max).
-If it is specified, each policy tested in the <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignalSearchSpace" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignalSearchSpace"><code class="xref any py py-attr docutils literal"><span class="pre">controlSignalSearchSpace</span></code></a> is saved in <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.EVC_policies" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.EVC_policies"><code class="xref any py py-attr docutils literal"><span class="pre">EVC_policies</span></code></a> and their
+If it is specified, each policy tested in the <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signal_search_space" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signal_search_space"><code class="xref any py py-attr docutils literal"><span class="pre">control_signal_search_space</span></code></a> is saved in <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.EVC_policies" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.EVC_policies"><code class="xref any py py-attr docutils literal"><span class="pre">EVC_policies</span></code></a> and their
 values are saved in <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.EVC_values" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.EVC_values"><code class="xref any py py-attr docutils literal"><span class="pre">EVC_values</span></code></a>.</p>
 </dd></dl>
 
 <dl class="attribute">
 <dt id="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.EVC_policies">
 <code class="descname">EVC_policies</code><a class="headerlink" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.EVC_policies" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>2d np.array</em> &#8211; array of allocation policies tested in <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignalSearchSpace" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignalSearchSpace"><code class="xref any py py-attr docutils literal"><span class="pre">controlSignalSearchSpace</span></code></a>.  The values of each are stored in
+<dd><p><em>2d np.array</em> &#8211; array of allocation policies tested in <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signal_search_space" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signal_search_space"><code class="xref any py py-attr docutils literal"><span class="pre">control_signal_search_space</span></code></a>.  The values of each are stored in
 <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.EVC_values" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.EVC_values"><code class="xref any py py-attr docutils literal"><span class="pre">EVC_values</span></code></a>.</p>
 </dd></dl>
 
@@ -710,7 +707,7 @@ <h3>Function<a class="headerlink" href="#function" title="Permalink to this head
 
 <dl class="method">
 <dt id="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.run_simulation">
-<code class="descname">run_simulation</code><span class="sig-paren">(</span><em>inputs</em>, <em>allocation_vector</em>, <em>runtime_params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 1&gt;</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.run_simulation" title="Permalink to this definition">¶</a></dt>
+<code class="descname">run_simulation</code><span class="sig-paren">(</span><em>inputs</em>, <em>allocation_vector</em>, <em>runtime_params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 2&gt;</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.run_simulation" title="Permalink to this definition">¶</a></dt>
 <dd><p>Run simulation of <a class="reference internal" href="System.html"><span class="doc">system</span></a> for which the EVCMechanism is the <a class="reference internal" href="System.html#System.System_Base.controller" title="System.System_Base.controller"><code class="xref any py py-attr docutils literal"><span class="pre">controller</span></code></a>.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
@@ -721,7 +718,7 @@ <h3>Function<a class="headerlink" href="#function" title="Permalink to this head
 should be the <code class="xref any docutils literal"><span class="pre">value</span></code> listed in the <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.predictionMechanisms" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.predictionMechanisms"><code class="xref any py py-attr docutils literal"><span class="pre">predictionMechanisms</span></code></a>
 attribute.  The inputs are available from the <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.predictedInput" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.predictedInput"><code class="xref any py py-attr docutils literal"><span class="pre">predictedInput</span></code></a> attribute.</li>
 <li><strong>allocation_vector</strong> (<em>(1D np.array)</em>) &#8211; the allocation policy to use in running the simulation, with one allocation value for each of the
-EVCMechanism&#8217;s ControlSignals (listed in <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignals" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignals"><code class="xref any py py-attr docutils literal"><span class="pre">controlSignals</span></code></a>).</li>
+EVCMechanism&#8217;s ControlSignals (listed in <a class="reference internal" href="#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signals" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signals"><code class="xref any py py-attr docutils literal"><span class="pre">control_signals</span></code></a>).</li>
 <li><strong>runtime_params</strong> (<em>Optional[Dict[str, Dict[str, Dict[str, value]]]]</em>) &#8211; a dictionary that can include any of the parameters used as arguments to instantiate the mechanisms,
 their functions, or projection(s) to any of their states.  See <a class="reference internal" href="Mechanism.html#mechanism-runtime-parameters"><span class="std std-ref">Runtime Parameters</span></a> for a full
 description.</li>
diff --git a/docs/build/html/Function.html b/docs/build/html/Function.html
index 17600985628..cbb00dfc3fe 100644
--- a/docs/build/html/Function.html
+++ b/docs/build/html/Function.html
@@ -87,7 +87,7 @@
 <li class="toctree-l1"><a class="reference internal" href="System.html">System</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Process.html">Process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Mechanism.html">Mechanism</a></li>
-<li class="toctree-l1"><a class="reference internal" href="State.html">States</a></li>
+<li class="toctree-l1"><a class="reference internal" href="State.html">State</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Projection.html">Projection</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Run.html">Run</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Component.html">Component</a></li>
@@ -170,7 +170,7 @@ <h1>Functions<a class="headerlink" href="#functions" title="Permalink to this he
 </dd>
 <dt>Integrator Functions:</dt>
 <dd><ul class="first last simple">
-<li><a class="reference internal" href="#Function.Integrator" title="Function.Integrator"><code class="xref any py py-class docutils literal"><span class="pre">Integrator</span></code></a></li>
+<li><code class="xref any docutils literal"><span class="pre">Integrator</span></code></li>
 <li><a class="reference internal" href="#Function.BogaczEtAl" title="Function.BogaczEtAl"><code class="xref any py py-class docutils literal"><span class="pre">BogaczEtAl</span></code></a></li>
 <li><a class="reference internal" href="#Function.NavarroAndFuss" title="Function.NavarroAndFuss"><code class="xref any py py-class docutils literal"><span class="pre">NavarroAndFuss</span></code></a></li>
 </ul>
@@ -184,6 +184,12 @@ <h1>Functions<a class="headerlink" href="#functions" title="Permalink to this he
 <li><a class="reference internal" href="#Function.WaldDist" title="Function.WaldDist"><code class="xref any py py-class docutils literal"><span class="pre">WaldDist</span></code></a></li>
 </ul>
 </dd>
+<dt>Objective Functions:</dt>
+<dd><ul class="first last simple">
+<li><a class="reference internal" href="#Function.Stability" title="Function.Stability"><code class="xref any py py-class docutils literal"><span class="pre">Stability</span></code></a></li>
+<li><a class="reference internal" href="#Function.Distance" title="Function.Distance"><code class="xref any py py-class docutils literal"><span class="pre">Distance</span></code></a></li>
+</ul>
+</dd>
 <dt>Learning Functions:</dt>
 <dd><ul class="first last simple">
 <li><a class="reference internal" href="#Function.Reinforcement" title="Function.Reinforcement"><code class="xref any py py-class docutils literal"><span class="pre">Reinforcement</span></code></a></li>
@@ -245,6 +251,39 @@ <h1>Functions<a class="headerlink" href="#functions" title="Permalink to this he
 </div>
 <div class="section" id="class-reference">
 <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalink to this headline">¶</a></h2>
+<dl class="class">
+<dt id="Function.ModulationParam">
+<em class="property">class </em><code class="descclassname">Function.</code><code class="descname">ModulationParam</code><a class="headerlink" href="#Function.ModulationParam" title="Permalink to this definition">¶</a></dt>
+<dd><p>Specify parameter of a <a class="reference internal" href="#"><span class="doc">Function</span></a> for <a class="reference internal" href="ModulatorySignal.html#modulatorysignal-modulation"><span class="std std-ref">modulation</span></a> by a ModulatorySignal</p>
+<dl class="attribute">
+<dt id="Function.ModulationParam.MULTIPLICATIVE">
+<code class="descname">MULTIPLICATIVE</code><a class="headerlink" href="#Function.ModulationParam.MULTIPLICATIVE" title="Permalink to this definition">¶</a></dt>
+<dd><p>assign the <a class="reference internal" href="ModulatorySignal.html#ModulatorySignals.ModulatorySignal.ModulatorySignal.value" title="ModulatorySignals.ModulatorySignal.ModulatorySignal.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> of the ModulatorySignal to the <em>MULTIPLICATIVE_PARAM</em>
+of the State&#8217;s <code class="xref any docutils literal"><span class="pre">function</span></code>;</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.ModulationParam.ADDITIVE">
+<code class="descname">ADDITIVE</code><a class="headerlink" href="#Function.ModulationParam.ADDITIVE" title="Permalink to this definition">¶</a></dt>
+<dd><p>assign the <a class="reference internal" href="ModulatorySignal.html#ModulatorySignals.ModulatorySignal.ModulatorySignal.value" title="ModulatorySignals.ModulatorySignal.ModulatorySignal.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> of the ModulatorySignal to the <em>ADDITIVE_PARAM</em>
+of the State&#8217;s <code class="xref any docutils literal"><span class="pre">function</span></code>;</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.ModulationParam.OVERRIDE">
+<code class="descname">OVERRIDE</code><a class="headerlink" href="#Function.ModulationParam.OVERRIDE" title="Permalink to this definition">¶</a></dt>
+<dd><p>assign the <a class="reference internal" href="ModulatorySignal.html#ModulatorySignals.ModulatorySignal.ModulatorySignal.value" title="ModulatorySignals.ModulatorySignal.ModulatorySignal.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> of the ModulatorySignal directly to the State&#8217;s
+<code class="xref any docutils literal"><span class="pre">value</span></code> (ignoring its <code class="xref any docutils literal"><span class="pre">variable</span></code> and <code class="xref any docutils literal"><span class="pre">function</span></code>);</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.ModulationParam.DISABLE">
+<code class="descname">DISABLE</code><a class="headerlink" href="#Function.ModulationParam.DISABLE" title="Permalink to this definition">¶</a></dt>
+<dd><p>ignore the ModulatorySignal when calculating the State&#8217;s <code class="xref any docutils literal"><span class="pre">value</span></code>.</p>
+</dd></dl>
+
+</dd></dl>
+
 <dl class="class">
 <dt id="Function.Function_Base">
 <em class="property">class </em><code class="descclassname">Function.</code><code class="descname">Function_Base</code><span class="sig-paren">(</span><em>variable_default</em>, <em>params=None</em>, <em>owner=None</em>, <em>name=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.Function_Base" title="Permalink to this definition">¶</a></dt>
@@ -288,7 +327,7 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 <dl class="attribute">
 <dt id="Function.Function_Base.prefs">
 <code class="descname">prefs</code><a class="headerlink" href="#Function.Function_Base.prefs" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <a class="reference internal" href="#Function.Function_Base.prefs" title="Function.Function_Base.prefs"><code class="xref any py py-attr docutils literal"><span class="pre">prefs</span></code></a> argument of the constructor for the function;
+<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <strong>prefs</strong> argument of the constructor for the function;
 if it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
 (see <span class="xref doc">PreferenceSet</span> for details).</p>
 </dd></dl>
@@ -345,7 +384,7 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 <dl class="attribute">
 <dt id="Function.ArgumentTherapy.prefs">
 <code class="descname">prefs</code><a class="headerlink" href="#Function.ArgumentTherapy.prefs" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <a class="reference internal" href="#Function.ArgumentTherapy.prefs" title="Function.ArgumentTherapy.prefs"><code class="xref any py py-attr docutils literal"><span class="pre">prefs</span></code></a> argument of the constructor for the function;
+<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <strong>prefs</strong> argument of the constructor for the function;
 if it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
 (see <span class="xref doc">PreferenceSet</span> for details).</p>
 </dd></dl>
@@ -358,7 +397,7 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 
 <dl class="method">
 <dt id="Function.ArgumentTherapy.function">
-<code class="descclassname">ArgumentTherapy.</code><code class="descname">function</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 1&gt;</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.ArgumentTherapy.function" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">ArgumentTherapy.</code><code class="descname">function</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 2&gt;</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.ArgumentTherapy.function" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a boolean that is (or tends to be) the same as or opposite the one passed in.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
@@ -404,7 +443,7 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>function</strong> (<a class="reference internal" href="#Function.BogaczEtAl.function" title="Function.BogaczEtAl.function"><em>function</em></a>) &#8211; specifies function to &#8220;wrap.&#8221; It can be any function, take any arguments (including standard ones,
+<li><strong>function</strong> (<a class="reference internal" href="#Function.Reduce.function" title="Function.Reduce.function"><em>function</em></a>) &#8211; specifies function to &#8220;wrap.&#8221; It can be any function, take any arguments (including standard ones,
 such as <code class="xref std std-keyword docutils literal"><span class="pre">params</span></code> and <code class="xref std std-keyword docutils literal"><span class="pre">context</span></code>) and return any value(s), so long as these are consistent
 with the context in which the UserDefinedFunction will be used.</li>
 <li><strong>variable</strong> (<em>value : default variableClassDefault</em>) &#8211; specifies the format and a default value for the input to <a class="reference internal" href="#"><span class="doc">function</span></a>.</li>
@@ -441,7 +480,7 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 <dl class="attribute">
 <dt id="Function.UserDefinedFunction.prefs">
 <code class="descname">prefs</code><a class="headerlink" href="#Function.UserDefinedFunction.prefs" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <a class="reference internal" href="#Function.UserDefinedFunction.prefs" title="Function.UserDefinedFunction.prefs"><code class="xref any py py-attr docutils literal"><span class="pre">prefs</span></code></a> argument of the constructor for the function;
+<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <strong>prefs</strong> argument of the constructor for the function;
 if it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
 (see <span class="xref doc">PreferenceSet</span> for details).</p>
 </dd></dl>
@@ -450,8 +489,10 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 
 <dl class="class">
 <dt id="Function.Reduce">
-<em class="property">class </em><code class="descclassname">Function.</code><code class="descname">Reduce</code><span class="sig-paren">(</span><em>variable_default=variableClassDefault</em>, <em>operation=SUM</em>, <em>params=None</em>, <em>owner=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.Reduce" title="Permalink to this definition">¶</a></dt>
-<dd><p id="reduce">Combine values in each of one or more arrays into a single value for each array.</p>
+<em class="property">class </em><code class="descclassname">Function.</code><code class="descname">Reduce</code><span class="sig-paren">(</span><em>variable_default=variableClassDefault</em>, <em>operation=SUM</em>, <em>scale=1.0</em>, <em>offset=0.0</em>, <em>params=None</em>, <em>owner=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.Reduce" title="Permalink to this definition">¶</a></dt>
+<dd><p id="reduce">Combine values in each of one or more arrays into a single value for each array.
+Use optional SCALE and OFFSET parameters to linearly transform the resulting value for each array.
+Returns a scalar value for each array of the input.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
@@ -460,6 +501,10 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 <li><strong>variable_default</strong> (<em>list or np.array : default variableClassDefault</em>) &#8211; specifies a template for the value to be transformed and its default value;  all entries must be numeric.</li>
 <li><strong>operation</strong> (<em>SUM or PRODUCT : default SUM</em>) &#8211; specifies whether to sum or multiply the elements in <code class="xref any docutils literal"><span class="pre">variable</span></code> of
 <a class="reference internal" href="#Function.Reduce.function" title="Function.Reduce.function"><code class="xref any py py-meth docutils literal"><span class="pre">function</span></code></a>.</li>
+<li><strong>scale</strong> (<em>float</em>) &#8211; specifies a value by which to multiply each element of the output of <a class="reference internal" href="#Function.Reduce.function" title="Function.Reduce.function"><code class="xref any py py-meth docutils literal"><span class="pre">function</span></code></a>
+(see <a class="reference internal" href="#Function.Reduce.scale" title="Function.Reduce.scale"><code class="xref any py py-attr docutils literal"><span class="pre">scale</span></code></a> for details)</li>
+<li><strong>offset</strong> (<em>float</em>) &#8211; specifies a value to add to each element of the output of <a class="reference internal" href="#Function.Reduce.function" title="Function.Reduce.function"><code class="xref any py py-meth docutils literal"><span class="pre">function</span></code></a>
+(see <a class="reference internal" href="#Function.Reduce.offset" title="Function.Reduce.offset"><code class="xref any py py-attr docutils literal"><span class="pre">offset</span></code></a> for details)</li>
 <li><strong>params</strong> (<em>Optional[Dict[param keyword, param value]]</em>) &#8211; a <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> that specifies the parameters for the
 function.  Values specified for parameters in the dictionary override any assigned to those parameters in
 arguments of the constructor.</li>
@@ -484,6 +529,21 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 <a class="reference internal" href="#Function.Reduce.function" title="Function.Reduce.function"><code class="xref any py py-meth docutils literal"><span class="pre">function</span></code></a> are summmed or multiplied.</p>
 </dd></dl>
 
+<dl class="attribute">
+<dt id="Function.Reduce.scale">
+<code class="descname">scale</code><a class="headerlink" href="#Function.Reduce.scale" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>float</em> &#8211; value is applied multiplicatively to each element of the array after applying the <a class="reference internal" href="#Function.Reduce.operation" title="Function.Reduce.operation"><code class="xref any py py-attr docutils literal"><span class="pre">operation</span></code></a>
+(see <a class="reference internal" href="#Function.Reduce.scale" title="Function.Reduce.scale"><code class="xref any py py-attr docutils literal"><span class="pre">scale</span></code></a> for details);  this done before applying the <a class="reference internal" href="#Function.Reduce.offset" title="Function.Reduce.offset"><code class="xref any py py-attr docutils literal"><span class="pre">offset</span></code></a>
+(if it is specified).</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.Reduce.offset">
+<code class="descname">offset</code><a class="headerlink" href="#Function.Reduce.offset" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>float</em> &#8211; value is added to each element of the array after applying the <a class="reference internal" href="#Function.Reduce.operation" title="Function.Reduce.operation"><code class="xref any py py-attr docutils literal"><span class="pre">operation</span></code></a>
+and <a class="reference internal" href="#Function.Reduce.scale" title="Function.Reduce.scale"><code class="xref any py py-attr docutils literal"><span class="pre">scale</span></code></a> (if it is specified).</p>
+</dd></dl>
+
 <dl class="attribute">
 <dt id="Function.Reduce.owner">
 <code class="descname">owner</code><a class="headerlink" href="#Function.Reduce.owner" title="Permalink to this definition">¶</a></dt>
@@ -493,16 +553,16 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 <dl class="attribute">
 <dt id="Function.Reduce.prefs">
 <code class="descname">prefs</code><a class="headerlink" href="#Function.Reduce.prefs" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <a class="reference internal" href="#Function.Reduce.prefs" title="Function.Reduce.prefs"><code class="xref any py py-attr docutils literal"><span class="pre">prefs</span></code></a> argument of the constructor for the function;
+<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <strong>prefs</strong> argument of the constructor for the function;
 if it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
 (see <span class="xref doc">PreferenceSet</span> for details).</p>
 </dd></dl>
 
 <dl class="method">
 <dt id="Function.Reduce.function">
-<code class="descname">function</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 1&gt;</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.Reduce.function" title="Permalink to this definition">¶</a></dt>
-<dd><p>Returns a scalar value for each array in <code class="xref any docutils literal"><span class="pre">variable</span></code> that is either the sum or
-product of the elements in that array.</p>
+<code class="descname">function</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 2&gt;</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.Reduce.function" title="Permalink to this definition">¶</a></dt>
+<dd><p>Calculate sum or product of the elements for each array in <code class="xref any docutils literal"><span class="pre">variable</span></code>,
+apply <a class="reference internal" href="#Function.Reduce.scale" title="Function.Reduce.scale"><code class="xref any py py-attr docutils literal"><span class="pre">scale</span></code></a> and/or <a class="reference internal" href="#Function.Reduce.offset" title="Function.Reduce.offset"><code class="xref any py py-attr docutils literal"><span class="pre">offset</span></code></a>, and return array of resulting values.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
@@ -530,7 +590,7 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 
 <dl class="class">
 <dt id="Function.LinearCombination">
-<em class="property">class </em><code class="descclassname">Function.</code><code class="descname">LinearCombination</code><span class="sig-paren">(</span><em>variable_default</em>, <em>weights=None</em>, <em>exponents=None</em>, <em>scale=1.0</em>, <em>offset=0.0</em>, <em>operation=SUM</em>, <em>params=None</em>, <em>owner=None</em>, <em>name=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.LinearCombination" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">Function.</code><code class="descname">LinearCombination</code><span class="sig-paren">(</span><em>variable_default</em>, <em>weights=None</em>, <em>exponents=None</em>, <em>operation=SUM</em>, <em>scale=None</em>, <em>offset=None</em>, <em>params=None</em>, <em>owner=None</em>, <em>name=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.LinearCombination" title="Permalink to this definition">¶</a></dt>
 <dd><p id="linearcombination">Linearly combine arrays of values with optional integration_type, exponentiation, scaling and/or offset.</p>
 <p>Combines the arrays in the items of the <a class="reference internal" href="#Function.LinearCombination.variable" title="Function.LinearCombination.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> argument.  Each array can be
 individually weighted and/or exponentiated; they can combined additively or multiplicatively; and the resulting
@@ -541,22 +601,22 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
 <li><strong>variable</strong> (<em>1d or 2d np.array : default variableClassDefault</em>) &#8211; specifies a template for the arrays to be combined.  If it is 2d, all items must have the same length.</li>
-<li><strong>weights</strong> (<em>1d or 2d np.array</em>) &#8211; specifies values used to multiply the elements of each array in <a class="reference internal" href="#Function.LinearCombination.variable" title="Function.LinearCombination.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>.
+<li><strong>weights</strong> (<em>1d or 2d np.array : default None</em>) &#8211; specifies values used to multiply the elements of each array in <a class="reference internal" href="#Function.LinearCombination.variable" title="Function.LinearCombination.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>.
 If it is 1d, its length must equal the number of items in <a class="reference internal" href="#Function.LinearCombination.variable" title="Function.LinearCombination.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>;
 if it is 2d, the length of each item must be the same as those in <a class="reference internal" href="#Function.LinearCombination.variable" title="Function.LinearCombination.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>,
 and there must be the same number of items as there are in <a class="reference internal" href="#Function.LinearCombination.variable" title="Function.LinearCombination.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>
 (see <a class="reference internal" href="#Function.LinearCombination.weights" title="Function.LinearCombination.weights"><code class="xref any py py-attr docutils literal"><span class="pre">weights</span></code></a> for details)</li>
-<li><strong>exponents</strong> (<em>1d or 2d np.array</em>) &#8211; specifies values used to exponentiate the elements of each array in <a class="reference internal" href="#Function.LinearCombination.variable" title="Function.LinearCombination.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>.
+<li><strong>exponents</strong> (<em>1d or 2d np.array : default None</em>) &#8211; specifies values used to exponentiate the elements of each array in <a class="reference internal" href="#Function.LinearCombination.variable" title="Function.LinearCombination.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>.
 If it is 1d, its length must equal the number of items in <a class="reference internal" href="#Function.LinearCombination.variable" title="Function.LinearCombination.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>;
 if it is 2d, the length of each item must be the same as those in <a class="reference internal" href="#Function.LinearCombination.variable" title="Function.LinearCombination.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>,
 and there must be the same number of items as there are in <a class="reference internal" href="#Function.LinearCombination.variable" title="Function.LinearCombination.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>
 (see <a class="reference internal" href="#Function.LinearCombination.exponents" title="Function.LinearCombination.exponents"><code class="xref any py py-attr docutils literal"><span class="pre">exponents</span></code></a> for details)</li>
-<li><strong>scale</strong> (<em>float</em>) &#8211; specifies a value by which to multiply each element of the output of <a class="reference internal" href="#Function.LinearCombination.function" title="Function.LinearCombination.function"><code class="xref any py py-meth docutils literal"><span class="pre">function</span></code></a>
-(see <a class="reference internal" href="#Function.LinearCombination.scale" title="Function.LinearCombination.scale"><code class="xref any py py-attr docutils literal"><span class="pre">scale</span></code></a> for details)</li>
-<li><strong>offset</strong> (<em>float</em>) &#8211; specifies a value to add to each element of the output of <a class="reference internal" href="#Function.LinearCombination.function" title="Function.LinearCombination.function"><code class="xref any py py-meth docutils literal"><span class="pre">function</span></code></a>
-(see <a class="reference internal" href="#Function.LinearCombination.offset" title="Function.LinearCombination.offset"><code class="xref any py py-attr docutils literal"><span class="pre">offset</span></code></a> for details)</li>
 <li><strong>operation</strong> (<em>SUM or PRODUCT</em>) &#8211; specifies whether the <a class="reference internal" href="#Function.LinearCombination.function" title="Function.LinearCombination.function"><code class="xref any py py-meth docutils literal"><span class="pre">function</span></code></a> takes the elementwise (Hadamarad)
 sum or product of the arrays in <a class="reference internal" href="#Function.LinearCombination.variable" title="Function.LinearCombination.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>.</li>
+<li><strong>scale</strong> (<em>float or np.ndarray : default None</em>) &#8211; specifies a value by which to multiply each element of the output of <a class="reference internal" href="#Function.LinearCombination.function" title="Function.LinearCombination.function"><code class="xref any py py-meth docutils literal"><span class="pre">function</span></code></a>
+(see <a class="reference internal" href="#Function.LinearCombination.scale" title="Function.LinearCombination.scale"><code class="xref any py py-attr docutils literal"><span class="pre">scale</span></code></a> for details)</li>
+<li><strong>offset</strong> (<em>float or np.ndarray : default None</em>) &#8211; specifies a value to add to each element of the output of <a class="reference internal" href="#Function.LinearCombination.function" title="Function.LinearCombination.function"><code class="xref any py py-meth docutils literal"><span class="pre">function</span></code></a>
+(see <a class="reference internal" href="#Function.LinearCombination.offset" title="Function.LinearCombination.offset"><code class="xref any py py-attr docutils literal"><span class="pre">offset</span></code></a> for details)</li>
 <li><strong>params</strong> (<em>Optional[Dict[param keyword, param value]]</em>) &#8211; a <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> that specifies the parameters for the
 function.  Values specified for parameters in the dictionary override any assigned to those parameters in
 arguments of the constructor.</li>
@@ -597,28 +657,28 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 (if any are specified).</p>
 </dd></dl>
 
+<dl class="attribute">
+<dt id="Function.LinearCombination.operation">
+<code class="descname">operation</code><a class="headerlink" href="#Function.LinearCombination.operation" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>SUM or PRODUCT</em> &#8211; determines whether the <a class="reference internal" href="#Function.LinearCombination.function" title="Function.LinearCombination.function"><code class="xref any py py-meth docutils literal"><span class="pre">function</span></code></a> takes the elementwise (Hadamard) sum or
+product of the arrays in <a class="reference internal" href="#Function.LinearCombination.variable" title="Function.LinearCombination.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>.</p>
+</dd></dl>
+
 <dl class="attribute">
 <dt id="Function.LinearCombination.scale">
 <code class="descname">scale</code><a class="headerlink" href="#Function.LinearCombination.scale" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>float</em> &#8211; value is multiplied by each element of the array after applying the <a class="reference internal" href="#Function.LinearCombination.operation" title="Function.LinearCombination.operation"><code class="xref any py py-attr docutils literal"><span class="pre">operation</span></code></a>
-(see <a class="reference internal" href="#Function.LinearCombination.scale" title="Function.LinearCombination.scale"><code class="xref any py py-attr docutils literal"><span class="pre">scale</span></code></a> for details);  this done before applying the
-<a class="reference internal" href="#Function.LinearCombination.offset" title="Function.LinearCombination.offset"><code class="xref any py py-attr docutils literal"><span class="pre">offset</span></code></a> (if it is specified).</p>
+<dd><p><em>float or np.ndarray</em> &#8211; value is applied multiplicatively to each element of the array after applying the
+<a class="reference internal" href="#Function.LinearCombination.operation" title="Function.LinearCombination.operation"><code class="xref any py py-attr docutils literal"><span class="pre">operation</span></code></a> (see <a class="reference internal" href="#Function.LinearCombination.scale" title="Function.LinearCombination.scale"><code class="xref any py py-attr docutils literal"><span class="pre">scale</span></code></a> for details);
+this done before applying the <a class="reference internal" href="#Function.LinearCombination.offset" title="Function.LinearCombination.offset"><code class="xref any py py-attr docutils literal"><span class="pre">offset</span></code></a> (if it is specified).</p>
 </dd></dl>
 
 <dl class="attribute">
 <dt id="Function.LinearCombination.offset">
 <code class="descname">offset</code><a class="headerlink" href="#Function.LinearCombination.offset" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>float</em> &#8211; value is added to each element of the array after applying the <a class="reference internal" href="#Function.LinearCombination.operation" title="Function.LinearCombination.operation"><code class="xref any py py-attr docutils literal"><span class="pre">operation</span></code></a>
+<dd><p><em>float or np.ndarray</em> &#8211; value is added to each element of the array after applying the <a class="reference internal" href="#Function.LinearCombination.operation" title="Function.LinearCombination.operation"><code class="xref any py py-attr docutils literal"><span class="pre">operation</span></code></a>
 and <a class="reference internal" href="#Function.LinearCombination.scale" title="Function.LinearCombination.scale"><code class="xref any py py-attr docutils literal"><span class="pre">scale</span></code></a> (if it is specified).</p>
 </dd></dl>
 
-<dl class="attribute">
-<dt id="Function.LinearCombination.operation">
-<code class="descname">operation</code><a class="headerlink" href="#Function.LinearCombination.operation" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>SUM or PRODUCT</em> &#8211; determines whether the <a class="reference internal" href="#Function.LinearCombination.function" title="Function.LinearCombination.function"><code class="xref any py py-meth docutils literal"><span class="pre">function</span></code></a> takes the elementwise (Hadamard) sum or
-product of the arrays in <a class="reference internal" href="#Function.LinearCombination.variable" title="Function.LinearCombination.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>.</p>
-</dd></dl>
-
 <dl class="attribute">
 <dt id="Function.LinearCombination.owner">
 <code class="descname">owner</code><a class="headerlink" href="#Function.LinearCombination.owner" title="Permalink to this definition">¶</a></dt>
@@ -628,14 +688,14 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 <dl class="attribute">
 <dt id="Function.LinearCombination.prefs">
 <code class="descname">prefs</code><a class="headerlink" href="#Function.LinearCombination.prefs" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <a class="reference internal" href="#Function.LinearCombination.prefs" title="Function.LinearCombination.prefs"><code class="xref any py py-attr docutils literal"><span class="pre">prefs</span></code></a> argument of the constructor for the function;
+<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <strong>prefs</strong> argument of the constructor for the function;
 if it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
 (see <span class="xref doc">PreferenceSet</span> for details).</p>
 </dd></dl>
 
 <dl class="method">
 <dt id="Function.LinearCombination.function">
-<code class="descname">function</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 1&gt;</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.LinearCombination.function" title="Permalink to this definition">¶</a></dt>
+<code class="descname">function</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 2&gt;</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.LinearCombination.function" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply <a class="reference internal" href="#Function.LinearCombination.weights" title="Function.LinearCombination.weights"><code class="xref any py py-attr docutils literal"><span class="pre">weights</span></code></a> and/or <code class="xref any docutils literal"><span class="pre">exponents</span></code> to the
 arrays in <a class="reference internal" href="#Function.LinearCombination.variable" title="Function.LinearCombination.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>, then take their sum or product (as specified by
 <a class="reference internal" href="#Function.LinearCombination.operation" title="Function.LinearCombination.operation"><code class="xref any py py-attr docutils literal"><span class="pre">operation</span></code></a>), apply <a class="reference internal" href="#Function.LinearCombination.scale" title="Function.LinearCombination.scale"><code class="xref any py py-attr docutils literal"><span class="pre">scale</span></code></a> and/or <a class="reference internal" href="#Function.LinearCombination.offset" title="Function.LinearCombination.offset"><code class="xref any py py-attr docutils literal"><span class="pre">offset</span></code></a>, and return the resulting array.</p>
@@ -708,6 +768,12 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 (if it is specified).</p>
 </dd></dl>
 
+<dl class="attribute">
+<dt id="Function.Linear.bounds">
+<code class="descname">bounds</code><a class="headerlink" href="#Function.Linear.bounds" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>None</em></p>
+</dd></dl>
+
 <dl class="attribute">
 <dt id="Function.Linear.owner">
 <code class="descname">owner</code><a class="headerlink" href="#Function.Linear.owner" title="Permalink to this definition">¶</a></dt>
@@ -717,14 +783,14 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 <dl class="attribute">
 <dt id="Function.Linear.prefs">
 <code class="descname">prefs</code><a class="headerlink" href="#Function.Linear.prefs" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <a class="reference internal" href="#Function.Linear.prefs" title="Function.Linear.prefs"><code class="xref any py py-attr docutils literal"><span class="pre">prefs</span></code></a> argument of the constructor for the function;
+<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <strong>prefs</strong> argument of the constructor for the function;
 if it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
 (see <span class="xref doc">PreferenceSet</span> for details).</p>
 </dd></dl>
 
 <dl class="method">
 <dt id="Function.Linear.function">
-<code class="descname">function</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 1&gt;</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.Linear.function" title="Permalink to this definition">¶</a></dt>
+<code class="descname">function</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 2&gt;</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.Linear.function" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return: <a class="reference internal" href="#Function.Linear.slope" title="Function.Linear.slope"><code class="xref any py py-attr docutils literal"><span class="pre">slope</span></code></a> * <a class="reference internal" href="#Function.Linear.variable" title="Function.Linear.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> + <a class="reference internal" href="#Function.Linear.intercept" title="Function.Linear.intercept"><code class="xref any py py-attr docutils literal"><span class="pre">intercept</span></code></a>.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
@@ -805,7 +871,13 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 <dl class="attribute">
 <dt id="Function.Exponential.scale">
 <code class="descname">scale</code><a class="headerlink" href="#Function.Exponential.scale" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>float</em> &#8211; value by which the exponentiated value is multipled.</p>
+<dd><p><em>float</em> &#8211; value by which the exponentiated value is multiplied.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.Exponential.bounds">
+<code class="descname">bounds</code><a class="headerlink" href="#Function.Exponential.bounds" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>(0, None)</em></p>
 </dd></dl>
 
 <dl class="attribute">
@@ -817,14 +889,14 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 <dl class="attribute">
 <dt id="Function.Exponential.prefs">
 <code class="descname">prefs</code><a class="headerlink" href="#Function.Exponential.prefs" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <a class="reference internal" href="#Function.Exponential.prefs" title="Function.Exponential.prefs"><code class="xref any py py-attr docutils literal"><span class="pre">prefs</span></code></a> argument of the constructor for the function;
+<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <strong>prefs</strong> argument of the constructor for the function;
 if it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
 (see <span class="xref doc">PreferenceSet</span> for details).</p>
 </dd></dl>
 
 <dl class="method">
 <dt id="Function.Exponential.function">
-<code class="descname">function</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 1&gt;</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.Exponential.function" title="Permalink to this definition">¶</a></dt>
+<code class="descname">function</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 2&gt;</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.Exponential.function" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return: <a class="reference internal" href="#Function.Exponential.scale" title="Function.Exponential.scale"><code class="xref any py py-attr docutils literal"><span class="pre">scale</span></code></a> * e**(<a class="reference internal" href="#Function.Exponential.rate" title="Function.Exponential.rate"><code class="xref any py py-attr docutils literal"><span class="pre">rate</span></code></a> * <a class="reference internal" href="#Function.Linear.variable" title="Function.Linear.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>).</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
@@ -911,6 +983,12 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 (if it is specified).</p>
 </dd></dl>
 
+<dl class="attribute">
+<dt id="Function.Logistic.bounds">
+<code class="descname">bounds</code><a class="headerlink" href="#Function.Logistic.bounds" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>(0,1)</em></p>
+</dd></dl>
+
 <dl class="attribute">
 <dt id="Function.Logistic.owner">
 <code class="descname">owner</code><a class="headerlink" href="#Function.Logistic.owner" title="Permalink to this definition">¶</a></dt>
@@ -920,14 +998,14 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 <dl class="attribute">
 <dt id="Function.Logistic.prefs">
 <code class="descname">prefs</code><a class="headerlink" href="#Function.Logistic.prefs" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <a class="reference internal" href="#Function.Logistic.prefs" title="Function.Logistic.prefs"><code class="xref any py py-attr docutils literal"><span class="pre">prefs</span></code></a> argument of the constructor for the function;
+<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <strong>prefs</strong> argument of the constructor for the function;
 if it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
 (see <span class="xref doc">PreferenceSet</span> for details).</p>
 </dd></dl>
 
 <dl class="method">
 <dt id="Function.Logistic.function">
-<code class="descname">function</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 1&gt;</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.Logistic.function" title="Permalink to this definition">¶</a></dt>
+<code class="descname">function</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 2&gt;</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.Logistic.function" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return: 1 / (1 + e**( (<a class="reference internal" href="#Function.Logistic.gain" title="Function.Logistic.gain"><code class="xref any py py-attr docutils literal"><span class="pre">gain</span></code></a> * <a class="reference internal" href="#Function.Logistic.variable" title="Function.Logistic.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>) + <a class="reference internal" href="#Function.Logistic.bias" title="Function.Logistic.bias"><code class="xref any py py-attr docutils literal"><span class="pre">bias</span></code></a>))</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
@@ -1024,22 +1102,22 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 </dd></dl>
 
 <dl class="attribute">
-<dt id="Function.SoftMax.owner">
-<code class="descname">owner</code><a class="headerlink" href="#Function.SoftMax.owner" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>Mechanism</em> &#8211; <a class="reference internal" href="Component.html"><span class="doc">component</span></a> to which the Function has been assigned.</p>
+<dt id="Function.SoftMax.bounds">
+<code class="descname">bounds</code><a class="headerlink" href="#Function.SoftMax.bounds" title="Permalink to this definition">¶</a></dt>
+<dd><p>None if <a href="#id1"><span class="problematic" id="id2">`</span></a>output &lt;SoftMax.output&gt;`==MAX_VAL, else (0,1) : default (0,1)</p>
 </dd></dl>
 
-<dl class="attribute">
-<dt id="Function.SoftMax.prefs">
-<code class="descname">prefs</code><a class="headerlink" href="#Function.SoftMax.prefs" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <a class="reference internal" href="#Function.SoftMax.prefs" title="Function.SoftMax.prefs"><code class="xref any py py-attr docutils literal"><span class="pre">prefs</span></code></a> argument of the constructor for the function;
+<dl class="docutils">
+<dt>owner</dt>
+ <span class="classifier-delimiter">:</span> <span class="classifier">Mechanism</span><dd><a class="reference internal" href="Component.html"><span class="doc">component</span></a> to which the Function has been assigned.</dd>
+<dt>prefs</dt>
+ <span class="classifier-delimiter">:</span> <span class="classifier">PreferenceSet or specification dict</span> <span class="classifier-delimiter">:</span> <span class="classifier">Projection.classPreferences</span><dd>the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <strong>prefs</strong> argument of the constructor for the function;
 if it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
-(see <span class="xref doc">PreferenceSet</span> for details).</p>
-</dd></dl>
-
+(see <span class="xref doc">PreferenceSet</span> for details).</dd>
+</dl>
 <dl class="method">
 <dt id="Function.SoftMax.function">
-<code class="descname">function</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 1&gt;</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.SoftMax.function" title="Permalink to this definition">¶</a></dt>
+<code class="descname">function</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 2&gt;</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.SoftMax.function" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return: e**(<a class="reference internal" href="#Function.SoftMax.gain" title="Function.SoftMax.gain"><code class="xref any py py-attr docutils literal"><span class="pre">gain</span></code></a> * <a class="reference internal" href="#Function.SoftMax.variable" title="Function.SoftMax.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>) /
 sum(e**(<a class="reference internal" href="#Function.SoftMax.gain" title="Function.SoftMax.gain"><code class="xref any py py-attr docutils literal"><span class="pre">gain</span></code></a> * <a class="reference internal" href="#Function.SoftMax.variable" title="Function.SoftMax.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>)),
 filtered by <a class="reference internal" href="#Function.SoftMax.output" title="Function.SoftMax.output"><code class="xref any py py-attr docutils literal"><span class="pre">ouptput</span></code></a> specification.</p>
@@ -1106,6 +1184,7 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 <li><strong>variable</strong> (<em>list or 1d np.array : default variableClassDefault</em>) &#8211; specifies a template for the value to be transformed; length must equal the number of rows of <a class="reference internal" href="#Function.LinearMatrix.matrix" title="Function.LinearMatrix.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a>.</li>
 <li><strong>matrix</strong> (<em>number, list, 1d or 2d np.ndarray, np.matrix, function, or matrix keyword : default IDENTITY_MATRIX</em>) &#8211; specifies matrix used to transform <a class="reference internal" href="#Function.LinearMatrix.variable" title="Function.LinearMatrix.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>
 (see <a class="reference internal" href="#Function.LinearMatrix.matrix" title="Function.LinearMatrix.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a> for specification details).</li>
+<li><strong>bounds</strong> (<em>None</em>) &#8211; </li>
 <li><strong>params</strong> (<em>Optional[Dict[param keyword, param value]]</em>) &#8211; a <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> that specifies the parameters for the
 function.  Values specified for parameters in the dictionary override any assigned to those parameters in
 arguments of the constructor.</li>
@@ -1148,7 +1227,7 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 <dl class="attribute">
 <dt id="Function.LinearMatrix.prefs">
 <code class="descname">prefs</code><a class="headerlink" href="#Function.LinearMatrix.prefs" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <a class="reference internal" href="#Function.LinearMatrix.prefs" title="Function.LinearMatrix.prefs"><code class="xref any py py-attr docutils literal"><span class="pre">prefs</span></code></a> argument of the constructor for the function;
+<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <strong>prefs</strong> argument of the constructor for the function;
 if it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
 (see <span class="xref doc">PreferenceSet</span> for details).</p>
 </dd></dl>
@@ -1181,7 +1260,7 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 
 <dl class="method">
 <dt id="Function.LinearMatrix.function">
-<code class="descname">function</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 1&gt;</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.LinearMatrix.function" title="Permalink to this definition">¶</a></dt>
+<code class="descname">function</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 2&gt;</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.LinearMatrix.function" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return: <a class="reference internal" href="#Function.LinearMatrix.variable" title="Function.LinearMatrix.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> • <a class="reference internal" href="#Function.LinearMatrix.matrix" title="Function.LinearMatrix.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a></p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
@@ -1208,160 +1287,6 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 
 </dd></dl>
 
-<dl class="class">
-<dt id="Function.Integrator">
-<em class="property">class </em><code class="descclassname">Function.</code><code class="descname">Integrator</code><span class="sig-paren">(</span><em>variable_default=None</em>, <em>rate=1.0</em>, <em>integration_type=CONSTANT</em>, <em>noise=0.0</em>, <em>time_step_size=1.0</em>, <em>initializer</em>, <em>params=None</em>, <em>owner=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.Integrator" title="Permalink to this definition">¶</a></dt>
-<dd><p id="integrator">Integrate current value of <a class="reference internal" href="#Function.Integrator.variable" title="Function.Integrator.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> with its prior value.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>variable_default</strong> (<em>number, list or np.array : default variableClassDefault</em>) &#8211; specifies a template for the value to be integrated;  if it is a list or array, each element is independently
-integrated.</li>
-<li><strong>rate</strong> (<em>float, list or 1d np.array : default 1.0</em>) &#8211; specifies the rate of integration.  If it is a list or array, it must be the same length as
-<code class="xref any docutils literal"><span class="pre">variable</span></code> (see <a class="reference internal" href="#Function.Integrator.rate" title="Function.Integrator.rate"><code class="xref any py py-attr docutils literal"><span class="pre">rate</span></code></a> for details).</li>
-<li><strong>integration_type</strong> (<em>CONSTANT, SIMPLE, ADAPTIVE, DIFFUSION : default CONSTANT</em>) &#8211; specifies type of integration (see <a class="reference internal" href="#Function.Integrator.integration_type" title="Function.Integrator.integration_type"><code class="xref any py py-attr docutils literal"><span class="pre">integration_type</span></code></a> for details).</li>
-<li><strong>noise</strong> (<em>float, PsyNeuLink Function, list or 1d np.array : default 0.0</em>) &#8211; specifies random value to be added in each call to <a class="reference internal" href="#Function.Integrator.function" title="Function.Integrator.function"><code class="xref any py py-meth docutils literal"><span class="pre">function</span></code></a>. (see
-<a class="reference internal" href="#Function.Integrator.noise" title="Function.Integrator.noise"><code class="xref any py py-attr docutils literal"><span class="pre">noise</span></code></a> for details).</li>
-<li><strong>time_step_size</strong> (<em>float : default 0.0</em>) &#8211; determines the timing precision of the integration process when <a class="reference internal" href="#Function.Integrator.integration_type" title="Function.Integrator.integration_type"><code class="xref any py py-attr docutils literal"><span class="pre">integration_type</span></code></a>
-is set to DIFFUSION (see <a class="reference internal" href="#Function.Integrator.time_step_size" title="Function.Integrator.time_step_size"><code class="xref any py py-attr docutils literal"><span class="pre">time_step_size</span></code></a> for details.</li>
-<li><strong>float, list or 1d np.array</strong> (<a class="reference internal" href="#Function.Integrator.initializer" title="Function.Integrator.initializer"><em>initializer</em></a>) &#8211; specifies starting value for integration.  If it is a list or array, it must be the same length as
-<code class="xref any docutils literal"><span class="pre">variable_default</span></code> (see <a class="reference internal" href="#Function.Integrator.initializer" title="Function.Integrator.initializer"><code class="xref any py py-attr docutils literal"><span class="pre">initializer</span></code></a> for details).</li>
-<li><strong>params</strong> (<em>Optional[Dict[param keyword, param value]]</em>) &#8211; a <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> that specifies the parameters for the
-function.  Values specified for parameters in the dictionary override any assigned to those parameters in
-arguments of the constructor.</li>
-<li><strong>owner</strong> (<a class="reference internal" href="Component.html#module-Component" title="Component"><em>Component</em></a>) &#8211; <a class="reference internal" href="Component.html"><span class="doc">component</span></a> to which to assign the Function.</li>
-<li><strong>prefs</strong> (<em>Optional[PreferenceSet or specification dict : Function.classPreferences]</em>) &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for the Function. If it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code>
-defined in __init__.py (see <span class="xref doc">PreferenceSet</span> for details).</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-<dl class="attribute">
-<dt id="Function.Integrator.variable">
-<code class="descname">variable</code><a class="headerlink" href="#Function.Integrator.variable" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>number or np.array</em> &#8211; current input value some portion of which (determined by <a class="reference internal" href="#Function.Integrator.rate" title="Function.Integrator.rate"><code class="xref any py py-attr docutils literal"><span class="pre">rate</span></code></a>) that will be
-added to the prior value;  if it is an array, each element is independently integrated.</p>
-</dd></dl>
-
-<dl class="attribute">
-<dt id="Function.Integrator.rate">
-<code class="descname">rate</code><a class="headerlink" href="#Function.Integrator.rate" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>float or 1d np.array</em> &#8211; determines the rate of integration based on current and prior values.  If integration_type is set to ADAPTIVE,
-all elements must be between 0 and 1 (0 = no change; 1 = instantaneous change). If it has a single element, it
-applies to all elements of <a class="reference internal" href="#Function.Integrator.variable" title="Function.Integrator.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>;  if it has more than one element, each element
-applies to the corresponding element of <a class="reference internal" href="#Function.Integrator.variable" title="Function.Integrator.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>.</p>
-</dd></dl>
-
-<dl class="attribute">
-<dt id="Function.Integrator.integration_type">
-<code class="descname">integration_type</code><a class="headerlink" href="#Function.Integrator.integration_type" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>CONSTANT, SIMPLE, ADAPTIVE, DIFFUSION</em> &#8211;</p>
-<dl class="docutils">
-<dt>specifies type of integration:</dt>
-<dd><ul class="first last simple">
-<li><strong>CONSTANT</strong>: <a class="reference internal" href="#Function.Integrator.previous_value" title="Function.Integrator.previous_value"><code class="xref any py py-attr docutils literal"><span class="pre">previous_value</span></code></a> + <a class="reference internal" href="#Function.Integrator.rate" title="Function.Integrator.rate"><code class="xref any py py-attr docutils literal"><span class="pre">rate</span></code></a> +
-<a class="reference internal" href="#Function.Integrator.noise" title="Function.Integrator.noise"><code class="xref any py py-attr docutils literal"><span class="pre">noise</span></code></a> (ignores <a class="reference internal" href="#Function.Integrator.variable" title="Function.Integrator.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>);</li>
-<li><strong>SIMPLE</strong>: <a class="reference internal" href="#Function.Integrator.previous_value" title="Function.Integrator.previous_value"><code class="xref any py py-attr docutils literal"><span class="pre">previous_value</span></code></a> + <a class="reference internal" href="#Function.Integrator.rate" title="Function.Integrator.rate"><code class="xref any py py-attr docutils literal"><span class="pre">rate</span></code></a> *
-<code class="xref any docutils literal"><span class="pre">variable</span></code> + <a class="reference internal" href="#Function.Integrator.noise" title="Function.Integrator.noise"><code class="xref any py py-attr docutils literal"><span class="pre">noise</span></code></a>;</li>
-<li><strong>ADAPTIVE</strong>: (1-<a class="reference internal" href="#Function.Integrator.rate" title="Function.Integrator.rate"><code class="xref any py py-attr docutils literal"><span class="pre">rate</span></code></a>) * <a class="reference internal" href="#Function.Integrator.variable" title="Function.Integrator.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> +
-(<a class="reference internal" href="#Function.Integrator.rate" title="Function.Integrator.rate"><code class="xref any py py-attr docutils literal"><span class="pre">rate</span></code></a> * <a class="reference internal" href="#Function.Integrator.previous_value" title="Function.Integrator.previous_value"><code class="xref any py py-attr docutils literal"><span class="pre">previous_value</span></code></a>) + <a class="reference internal" href="#Function.Integrator.noise" title="Function.Integrator.noise"><code class="xref any py py-attr docutils literal"><span class="pre">noise</span></code></a>
-(<a class="reference external" href="https://en.wikipedia.org/wiki/Wiener_filter">Weiner filter</a> or
-<a class="reference external" href="https://en.wikipedia.org/wiki/Delta_rule">Delta rule</a>);</li>
-<li><strong>DIFFUSION</strong>: <a class="reference internal" href="#Function.Integrator.previous_value" title="Function.Integrator.previous_value"><code class="xref any py py-attr docutils literal"><span class="pre">previous_value</span></code></a> +
-(<a class="reference internal" href="#Function.Integrator.rate" title="Function.Integrator.rate"><code class="xref any py py-attr docutils literal"><span class="pre">rate</span></code></a> * <a class="reference internal" href="#Function.Integrator.previous_value" title="Function.Integrator.previous_value"><code class="xref any py py-attr docutils literal"><span class="pre">previous_value</span></code></a> * <a class="reference internal" href="#Function.Integrator.time_step_size" title="Function.Integrator.time_step_size"><code class="xref any py py-attr docutils literal"><span class="pre">time_step_size</span></code></a>) +
-√(<a class="reference internal" href="#Function.Integrator.time_step_size" title="Function.Integrator.time_step_size"><code class="xref any py py-attr docutils literal"><span class="pre">time_step_size</span></code></a> * <a class="reference internal" href="#Function.Integrator.noise" title="Function.Integrator.noise"><code class="xref any py py-attr docutils literal"><span class="pre">noise</span></code></a> * Gaussian(0,1))
-(<a class="reference external" href="https://en.wikipedia.org/wiki/Two-alternative_forced_choice#Drift-diffusion_model">Drift Diffusion Model</a>).</li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="attribute">
-<dt id="Function.Integrator.noise">
-<code class="descname">noise</code><a class="headerlink" href="#Function.Integrator.noise" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>float, function, list, or 1d np.array</em> &#8211; specifies random value to be added in each call to <a class="reference internal" href="#Function.Integrator.function" title="Function.Integrator.function"><code class="xref any py py-meth docutils literal"><span class="pre">function</span></code></a>.</p>
-<p>If noise is a list or array, it must be the same length as <code class="xref any docutils literal"><span class="pre">variable</span></code>. If noise is
-specified as a single float or function, while <a class="reference internal" href="#Function.Integrator.variable" title="Function.Integrator.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> is a list or array,
-noise will be applied to each variable element. In the case of a noise function, this means that the function
-will be executed separately for each variable element.</p>
-<p>Note that in the case of DIFFUSION, noise must be specified as a float (or list or array of floats) because this
-value will be used to construct the standard DDM probability distribution. For all other types of integration,
-in order to generate random noise, we recommend that you instead select a probability distribution function
-(see <code class="xref any docutils literal"><span class="pre">Distribution</span> <span class="pre">Functions</span></code> for details), which will generate a new noise value from
-its distribution on each execution. If noise is specified as a float or as a function with a fixed output (or a
-list or array of these), then the noise will simply be an offset that remains the same across all executions.</p>
-</dd></dl>
-
-<dl class="attribute">
-<dt id="Function.Integrator.time_step_size">
-<code class="descname">time_step_size</code><a class="headerlink" href="#Function.Integrator.time_step_size" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>float</em> &#8211; determines the timing precision of the integration process when <a class="reference internal" href="#Function.Integrator.integration_type" title="Function.Integrator.integration_type"><code class="xref any py py-attr docutils literal"><span class="pre">integration_type</span></code></a>
-is set to DIFFUSION (and used to scale the <a class="reference internal" href="#Function.Integrator.noise" title="Function.Integrator.noise"><code class="xref any py py-attr docutils literal"><span class="pre">noise</span></code></a> parameter appropriately).</p>
-</dd></dl>
-
-<dl class="attribute">
-<dt id="Function.Integrator.initializer">
-<code class="descname">initializer</code><a class="headerlink" href="#Function.Integrator.initializer" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>1d np.array or list</em> &#8211; determines the starting value for integration (i.e., the value to which
-<a class="reference internal" href="#Function.Integrator.previous_value" title="Function.Integrator.previous_value"><code class="xref any py py-attr docutils literal"><span class="pre">previous_value</span></code></a> is set.</p>
-<p>If initializer is a list or array, it must be the same length as <code class="xref any docutils literal"><span class="pre">variable</span></code>. If
-initializer is specified as a single float or function, while <a class="reference internal" href="#Function.Integrator.variable" title="Function.Integrator.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> is a list or
-array, initializer will be applied to each variable element. In the case of an initializer function, this means
-that the function will be executed separately for each variable element.</p>
-</dd></dl>
-
-<dl class="attribute">
-<dt id="Function.Integrator.previous_value">
-<code class="descname">previous_value</code><a class="headerlink" href="#Function.Integrator.previous_value" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>1d np.array : default variableClassDefault</em> &#8211; stores previous value with which <a class="reference internal" href="#Function.Integrator.variable" title="Function.Integrator.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> is integrated.</p>
-</dd></dl>
-
-<dl class="attribute">
-<dt id="Function.Integrator.owner">
-<code class="descname">owner</code><a class="headerlink" href="#Function.Integrator.owner" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>Mechanism</em> &#8211; <a class="reference internal" href="Component.html"><span class="doc">component</span></a> to which the Function has been assigned.</p>
-</dd></dl>
-
-<dl class="attribute">
-<dt id="Function.Integrator.prefs">
-<code class="descname">prefs</code><a class="headerlink" href="#Function.Integrator.prefs" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <a class="reference internal" href="#Function.Integrator.prefs" title="Function.Integrator.prefs"><code class="xref any py py-attr docutils literal"><span class="pre">prefs</span></code></a> argument of the constructor for the function;
-if it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
-(see <span class="xref doc">PreferenceSet</span> for details).</p>
-</dd></dl>
-
-<dl class="method">
-<dt id="Function.Integrator.function">
-<code class="descname">function</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 1&gt;</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.Integrator.function" title="Permalink to this definition">¶</a></dt>
-<dd><p>Return: some fraction of <a class="reference internal" href="#Function.Linear.slope" title="Function.Linear.slope"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> combined with some fraction of <a class="reference internal" href="#Function.Integrator.previous_value" title="Function.Integrator.previous_value"><code class="xref any py py-attr docutils literal"><span class="pre">previous_value</span></code></a> (see <a class="reference internal" href="#Function.Integrator.integration_type" title="Function.Integrator.integration_type"><code class="xref any py py-attr docutils literal"><span class="pre">integration_type</span></code></a>).</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>variable</strong> (<em>number, list or np.array : default variableClassDefault</em>) &#8211; a single value or array of values to be integrated.</li>
-<li><strong>params</strong> (<em>Optional[Dict[param keyword, param value]]</em>) &#8211; a <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> that specifies the parameters for the
-function.  Values specified for parameters in the dictionary override any assigned to those parameters in
-arguments of the constructor.</li>
-<li><strong>time_scale</strong> (<em>TimeScale : default TimeScale.TRIAL</em>) &#8211; specifies whether the function is executed on the time_step or trial time scale.</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>updated value of integral</strong></p>
-</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">2d np.array</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
-</dd></dl>
-
 <dl class="class">
 <dt id="Function.BogaczEtAl">
 <em class="property">class </em><code class="descclassname">Function.</code><code class="descname">BogaczEtAl</code><span class="sig-paren">(</span><em>variable_default=variableCLassDefault</em>, <em>drift_rate=1.0</em>, <em>threshold=1.0</em>, <em>starting_point=0.0</em>, <em>t0=0.2                                          noise=0.5</em>, <em>params=None</em>, <em>owner=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.BogaczEtAl" title="Permalink to this definition">¶</a></dt>
@@ -1453,14 +1378,14 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 <dl class="attribute">
 <dt id="Function.BogaczEtAl.prefs">
 <code class="descname">prefs</code><a class="headerlink" href="#Function.BogaczEtAl.prefs" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <a class="reference internal" href="#Function.BogaczEtAl.prefs" title="Function.BogaczEtAl.prefs"><code class="xref any py py-attr docutils literal"><span class="pre">prefs</span></code></a> argument of the constructor for the function;
+<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <strong>prefs</strong> argument of the constructor for the function;
 if it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
 (see <span class="xref doc">PreferenceSet</span> for details).</p>
 </dd></dl>
 
 <dl class="method">
 <dt id="Function.BogaczEtAl.function">
-<code class="descname">function</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 1&gt;</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.BogaczEtAl.function" title="Permalink to this definition">¶</a></dt>
+<code class="descname">function</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 2&gt;</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.BogaczEtAl.function" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return: terminal value of decision variable (equal to threshold), mean accuracy (error rate; ER) and mean
 response time (RT)</p>
 <table class="docutils field-list" frame="void" rules="none">
@@ -1609,13 +1534,13 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 <dt>owner</dt>
  <span class="classifier-delimiter">:</span> <span class="classifier">Mechanism</span><dd><a class="reference internal" href="Component.html"><span class="doc">component</span></a> to which the Function has been assigned.</dd>
 <dt>prefs</dt>
- <span class="classifier-delimiter">:</span> <span class="classifier">PreferenceSet or specification dict</span> <span class="classifier-delimiter">:</span> <span class="classifier">Projection.classPreferences</span><dd>the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <a class="reference internal" href="#Function.UserDefinedFunction.prefs" title="Function.UserDefinedFunction.prefs"><code class="xref any py py-attr docutils literal"><span class="pre">prefs</span></code></a> argument of the constructor for the function;
+ <span class="classifier-delimiter">:</span> <span class="classifier">PreferenceSet or specification dict</span> <span class="classifier-delimiter">:</span> <span class="classifier">Projection.classPreferences</span><dd>the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <strong>prefs</strong> argument of the constructor for the function;
 if it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
 (see <span class="xref doc">PreferenceSet</span> for details).</dd>
 </dl>
 <dl class="method">
 <dt id="Function.NavarroAndFuss.function">
-<code class="descname">function</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 1&gt;</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.NavarroAndFuss.function" title="Permalink to this definition">¶</a></dt>
+<code class="descname">function</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 2&gt;</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.NavarroAndFuss.function" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return: terminal value of decision variable, mean accuracy (error rate; ER), mean response time (RT),
 correct RT mean, correct RT variance and correct RT skew.  <strong>Requires that the MatLab engine is installed.</strong></p>
 <table class="docutils field-list" frame="void" rules="none">
@@ -1809,15 +1734,15 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 
 <dl class="class">
 <dt id="Function.GammaDist">
-<em class="property">class </em><code class="descclassname">Function.</code><code class="descname">GammaDist</code><span class="sig-paren">(</span><em>scale=1.0</em>, <em>shape=1.0</em>, <em>params=None</em>, <em>owner=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.GammaDist" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">Function.</code><code class="descname">GammaDist</code><span class="sig-paren">(</span><em>scale=1.0</em>, <em>dist_shape=1.0</em>, <em>params=None</em>, <em>owner=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.GammaDist" title="Permalink to this definition">¶</a></dt>
 <dd><p id="gammadist">Return a random sample from a gamma distribution using numpy.random.gamma</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>scale</strong> (<em>float : default 1.0</em>) &#8211; The shape of the gamma distribution. Should be greater than zero.</li>
-<li><strong>shape</strong> (<em>float : default 1.0</em>) &#8211; The scale of the gamma distribution. Should be greater than zero.</li>
+<li><strong>scale</strong> (<em>float : default 1.0</em>) &#8211; The scale of the gamma distribution. Should be greater than zero.</li>
+<li><strong>dist_shape</strong> (<em>float : default 1.0</em>) &#8211; The shape of the gamma distribution. Should be greater than zero.</li>
 <li><strong>params</strong> (<em>Optional[Dict[param keyword, param value]]</em>) &#8211; a <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> that specifies the parameters for the
 function.  Values specified for parameters in the dictionary override any assigned to those parameters in
 arguments of the constructor.</li>
@@ -1832,12 +1757,12 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 <dl class="attribute">
 <dt id="Function.GammaDist.scale">
 <code class="descname">scale</code><a class="headerlink" href="#Function.GammaDist.scale" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>float : default 1.0</em> &#8211; The shape of the gamma distribution. Should be greater than zero.</p>
+<dd><p><em>float : default 1.0</em> &#8211; The dist_shape of the gamma distribution. Should be greater than zero.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="Function.GammaDist.shape">
-<code class="descname">shape</code><a class="headerlink" href="#Function.GammaDist.shape" title="Permalink to this definition">¶</a></dt>
+<dt id="Function.GammaDist.dist_shape">
+<code class="descname">dist_shape</code><a class="headerlink" href="#Function.GammaDist.dist_shape" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>float : default 1.0</em> &#8211; The scale of the gamma distribution. Should be greater than zero.</p>
 </dd></dl>
 
@@ -1921,12 +1846,414 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 
 </dd></dl>
 
+<dl class="class">
+<dt id="Function.Stability">
+<em class="property">class </em><code class="descclassname">Function.</code><code class="descname">Stability</code><span class="sig-paren">(</span><em>variable_default=variableCLassDefault</em>, <em>matrix=HOLLOW_MATRIX</em>, <em>metric=ENERGY                                   transfer_fct=None                               normalize=False</em>, <em>params=None</em>, <em>owner=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.Stability" title="Permalink to this definition">¶</a></dt>
+<dd><p id="stability">Return the stability of a vector based an a weight matrix from each element to every other element in the vector.
+The value of <a class="reference internal" href="#Function.Stability.variable" title="Function.Stability.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> is passed through the <a class="reference internal" href="#Function.Stability.matrix" title="Function.Stability.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a>, transformed
+using the <a class="reference internal" href="#Function.Stability.transfer_fct" title="Function.Stability.transfer_fct"><code class="xref any py py-attr docutils literal"><span class="pre">transfer_fct</span></code></a> (if specified), and then compared with its initial value
+using the specified <a class="reference internal" href="#Function.Stability.metric" title="Function.Stability.metric"><code class="xref any py py-attr docutils literal"><span class="pre">metric</span></code></a>.  If <a class="reference internal" href="#Function.Stability.normalize" title="Function.Stability.normalize"><code class="xref any py py-attr docutils literal"><span class="pre">normalize</span></code></a> is specified, the result
+is normalized by the number of elements in the <a class="reference internal" href="#Function.Stability.variable" title="Function.Stability.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>variable</strong> (<em>list of numbers or 1d np.array : Default variableClassDefault</em>) &#8211; the array for which stabilty is calculated.</li>
+<li><strong>matrix</strong> (<em>list, np.ndarray, np.matrix, function keyword, or MappingProjection : default HOLLOW_MATRIX</em>) &#8211; specifies the matrix of recurrent weights;  must be a square matrix with the same width as the
+length of <a class="reference internal" href="#Function.Stability.variable" title="Function.Stability.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>.</li>
+<li><strong>metric</strong> (<em>ENERGY, ENTROPY or keyword in DISTANCE_METRICS : Default ENERGY</em>) &#8211; specifies the metric used to compute stability.</li>
+<li><strong>transfer_fct</strong> (<em>function or method : Default None</em>) &#8211; specifies the function used to transform output of weight <a class="reference internal" href="#Function.Stability.matrix" title="Function.Stability.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a>.</li>
+<li><strong>normalize</strong> (<em>bool : Default False</em>) &#8211; specifies whether to normalize the stability value by the length of <a class="reference internal" href="#Function.Stability.variable" title="Function.Stability.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>.</li>
+<li><strong>params</strong> (<em>Optional[Dict[param keyword, param value]]</em>) &#8211; a <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> that specifies the parameters for the
+function.  Values specified for parameters in the dictionary override any assigned to those parameters in
+arguments of the constructor.</li>
+<li><strong>owner</strong> (<a class="reference internal" href="Component.html#module-Component" title="Component"><em>Component</em></a>) &#8211; <a class="reference internal" href="Component.html"><span class="doc">component</span></a> to which to assign the Function.</li>
+<li><strong>prefs</strong> (<em>Optional[PreferenceSet or specification dict : Function.classPreferences]</em>) &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for the Function. If it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code>
+defined in __init__.py (see <span class="xref doc">PreferenceSet</span> for details).</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+<dl class="attribute">
+<dt id="Function.Stability.variable">
+<code class="descname">variable</code><a class="headerlink" href="#Function.Stability.variable" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>1d np.array</em> &#8211; array for which stability is calculated.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.Stability.matrix">
+<code class="descname">matrix</code><a class="headerlink" href="#Function.Stability.matrix" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>list, np.ndarray, np.matrix, function keyword, or MappingProjection : default HOLLOW_MATRIX</em> &#8211; weight matrix from each element of <code class="xref any docutils literal"><span class="pre">variable</span></code> to each other;  if a matrix other
+than HOLLOW_MATRIX is assigned, it is convolved with HOLLOW_MATRIX to eliminate self-connections from the
+stability calculation.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.Stability.metric">
+<code class="descname">metric</code><a class="headerlink" href="#Function.Stability.metric" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>ENERGY, ENTROPY or keyword in DISTANCE_METRICS</em> &#8211; metric used to compute stability.  If ENTROPY or DISTANCE_METRICS keyword is used, the <a class="reference internal" href="#Function.Distance" title="Function.Distance"><code class="xref any py py-class docutils literal"><span class="pre">Distance</span></code></a> Function
+is used to compute the stability of <a class="reference internal" href="#Function.Stability.variable" title="Function.Stability.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> with respect to its value after
+transformation by <a class="reference internal" href="#Function.Stability.matrix" title="Function.Stability.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a> and <a class="reference internal" href="#Function.Stability.transfer_fct" title="Function.Stability.transfer_fct"><code class="xref any py py-attr docutils literal"><span class="pre">transfer_fct</span></code></a>.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.Stability.transfer_fct">
+<code class="descname">transfer_fct</code><a class="headerlink" href="#Function.Stability.transfer_fct" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>function or method</em> &#8211; function used to transform output of weight <a class="reference internal" href="#Function.Stability.matrix" title="Function.Stability.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a> prior to computing stability.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.Stability.normalize">
+<code class="descname">normalize</code><a class="headerlink" href="#Function.Stability.normalize" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>bool</em> &#8211; if <code class="xref any docutils literal"><span class="pre">True</span></code>, result of stability calculation is normalized by the length of <a class="reference internal" href="#Function.Stability.variable" title="Function.Stability.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.Stability.params">
+<code class="descname">params</code><a class="headerlink" href="#Function.Stability.params" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>Optional[Dict[param keyword, param value]]</em> &#8211; a <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> that specifies the parameters for the
+function.  Values specified for parameters in the dictionary override any assigned to those parameters in
+arguments of the constructor.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.Stability.owner">
+<code class="descname">owner</code><a class="headerlink" href="#Function.Stability.owner" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>Component</em> &#8211; <a class="reference internal" href="Component.html"><span class="doc">component</span></a> to which to assign the Function.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.Stability.prefs">
+<code class="descname">prefs</code><a class="headerlink" href="#Function.Stability.prefs" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>Optional[PreferenceSet or specification dict : Function.classPreferences]</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for the Function. If it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code>
+defined in __init__.py (see <span class="xref doc">PreferenceSet</span> for details).</p>
+</dd></dl>
+
+<dl class="method">
+<dt id="Function.Stability.function">
+<code class="descname">function</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 2&gt;</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.Stability.function" title="Permalink to this definition">¶</a></dt>
+<dd><p>Calculate the stability of <a class="reference internal" href="#Function.Stability.variable" title="Function.Stability.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>.</p>
+<blockquote>
+<div>Compare the value of <a class="reference internal" href="#Function.Stability.variable" title="Function.Stability.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> with its value after transformation by
+<a class="reference internal" href="#Function.Stability.matrix" title="Function.Stability.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a> and <a class="reference internal" href="#Function.Stability.transfer_fct" title="Function.Stability.transfer_fct"><code class="xref any py py-attr docutils literal"><span class="pre">transfer_fct</span></code></a> (if specified), using the specified
+<a class="reference internal" href="#Function.Stability.metric" title="Function.Stability.metric"><code class="xref any py py-attr docutils literal"><span class="pre">metric</span></code></a>.  If <a class="reference internal" href="#Function.Stability.normalize" title="Function.Stability.normalize"><code class="xref any py py-attr docutils literal"><span class="pre">normalize</span></code></a> is <code class="xref any docutils literal"><span class="pre">True</span></code>, the result is divided
+by the length of <a class="reference internal" href="#Function.Stability.variable" title="Function.Stability.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>.</div></blockquote>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>stability</strong></td>
+</tr>
+<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body">scalar</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="class">
+<dt id="Function.Stability.MappingProjection">
+<em class="property">class </em><code class="descname">MappingProjection</code><span class="sig-paren">(</span><em>sender=None</em>, <em>receiver=None</em>, <em>matrix=DEFAULT_MATRIX</em>, <em>params=None</em>, <em>name=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.Stability.MappingProjection" title="Permalink to this definition">¶</a></dt>
+<dd><p>Implements a projection that transmits the output of one mechanism to the input of another.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>sender</strong> (<em>Optional[OutputState or Mechanism]</em>) &#8211; specifies the source of the projection&#8217;s input. If a mechanism is specified, its
+<a class="reference internal" href="OutputState.html#outputstate-primary"><span class="std std-ref">primary outputState</span></a> will be used. If it is not specified, it will be assigned in
+the context in which the projection is used.</li>
+<li><strong>receiver</strong> (<em>Optional[InputState or Mechanism]</em>) &#8211; specifies the destination of the projection&#8217;s output.  If a mechanism is specified, its
+<a class="reference internal" href="Mechanism.html#mechanism-inputstates"><span class="std std-ref">primary inputState</span></a> will be used. If it is not specified, it will be assigned in
+the context in which the projection is used.</li>
+<li><strong>matrix</strong> (<em>list, np.ndarray, np.matrix, function or keyword : default DEFAULT_MATRIX</em>) &#8211; the matrix used by <code class="xref any docutils literal"><span class="pre">function</span></code> (default: <a class="reference internal" href="#Function.LinearCombination" title="Function.LinearCombination"><code class="xref any py py-class docutils literal"><span class="pre">LinearCombination</span></code></a>) to transform the
+value of the <a class="reference internal" href="#Function.Stability.MappingProjection.sender" title="Function.Stability.MappingProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a>.</li>
+<li><strong>params</strong> (<em>Optional[Dict[param keyword, param value]]</em>) &#8211; a <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> that can be used to specify the parameters for
+the projection, its function, and/or a custom function and its parameters. By default, it contains an entry for
+the projection&#8217;s default assignment (<a class="reference internal" href="#Function.LinearCombination" title="Function.LinearCombination"><code class="xref any py py-class docutils literal"><span class="pre">LinearCombination</span></code></a>).  Values specified for parameters in the dictionary
+override any assigned to those parameters in arguments of the constructor.</li>
+<li><strong>name</strong> (<em>str : default MappingProjection-&lt;index&gt;</em>) &#8211; a string used for the name of the MappingProjection.
+If not is specified, a default is assigned by <code class="xref any docutils literal"><span class="pre">ProjectionRegistry</span></code>
+(see <code class="xref any docutils literal"><span class="pre">Registry</span></code> for conventions used in naming, including for default and duplicate names).</li>
+<li><strong>prefs</strong> (<em>Optional[PreferenceSet or specification dict : Projection.classPreferences]</em>) &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for the MappingProjection.
+If it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
+(see <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for details).</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+<dl class="attribute">
+<dt id="Function.Stability.MappingProjection.componentType">
+<code class="descname">componentType</code><a class="headerlink" href="#Function.Stability.MappingProjection.componentType" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>MAPPING_PROJECTION</em></p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.Stability.MappingProjection.sender">
+<code class="descname">sender</code><a class="headerlink" href="#Function.Stability.MappingProjection.sender" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>OutputState</em> &#8211; identifies the source of the projection&#8217;s input.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.Stability.MappingProjection.receiver">
+<code class="descname">receiver</code><a class="headerlink" href="#Function.Stability.MappingProjection.receiver" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>InputState</em> &#8211; identifies the destination of the projection.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.Stability.MappingProjection.monitoringMechanism">
+<code class="descname">monitoringMechanism</code><a class="headerlink" href="#Function.Stability.MappingProjection.monitoringMechanism" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>MonitoringMechanism</em> &#8211; source of error signal for that determine changes to the <a class="reference internal" href="#Function.Stability.MappingProjection.matrix" title="Function.Stability.MappingProjection.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a> when
+<a class="reference internal" href="LearningProjection.html"><span class="doc">learning</span></a> is used.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.Stability.MappingProjection.matrix">
+<code class="descname">matrix</code><a class="headerlink" href="#Function.Stability.MappingProjection.matrix" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>2d np.array</em> &#8211; matrix used by <code class="xref any docutils literal"><span class="pre">function</span></code> to transform input from the
+<a class="reference internal" href="#Function.Stability.MappingProjection.sender" title="Function.Stability.MappingProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a> to the value provided to the <a class="reference internal" href="#Function.Stability.MappingProjection.receiver" title="Function.Stability.MappingProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a>.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.Stability.MappingProjection.has_learning_projection">
+<code class="descname">has_learning_projection</code><a class="headerlink" href="#Function.Stability.MappingProjection.has_learning_projection" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>bool : False</em> &#8211; identifies whether the MappingProjection&#8217;s <code class="xref any docutils literal"><span class="pre">MATRIX</span></code> <a class="reference internal" href="ParameterState.html"><span class="doc">parameterState</span></a> has been assigned a
+<a class="reference internal" href="LearningProjection.html"><span class="doc">LearningProjection</span></a>.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.Stability.MappingProjection.name">
+<code class="descname">name</code><a class="headerlink" href="#Function.Stability.MappingProjection.name" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>str : default MappingProjection-&lt;index&gt;</em> &#8211; the name of the MappingProjection.
+Specified in the <strong>name</strong> argument of the constructor for the projection;
+if not is specified, a default is assigned by ProjectionRegistry
+(see <span class="xref doc">Registry</span> for conventions used in naming, including for default and duplicate names).</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.Stability.MappingProjection.prefs">
+<code class="descname">prefs</code><a class="headerlink" href="#Function.Stability.MappingProjection.prefs" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for projection.
+Specified in the <strong>prefs</strong> argument of the constructor for the projection;
+if it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
+(see <span class="xref doc">PreferenceSet</span> for details).</p>
+</dd></dl>
+
+<dl class="method">
+<dt id="Function.Stability.MappingProjection.execute">
+<code class="descname">execute</code><span class="sig-paren">(</span><em>input=None</em>, <em>clock=&lt;PsyNeuLink.Globals.TimeScale.Clock object&gt;</em>, <em>time_scale=None</em>, <em>params=None</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.Stability.MappingProjection.execute" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="docutils">
+<dt>If there is a functionParameterStates[LEARNING_PROJECTION], update the matrix parameterState:</dt>
+<dd><blockquote class="first">
+<div>it should set params[PARAMETER_STATE_PARAMS] = {kwLinearCombinationOperation:SUM (OR ADD??)}
+and then call its super().execute</div></blockquote>
+<ul class="last simple">
+<li>use its value to update MATRIX using CombinationOperation (see State update ??execute method??)</li>
+</ul>
+</dd>
+</dl>
+<p>Assumes that if self.monitoringMechanism is assigned <em>and</em> parameterState[MATRIX] has been instantiated
+then learningSignal exists;  this averts duck typing which otherwise would be required for the most
+frequent cases (i.e., <em>no</em> learningSignal).</p>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="class">
+<dt id="Function.Stability.ParameterState">
+<em class="property">class </em><code class="descclassname">Stability.</code><code class="descname">ParameterState</code><span class="sig-paren">(</span><em>owner</em>, <em>reference_value=None                                             function=LinearCombination(operation=PRODUCT)</em>, <em>variable=None</em>, <em>parameter_modulation_operation=Modulation.MULTIPLY</em>, <em>params=None</em>, <em>name=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.Stability.ParameterState" title="Permalink to this definition">¶</a></dt>
+<dd><p>Implements a subclass of <a class="reference internal" href="State.html"><span class="doc">State</span></a> that represents and possibly modifies the value of a parameter for a mechanism,
+projection, or function.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>owner</strong> (<em>Mechanism or MappingProjection</em>) &#8211; the <a class="reference internal" href="Mechanism.html"><span class="doc">Mechanism</span></a> or <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a> to which to which the ParameterState belongs; it must be specified or
+determinable from the context in which the ParameterState is created. The owner of a ParameterState for the
+parameter of a <code class="xref any docutils literal"><span class="pre">function</span></code> should be specified as the Mechanism or Projection
+to which the function belongs.</li>
+<li><strong>reference_value</strong> (<em>number, list or np.ndarray</em>) &#8211; specifies the default value of the parameter for which the ParameterState is responsible.</li>
+<li><strong>variable</strong> (<em>number, list or np.ndarray</em>) &#8211; specifies the parameter&#8217;s initial value and attribute value —&nbsp;that is, the value of the attribute of the
+ParameterState&#8217;s owner or its <code class="xref any docutils literal"><span class="pre">function</span></code> assigned to the parameter.</li>
+<li><strong>function</strong> (<em>Function or method : default LinearCombination(operation=SUM)</em>) &#8211; specifies the function used to convert the parameter&#8217;s attribute value (same as the ParameterState&#8217;s
+<a class="reference internal" href="#Function.Stability.ParameterState.variable" title="Function.Stability.ParameterState.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>) to the ParameterState&#8217;s <a class="reference internal" href="ParameterState.html#ParameterState.ParameterState.value" title="ParameterState.ParameterState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>.</li>
+<li><strong>params</strong> (<em>Optional[Dict[param keyword, param value]]</em>) &#8211; a <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> that can be used to specify the parameters for
+the parameterState or its function, and/or a custom function and its parameters.  Values specified for
+parameters in the dictionary override any assigned to those parameters in arguments of the constructor.</li>
+<li><strong>name</strong> (<em>str : default InputState-&lt;index&gt;</em>) &#8211; a string used for the name of the inputState.
+If not is specified, a default is assigned by StateRegistry of the mechanism to which the inputState belongs
+(see <span class="xref doc">Registry</span> for conventions used in naming, including for default and duplicate names).</li>
+<li><strong>prefs</strong> (<em>Optional[PreferenceSet or specification dict : State.classPreferences]</em>) &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for the inputState.
+If it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
+(see <span class="xref doc">PreferenceSet</span> for details).</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+<dl class="attribute">
+<dt id="Function.Stability.ParameterState.owner">
+<code class="descname">owner</code><a class="headerlink" href="#Function.Stability.ParameterState.owner" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>Mechanism or MappingProjection</em> &#8211; the <a class="reference internal" href="Mechanism.html"><span class="doc">Mechanism</span></a> or <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a> to which the parameterState belongs.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.Stability.ParameterState.variable">
+<code class="descname">variable</code><a class="headerlink" href="#Function.Stability.ParameterState.variable" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>number, list or np.ndarray</em> &#8211; the parameter&#8217;s attribute value —&nbsp;that is, the value of the attribute of the
+ParameterState&#8217;s owner or its <code class="xref any docutils literal"><span class="pre">function</span></code> assigned to the parameter.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.Stability.ParameterState.mod_afferents">
+<code class="descname">mod_afferents</code><a class="headerlink" href="#Function.Stability.ParameterState.mod_afferents" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>Optional[List[Projection]]</em> &#8211; a list of the <a class="reference internal" href="ModulatoryProjection.html"><span class="doc">ModulatoryProjection</span></a> that project to the ParameterState (i.e.,
+for which it is a <code class="xref any docutils literal"><span class="pre">receiver</span></code>); these can be
+<a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjection(s)</span></a> and/or <a class="reference internal" href="LearningProjection.html"><span class="doc">LearningProjection(s)</span></a>,
+but not <a class="reference internal" href="GatingProjection.html"><span class="doc">GatingProjection</span></a>.  The <code class="xref any docutils literal"><span class="pre">value</span></code> of each
+must match the format (number and types of elements) of the ParameterState&#8217;s
+<a class="reference internal" href="#Function.Stability.ParameterState.variable" title="Function.Stability.ParameterState.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.Stability.ParameterState.function">
+<code class="descname">function</code><a class="headerlink" href="#Function.Stability.ParameterState.function" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>Function : default Linear</em> &#8211; converts the parameter&#8217;s attribute value (same as the ParameterState&#8217;s <a class="reference internal" href="#Function.Stability.ParameterState.variable" title="Function.Stability.ParameterState.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>)
+to the ParameterState&#8217;s <a class="reference internal" href="ParameterState.html#ParameterState.ParameterState.value" title="ParameterState.ParameterState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>, under the influence of any
+<a class="reference internal" href="ModulatoryProjection.html"><span class="doc">ModulatoryProjections</span></a> received by the ParameterState (and listed in its
+<a class="reference internal" href="ParameterState.html#ParameterState.ParameterState.mod_afferents" title="ParameterState.ParameterState.mod_afferents"><code class="xref any py py-attr docutils literal"><span class="pre">mod_afferents</span></code></a> attribute.  The result is assigned as the ParameterState&#8217;s
+<a class="reference internal" href="ParameterState.html"><span class="doc">value</span></a>.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.Stability.ParameterState.value">
+<code class="descname">value</code><a class="headerlink" href="#Function.Stability.ParameterState.value" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>number, list or np.ndarray</em> &#8211; the result returned by the ParameterState&#8217;s <a class="reference internal" href="ParameterState.html#ParameterState.ParameterState.function" title="ParameterState.ParameterState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>, and used by the
+ParameterState&#8217;s owner or its <code class="xref any docutils literal"><span class="pre">function</span></code> as the value of the parameter for which the
+ParmeterState is responsible.  Note that this is not necessarily the same as the parameter&#8217;s attribute value
+(that is, the value of the owner&#8217;s attribute for the parameter), since the ParameterState&#8217;s
+<a class="reference internal" href="ParameterState.html#ParameterState.ParameterState.function" title="ParameterState.ParameterState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> may modify the latter under the influence of its
+<a class="reference internal" href="ParameterState.html#ParameterState.ParameterState.mod_afferents" title="ParameterState.ParameterState.mod_afferents"><code class="xref any py py-attr docutils literal"><span class="pre">mod_afferents</span></code></a>.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.Stability.ParameterState.name">
+<code class="descname">name</code><a class="headerlink" href="#Function.Stability.ParameterState.name" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>str : default &lt;State subclass&gt;-&lt;index&gt;</em> &#8211; the name of the inputState.
+Specified in the <strong>name</strong> argument of the constructor for the outputState.  If not is specified, a default is
+assigned by the StateRegistry of the mechanism to which the outputState belongs
+(see <span class="xref doc">Registry</span> for conventions used in naming, including for default and duplicate names).</p>
+<div class="admonition note">
+<p class="first admonition-title">Note</p>
+<p class="last">Unlike other PsyNeuLink components, states names are &#8220;scoped&#8221; within a mechanism, meaning that states with
+the same name are permitted in different mechanisms.  However, they are <em>not</em> permitted in the same
+mechanism: states within a mechanism with the same base name are appended an index in the order of their
+creation.</p>
+</div>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.Stability.ParameterState.prefs">
+<code class="descname">prefs</code><a class="headerlink" href="#Function.Stability.ParameterState.prefs" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>PreferenceSet or specification dict : State.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for the inputState.
+Specified in the <strong>prefs</strong> argument of the constructor for the projection;  if it is not specified, a default is
+assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
+(see <span class="xref doc">PreferenceSet</span> for details).</p>
+</dd></dl>
+
+</dd></dl>
+
+</dd></dl>
+
+<dl class="class">
+<dt id="Function.Distance">
+<em class="property">class </em><code class="descclassname">Function.</code><code class="descname">Distance</code><span class="sig-paren">(</span><em>variable_default=variableCLassDefault</em>, <em>metric=EUCLIDEAN                               normalize=False</em>, <em>params=None</em>, <em>owner=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.Distance" title="Permalink to this definition">¶</a></dt>
+<dd><p id="distance">Return the distance between two vectors based on a specified metric.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>variable</strong> (<em>2d np.array with two items : Default variableClassDefault</em>) &#8211; the arrays between which the distance is calculated.</li>
+<li><strong>metric</strong> (<em>keyword in DISTANCE_METRICS : Default EUCLIDEAN</em>) &#8211; specifies the metric used to compute the distance between the two items in <a class="reference internal" href="#Function.Distance.variable" title="Function.Distance.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>.</li>
+<li><strong>normalize</strong> (<em>bool : Default False</em>) &#8211; specifies whether to normalize the distance by the length of <a class="reference internal" href="#Function.Distance.variable" title="Function.Distance.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>.</li>
+<li><strong>params</strong> (<em>Optional[Dict[param keyword, param value]]</em>) &#8211; a <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> that specifies the parameters for the
+function.  Values specified for parameters in the dictionary override any assigned to those parameters in
+arguments of the constructor.</li>
+<li><strong>owner</strong> (<a class="reference internal" href="Component.html#module-Component" title="Component"><em>Component</em></a>) &#8211; <a class="reference internal" href="Component.html"><span class="doc">component</span></a> to which to assign the Function.</li>
+<li><strong>prefs</strong> (<em>Optional[PreferenceSet or specification dict : Function.classPreferences]</em>) &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for the Function. If it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code>
+defined in __init__.py (see <span class="xref doc">PreferenceSet</span> for details).</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+<dl class="attribute">
+<dt id="Function.Distance.variable">
+<code class="descname">variable</code><a class="headerlink" href="#Function.Distance.variable" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>2d np.array with two items</em> &#8211; contains the arrays between which the distance is calculated.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.Distance.metric">
+<code class="descname">metric</code><a class="headerlink" href="#Function.Distance.metric" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>keyword in DISTANCE_METRICS</em> &#8211; specifies the metric used to compute the distance between the two items in <a class="reference internal" href="#Function.Distance.variable" title="Function.Distance.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.Distance.normalize">
+<code class="descname">normalize</code><a class="headerlink" href="#Function.Distance.normalize" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>bool</em> &#8211; specifies whether to normalize the distance by the length of <a class="reference internal" href="#Function.Distance.variable" title="Function.Distance.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.Distance.params">
+<code class="descname">params</code><a class="headerlink" href="#Function.Distance.params" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>Optional[Dict[param keyword, param value]]</em> &#8211; a <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> that specifies the parameters for the
+function.  Values specified for parameters in the dictionary override any assigned to those parameters in
+arguments of the constructor.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.Distance.owner">
+<code class="descname">owner</code><a class="headerlink" href="#Function.Distance.owner" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>Component</em> &#8211; <a class="reference internal" href="Component.html"><span class="doc">component</span></a> to which to assign the Function.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Function.Distance.prefs">
+<code class="descname">prefs</code><a class="headerlink" href="#Function.Distance.prefs" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>Optional[PreferenceSet or specification dict : Function.classPreferences]</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for the Function. If it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code>
+defined in __init__.py (see <span class="xref doc">PreferenceSet</span> for details).</p>
+</dd></dl>
+
+<dl class="method">
+<dt id="Function.Distance.function">
+<code class="descname">function</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 2&gt;</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.Distance.function" title="Permalink to this definition">¶</a></dt>
+<dd><p>Calculate the distance between the two arrays in <a class="reference internal" href="#Function.Stability.variable" title="Function.Stability.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>distance</strong></td>
+</tr>
+<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body">scalar</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+</dd></dl>
+
 <dl class="class">
 <dt id="Function.LearningFunction">
 <em class="property">class </em><code class="descclassname">Function.</code><code class="descname">LearningFunction</code><span class="sig-paren">(</span><em>variable_default</em>, <em>params</em>, <em>owner=None</em>, <em>name=None</em>, <em>prefs=None</em>, <em>context='Function_Base Init'</em><span class="sig-paren">)</span><a class="headerlink" href="#Function.LearningFunction" title="Permalink to this definition">¶</a></dt>
 <dd><p>Abstract class of <a class="reference internal" href="#"><span class="doc">Functions</span></a> used for learning.</p>
 <p>All LearningFunctions take three input values (specified in each of the three required items of the
-<a class="reference internal" href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.variable" title="AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> argument), and return two output values.</p>
+<a class="reference internal" href="#Function.LinearMatrix.variable" title="Function.LinearMatrix.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> argument), and return two output values.</p>
 <dl class="docutils">
 <dt>Input values:</dt>
 <dd><ul class="first last simple">
@@ -2043,14 +2370,14 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 <dl class="attribute">
 <dt id="Function.Reinforcement.prefs">
 <code class="descname">prefs</code><a class="headerlink" href="#Function.Reinforcement.prefs" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <a class="reference internal" href="#Function.Reinforcement.prefs" title="Function.Reinforcement.prefs"><code class="xref any py py-attr docutils literal"><span class="pre">prefs</span></code></a> argument of the constructor for the function;
+<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <strong>prefs</strong> argument of the constructor for the function;
 if it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
 (see <span class="xref doc">PreferenceSet</span> for details).</p>
 </dd></dl>
 
 <dl class="method">
 <dt>
-<code class="descname">function</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 1&gt;</em>, <em>context=None</em><span class="sig-paren">)</span></dt>
+<code class="descname">function</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 2&gt;</em>, <em>context=None</em><span class="sig-paren">)</span></dt>
 <dd><p>Calculate a matrix of weight changes from a single (scalar) error term</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
@@ -2069,9 +2396,9 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 </td>
 </tr>
 <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple">
+<li><strong>error signal</strong> (<em>1d np.array</em>) &#8211; same as value received in <a class="reference internal" href="#Function.Reinforcement.error_signal" title="Function.Reinforcement.error_signal"><code class="xref any py py-attr docutils literal"><span class="pre">error_signal</span></code></a> argument.</li>
 <li><strong>diagonal weight change matrix</strong> (<em>2d np.array</em>) &#8211; has a single non-zero entry in the same row and column as the one in
 <a class="reference internal" href="#Function.Reinforcement.activation_output" title="Function.Reinforcement.activation_output"><code class="xref any py py-attr docutils literal"><span class="pre">activation_output</span></code></a> and <a class="reference internal" href="#Function.Reinforcement.error_signal" title="Function.Reinforcement.error_signal"><code class="xref any py py-attr docutils literal"><span class="pre">error_signal</span></code></a>.</li>
-<li><strong>error signal</strong> (<em>1d np.array</em>) &#8211; same as value received in <a class="reference internal" href="#Function.Reinforcement.error_signal" title="Function.Reinforcement.error_signal"><code class="xref any py py-attr docutils literal"><span class="pre">error_signal</span></code></a> argument.</li>
 </ul>
 </p>
 </td>
@@ -2102,7 +2429,7 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 <li><strong>error_derivative</strong> (<em>Function or function</em>) &#8211; specifies the derivative for the function of the mechanism that is the receiver of the
 <a class="reference internal" href="#Function.BackPropagation.error_matrix" title="Function.BackPropagation.error_matrix"><code class="xref any py py-attr docutils literal"><span class="pre">error_matrix</span></code></a>.</li>
 <li><strong>error_matrix</strong> (<em>List, 2d np.array, np.matrix, ParameterState, or MappingProjection</em>) &#8211; matrix, the output of which is used to calculate the <a class="reference internal" href="#Function.BackPropagation.error_signal" title="Function.BackPropagation.error_signal"><code class="xref any py py-attr docutils literal"><span class="pre">error_signal</span></code></a>.
-If it is specified as a ParameterState it must be one for the <a class="reference internal" href="MappingProjection.html#MappingProjection.MappingProjection.matrix" title="MappingProjection.MappingProjection.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a>
+If it is specified as a ParameterState it must be one for the <a class="reference internal" href="#Function.Stability.MappingProjection.matrix" title="Function.Stability.MappingProjection.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a>
 parameter of a <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a>;  if it is a MappingProjection, it must be one with a
 MATRIX parameterState.</li>
 <li><strong>learning_rate</strong> (<em>float : default default_learning_rate</em>) &#8211; supercedes any specification for the <a class="reference internal" href="Process.html"><span class="doc">process</span></a> and/or <a class="reference internal" href="System.html"><span class="doc">system</span></a> to which the function&#8217;s
@@ -2130,14 +2457,14 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 <dl class="attribute">
 <dt id="Function.BackPropagation.activation_input">
 <code class="descname">activation_input</code><a class="headerlink" href="#Function.BackPropagation.activation_input" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>1d np.array</em> &#8211; the input to the matrix being modified; same as 1st item of <a href="#id1"><span class="problematic" id="id2">`</span></a>variable &lt;BackPropagation.variable&gt;.</p>
+<dd><p><em>1d np.array</em> &#8211; the input to the matrix being modified; same as 1st item of <a href="#id3"><span class="problematic" id="id4">`</span></a>variable &lt;BackPropagation.variable&gt;.</p>
 </dd></dl>
 
 <dl class="attribute">
 <dt id="Function.BackPropagation.activation_output">
 <code class="descname">activation_output</code><a class="headerlink" href="#Function.BackPropagation.activation_output" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>1d np.array</em> &#8211; the output of the function for which the matrix being modified provides the input;
-same as 2nd item of <a href="#id3"><span class="problematic" id="id4">`</span></a>variable &lt;BackPropagation.variable&gt;.</p>
+same as 2nd item of <a href="#id5"><span class="problematic" id="id6">`</span></a>variable &lt;BackPropagation.variable&gt;.</p>
 </dd></dl>
 
 <dl class="attribute">
@@ -2145,7 +2472,7 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 <code class="descname">error_signal</code><a class="headerlink" href="#Function.BackPropagation.error_signal" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>1d np.array</em> &#8211; the error signal for the next matrix (layer above) in the learning sequence, or the error computed from the
 target (training signal) and the output of the last mechanism in the sequence;
-same as 3rd item of <a href="#id5"><span class="problematic" id="id6">`</span></a>variable &lt;BackPropagation.variable&gt;.</p>
+same as 3rd item of <a href="#id7"><span class="problematic" id="id8">`</span></a>variable &lt;BackPropagation.variable&gt;.</p>
 </dd></dl>
 
 <dl class="attribute">
@@ -2189,14 +2516,14 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 <dl class="attribute">
 <dt id="Function.BackPropagation.prefs">
 <code class="descname">prefs</code><a class="headerlink" href="#Function.BackPropagation.prefs" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <a class="reference internal" href="#Function.BackPropagation.prefs" title="Function.BackPropagation.prefs"><code class="xref any py py-attr docutils literal"><span class="pre">prefs</span></code></a> argument of the constructor for the function;
+<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <strong>prefs</strong> argument of the constructor for the function;
 if it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
 (see <span class="xref doc">PreferenceSet</span> for details).</p>
 </dd></dl>
 
 <dl class="method">
 <dt>
-<code class="descname">function</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 1&gt;</em>, <em>context=None</em><span class="sig-paren">)</span></dt>
+<code class="descname">function</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 2&gt;</em>, <em>context=None</em><span class="sig-paren">)</span></dt>
 <dd><p>Calculate and return a matrix of weight changes from arrays of inputs, outputs and error terms</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
@@ -2215,10 +2542,10 @@ <h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalin
 </td>
 </tr>
 <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul class="simple">
-<li><strong>weight change matrix</strong> (<em>2d np.array</em>) &#8211; the modifications to make to the matrix.</li>
 <li><strong>weighted error signal</strong> (<em>1d np.array</em>) &#8211; <a class="reference internal" href="#Function.BackPropagation.error_signal" title="Function.BackPropagation.error_signal"><code class="xref any py py-attr docutils literal"><span class="pre">error_signal</span></code></a>, weighted by the contribution made by each element of
 <a class="reference internal" href="#Function.BackPropagation.activation_output" title="Function.BackPropagation.activation_output"><code class="xref any py py-attr docutils literal"><span class="pre">activation_output</span></code></a> as a function of
 <a class="reference internal" href="#Function.BackPropagation.error_matrix" title="Function.BackPropagation.error_matrix"><code class="xref any py py-attr docutils literal"><span class="pre">error_matrix</span></code></a>.</li>
+<li><strong>weight change matrix</strong> (<em>2d np.array</em>) &#8211; the modifications to make to the matrix.</li>
 </ul>
 </p>
 </td>
diff --git a/docs/build/html/InputState.html b/docs/build/html/InputState.html
index c90e8987ddf..30d9e45597f 100644
--- a/docs/build/html/InputState.html
+++ b/docs/build/html/InputState.html
@@ -34,9 +34,9 @@
               href="genindex.html"/>
         <link rel="search" title="Search" href="search.html"/>
     <link rel="top" title="PsyNeuLink 0.1 documentation" href="index.html"/>
-        <link rel="up" title="States" href="State.html"/>
+        <link rel="up" title="State" href="State.html"/>
         <link rel="next" title="ParameterState" href="ParameterState.html"/>
-        <link rel="prev" title="States" href="State.html"/> 
+        <link rel="prev" title="State" href="State.html"/> 
 
   
   <script src="_static/js/modernizr.min.js"></script>
@@ -89,7 +89,7 @@
 <li class="toctree-l1"><a class="reference internal" href="System.html">System</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Process.html">Process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Mechanism.html">Mechanism</a></li>
-<li class="toctree-l1 current"><a class="reference internal" href="State.html">States</a><ul class="current">
+<li class="toctree-l1 current"><a class="reference internal" href="State.html">State</a><ul class="current">
 <li class="toctree-l2 current"><a class="current reference internal" href="#">InputState</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#overview">Overview</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#creating-an-inputstate">Creating an InputState</a></li>
@@ -100,6 +100,7 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="ParameterState.html">ParameterState</a></li>
 <li class="toctree-l2"><a class="reference internal" href="OutputState.html">OutputState</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ModulatorySignal.html">ModulatorySignal</a></li>
 <li class="toctree-l2"><a class="reference internal" href="State.html#overview">Overview</a></li>
 <li class="toctree-l2"><a class="reference internal" href="State.html#creating-a-state">Creating a State</a></li>
 <li class="toctree-l2"><a class="reference internal" href="State.html#structure">Structure</a></li>
@@ -141,7 +142,7 @@
   <ul class="wy-breadcrumbs">
     <li><a href="index.html">Docs</a> &raquo;</li>
       
-          <li><a href="State.html">States</a> &raquo;</li>
+          <li><a href="State.html">State</a> &raquo;</li>
       
     <li>InputState</li>
       <li class="wy-breadcrumbs-aside">
@@ -165,19 +166,19 @@ <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this head
 or system.  If the inputState belongs to an <a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> mechanism (see
 <a class="reference internal" href="Mechanism.html#mechanism-role-in-processes-and-systems"><span class="std std-ref">role of mechanisms in processes and systems</span></a>), then it receives the input
 specified when that process or system is <a class="reference internal" href="Run.html"><span class="doc">run</span></a>.  The projections received by an inputState are
-listed in its <a class="reference internal" href="#InputState.InputState.receivesFromProjections" title="InputState.InputState.receivesFromProjections"><code class="xref any py py-attr docutils literal"><span class="pre">receivesFromProjections</span></code></a> attribute. Its
+listed in its <code class="xref any docutils literal"><span class="pre">path_afferents</span></code> attribute. Its
 <a class="reference internal" href="#InputState.InputState.function" title="InputState.InputState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> combines the values of these inputs, and the result is assigned to an item
 corresponding to the inputState in the owner mechanism&#8217;s <code class="xref std std-keyword docutils literal"><span class="pre">variable</span></code> and
-<a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.inputValue" title="Mechanism.Mechanism_Base.inputValue"><code class="xref any py py-attr docutils literal"><span class="pre">inputValue</span></code></a> attributes  (see <a class="reference internal" href="Mechanism.html#mechanism-inputstates"><span class="std std-ref">Mechanism InputStates</span></a>
-for additional details about the role of inputStates in mechanisms).</p>
+<a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.input_value" title="Mechanism.Mechanism_Base.input_value"><code class="xref any py py-attr docutils literal"><span class="pre">input_value</span></code></a> attributes  (see <a class="reference internal" href="Mechanism.html#mechanism-inputstates"><span class="std std-ref">Mechanism InputStates</span></a>
+for additional details about the role of input_states in mechanisms).</p>
 </div>
 <div class="section" id="creating-an-inputstate">
 <span id="inputstate-creation"></span><h2>Creating an InputState<a class="headerlink" href="#creating-an-inputstate" title="Permalink to this headline">¶</a></h2>
 <p>InputStates are created automatically when a mechanism is created.  For example, if a mechanism is created within
 the <a class="reference internal" href="Process.html#Process.Process_Base.pathway" title="Process.Process_Base.pathway"><code class="xref any py py-attr docutils literal"><span class="pre">pathway</span></code></a> of a process`, the inputState for that mechanism will be created and assigned as the
-<a class="reference internal" href="MappingProjection.html#MappingProjection.MappingProjection.receiver" title="MappingProjection.MappingProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a> of a <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a> from the  preceding mechanism in the
+<code class="xref any docutils literal"><span class="pre">receiver</span></code> of a <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a> from the  preceding mechanism in the
 pathway;  and a <a class="reference internal" href="ControlMechanism.html"><span class="doc">ControlMechanism</span></a> automatically creates an inputState for each mechanism that it
-monitors. PsyNeuLink does not currently support the explicit creation of inputStates (this may be implemented in the
+monitors. PsyNeuLink does not currently support the explicit creation of input_states (this may be implemented in the
 future). However they can modified as described below.</p>
 </div>
 <div class="section" id="structure">
@@ -185,7 +186,7 @@ <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this head
 <p>Every inputState is owned by a <a class="reference internal" href="Mechanism.html"><span class="doc">mechanism</span></a>. It can receive one or more
 <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjections</span></a> from other mechanisms, as well as from the process or system to which its
 owner belongs (if it is the <a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> mechanism for that process or system).  The projections received by an
-inputState are listed in its <a class="reference internal" href="#InputState.InputState.receivesFromProjections" title="InputState.InputState.receivesFromProjections"><code class="xref any py py-attr docutils literal"><span class="pre">receivesFromProjections</span></code></a> attribute.</p>
+inputState are listed in its <code class="xref any docutils literal"><span class="pre">path_afferents</span></code> attribute.</p>
 <p>Like all PsyNeuLink components, an inputState has the three following core attributes:</p>
 <ul class="simple">
 <li><a class="reference internal" href="#InputState.InputState.variable" title="InputState.InputState.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>:  this serves as a template for the <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code> of each projection that the
@@ -212,14 +213,14 @@ <h2>Execution<a class="headerlink" href="#execution" title="Permalink to this he
 When this occurs, the inputState executes any projections it receives, calls its <a class="reference internal" href="#InputState.InputState.function" title="InputState.InputState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> to
 aggregate their values, and then assigns the result to the inputState&#8217;s <a class="reference internal" href="#InputState.InputState.value" title="InputState.InputState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> attribute.  This,
 in turn, is assigned to the item of the mechanism&#8217;s <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.variable" title="Mechanism.Mechanism_Base.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> and
-<a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.inputValue" title="Mechanism.Mechanism_Base.inputValue"><code class="xref any py py-attr docutils literal"><span class="pre">inputValue</span></code></a> attributes corresponding to that inputState
-(see <a class="reference internal" href="Mechanism.html#mechanism-variable"><span class="std std-ref">mechanism variable and inputValue attributes</span></a> for additional details).</p>
+<a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.input_value" title="Mechanism.Mechanism_Base.input_value"><code class="xref any py py-attr docutils literal"><span class="pre">input_value</span></code></a> attributes corresponding to that inputState
+(see <a class="reference internal" href="Mechanism.html#mechanism-variable"><span class="std std-ref">mechanism variable and input_value attributes</span></a> for additional details).</p>
 </div>
 <div class="section" id="class-reference">
 <span id="inputstate-class-reference"></span><h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalink to this headline">¶</a></h2>
 <dl class="class">
 <dt id="InputState.InputState">
-<em class="property">class </em><code class="descclassname">InputState.</code><code class="descname">InputState</code><span class="sig-paren">(</span><em>owner</em>, <em>reference_value=None</em>, <em>value=None</em>, <em>function=LinearCombination(operation=SUM)</em>, <em>params=None</em>, <em>name=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#InputState.InputState" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">InputState.</code><code class="descname">InputState</code><span class="sig-paren">(</span><em>owner</em>, <em>reference_value=None</em>, <em>function=LinearCombination(operation=SUM)</em>, <em>value=None</em>, <em>params=None</em>, <em>name=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#InputState.InputState" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements a subclass of State that calculates and represents the input to a mechanism.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
@@ -230,9 +231,7 @@ <h2>Execution<a class="headerlink" href="#execution" title="Permalink to this he
 the inputState is created.</li>
 <li><strong>reference_value</strong> (<em>number, list or np.ndarray</em>) &#8211; the value of the item of the owner mechanism&#8217;s <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.variable" title="Mechanism.Mechanism_Base.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> attribute to which
 the inputState is assigned; used as the template for the inputState&#8217;s <a class="reference internal" href="#InputState.InputState.value" title="InputState.InputState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> attribute.</li>
-<li><strong>value</strong> (<em>number, list or np.ndarray</em>) &#8211; specifies the template for the inputState&#8217;s <a class="reference internal" href="#InputState.InputState.variable" title="InputState.InputState.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> attribute (since an inputState&#8217;s
-<a class="reference internal" href="#InputState.InputState.variable" title="InputState.InputState.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> and <a class="reference internal" href="#InputState.InputState.value" title="InputState.InputState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> attributes must have the same format
-(number and type of elements).</li>
+<li><strong>variable</strong> (<em>number, list or np.ndarray</em>) &#8211; specifies the template for the inputState&#8217;s <a class="reference internal" href="#InputState.InputState.variable" title="InputState.InputState.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> attribute.</li>
 <li><strong>function</strong> (<em>Function or method : default LinearCombination(operation=SUM)</em>) &#8211; specifies the function used to aggregate the values of the projections received by the inputState.
 It must produce a result that has the same format (number and type of elements) as its input.</li>
 <li><strong>params</strong> (<em>Optional[Dict[param keyword, param value]]</em>) &#8211; a <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> that can be used to specify the parameters for
@@ -256,8 +255,8 @@ <h2>Execution<a class="headerlink" href="#execution" title="Permalink to this he
 </dd></dl>
 
 <dl class="attribute">
-<dt id="InputState.InputState.receivesFromProjections">
-<code class="descname">receivesFromProjections</code><a class="headerlink" href="#InputState.InputState.receivesFromProjections" title="Permalink to this definition">¶</a></dt>
+<dt id="InputState.InputState.afferents">
+<code class="descname">afferents</code><a class="headerlink" href="#InputState.InputState.afferents" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>Optional[List[Projection]]</em> &#8211; a list of the projections received by the inputState
 (i.e., for which it is a <code class="xref any docutils literal"><span class="pre">receiver</span></code>).</p>
 </dd></dl>
@@ -286,7 +285,7 @@ <h2>Execution<a class="headerlink" href="#execution" title="Permalink to this he
 <dt id="InputState.InputState.name">
 <code class="descname">name</code><a class="headerlink" href="#InputState.InputState.name" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>str : default &lt;State subclass&gt;-&lt;index&gt;</em> &#8211; the name of the inputState.
-Specified in the <a class="reference internal" href="#InputState.InputState.name" title="InputState.InputState.name"><code class="xref any py py-attr docutils literal"><span class="pre">name</span></code></a> argument of the constructor for the outputState.  If not is specified, a default is
+Specified in the <strong>name</strong> argument of the constructor for the outputState.  If not is specified, a default is
 assigned by the StateRegistry of the mechanism to which the outputState belongs
 (see <span class="xref doc">Registry</span> for conventions used in naming, including for default and duplicate names).</p>
 <div class="admonition note">
@@ -302,7 +301,7 @@ <h2>Execution<a class="headerlink" href="#execution" title="Permalink to this he
 <dt id="InputState.InputState.prefs">
 <code class="descname">prefs</code><a class="headerlink" href="#InputState.InputState.prefs" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>PreferenceSet or specification dict : State.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for the inputState.
-Specified in the <a class="reference internal" href="#InputState.InputState.prefs" title="InputState.InputState.prefs"><code class="xref any py py-attr docutils literal"><span class="pre">prefs</span></code></a> argument of the constructor for the projection;  if it is not specified, a default is
+Specified in the <strong>prefs</strong> argument of the constructor for the projection;  if it is not specified, a default is
 assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
 (see <span class="xref doc">PreferenceSet</span> for details).</p>
 </dd></dl>
@@ -322,7 +321,7 @@ <h2>Execution<a class="headerlink" href="#execution" title="Permalink to this he
         <a href="ParameterState.html" class="btn btn-neutral float-right" title="ParameterState" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
       
       
-        <a href="State.html" class="btn btn-neutral" title="States" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
+        <a href="State.html" class="btn btn-neutral" title="State" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
       
     </div>
   
diff --git a/docs/build/html/IntegratorMechanism.html b/docs/build/html/IntegratorMechanism.html
index 93ef9563fc6..f243acaa463 100644
--- a/docs/build/html/IntegratorMechanism.html
+++ b/docs/build/html/IntegratorMechanism.html
@@ -36,7 +36,7 @@
     <link rel="top" title="PsyNeuLink 0.1 documentation" href="index.html"/>
         <link rel="up" title="Processing Mechanism" href="ProcessingMechanism.html"/>
         <link rel="next" title="DDM" href="DDM.html"/>
-        <link rel="prev" title="TransferMechanism" href="TransferMechanism.html"/> 
+        <link rel="prev" title="Processing Mechanism" href="ProcessingMechanism.html"/> 
 
   
   <script src="_static/js/modernizr.min.js"></script>
@@ -85,37 +85,11 @@
           
             
             
-                <ul class="current">
+                <ul>
 <li class="toctree-l1"><a class="reference internal" href="System.html">System</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Process.html">Process</a></li>
-<li class="toctree-l1 current"><a class="reference internal" href="Mechanism.html">Mechanism</a><ul class="current">
-<li class="toctree-l2 current"><a class="reference internal" href="ProcessingMechanism.html">Processing Mechanism</a><ul class="current">
-<li class="toctree-l3"><a class="reference internal" href="TransferMechanism.html">TransferMechanism</a></li>
-<li class="toctree-l3 current"><a class="current reference internal" href="#">IntegratorMechanism</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#overview">Overview</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#creating-an-integratormechanism">Creating an IntegratorMechanism</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#structure">Structure</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#execution">Execution</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#class-reference">Class Reference</a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="DDM.html">DDM</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ObjectiveMechanism.html">ObjectiveMechanism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ProcessingMechanism.html#overview">Overview</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ProcessingMechanism.html#creating-a-processingmechanism">Creating a ProcessingMechanism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ProcessingMechanism.html#structure">Structure</a></li>
-<li class="toctree-l3"><a class="reference internal" href="ProcessingMechanism.html#execution">Execution</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="AdaptiveMechanism.html">Adaptive Mechanism</a></li>
-<li class="toctree-l2"><a class="reference internal" href="Mechanism.html#overview">Overview</a></li>
-<li class="toctree-l2"><a class="reference internal" href="Mechanism.html#creating-a-mechanism">Creating a Mechanism</a></li>
-<li class="toctree-l2"><a class="reference internal" href="Mechanism.html#structure">Structure</a></li>
-<li class="toctree-l2"><a class="reference internal" href="Mechanism.html#execution">Execution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="Mechanism.html#class-reference">Class Reference</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="State.html">States</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Mechanism.html">Mechanism</a></li>
+<li class="toctree-l1"><a class="reference internal" href="State.html">State</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Projection.html">Projection</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Run.html">Run</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Component.html">Component</a></li>
@@ -186,15 +160,15 @@ <h1>IntegratorMechanism<a class="headerlink" href="#integratormechanism" title="
 <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">¶</a></h2>
 <p>An IntegratorMechanism integrates its input, possibly based on its prior values.  The input can be a single
 scalar value or an array of scalars (list or 1d np.array).  If it is a list or array, then each value is
-independently integrated.  The default function (<a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.Integrator" title="IntegratorMechanism.IntegratorMechanism.Integrator"><code class="xref any py py-class docutils literal"><span class="pre">Integrator</span></code></a>) can be parameterized to implement either a simple
+independently integrated.  The default function (<code class="xref any docutils literal"><span class="pre">Integrator</span></code>) can be parameterized to implement either a simple
 increment rate, additive accumulator, or an (exponentially weighted) time-averaging of its input.  It can also be
 assigned a custom function.</p>
 </div>
 <div class="section" id="creating-an-integratormechanism">
 <span id="integratormechanism-creation"></span><h2>Creating an IntegratorMechanism<a class="headerlink" href="#creating-an-integratormechanism" title="Permalink to this headline">¶</a></h2>
 <p>An IntegratorMechanism can be created directly by calling its constructor, or using the
-<a class="reference internal" href="Mechanism.html#Mechanism.mechanism" title="Mechanism.mechanism"><code class="xref py py-func docutils literal"><span class="pre">mechanism</span></code></a> function and specifying <code class="xref std std-keyword docutils literal"><span class="pre">IntegratorMechanism</span></code> as its <code class="xref any docutils literal"><span class="pre">mech_spec</span></code>
-argument.  Its function is specified in the <code class="xref std std-keyword docutils literal"><span class="pre">function</span></code> argument, which can be parameterized by calling its
+<a class="reference internal" href="Mechanism.html#Mechanism.mechanism" title="Mechanism.mechanism"><code class="xref any py py-func docutils literal"><span class="pre">mechanism()</span></code></a> function and specifying INTEGRATOR_MECHANISM as its <strong>mech_spec</strong>
+argument.  Its function is specified in the <strong>function</strong> argument, which can be parameterized by calling its
 constructor with parameter values:</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">my_time_averaging_mechanism</span> <span class="o">=</span> <span class="n">IntegratorMechanism</span><span class="p">(</span><span class="n">function</span><span class="o">=</span><span class="n">Integrator</span><span class="p">(</span><span class="n">integration_type</span><span class="o">=</span><span class="n">ADAPTIVE</span><span class="p">,</span> <span class="n">rate</span><span class="o">=</span><span class="mf">0.5</span><span class="p">))</span>
 </pre></div>
@@ -215,7 +189,7 @@ <h2>Structure<a class="headerlink" href="#structure" title="Permalink to this he
 <h2>Execution<a class="headerlink" href="#execution" title="Permalink to this headline">¶</a></h2>
 <p>When an IntegratorMechanism is executed, it carries out the specified integration, and assigns the
 result to the <code class="xref any docutils literal"><span class="pre">value</span></code> of its (primary) outputState.  For the default function
-(<a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.Integrator" title="IntegratorMechanism.IntegratorMechanism.Integrator"><code class="xref any py py-class docutils literal"><span class="pre">Integrator</span></code></a>), if the value specified for <code class="xref std std-keyword docutils literal"><span class="pre">default_input_value</span></code> is a list or array, each element of the array
+(<code class="xref any docutils literal"><span class="pre">Integrator</span></code>), if the value specified for <code class="xref std std-keyword docutils literal"><span class="pre">default_input_value</span></code> is a list or array, each element of the array
 is independently integrated.  If its <code class="xref std std-keyword docutils literal"><span class="pre">rate</span></code> parameter is a single value, that rate will be used for
 integrating each element.  If the <code class="xref std std-keyword docutils literal"><span class="pre">rate</span></code> parameter is a list or array, then each element will be used as the
 rate for the corresponding element of the input (in this case, <code class="xref std std-keyword docutils literal"><span class="pre">rate</span></code> must be the same length as the value
@@ -272,7 +246,7 @@ <h2>Execution<a class="headerlink" href="#execution" title="Permalink to this he
 <dt id="IntegratorMechanism.IntegratorMechanism.name">
 <code class="descname">name</code><a class="headerlink" href="#IntegratorMechanism.IntegratorMechanism.name" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>str : default IntegratorMechanism-&lt;index&gt;</em> &#8211; the name of the mechanism.
-Specified in the <code class="xref std std-keyword docutils literal"><span class="pre">name</span></code> argument of the constructor for the mechanism;
+Specified in the :keyword:<strong>name</strong> argument of the constructor for the mechanism;
 if not is specified, a default is assigned by <code class="xref any docutils literal"><span class="pre">MechanismRegistry</span></code>
 (see <span class="xref doc">Registry</span> for conventions used in naming, including for default and duplicate names).</p>
 </dd></dl>
@@ -281,15 +255,15 @@ <h2>Execution<a class="headerlink" href="#execution" title="Permalink to this he
 <dt id="IntegratorMechanism.IntegratorMechanism.prefs">
 <code class="descname">prefs</code><a class="headerlink" href="#IntegratorMechanism.IntegratorMechanism.prefs" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>Optional[PreferenceSet or specification dict : Mechanism.classPreferences]</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for mechanism.
-Specified in the <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.prefs" title="IntegratorMechanism.IntegratorMechanism.prefs"><code class="xref any py py-attr docutils literal"><span class="pre">prefs</span></code></a> argument of the constructor for the mechanism;
+Specified in the <strong>prefs</strong> argument of the constructor for the mechanism;
 if it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
 (see <span class="xref doc">PreferenceSet</span> for details).</p>
 </dd></dl>
 
 <dl class="class">
-<dt id="IntegratorMechanism.IntegratorMechanism.Integrator">
-<em class="property">class </em><code class="descname">Integrator</code><span class="sig-paren">(</span><em>variable_default=None</em>, <em>rate=1.0</em>, <em>integration_type=CONSTANT</em>, <em>noise=0.0</em>, <em>time_step_size=1.0</em>, <em>initializer</em>, <em>params=None</em>, <em>owner=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#IntegratorMechanism.IntegratorMechanism.Integrator" title="Permalink to this definition">¶</a></dt>
-<dd><p id="integrator">Integrate current value of <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.Integrator.variable" title="IntegratorMechanism.IntegratorMechanism.Integrator.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> with its prior value.</p>
+<dt id="IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator">
+<em class="property">class </em><code class="descname">AdaptiveIntegrator</code><span class="sig-paren">(</span><em>variable_default=None</em>, <em>rate=1.0</em>, <em>noise=0.0</em>, <em>scale: parameter_spec = 1.0</em>, <em>offset: parameter_spec = 0.0</em>, <em>initializer</em>, <em>params=None</em>, <em>owner=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator" title="Permalink to this definition">¶</a></dt>
+<dd><p id="adaptiveintegrator">Integrate current value of <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.variable" title="IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> with its prior value.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
@@ -298,14 +272,11 @@ <h2>Execution<a class="headerlink" href="#execution" title="Permalink to this he
 <li><strong>variable_default</strong> (<em>number, list or np.array : default variableClassDefault</em>) &#8211; specifies a template for the value to be integrated;  if it is a list or array, each element is independently
 integrated.</li>
 <li><strong>rate</strong> (<em>float, list or 1d np.array : default 1.0</em>) &#8211; specifies the rate of integration.  If it is a list or array, it must be the same length as
-<code class="xref any docutils literal"><span class="pre">variable</span></code> (see <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.Integrator.rate" title="IntegratorMechanism.IntegratorMechanism.Integrator.rate"><code class="xref any py py-attr docutils literal"><span class="pre">rate</span></code></a> for details).</li>
-<li><strong>integration_type</strong> (<em>CONSTANT, SIMPLE, ADAPTIVE, DIFFUSION : default CONSTANT</em>) &#8211; specifies type of integration (see <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.Integrator.integration_type" title="IntegratorMechanism.IntegratorMechanism.Integrator.integration_type"><code class="xref any py py-attr docutils literal"><span class="pre">integration_type</span></code></a> for details).</li>
-<li><strong>noise</strong> (<em>float, PsyNeuLink Function, list or 1d np.array : default 0.0</em>) &#8211; specifies random value to be added in each call to <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.Integrator.function" title="IntegratorMechanism.IntegratorMechanism.Integrator.function"><code class="xref any py py-meth docutils literal"><span class="pre">function</span></code></a>. (see
-<a class="reference internal" href="Function.html#Function.Integrator.noise" title="Function.Integrator.noise"><code class="xref any py py-attr docutils literal"><span class="pre">noise</span></code></a> for details).</li>
-<li><strong>time_step_size</strong> (<em>float : default 0.0</em>) &#8211; determines the timing precision of the integration process when <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.Integrator.integration_type" title="IntegratorMechanism.IntegratorMechanism.Integrator.integration_type"><code class="xref any py py-attr docutils literal"><span class="pre">integration_type</span></code></a>
-is set to DIFFUSION (see <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.Integrator.time_step_size" title="IntegratorMechanism.IntegratorMechanism.Integrator.time_step_size"><code class="xref any py py-attr docutils literal"><span class="pre">time_step_size</span></code></a> for details.</li>
-<li><strong>float, list or 1d np.array</strong> (<a class="reference internal" href="Function.html#Function.Integrator.initializer" title="Function.Integrator.initializer"><em>initializer</em></a>) &#8211; specifies starting value for integration.  If it is a list or array, it must be the same length as
-<code class="xref any docutils literal"><span class="pre">variable_default</span></code> (see <a class="reference internal" href="Function.html#Function.Integrator.initializer" title="Function.Integrator.initializer"><code class="xref any py py-attr docutils literal"><span class="pre">initializer</span></code></a> for details).</li>
+<code class="xref any docutils literal"><span class="pre">variable</span></code> (see <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.rate" title="IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.rate"><code class="xref any py py-attr docutils literal"><span class="pre">rate</span></code></a> for details).</li>
+<li><strong>noise</strong> (<em>float, PsyNeuLink Function, list or 1d np.array : default 0.0</em>) &#8211; specifies random value to be added in each call to <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.function" title="IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.function"><code class="xref any py py-meth docutils literal"><span class="pre">function</span></code></a>. (see
+<a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.noise" title="IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.noise"><code class="xref any py py-attr docutils literal"><span class="pre">noise</span></code></a> for details).</li>
+<li><strong>float, list or 1d np.array</strong> (<a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.initializer" title="IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.initializer"><em>initializer</em></a>) &#8211; specifies starting value for integration.  If it is a list or array, it must be the same length as
+<code class="xref any docutils literal"><span class="pre">variable_default</span></code> (see <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.initializer" title="IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.initializer"><code class="xref any py py-attr docutils literal"><span class="pre">initializer</span></code></a> for details).</li>
 <li><strong>params</strong> (<em>Optional[Dict[param keyword, param value]]</em>) &#8211; a <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> that specifies the parameters for the
 function.  Values specified for parameters in the dictionary override any assigned to those parameters in
 arguments of the constructor.</li>
@@ -318,103 +289,74 @@ <h2>Execution<a class="headerlink" href="#execution" title="Permalink to this he
 </tbody>
 </table>
 <dl class="attribute">
-<dt id="IntegratorMechanism.IntegratorMechanism.Integrator.variable">
-<code class="descname">variable</code><a class="headerlink" href="#IntegratorMechanism.IntegratorMechanism.Integrator.variable" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>number or np.array</em> &#8211; current input value some portion of which (determined by <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.Integrator.rate" title="IntegratorMechanism.IntegratorMechanism.Integrator.rate"><code class="xref any py py-attr docutils literal"><span class="pre">rate</span></code></a>) that will be
+<dt id="IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.variable">
+<code class="descname">variable</code><a class="headerlink" href="#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.variable" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>number or np.array</em> &#8211; current input value some portion of which (determined by <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.rate" title="IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.rate"><code class="xref any py py-attr docutils literal"><span class="pre">rate</span></code></a>) that will be
 added to the prior value;  if it is an array, each element is independently integrated.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="IntegratorMechanism.IntegratorMechanism.Integrator.rate">
-<code class="descname">rate</code><a class="headerlink" href="#IntegratorMechanism.IntegratorMechanism.Integrator.rate" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>float or 1d np.array</em> &#8211; determines the rate of integration based on current and prior values.  If integration_type is set to ADAPTIVE,
-all elements must be between 0 and 1 (0 = no change; 1 = instantaneous change). If it has a single element, it
-applies to all elements of <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.Integrator.variable" title="IntegratorMechanism.IntegratorMechanism.Integrator.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>;  if it has more than one element, each element
-applies to the corresponding element of <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.Integrator.variable" title="IntegratorMechanism.IntegratorMechanism.Integrator.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>.</p>
+<dt id="IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.rate">
+<code class="descname">rate</code><a class="headerlink" href="#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.rate" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>float or 1d np.array</em> &#8211; determines the rate of integration based on current and prior values.  All rate elements must be between 0 and 1
+(0 = no change; 1 = instantaneous change).</p>
+<p>If rate is a float, it is applied to all elements of <code class="xref any docutils literal"><span class="pre">variable</span></code>;
+if it has more than one element, each element is applied to the corresponding element of
+<code class="xref any docutils literal"><span class="pre">variable</span></code>.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="IntegratorMechanism.IntegratorMechanism.Integrator.integration_type">
-<code class="descname">integration_type</code><a class="headerlink" href="#IntegratorMechanism.IntegratorMechanism.Integrator.integration_type" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>CONSTANT, SIMPLE, ADAPTIVE, DIFFUSION</em> &#8211;</p>
-<dl class="docutils">
-<dt>specifies type of integration:</dt>
-<dd><ul class="first last simple">
-<li><strong>CONSTANT</strong>: <a class="reference internal" href="Function.html#Function.Integrator.previous_value" title="Function.Integrator.previous_value"><code class="xref any py py-attr docutils literal"><span class="pre">previous_value</span></code></a> + <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.Integrator.rate" title="IntegratorMechanism.IntegratorMechanism.Integrator.rate"><code class="xref any py py-attr docutils literal"><span class="pre">rate</span></code></a> +
-<a class="reference internal" href="Function.html#Function.Integrator.noise" title="Function.Integrator.noise"><code class="xref any py py-attr docutils literal"><span class="pre">noise</span></code></a> (ignores <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.Integrator.variable" title="IntegratorMechanism.IntegratorMechanism.Integrator.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>);</li>
-<li><strong>SIMPLE</strong>: <a class="reference internal" href="Function.html#Function.Integrator.previous_value" title="Function.Integrator.previous_value"><code class="xref any py py-attr docutils literal"><span class="pre">previous_value</span></code></a> + <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.Integrator.rate" title="IntegratorMechanism.IntegratorMechanism.Integrator.rate"><code class="xref any py py-attr docutils literal"><span class="pre">rate</span></code></a> *
-<code class="xref any docutils literal"><span class="pre">variable</span></code> + <a class="reference internal" href="Function.html#Function.Integrator.noise" title="Function.Integrator.noise"><code class="xref any py py-attr docutils literal"><span class="pre">noise</span></code></a>;</li>
-<li><strong>ADAPTIVE</strong>: (1-<a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.Integrator.rate" title="IntegratorMechanism.IntegratorMechanism.Integrator.rate"><code class="xref any py py-attr docutils literal"><span class="pre">rate</span></code></a>) * <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.Integrator.variable" title="IntegratorMechanism.IntegratorMechanism.Integrator.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> +
-(<a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.Integrator.rate" title="IntegratorMechanism.IntegratorMechanism.Integrator.rate"><code class="xref any py py-attr docutils literal"><span class="pre">rate</span></code></a> * <a class="reference internal" href="Function.html#Function.Integrator.previous_value" title="Function.Integrator.previous_value"><code class="xref any py py-attr docutils literal"><span class="pre">previous_value</span></code></a>) + <a class="reference internal" href="Function.html#Function.Integrator.noise" title="Function.Integrator.noise"><code class="xref any py py-attr docutils literal"><span class="pre">noise</span></code></a>
-(<a class="reference external" href="https://en.wikipedia.org/wiki/Wiener_filter">Weiner filter</a> or
-<a class="reference external" href="https://en.wikipedia.org/wiki/Delta_rule">Delta rule</a>);</li>
-<li><strong>DIFFUSION</strong>: <a class="reference internal" href="Function.html#Function.Integrator.previous_value" title="Function.Integrator.previous_value"><code class="xref any py py-attr docutils literal"><span class="pre">previous_value</span></code></a> +
-(<a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.Integrator.rate" title="IntegratorMechanism.IntegratorMechanism.Integrator.rate"><code class="xref any py py-attr docutils literal"><span class="pre">rate</span></code></a> * <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.Integrator.previous_value" title="IntegratorMechanism.IntegratorMechanism.Integrator.previous_value"><code class="xref any py py-attr docutils literal"><span class="pre">previous_value</span></code></a> * <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.Integrator.time_step_size" title="IntegratorMechanism.IntegratorMechanism.Integrator.time_step_size"><code class="xref any py py-attr docutils literal"><span class="pre">time_step_size</span></code></a>) +
-√(<a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.Integrator.time_step_size" title="IntegratorMechanism.IntegratorMechanism.Integrator.time_step_size"><code class="xref any py py-attr docutils literal"><span class="pre">time_step_size</span></code></a> * <a class="reference internal" href="Function.html#Function.Integrator.noise" title="Function.Integrator.noise"><code class="xref any py py-attr docutils literal"><span class="pre">noise</span></code></a> * Gaussian(0,1))
-(<a class="reference external" href="https://en.wikipedia.org/wiki/Two-alternative_forced_choice#Drift-diffusion_model">Drift Diffusion Model</a>).</li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="attribute">
-<dt id="IntegratorMechanism.IntegratorMechanism.Integrator.noise">
-<code class="descname">noise</code><a class="headerlink" href="#IntegratorMechanism.IntegratorMechanism.Integrator.noise" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>float, function, list, or 1d np.array</em> &#8211; specifies random value to be added in each call to <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.Integrator.function" title="IntegratorMechanism.IntegratorMechanism.Integrator.function"><code class="xref any py py-meth docutils literal"><span class="pre">function</span></code></a>.</p>
-<p>If noise is a list or array, it must be the same length as <code class="xref any docutils literal"><span class="pre">variable</span></code>. If noise is
-specified as a single float or function, while <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.Integrator.variable" title="IntegratorMechanism.IntegratorMechanism.Integrator.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> is a list or array,
+<dt id="IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.noise">
+<code class="descname">noise</code><a class="headerlink" href="#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.noise" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>float, function, list, or 1d np.array</em> &#8211; specifies random value to be added in each call to <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.function" title="IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.function"><code class="xref any py py-meth docutils literal"><span class="pre">function</span></code></a>.</p>
+<p>If noise is a list or array, it must be the same length as <code class="xref any docutils literal"><span class="pre">variable</span></code>.</p>
+<p>If noise is specified as a single float or function, while <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.variable" title="IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> is a list or array,
 noise will be applied to each variable element. In the case of a noise function, this means that the function
 will be executed separately for each variable element.</p>
-<p>Note that in the case of DIFFUSION, noise must be specified as a float (or list or array of floats) because this
-value will be used to construct the standard DDM probability distribution. For all other types of integration,
-in order to generate random noise, we recommend that you instead select a probability distribution function
+<p><strong>Note:</strong>
+In order to generate random noise, we recommend selecting a probability distribution function
 (see <code class="xref any docutils literal"><span class="pre">Distribution</span> <span class="pre">Functions</span></code> for details), which will generate a new noise value from
-its distribution on each execution. If noise is specified as a float or as a function with a fixed output (or a
-list or array of these), then the noise will simply be an offset that remains the same across all executions.</p>
-</dd></dl>
-
-<dl class="attribute">
-<dt id="IntegratorMechanism.IntegratorMechanism.Integrator.time_step_size">
-<code class="descname">time_step_size</code><a class="headerlink" href="#IntegratorMechanism.IntegratorMechanism.Integrator.time_step_size" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>float</em> &#8211; determines the timing precision of the integration process when <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.Integrator.integration_type" title="IntegratorMechanism.IntegratorMechanism.Integrator.integration_type"><code class="xref any py py-attr docutils literal"><span class="pre">integration_type</span></code></a>
-is set to DIFFUSION (and used to scale the <a class="reference internal" href="Function.html#Function.Integrator.noise" title="Function.Integrator.noise"><code class="xref any py py-attr docutils literal"><span class="pre">noise</span></code></a> parameter appropriately).</p>
+its distribution on each execution. If noise is specified as a float or as a function with a fixed output, then
+the noise will simply be an offset that remains the same across all executions.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="IntegratorMechanism.IntegratorMechanism.Integrator.initializer">
-<code class="descname">initializer</code><a class="headerlink" href="#IntegratorMechanism.IntegratorMechanism.Integrator.initializer" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>1d np.array or list</em> &#8211; determines the starting value for integration (i.e., the value to which
-<a class="reference internal" href="Function.html#Function.Integrator.previous_value" title="Function.Integrator.previous_value"><code class="xref any py py-attr docutils literal"><span class="pre">previous_value</span></code></a> is set.</p>
-<p>If initializer is a list or array, it must be the same length as <code class="xref any docutils literal"><span class="pre">variable</span></code>. If
-initializer is specified as a single float or function, while <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.Integrator.variable" title="IntegratorMechanism.IntegratorMechanism.Integrator.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> is a list or
-array, initializer will be applied to each variable element. In the case of an initializer function, this means
-that the function will be executed separately for each variable element.</p>
+<dt id="IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.initializer">
+<code class="descname">initializer</code><a class="headerlink" href="#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.initializer" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>float, 1d np.array or list</em> &#8211; determines the starting value for integration (i.e., the value to which
+<a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.previous_value" title="IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.previous_value"><code class="xref any py py-attr docutils literal"><span class="pre">previous_value</span></code></a> is set.</p>
+<p>If initializer is a list or array, it must be the same length as <code class="xref any docutils literal"><span class="pre">variable</span></code>.</p>
+<p>TBI:</p>
+<p>Initializer may be a function or list/array of functions.</p>
+<p>If initializer is specified as a single float or function, while <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.variable" title="IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> is
+a list or array, initializer will be applied to each variable element. In the case of an initializer function,
+this means that the function will be executed separately for each variable element.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="IntegratorMechanism.IntegratorMechanism.Integrator.previous_value">
-<code class="descname">previous_value</code><a class="headerlink" href="#IntegratorMechanism.IntegratorMechanism.Integrator.previous_value" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>1d np.array : default variableClassDefault</em> &#8211; stores previous value with which <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.Integrator.variable" title="IntegratorMechanism.IntegratorMechanism.Integrator.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> is integrated.</p>
+<dt id="IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.previous_value">
+<code class="descname">previous_value</code><a class="headerlink" href="#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.previous_value" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>1d np.array : default variableClassDefault</em> &#8211; stores previous value with which <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.variable" title="IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> is integrated.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="IntegratorMechanism.IntegratorMechanism.Integrator.owner">
-<code class="descname">owner</code><a class="headerlink" href="#IntegratorMechanism.IntegratorMechanism.Integrator.owner" title="Permalink to this definition">¶</a></dt>
+<dt id="IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.owner">
+<code class="descname">owner</code><a class="headerlink" href="#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.owner" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>Mechanism</em> &#8211; <a class="reference internal" href="Component.html"><span class="doc">component</span></a> to which the Function has been assigned.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="IntegratorMechanism.IntegratorMechanism.Integrator.prefs">
-<code class="descname">prefs</code><a class="headerlink" href="#IntegratorMechanism.IntegratorMechanism.Integrator.prefs" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.Integrator.prefs" title="IntegratorMechanism.IntegratorMechanism.Integrator.prefs"><code class="xref any py py-attr docutils literal"><span class="pre">prefs</span></code></a> argument of the constructor for the function;
+<dt id="IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.prefs">
+<code class="descname">prefs</code><a class="headerlink" href="#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.prefs" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for function. Specified in the <strong>prefs</strong> argument of the constructor for the function;
 if it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
 (see <span class="xref doc">PreferenceSet</span> for details).</p>
 </dd></dl>
 
 <dl class="method">
-<dt id="IntegratorMechanism.IntegratorMechanism.Integrator.function">
-<code class="descname">function</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 1&gt;</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#IntegratorMechanism.IntegratorMechanism.Integrator.function" title="Permalink to this definition">¶</a></dt>
-<dd><p>Return: some fraction of <a class="reference internal" href="Function.html#Function.Linear.slope" title="Function.Linear.slope"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> combined with some fraction of <a class="reference internal" href="Function.html#Function.Integrator.previous_value" title="Function.Integrator.previous_value"><code class="xref any py py-attr docutils literal"><span class="pre">previous_value</span></code></a> (see <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.Integrator.integration_type" title="IntegratorMechanism.IntegratorMechanism.Integrator.integration_type"><code class="xref any py py-attr docutils literal"><span class="pre">integration_type</span></code></a>).</p>
+<dt id="IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.function">
+<code class="descname">function</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>time_scale=&lt;TimeScale.TRIAL: 2&gt;</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.function" title="Permalink to this definition">¶</a></dt>
+<dd><p>Return: some fraction of <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.variable" title="IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> combined with some fraction of <a class="reference internal" href="#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.previous_value" title="IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.previous_value"><code class="xref any py py-attr docutils literal"><span class="pre">previous_value</span></code></a>.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
@@ -455,7 +397,7 @@ <h2>Execution<a class="headerlink" href="#execution" title="Permalink to this he
         <a href="DDM.html" class="btn btn-neutral float-right" title="DDM" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
       
       
-        <a href="TransferMechanism.html" class="btn btn-neutral" title="TransferMechanism" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
+        <a href="ProcessingMechanism.html" class="btn btn-neutral" title="Processing Mechanism" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
       
     </div>
   
diff --git a/docs/build/html/Keywords.html b/docs/build/html/Keywords.html
index 54a307b889c..adf56825f76 100644
--- a/docs/build/html/Keywords.html
+++ b/docs/build/html/Keywords.html
@@ -86,7 +86,7 @@
 <li class="toctree-l1"><a class="reference internal" href="System.html">System</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Process.html">Process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Mechanism.html">Mechanism</a></li>
-<li class="toctree-l1"><a class="reference internal" href="State.html">States</a></li>
+<li class="toctree-l1"><a class="reference internal" href="State.html">State</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Projection.html">Projection</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Run.html">Run</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Component.html">Component</a></li>
@@ -150,7 +150,7 @@
 receive projections from other processes in the system. The <a class="reference internal" href="#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> mechanisms of a process or
 system are listed in its <code class="xref std std-keyword docutils literal"><span class="pre">originMechanisms</span></code> attribute, and can be displayed using its <code class="xref std std-keyword docutils literal"><span class="pre">show</span></code>
 method.  For additional details about <a class="reference internal" href="#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> mechanisms in processes, see
-<a class="reference internal" href="Process.html#process-mechanisms"><span class="std std-ref">Process Mechanisms</span></a> and <a class="reference internal" href="Process.html#process-input-and-ouput"><span class="std std-ref">Process Input and Output</span></a>;
+<a class="reference internal" href="Process.html#process-mechanisms"><span class="std std-ref">Process Mechanisms</span></a> and <code class="xref any docutils literal"><span class="pre">Process</span> <span class="pre">Input</span> <span class="pre">and</span> <span class="pre">Output</span></code>;
 and for systems see <a class="reference internal" href="System.html#system-mechanisms"><span class="std std-ref">System Mechanisms</span></a> and
 <a class="reference internal" href="System.html#system-execution-input-and-initialization"><span class="std std-ref">System Input and Initialization</span></a>.</p>
 </dd></dl>
@@ -190,7 +190,7 @@
 as it may send projections to other processes in the system.  The <a class="reference internal" href="#Keywords.Keywords.TERMINAL" title="Keywords.Keywords.TERMINAL"><code class="xref any py py-attr docutils literal"><span class="pre">TERMINAL</span></code></a> mechanisms of a process
 or system are listed in its <code class="xref std std-keyword docutils literal"><span class="pre">terminalMechanisms</span></code> attribute, and can be displayed using its
 <code class="xref std std-keyword docutils literal"><span class="pre">show</span></code> method.  For additional details about <a class="reference internal" href="#Keywords.Keywords.TERMINAL" title="Keywords.Keywords.TERMINAL"><code class="xref any py py-attr docutils literal"><span class="pre">TERMINAL</span></code></a> mechanisms in processes, see
-<a class="reference internal" href="Process.html#process-mechanisms"><span class="std std-ref">Mechanisms</span></a> and <a class="reference internal" href="Process.html#process-input-and-ouput"><span class="std std-ref">Process input and output</span></a>; and for systems see <a class="reference internal" href="System.html#system-mechanisms"><span class="std std-ref">Mechanisms</span></a>.</p>
+<a class="reference internal" href="Process.html#process-mechanisms"><span class="std std-ref">Mechanisms</span></a> and <code class="xref any docutils literal"><span class="pre">Process_Input_And_Output</span></code>; and for systems see <a class="reference internal" href="System.html#system-mechanisms"><span class="std std-ref">Mechanisms</span></a>.</p>
 </dd></dl>
 
 <dl class="attribute">
@@ -233,8 +233,8 @@
 </dd></dl>
 
 <dl class="attribute">
-<dt id="Keywords.MatrixKeywords.OFF_DIAGONAL_MATRIX">
-<code class="descname">OFF_DIAGONAL_MATRIX</code><a class="headerlink" href="#Keywords.MatrixKeywords.OFF_DIAGONAL_MATRIX" title="Permalink to this definition">¶</a></dt>
+<dt id="Keywords.MatrixKeywords.HOLLOW_MATRIX">
+<code class="descname">HOLLOW_MATRIX</code><a class="headerlink" href="#Keywords.MatrixKeywords.HOLLOW_MATRIX" title="Permalink to this definition">¶</a></dt>
 <dd><p>a square matrix of 0&#8217;s along the diagnoal, 1&#8217;s elsewhere; this requires that the length of the sender and
 receiver values are the same.</p>
 </dd></dl>
diff --git a/docs/build/html/LearningProjection.html b/docs/build/html/LearningProjection.html
index 9602cd59790..fa64f6cdf1e 100644
--- a/docs/build/html/LearningProjection.html
+++ b/docs/build/html/LearningProjection.html
@@ -34,9 +34,9 @@
               href="genindex.html"/>
         <link rel="search" title="Search" href="search.html"/>
     <link rel="top" title="PsyNeuLink 0.1 documentation" href="index.html"/>
-        <link rel="up" title="Learning Mechanism" href="LearningMechanism.html"/>
-        <link rel="next" title="Control Mechanism" href="ControlMechanism.html"/>
-        <link rel="prev" title="Learning Mechanism" href="LearningMechanism.html"/> 
+        <link rel="up" title="ModulatoryProjection" href="ModulatoryProjection.html"/>
+        <link rel="next" title="GatingProjection" href="GatingProjection.html"/>
+        <link rel="prev" title="ModulatoryProjection" href="ModulatoryProjection.html"/> 
 
   
   <script src="_static/js/modernizr.min.js"></script>
@@ -89,18 +89,27 @@
 <li class="toctree-l1"><a class="reference internal" href="System.html">System</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Process.html">Process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Mechanism.html">Mechanism</a></li>
-<li class="toctree-l1"><a class="reference internal" href="State.html">States</a></li>
+<li class="toctree-l1"><a class="reference internal" href="State.html">State</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="Projection.html">Projection</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="MappingProjection.html">MappingProjection</a></li>
-<li class="toctree-l2 current"><a class="current reference internal" href="#">LearningProjection</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#overview">Overview</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#creating-a-learningprojection">Creating a LearningProjection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#structure">Structure</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#execution">Execution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#class-reference">Class Reference</a></li>
+<li class="toctree-l2"><a class="reference internal" href="PathwayProjection.html">PathwayProjection</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="ModulatoryProjection.html">ModulatoryProjection</a><ul class="current">
+<li class="toctree-l3 current"><a class="current reference internal" href="#">LearningProjection</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#overview">Overview</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#creating-a-learningprojection">Creating a LearningProjection</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#structure">Structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#execution">Execution</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#class-reference">Class Reference</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="ControlProjection.html">ControlProjection</a></li>
+<li class="toctree-l3"><a class="reference internal" href="GatingProjection.html">GatingProjection</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ModulatoryProjection.html#overview">Overview</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ModulatoryProjection.html#creating-a-modulatoryprojection">Creating a ModulatoryProjection</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ModulatoryProjection.html#structure">Structure</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ModulatoryProjection.html#execution">Execution</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ModulatoryProjection.html#class-reference">Class Reference</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="ControlProjection.html">ControlProjection</a></li>
 <li class="toctree-l2"><a class="reference internal" href="Projection.html#overview">Overview</a></li>
 <li class="toctree-l2"><a class="reference internal" href="Projection.html#creating-a-projection">Creating a Projection</a></li>
 <li class="toctree-l2"><a class="reference internal" href="Projection.html#structure">Structure</a></li>
@@ -140,11 +149,9 @@
   <ul class="wy-breadcrumbs">
     <li><a href="index.html">Docs</a> &raquo;</li>
       
-          <li><a href="Mechanism.html">Mechanism</a> &raquo;</li>
-      
-          <li><a href="AdaptiveMechanism.html">Adaptive Mechanism</a> &raquo;</li>
+          <li><a href="Projection.html">Projection</a> &raquo;</li>
       
-          <li><a href="LearningMechanism.html">Learning Mechanism</a> &raquo;</li>
+          <li><a href="ModulatoryProjection.html">ModulatoryProjection</a> &raquo;</li>
       
     <li>LearningProjection</li>
       <li class="wy-breadcrumbs-aside">
@@ -160,85 +167,94 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
             
-  <div class="section" id="module-LearningProjection">
-<span id="learningprojection"></span><h1>LearningProjection<a class="headerlink" href="#module-LearningProjection" title="Permalink to this headline">¶</a></h1>
+  <div class="section" id="module-ModulatoryProjections.LearningProjection">
+<span id="learningprojection"></span><h1>LearningProjection<a class="headerlink" href="#module-ModulatoryProjections.LearningProjection" title="Permalink to this headline">¶</a></h1>
 <div class="section" id="overview">
 <span id="learningprojection-overview"></span><h2>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">¶</a></h2>
-<p>A LearningProjection is a subclass of <a class="reference internal" href="Projection.html"><span class="doc">Projection</span></a> that projects from a <a class="reference internal" href="LearningMechanism.html"><span class="doc">Learning Mechanism</span></a> to the
+<p>A LearningProjection is a subclass of <a class="reference internal" href="ModulatoryProjection.html"><span class="doc">ModulatoryProjection</span></a> that projects from a <a class="reference internal" href="LearningMechanism.html"><span class="doc">Learning Mechanism</span></a> to the
 MATRIX <a class="reference internal" href="ParameterState.html"><span class="doc">parameterState</span></a> of a <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a>, and modifies the value of the
-<a class="reference internal" href="MappingProjection.html#MappingProjection.MappingProjection.matrix" title="MappingProjection.MappingProjection.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a> parameter of that MappingProjection.  All of the LearningProjections in a system,
+<a class="reference internal" href="Function.html#Function.Stability.MappingProjection.matrix" title="Function.Stability.MappingProjection.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a> parameter of that MappingProjection.  All of the LearningProjections in a system,
 along with its other <a class="reference internal" href="LearningMechanism.html"><span class="doc">learning components</span></a>, can be displayed using the system&#8217;s <a class="reference internal" href="System.html#System.System_Base.show_graph" title="System.System_Base.show_graph"><code class="xref any py py-meth docutils literal"><span class="pre">show_graph</span></code></a> method
-with its <strong>show_learning</strong> argument assigned <code class="xref std std-keyword docutils literal"><span class="pre">True</span></code>.</p>
+with its <strong>show_learning</strong> argument assigned as <code class="xref std std-keyword docutils literal"><span class="pre">True</span></code>.</p>
 </div>
 <div class="section" id="creating-a-learningprojection">
 <span id="learningprojection-creation"></span><h2>Creating a LearningProjection<a class="headerlink" href="#creating-a-learningprojection" title="Permalink to this headline">¶</a></h2>
 <p>A LearningProjection can be created using any of the standard ways to <a class="reference internal" href="Projection.html#projection-creation"><span class="std std-ref">create a projection</span></a>,
-or by including it in a tuple that specifies the <a class="reference internal" href="MappingProjection.html#mapping-matrix-specification"><span class="std std-ref">matrix</span></a> parameter for a
+or by including it in a tuple that specifies the <code class="xref any docutils literal"><span class="pre">matrix</span></code> parameter for a
 <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a>.  LearningProjections are also created automatically, along with the other
 <a class="reference internal" href="LearningMechanism.html#learningmechanism-learning-configurations"><span class="std std-ref">components required for learning</span></a>, when learning is specified for a
-<a class="reference internal" href="Process.html#process-learning"><span class="std std-ref">process</span></a> or a <a class="reference internal" href="System.html#system-execution-learning"><span class="std std-ref">system</span></a>.</p>
-<p>If a LearningProjection is created using its constructor on its own, the <a class="reference internal" href="ControlProjection.html#ControlProjection.ControlProjection.receiver" title="ControlProjection.ControlProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a>
-argument must be specified.  If it is included in the <a class="reference internal" href="MappingProjection.html#mapping-matrix-specification"><span class="std std-ref">matrix specification</span></a> for a
-MappingProjection, the parameterState for the MappingProjection&#8217;s MATRIX will be assigned as the
-LearningProjection&#8217;s <a class="reference internal" href="#LearningProjection.LearningProjection.receiver" title="LearningProjection.LearningProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a>.  If its <a class="reference internal" href="#LearningProjection.LearningProjection.sender" title="LearningProjection.LearningProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a> is not
-specified, its assignment depends on the <a class="reference internal" href="#LearningProjection.LearningProjection.receiver" title="LearningProjection.LearningProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a>.  If the receiver belongs to a
-MappingProjection that projects between two mechanisms that are both in the same <a class="reference internal" href="Process.html#process-learning"><span class="std std-ref">process</span></a> or
-<a class="reference internal" href="System.html#system-execution-learning"><span class="std std-ref">system</span></a>, then the LearningProjection&#8217;s <a class="reference internal" href="#LearningProjection.LearningProjection.sender" title="LearningProjection.LearningProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a> is assigned
-to the <a class="reference internal" href="LearningMechanism.html#learningmechanism-learning-signal"><span class="std std-ref">LEARNING_SIGNAL</span></a> outputState of the <a class="reference internal" href="LearningMechanism.html"><span class="doc">Learning Mechanism</span></a> for the
-MappingProjection. If there is none, it is <a class="reference internal" href="LearningMechanism.html#learningmechanism-creation"><span class="std std-ref">created</span></a> along with any other components
+<a class="reference internal" href="Process.html#process-learning"><span class="std std-ref">Process</span></a> or a <a class="reference internal" href="System.html#system-execution-learning"><span class="std std-ref">System</span></a>.</p>
+<p>If a LearningProjection is created using its constructor on its own, the <a class="reference internal" href="ControlProjection.html#ModulatoryProjections.ControlProjection.ControlProjection.receiver" title="ModulatoryProjections.ControlProjection.ControlProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a>
+argument must be specified.  If it is included in the <code class="xref any docutils literal"><span class="pre">matrix</span> <span class="pre">specification</span></code> for a
+<a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a>, the <em>MATRIX</em> <a class="reference internal" href="ParameterState.html"><span class="doc">ParameterState</span></a> for the MappingProjection will be assigned as the
+LearningProjection&#8217;s <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.receiver" title="ModulatoryProjections.LearningProjection.LearningProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a>.  If its <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.sender" title="ModulatoryProjections.LearningProjection.LearningProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a> is not
+specified, its assignment depends on the <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.receiver" title="ModulatoryProjections.LearningProjection.LearningProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a>.  If the receiver belongs to a
+MappingProjection that projects between two mechanisms that are both in the same <a class="reference internal" href="Process.html#process-learning"><span class="std std-ref">Process</span></a> or
+<a class="reference internal" href="System.html#system-execution-learning"><span class="std std-ref">System</span></a>, then the LearningProjection&#8217;s <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.sender" title="ModulatoryProjections.LearningProjection.LearningProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a> is assigned
+to a <a class="reference internal" href="LearningSignal.html"><span class="doc">LearningSignal</span></a> of the <a class="reference internal" href="LearningMechanism.html"><span class="doc">Learning Mechanism</span></a> for the MappingProjection. If there is none, it is
+<a class="reference internal" href="LearningMechanism.html#learningmechanism-creation"><span class="std std-ref">created</span></a> along with any other components
 needed to implement learning for the MappingProjection (see <a class="reference internal" href="LearningMechanism.html#learningmechanism-learning-configurations"><span class="std std-ref">Learning Configurations</span></a>).</p>
+<div class="section" id="deferred-initialization">
+<span id="learningprojection-deferred-initialization"></span><h3>Deferred Initialization<a class="headerlink" href="#deferred-initialization" title="Permalink to this headline">¶</a></h3>
 <p>When a LearningProjection is created, its full initialization is <span class="xref std std-ref">deferred</span> until its
-<a class="reference internal" href="#LearningProjection.LearningProjection.sender" title="LearningProjection.LearningProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a> and <a class="reference internal" href="#LearningProjection.LearningProjection.receiver" title="LearningProjection.LearningProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a> have been fully specified.  This
-allows a LearningProjection to be created before its <a class="reference internal" href="ControlProjection.html#ControlProjection.ControlProjection.sender" title="ControlProjection.ControlProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a> and/or <a class="reference internal" href="#LearningProjection.LearningProjection.receiver" title="LearningProjection.LearningProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a> have been created (e.g., before them
+<a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.sender" title="ModulatoryProjections.LearningProjection.LearningProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a> and <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.receiver" title="ModulatoryProjections.LearningProjection.LearningProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a> have been fully specified.  This
+allows a LearningProjection to be created before its <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.sender" title="ModulatoryProjections.LearningProjection.LearningProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a> and/or <a class="reference internal" href="Function.html#Function.Stability.MappingProjection.receiver" title="Function.Stability.MappingProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a> have been created (e.g., before them
 in a script), by calling its constructor without specifying its <strong>sender</strong> or <strong>receiver</strong> arguments.
 However, for the LearningProjection to be operational, initialization must be completed by calling its <code class="xref any docutils literal"><span class="pre">deferred_init</span></code>
 method.  This is not necessary if learning has been specified for a <a class="reference internal" href="System.html#system-execution-learning"><span class="std std-ref">system</span></a>,
-<a class="reference internal" href="Process.html#process-learning"><span class="std std-ref">process</span></a>, or as the <a class="reference internal" href="MappingProjection.html#mappingprojection-tuple-specification"><span class="std std-ref">projection</span></a> in the <a class="reference internal" href="Process.html#Process.Process_Base.pathway" title="Process.Process_Base.pathway"><code class="xref any py py-attr docutils literal"><span class="pre">pathway</span></code></a> of a
+<a class="reference internal" href="Process.html#process-learning"><span class="std std-ref">process</span></a>, or as the <code class="xref any docutils literal"><span class="pre">projection</span></code> in the <a class="reference internal" href="Process.html#Process.Process_Base.pathway" title="Process.Process_Base.pathway"><code class="xref any py py-attr docutils literal"><span class="pre">pathway</span></code></a> of a
 process &#8211; in those cases, deferred initialization is completed automatically.</p>
 </div>
+</div>
 <div class="section" id="structure">
 <span id="learningprojection-structure"></span><h2>Structure<a class="headerlink" href="#structure" title="Permalink to this headline">¶</a></h2>
-<p>The <a class="reference internal" href="#LearningProjection.LearningProjection.sender" title="LearningProjection.LearningProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a> of a LearningProjection is the
-<code class="xref any docutils literal"><span class="pre">LEARNING_SIGNAL</span></code> outputState of a LearningMechanism. Its
-<a class="reference internal" href="#LearningProjection.LearningProjection.receiver" title="LearningProjection.LearningProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a> is the <code class="xref any docutils literal"><span class="pre">MATRIX</span></code> parameterState of a MappingProjection,
-Its <a class="reference internal" href="#LearningProjection.LearningProjection.learning_signal" title="LearningProjection.LearningProjection.learning_signal"><code class="xref any py py-attr docutils literal"><span class="pre">function</span> <span class="pre">simply</span> <span class="pre">conveys</span> <span class="pre">the</span> <span class="pre">`learning_signal</span></code></a> received from its
-<a class="reference internal" href="#LearningProjection.LearningProjection.sender" title="LearningProjection.LearningProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a> to the <a class="reference internal" href="#LearningProjection.LearningProjection.receiver" title="LearningProjection.LearningProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a>, possibly modulated by
-the <a class="reference internal" href="#LearningProjection.LearningProjection.learning_rate" title="LearningProjection.LearningProjection.learning_rate"><code class="xref any py py-attr docutils literal"><span class="pre">learning_rate</span></code></a>.</p>
+<p>The <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.sender" title="ModulatoryProjections.LearningProjection.LearningProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a> of a LearningProjection is a <a class="reference internal" href="LearningSignal.html"><span class="doc">LearningSignal</span></a> of a <a class="reference internal" href="LearningMechanism.html"><span class="doc">Learning Mechanism</span></a>.  Its
+<a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.receiver" title="ModulatoryProjections.LearningProjection.LearningProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a> is the <em>MATRIX</em> <a class="reference internal" href="ParameterState.html"><span class="doc">ParameterState</span></a> of a <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a>,  The
+<a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.function" title="ModulatoryProjections.LearningProjection.LearningProjection.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> of a LearningProjection is, by default, the identity function;  that is,
+it conveys the <code class="xref any docutils literal"><span class="pre">value</span></code> of its <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.sender" title="ModulatoryProjections.LearningProjection.LearningProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a> to
+its <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.receiver" title="ModulatoryProjections.LearningProjection.LearningProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a>, for use in modifying the <a class="reference internal" href="Function.html#Function.Stability.MappingProjection.matrix" title="Function.Stability.MappingProjection.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a> parameter
+of the <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a> being learned.</p>
 </div>
 <div class="section" id="execution">
 <span id="learningprojection-execution"></span><h2>Execution<a class="headerlink" href="#execution" title="Permalink to this headline">¶</a></h2>
-<p>A LearningProjection cannot be executed directly.  It is executed when its
-<a class="reference internal" href="#LearningProjection.LearningProjection.learned_projection" title="LearningProjection.LearningProjection.learned_projection"><code class="xref any py py-attr docutils literal"><span class="pre">learned_projection</span></code></a> is executed the MATRIX parameterState
-for the <a class="reference internal" href="#LearningProjection.LearningProjection.learned_projection" title="LearningProjection.LearningProjection.learned_projection"><code class="xref any py py-attr docutils literal"><span class="pre">learned_projection</span></code></a> is updated.  Note that these events only occur
-when the ProcessingMechanism to which the <a class="reference internal" href="#LearningProjection.LearningProjection.learned_projection" title="LearningProjection.LearningProjection.learned_projection"><code class="xref any py py-attr docutils literal"><span class="pre">learned_projection</span></code></a> projects is
+<p>A LearningProjection cannot be executed directly.  It is executed when the <em>MATRIX</em> ParameterState to which it
+projects is updated.  This occurs when the <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.learned_projection" title="ModulatoryProjections.LearningProjection.LearningProjection.learned_projection"><code class="xref any py py-attr docutils literal"><span class="pre">learned_projection</span></code></a> (i.e.,
+<a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a> to which the <em>MATRIX</em> ParameterState belongs) is updated. Note that these events occur only
+when the ProcessingMechanism that receives the <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.learned_projection" title="ModulatoryProjections.LearningProjection.LearningProjection.learned_projection"><code class="xref any py py-attr docutils literal"><span class="pre">learned_projection</span></code></a> is
 executed (see <span class="xref std std-ref">Lazy Evaluation</span> for an explanation of &#8220;lazy&#8221; updating). When the LearningProjection is
-executed, it gets the <a class="reference internal" href="#LearningProjection.LearningProjection.learning_signal" title="LearningProjection.LearningProjection.learning_signal"><code class="xref any py py-attr docutils literal"><span class="pre">learning_signal</span></code></a> from its <a class="reference internal" href="#LearningProjection.LearningProjection.sender" title="LearningProjection.LearningProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a>
-and conveys this to its <a class="reference internal" href="#LearningProjection.LearningProjection.receiver" title="LearningProjection.LearningProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a>, modified only by the <a class="reference internal" href="#LearningProjection.LearningProjection.learning_rate" title="LearningProjection.LearningProjection.learning_rate"><code class="xref any py py-attr docutils literal"><span class="pre">learning_rate</span></code></a> if that is specified.  Additional attributes are described under
-<a class="reference internal" href="#learningprojection-class-reference"><span class="std std-ref">Class Reference</span></a> below.</p>
+executed, it gets the <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.learning_signal" title="ModulatoryProjections.LearningProjection.LearningProjection.learning_signal"><code class="xref any py py-attr docutils literal"><span class="pre">learning_signal</span></code></a> from its
+<a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.sender" title="ModulatoryProjections.LearningProjection.LearningProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a> and conveys this to its <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.receiver" title="ModulatoryProjections.LearningProjection.LearningProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a>,
+possibly modified by its <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.learning_rate" title="ModulatoryProjections.LearningProjection.LearningProjection.learning_rate"><code class="xref any py py-attr docutils literal"><span class="pre">learning_rate</span></code></a> parameter if that is specified.</p>
+<div class="admonition note">
+<p class="first admonition-title">Note</p>
+<p class="last">The changes in the <code class="xref any docutils literal"><span class="pre">matrix</span></code> parameter of a <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a> in response to the
+execution of a LearningProjection are not applied until the <a class="reference internal" href="Mechanism.html"><span class="doc">Mechanism</span></a> that receives MappingProjection are next
+executed; see <span class="xref std std-ref">Lazy Evaluation</span> for an explanation of &#8220;lazy&#8221; updating).</p>
+</div>
 </div>
 <div class="section" id="class-reference">
 <span id="learningprojection-class-reference"></span><h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalink to this headline">¶</a></h2>
 <dl class="class">
-<dt id="LearningProjection.LearningProjection">
-<em class="property">class </em><code class="descclassname">LearningProjection.</code><code class="descname">LearningProjection</code><span class="sig-paren">(</span><em>sender=None</em>, <em>receiver=None</em>, <em>learning_function</em>, <em>learning_rate=None</em>, <em>params=None</em>, <em>name=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#LearningProjection.LearningProjection" title="Permalink to this definition">¶</a></dt>
+<dt id="ModulatoryProjections.LearningProjection.LearningProjection">
+<em class="property">class </em><code class="descclassname">ModulatoryProjections.LearningProjection.</code><code class="descname">LearningProjection</code><span class="sig-paren">(</span><em>sender=None</em>, <em>receiver=None</em>, <em>learning_function</em>, <em>learning_rate=None</em>, <em>params=None</em>, <em>name=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#ModulatoryProjections.LearningProjection.LearningProjection" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements a projection that modifies the matrix parameter of a MappingProjection.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>sender</strong> (<em>Optional[LearningMechanism or LEARNING_SIGNAL OutputState of one]</em>) &#8211; the source of the <a class="reference internal" href="Function.html#Function.BackPropagation.error_signal" title="Function.BackPropagation.error_signal"><code class="xref any py py-attr docutils literal"><span class="pre">error_signal</span></code></a> for the LearningProjection. If it is not specified, one will be
+<li><strong>sender</strong> (<em>Optional[LearningMechanism or LearningSignal]</em>) &#8211; the source of the <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.learning_signal" title="ModulatoryProjections.LearningProjection.LearningProjection.learning_signal"><code class="xref any py py-attr docutils literal"><span class="pre">learning_signal</span></code></a> for the LearningProjection;  If it is not specified, one will be
 <code class="xref any docutils literal"><span class="pre">automatically</span> <span class="pre">created</span></code> that is appropriate for the
 LearningProjection&#8217;s <code class="xref any docutils literal"><span class="pre">errorSource</span></code>.</li>
 <li><strong>receiver</strong> (Optional[MappingProjection or ParameterState for <code class="docutils literal"><span class="pre">matrix</span></code> parameter of one]) &#8211; the <a class="reference internal" href="ParameterState.html"><span class="doc">parameterState</span></a> (or the <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a> that owns it) for the
-<a class="reference internal" href="MappingProjection.html#MappingProjection.MappingProjection.matrix" title="MappingProjection.MappingProjection.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a> to be modified by the LearningProjection.</li>
+<code class="xref any docutils literal"><span class="pre">matrix</span></code> to be modified by the LearningProjection.</li>
 <li><strong>learning_function</strong> (<em>Optional[LearningFunction or function] : default BackPropagation</em>) &#8211; specifies a function to be used for learning by the <code class="xref any docutils literal"><span class="pre">sender</span></code> (i.e., its
 <a class="reference internal" href="LearningMechanism.html#AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism.function" title="AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> attribute).</li>
-<li><strong>learning_rate</strong> (<em>Optional[float or int]</em>) &#8211; if specified, it is applied mulitiplicatively to <a class="reference internal" href="#LearningProjection.LearningProjection.learning_signal" title="LearningProjection.LearningProjection.learning_signal"><code class="xref any py py-attr docutils literal"><span class="pre">learning_signal</span></code></a> received from the <a class="reference internal" href="LearningMechanism.html"><span class="doc">Learning Mechanism</span></a>
-from which it projects (see <a class="reference internal" href="#LearningProjection.LearningProjection.learning_rate" title="LearningProjection.LearningProjection.learning_rate"><code class="xref any py py-attr docutils literal"><span class="pre">learning_rate</span></code></a> for additional details).</li>
+<li><strong>learning_rate</strong> (<em>Optional[float or int]</em>) &#8211; if specified, it is applied mulitiplicatively to <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.learning_signal" title="ModulatoryProjections.LearningProjection.LearningProjection.learning_signal"><code class="xref any py py-attr docutils literal"><span class="pre">learning_signal</span></code></a> received from the <a class="reference internal" href="LearningSignal.html"><span class="doc">LearningSignal</span></a>
+from which it projects (see <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.learning_rate" title="ModulatoryProjections.LearningProjection.LearningProjection.learning_rate"><code class="xref any py py-attr docutils literal"><span class="pre">learning_rate</span></code></a> for additional details).</li>
 <li><strong>params</strong> (<em>Optional[Dict[param keyword, param value]]</em>) &#8211; a <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> that specifies the parameters for the
 projection, its function, and/or a custom function and its parameters. By default, it contains an entry for
-the projection&#8217;s default <a class="reference internal" href="#LearningProjection.LearningProjection.function" title="LearningProjection.LearningProjection.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> and parameter assignments.  Values specified
+the projection&#8217;s default <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.function" title="ModulatoryProjections.LearningProjection.LearningProjection.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> and parameter assignments.  Values specified
 for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.</li>
 <li><strong>name</strong> (<em>str : default LearningProjection-&lt;index&gt;</em>) &#8211; a string used for the name of the LearningProjection.
 If not is specified, a default is assigned by ProjectionRegistry
@@ -252,103 +268,103 @@
 </tbody>
 </table>
 <dl class="attribute">
-<dt id="LearningProjection.LearningProjection.componentType">
-<code class="descname">componentType</code><a class="headerlink" href="#LearningProjection.LearningProjection.componentType" title="Permalink to this definition">¶</a></dt>
+<dt id="ModulatoryProjections.LearningProjection.LearningProjection.componentType">
+<code class="descname">componentType</code><a class="headerlink" href="#ModulatoryProjections.LearningProjection.LearningProjection.componentType" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>LEARNING_PROJECTION</em></p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="LearningProjection.LearningProjection.sender">
-<code class="descname">sender</code><a class="headerlink" href="#LearningProjection.LearningProjection.sender" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>LEARNING_SIGNAL OutputState of a LearningMechanism</em> &#8211; source of <a class="reference internal" href="#LearningProjection.LearningProjection.learning_signal" title="LearningProjection.LearningProjection.learning_signal"><code class="xref any py py-attr docutils literal"><span class="pre">learning_signal</span></code></a>.</p>
+<dt id="ModulatoryProjections.LearningProjection.LearningProjection.sender">
+<code class="descname">sender</code><a class="headerlink" href="#ModulatoryProjections.LearningProjection.LearningProjection.sender" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>LEARNING_SIGNAL OutputState of a LearningMechanism</em> &#8211; source of <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.learning_signal" title="ModulatoryProjections.LearningProjection.LearningProjection.learning_signal"><code class="xref any py py-attr docutils literal"><span class="pre">learning_signal</span></code></a>.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="LearningProjection.LearningProjection.receiver">
-<code class="descname">receiver</code><a class="headerlink" href="#LearningProjection.LearningProjection.receiver" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>MATRIX ParameterState of a MappingProjection</em> &#8211; parameterState for the <a class="reference internal" href="MappingProjection.html#MappingProjection.MappingProjection.matrix" title="MappingProjection.MappingProjection.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a> parameter of
-the <a class="reference internal" href="#LearningProjection.LearningProjection.learned_projection" title="LearningProjection.LearningProjection.learned_projection"><code class="xref any py py-attr docutils literal"><span class="pre">learned_projection</span></code></a> to be modified by the LearningProjection.</p>
+<dt id="ModulatoryProjections.LearningProjection.LearningProjection.receiver">
+<code class="descname">receiver</code><a class="headerlink" href="#ModulatoryProjections.LearningProjection.LearningProjection.receiver" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>MATRIX ParameterState of a MappingProjection</em> &#8211; parameterState for the <code class="xref any docutils literal"><span class="pre">matrix</span></code> parameter of
+the <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.learned_projection" title="ModulatoryProjections.LearningProjection.LearningProjection.learned_projection"><code class="xref any py py-attr docutils literal"><span class="pre">learned_projection</span></code></a> to be modified by the LearningProjection.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="LearningProjection.LearningProjection.learned_projection">
-<code class="descname">learned_projection</code><a class="headerlink" href="#LearningProjection.LearningProjection.learned_projection" title="Permalink to this definition">¶</a></dt>
+<dt id="ModulatoryProjections.LearningProjection.LearningProjection.learned_projection">
+<code class="descname">learned_projection</code><a class="headerlink" href="#ModulatoryProjections.LearningProjection.LearningProjection.learned_projection" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>MappingProjection</em> &#8211; the <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a> to which LearningProjection projects, and to which its
 <code class="xref any docutils literal"><span class="pre">receiver</span> <span class="pre">&lt;LearningProjection.receiver</span></code> and the <a class="reference internal" href="MappingProjection.html"><span class="doc">matrix</span></a> parameter to be modified belong.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="LearningProjection.LearningProjection.variable">
-<code class="descname">variable</code><a class="headerlink" href="#LearningProjection.LearningProjection.variable" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>2d np.array</em> &#8211; same as <a class="reference internal" href="#LearningProjection.LearningProjection.learning_signal" title="LearningProjection.LearningProjection.learning_signal"><code class="xref any py py-attr docutils literal"><span class="pre">learning_signal</span></code></a>.</p>
+<dt id="ModulatoryProjections.LearningProjection.LearningProjection.variable">
+<code class="descname">variable</code><a class="headerlink" href="#ModulatoryProjections.LearningProjection.LearningProjection.variable" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>2d np.array</em> &#8211; same as <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.learning_signal" title="ModulatoryProjections.LearningProjection.LearningProjection.learning_signal"><code class="xref any py py-attr docutils literal"><span class="pre">learning_signal</span></code></a>.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="LearningProjection.LearningProjection.learning_signal">
-<code class="descname">learning_signal</code><a class="headerlink" href="#LearningProjection.LearningProjection.learning_signal" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>2d np.array</em> &#8211; matrix of &#8220;weight&#8221; changes calculated by the LearningProjection&#8217;s <a class="reference internal" href="#LearningProjection.LearningProjection.sender" title="LearningProjection.LearningProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a> and
-used to modify the <a class="reference internal" href="MappingProjection.html#MappingProjection.MappingProjection.matrix" title="MappingProjection.MappingProjection.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a> parameter of its <a class="reference internal" href="#LearningProjection.LearningProjection.receiver" title="LearningProjection.LearningProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a>
-(i.e., the <a class="reference internal" href="#LearningProjection.LearningProjection.learned_projection" title="LearningProjection.LearningProjection.learned_projection"><code class="xref any py py-attr docutils literal"><span class="pre">learned_projection</span></code></a>). Rows correspond to sender,
+<dt id="ModulatoryProjections.LearningProjection.LearningProjection.learning_signal">
+<code class="descname">learning_signal</code><a class="headerlink" href="#ModulatoryProjections.LearningProjection.LearningProjection.learning_signal" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>2d np.array</em> &#8211; matrix of &#8220;weight&#8221; changes calculated by the LearningProjection&#8217;s <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.sender" title="ModulatoryProjections.LearningProjection.LearningProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a> and
+used to modify the <a class="reference internal" href="Function.html#Function.Stability.MappingProjection.matrix" title="Function.Stability.MappingProjection.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a> parameter of its <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.receiver" title="ModulatoryProjections.LearningProjection.LearningProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a>
+(i.e., the <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.learned_projection" title="ModulatoryProjections.LearningProjection.LearningProjection.learned_projection"><code class="xref any py py-attr docutils literal"><span class="pre">learned_projection</span></code></a>). Rows correspond to sender,
 columns to receiver (i.e., the input and output of the MappingProjection, respectively).</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="LearningProjection.LearningProjection.function">
-<code class="descname">function</code><a class="headerlink" href="#LearningProjection.LearningProjection.function" title="Permalink to this definition">¶</a></dt>
+<dt id="ModulatoryProjections.LearningProjection.LearningProjection.function">
+<code class="descname">function</code><a class="headerlink" href="#ModulatoryProjections.LearningProjection.LearningProjection.function" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>Function : default Linear</em> &#8211; assigns the learning_signal received from <a class="reference internal" href="LearningMechanism.html"><span class="doc">Learning Mechanism</span></a> as the value of the projection.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="LearningProjection.LearningProjection.weight_change_params">
-<code class="descname">weight_change_params</code><a class="headerlink" href="#LearningProjection.LearningProjection.weight_change_params" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>dict : default: see below</em> &#8211; specifies to the <a class="reference internal" href="#LearningProjection.LearningProjection.receiver" title="LearningProjection.LearningProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a> how the <a class="reference internal" href="#LearningProjection.LearningProjection.weight_change_matrix" title="LearningProjection.LearningProjection.weight_change_matrix"><code class="xref any py py-attr docutils literal"><span class="pre">weight_change_matrix</span></code></a> should be applied to the
+<dt id="ModulatoryProjections.LearningProjection.LearningProjection.weight_change_params">
+<code class="descname">weight_change_params</code><a class="headerlink" href="#ModulatoryProjections.LearningProjection.LearningProjection.weight_change_params" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>dict : default: see below</em> &#8211; specifies to the <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.receiver" title="ModulatoryProjections.LearningProjection.LearningProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a> how the <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.weight_change_matrix" title="ModulatoryProjections.LearningProjection.LearningProjection.weight_change_matrix"><code class="xref any py py-attr docutils literal"><span class="pre">weight_change_matrix</span></code></a> should be applied to the
 <code class="xref any docutils literal"><span class="pre">matrix</span></code>.  It assumes that the
 <code class="xref any docutils literal"><span class="pre">function</span></code> of the receiver is <a class="reference internal" href="Function.html#Function.LinearCombination" title="Function.LinearCombination"><code class="xref any py py-class docutils literal"><span class="pre">LinearCombination</span></code></a>.  By default it passes
-the following dictionary of specifications to the <a class="reference internal" href="#LearningProjection.LearningProjection.receiver" title="LearningProjection.LearningProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a>:</p>
+the following dictionary of specifications to the <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.receiver" title="ModulatoryProjections.LearningProjection.LearningProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a>:</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">FUNCTION_PARAMS</span><span class="p">:</span> <span class="p">{</span><span class="n">OPERATION</span><span class="p">:</span> <span class="n">SUM</span><span class="p">,</span>
-                  <span class="n">PARAMETER_MODULATION_OPERATION</span><span class="p">:</span> <span class="n">ModulationOperation</span><span class="o">.</span><span class="n">ADD</span><span class="p">,</span>
+                  <span class="n">PARAMETER_MODULATION_OPERATION</span><span class="p">:</span> <span class="n">Modulation</span><span class="o">.</span><span class="n">ADD</span><span class="p">,</span>
                   <span class="n">PROJECTION_TYPE</span><span class="p">:</span> <span class="n">LEARNING_PROJECTION</span><span class="p">}</span>
 </pre></div>
 </div>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="LearningProjection.LearningProjection.learning_rate">
-<code class="descname">learning_rate</code><a class="headerlink" href="#LearningProjection.LearningProjection.learning_rate" title="Permalink to this definition">¶</a></dt>
+<dt id="ModulatoryProjections.LearningProjection.LearningProjection.learning_rate">
+<code class="descname">learning_rate</code><a class="headerlink" href="#ModulatoryProjections.LearningProjection.LearningProjection.learning_rate" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>Optional[float]</em> &#8211; determines the <code class="xref std std-keyword docutils literal"><span class="pre">learning_rate</span></code> for the LearningProjection.  If specified, it is applied
-multiplicatively to the <a class="reference internal" href="#LearningProjection.LearningProjection.learning_signal" title="LearningProjection.LearningProjection.learning_signal"><code class="xref any py py-attr docutils literal"><span class="pre">learning_signal</span></code></a> and thus can be used to
+multiplicatively to the <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.learning_signal" title="ModulatoryProjections.LearningProjection.LearningProjection.learning_signal"><code class="xref any py py-attr docutils literal"><span class="pre">learning_signal</span></code></a> and thus can be used to
 modulate the learning rate in addition to (and on top of) the one specified for the <a class="reference internal" href="LearningMechanism.html"><span class="doc">Learning Mechanism</span></a>,
 its <a class="reference internal" href="LearningMechanism.html#AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism.function" title="AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>, and/or the process or system to which it belongs
 (see <a class="reference internal" href="LearningMechanism.html#learningmechanism-learning-rate"><span class="std std-ref">learning_rate</span></a> of LearningMechanism for additional details).</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="LearningProjection.LearningProjection.weight_change_matrix">
-<code class="descname">weight_change_matrix</code><a class="headerlink" href="#LearningProjection.LearningProjection.weight_change_matrix" title="Permalink to this definition">¶</a></dt>
+<dt id="ModulatoryProjections.LearningProjection.LearningProjection.weight_change_matrix">
+<code class="descname">weight_change_matrix</code><a class="headerlink" href="#ModulatoryProjections.LearningProjection.LearningProjection.weight_change_matrix" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>2d np.array</em> &#8211; matrix of changes to be made to the <code class="xref any docutils literal"><span class="pre">mappingWeightMatrix</span></code>, after learning_rate has been applied to the
-<a class="reference internal" href="#LearningProjection.LearningProjection.learning_signal" title="LearningProjection.LearningProjection.learning_signal"><code class="xref any py py-attr docutils literal"><span class="pre">learning_signal</span></code></a>; same as <a class="reference internal" href="#LearningProjection.LearningProjection.value" title="LearningProjection.LearningProjection.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>.</p>
+<a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.learning_signal" title="ModulatoryProjections.LearningProjection.LearningProjection.learning_signal"><code class="xref any py py-attr docutils literal"><span class="pre">learning_signal</span></code></a>; same as <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.value" title="ModulatoryProjections.LearningProjection.LearningProjection.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="LearningProjection.LearningProjection.value">
-<code class="descname">value</code><a class="headerlink" href="#LearningProjection.LearningProjection.value" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>2d np.array</em> &#8211; same as <a class="reference internal" href="#LearningProjection.LearningProjection.weight_change_matrix" title="LearningProjection.LearningProjection.weight_change_matrix"><code class="xref any py py-attr docutils literal"><span class="pre">weight_change_matrix</span></code></a>.</p>
+<dt id="ModulatoryProjections.LearningProjection.LearningProjection.value">
+<code class="descname">value</code><a class="headerlink" href="#ModulatoryProjections.LearningProjection.LearningProjection.value" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>2d np.array</em> &#8211; same as <a class="reference internal" href="#ModulatoryProjections.LearningProjection.LearningProjection.weight_change_matrix" title="ModulatoryProjections.LearningProjection.LearningProjection.weight_change_matrix"><code class="xref any py py-attr docutils literal"><span class="pre">weight_change_matrix</span></code></a>.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="LearningProjection.LearningProjection.name">
-<code class="descname">name</code><a class="headerlink" href="#LearningProjection.LearningProjection.name" title="Permalink to this definition">¶</a></dt>
+<dt id="ModulatoryProjections.LearningProjection.LearningProjection.name">
+<code class="descname">name</code><a class="headerlink" href="#ModulatoryProjections.LearningProjection.LearningProjection.name" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>str : default LearningProjection-&lt;index&gt;</em> &#8211; the name of the LearningProjection.
-Specified in the <a class="reference internal" href="#LearningProjection.LearningProjection.name" title="LearningProjection.LearningProjection.name"><code class="xref any py py-attr docutils literal"><span class="pre">name</span></code></a> argument of the constructor for the projection;
+Specified in the <strong>name</strong> argument of the constructor for the projection;
 if not is specified, a default is assigned by ProjectionRegistry
 (see <span class="xref doc">Registry</span> for conventions used in naming, including for default and duplicate names).</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="LearningProjection.LearningProjection.prefs">
-<code class="descname">prefs</code><a class="headerlink" href="#LearningProjection.LearningProjection.prefs" title="Permalink to this definition">¶</a></dt>
+<dt id="ModulatoryProjections.LearningProjection.LearningProjection.prefs">
+<code class="descname">prefs</code><a class="headerlink" href="#ModulatoryProjections.LearningProjection.LearningProjection.prefs" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for projection.
-Specified in the <a class="reference internal" href="#LearningProjection.LearningProjection.prefs" title="LearningProjection.LearningProjection.prefs"><code class="xref any py py-attr docutils literal"><span class="pre">prefs</span></code></a> argument of the constructor for the projection;
+Specified in the <strong>prefs</strong> argument of the constructor for the projection;
 if it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
 (see <span class="xref doc">PreferenceSet</span> for details).</p>
 </dd></dl>
@@ -365,10 +381,10 @@
   
     <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
       
-        <a href="ControlMechanism.html" class="btn btn-neutral float-right" title="Control Mechanism" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
+        <a href="GatingProjection.html" class="btn btn-neutral float-right" title="GatingProjection" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
       
       
-        <a href="LearningMechanism.html" class="btn btn-neutral" title="Learning Mechanism" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
+        <a href="ModulatoryProjection.html" class="btn btn-neutral" title="ModulatoryProjection" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
       
     </div>
   
diff --git a/docs/build/html/Log.html b/docs/build/html/Log.html
index e6c8cd9e667..c92a2baa8c3 100644
--- a/docs/build/html/Log.html
+++ b/docs/build/html/Log.html
@@ -86,7 +86,7 @@
 <li class="toctree-l1"><a class="reference internal" href="System.html">System</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Process.html">Process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Mechanism.html">Mechanism</a></li>
-<li class="toctree-l1"><a class="reference internal" href="State.html">States</a></li>
+<li class="toctree-l1"><a class="reference internal" href="State.html">State</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Projection.html">Projection</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Run.html">Run</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Component.html">Component</a></li>
diff --git a/docs/build/html/MappingProjection.html b/docs/build/html/MappingProjection.html
index f201191a528..ec60fa3765e 100644
--- a/docs/build/html/MappingProjection.html
+++ b/docs/build/html/MappingProjection.html
@@ -34,9 +34,9 @@
               href="genindex.html"/>
         <link rel="search" title="Search" href="search.html"/>
     <link rel="top" title="PsyNeuLink 0.1 documentation" href="index.html"/>
-        <link rel="up" title="Projection" href="Projection.html"/>
-        <link rel="next" title="Run" href="Run.html"/>
-        <link rel="prev" title="Projection" href="Projection.html"/> 
+        <link rel="up" title="PathwayProjection" href="PathwayProjection.html"/>
+        <link rel="next" title="ModulatoryProjection" href="ModulatoryProjection.html"/>
+        <link rel="prev" title="PathwayProjection" href="PathwayProjection.html"/> 
 
   
   <script src="_static/js/modernizr.min.js"></script>
@@ -89,18 +89,17 @@
 <li class="toctree-l1"><a class="reference internal" href="System.html">System</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Process.html">Process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Mechanism.html">Mechanism</a></li>
-<li class="toctree-l1"><a class="reference internal" href="State.html">States</a></li>
+<li class="toctree-l1"><a class="reference internal" href="State.html">State</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="Projection.html">Projection</a><ul class="current">
-<li class="toctree-l2 current"><a class="current reference internal" href="#">MappingProjection</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#overview">Overview</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#creating-a-mappingprojection">Creating a MappingProjection</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#structure">Structure</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#execution">Execution</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#class-reference">Class Reference</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="PathwayProjection.html">PathwayProjection</a><ul class="current">
+<li class="toctree-l3 current"><a class="current reference internal" href="#">MappingProjection</a></li>
+<li class="toctree-l3"><a class="reference internal" href="PathwayProjection.html#overview">Overview</a></li>
+<li class="toctree-l3"><a class="reference internal" href="PathwayProjection.html#creating-a-pathwayprojection">Creating a PathwayProjection</a></li>
+<li class="toctree-l3"><a class="reference internal" href="PathwayProjection.html#structure">Structure</a></li>
+<li class="toctree-l3"><a class="reference internal" href="PathwayProjection.html#execution">Execution</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="LearningProjection.html">LearningProjection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ControlProjection.html">ControlProjection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ModulatoryProjection.html">ModulatoryProjection</a></li>
 <li class="toctree-l2"><a class="reference internal" href="Projection.html#overview">Overview</a></li>
 <li class="toctree-l2"><a class="reference internal" href="Projection.html#creating-a-projection">Creating a Projection</a></li>
 <li class="toctree-l2"><a class="reference internal" href="Projection.html#structure">Structure</a></li>
@@ -142,6 +141,8 @@
       
           <li><a href="Projection.html">Projection</a> &raquo;</li>
       
+          <li><a href="PathwayProjection.html">PathwayProjection</a> &raquo;</li>
+      
     <li>MappingProjection</li>
       <li class="wy-breadcrumbs-aside">
         
@@ -156,204 +157,8 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
             
-  <div class="section" id="module-MappingProjection">
-<span id="mappingprojection"></span><h1>MappingProjection<a class="headerlink" href="#module-MappingProjection" title="Permalink to this headline">¶</a></h1>
-<div class="section" id="overview">
-<span id="mapping-overview"></span><h2>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">¶</a></h2>
-<p>A MappingProjection transmits the <a class="reference internal" href="OutputState.html#OutputState.OutputState.value" title="OutputState.OutputState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> of an <a class="reference internal" href="OutputState.html"><span class="doc">outputState</span></a> of one
-<a class="reference internal" href="ProcessingMechanism.html"><span class="doc">Processing Mechanism</span></a> (its sender) to the <a class="reference internal" href="InputState.html"><span class="doc">inputState</span></a> of another (its receiver).  The default function
-for a MappingProjection is  <a class="reference internal" href="Function.html#Function.LinearMatrix" title="Function.LinearMatrix"><code class="xref any py py-class docutils literal"><span class="pre">LinearMatrix</span></code></a>, which uses the MappingProjection&#8217;s <a class="reference internal" href="#MappingProjection.MappingProjection.matrix" title="MappingProjection.MappingProjection.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a>
-attribute to transform the value received from its sender and provide the result to its receiver.</p>
-</div>
-<div class="section" id="creating-a-mappingprojection">
-<span id="mapping-creation"></span><h2>Creating a MappingProjection<a class="headerlink" href="#creating-a-mappingprojection" title="Permalink to this headline">¶</a></h2>
-<p>A MappingProjection can be created in any of the ways that can be used to create a <a class="reference internal" href="Projection.html#projection-creation"><span class="std std-ref">projection</span></a>).
-MappingProjections are also generated automatically in the following circumstances, using a <a class="reference internal" href="#mapping-matrix"><span class="std std-ref">matrix</span></a>
-appropriate to the circumstance:</p>
-<blockquote>
-<div><ul class="simple">
-<li>by a <a class="reference internal" href="Process.html"><span class="doc">process</span></a>, when two adjacent mechanisms in its <a class="reference internal" href="Process.html#Process.Process_Base.pathway" title="Process.Process_Base.pathway"><code class="xref any py py-attr docutils literal"><span class="pre">pathway</span></code></a> do not already have a projection assigned
-between them (<a class="reference internal" href="Keywords.html#Keywords.MatrixKeywords.AUTO_ASSIGN_MATRIX" title="Keywords.MatrixKeywords.AUTO_ASSIGN_MATRIX"><code class="xref any py py-attr docutils literal"><span class="pre">AUTO_ASSIGN_MATRIX</span></code></a> is used as the matrix specification, which determines the appropriate matrix by
-context);</li>
-</ul>
-<ul class="simple">
-<li>by an <a class="reference internal" href="ObjectiveMechanism.html"><span class="doc">ObjectiveMechanism</span></a>, from each outputState listed in its <a class="reference internal" href="ObjectiveMechanism.html#ObjectiveMechanism.ObjectiveMechanism.monitored_values" title="ObjectiveMechanism.ObjectiveMechanism.monitored_values"><code class="xref any py py-attr docutils literal"><span class="pre">monitored_values</span></code></a> attribute to the corresponding
-inputState of the ObjectiveMechanism (<a class="reference internal" href="Keywords.html#Keywords.MatrixKeywords.AUTO_ASSIGN_MATRIX" title="Keywords.MatrixKeywords.AUTO_ASSIGN_MATRIX"><code class="xref any py py-attr docutils literal"><span class="pre">AUTO_ASSIGN_MATRIX</span></code></a> is used as the matrix specification, which determines
-the appropriate matrix by context).</li>
-</ul>
-<ul class="simple">
-<li>by a <a class="reference internal" href="ControlMechanism.html"><span class="doc">ControlMechanism</span></a>, from the ObjectiveMechanism that provides it with the outcome of the
-outputStates that it monitors, and from those outputStates (listed in its
-<code class="xref any docutils literal"><span class="pre">monitored_output_states</span></code> attribute) to
-the ObjectiveMechanism (an <a class="reference internal" href="Keywords.html#Keywords.MatrixKeywords.IDENTITY_MATRIX" title="Keywords.MatrixKeywords.IDENTITY_MATRIX"><code class="xref any py py-attr docutils literal"><span class="pre">IDENTITY_MATRIX</span></code></a> is used for all of these);</li>
-</ul>
-<ul class="simple">
-<li>by a <a class="reference internal" href="LearningMechanism.html"><span class="doc">Learning Mechanism</span></a>, between it and the other components required to implement learning
-(see <a class="reference internal" href="LearningMechanism.html#learningmechanism-learning-configurations"><span class="std std-ref">Learning Configurations</span></a> for details);</li>
-</ul>
-</div></blockquote>
-<p id="mapping-matrix-specification">The <a class="reference internal" href="#MappingProjection.MappingProjection.matrix" title="MappingProjection.MappingProjection.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a> parameter of a MappingProjection is used to transform the input from its
-<a class="reference internal" href="#MappingProjection.MappingProjection.sender" title="MappingProjection.MappingProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a>, the result of which is provided to its <a class="reference internal" href="#MappingProjection.MappingProjection.receiver" title="MappingProjection.MappingProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a>.
-It can be specified in any of the following formats:</p>
-<blockquote>
-<div><ul class="simple">
-<li><strong>List, array or matrix</strong>.  If it is a list, each item must be a list or 1d np.array of numbers.  Otherwise,
-it must be a 2d np.array or np.matrix.  In each case, the outer dimension (outer list items, array axis 0,
-or matrix rows) corresponds to the elements of the <a class="reference internal" href="#MappingProjection.MappingProjection.sender" title="MappingProjection.MappingProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a>, and the inner dimension
-(inner list items, array axis 1, or matrix columns) corresponds to the weighting of the contribution that a
-given <a class="reference internal" href="#MappingProjection.MappingProjection.sender" title="MappingProjection.MappingProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a> makes to the <a class="reference internal" href="#MappingProjection.MappingProjection.receiver" title="MappingProjection.MappingProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a>.</li>
-</ul>
-<ul class="simple">
-<li><strong>Matrix keyword</strong>.  This is used to specify a type of matrix without having to specify its individual values.
-Any of the <a class="reference internal" href="Keywords.html#Keywords.MatrixKeywords" title="Keywords.MatrixKeywords"><code class="xref any py py-class docutils literal"><span class="pre">matrix</span> <span class="pre">keywords</span></code></a> can be used.</li>
-</ul>
-<ul class="simple">
-<li><strong>Random matrix function</strong> (<a class="reference internal" href="Utilities.html#Utilities.random_matrix" title="Utilities.random_matrix"><code class="xref py py-func docutils literal"><span class="pre">random_matrix</span></code></a>).  This is a convenience function
-that provides more flexibility than <a class="reference internal" href="Keywords.html#Keywords.MatrixKeywords.RANDOM_CONNECTIVITY_MATRIX" title="Keywords.MatrixKeywords.RANDOM_CONNECTIVITY_MATRIX"><code class="xref any py py-attr docutils literal"><span class="pre">RANDOM_CONNECTIVITY_MATRIX</span></code></a>.  It generates a random matrix sized for a
-sender and receiver, with random numbers drawn from a uniform distribution within a specified range and with a
-specified offset.</li>
-</ul>
-<ul class="simple">
-<li><strong>Tuple</strong>.  This is used to specify a projection to the <a class="reference internal" href="ParameterState.html"><span class="doc">parameterState</span></a> for the matrix
-along with the <a class="reference internal" href="#MappingProjection.MappingProjection.matrix" title="MappingProjection.MappingProjection.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a>  itself. The tuple must have two items:
-the first can be any of the specifications described above;  the second must be a
-<a class="reference internal" href="Projection.html#projection-in-context-specification"><span class="std std-ref">projection specification</span></a>.</li>
-</ul>
-</div></blockquote>
-</div>
-<div class="section" id="structure">
-<span id="mapping-structure"></span><h2>Structure<a class="headerlink" href="#structure" title="Permalink to this headline">¶</a></h2>
-<p>In addition to its <code class="xref any docutils literal"><span class="pre">function</span></code>, MappingProjections use the following two parameters:</p>
-<ul id="mapping-matrix">
-<li><p class="first"><a class="reference internal" href="#MappingProjection.MappingProjection.matrix" title="MappingProjection.MappingProjection.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a></p>
-<p>Used by the MappingProjection&#8217;s <code class="xref any docutils literal"><span class="pre">function</span></code> to carry out a matrix transformation of its
-input, that is then provided to its <a class="reference internal" href="#MappingProjection.MappingProjection.receiver" title="MappingProjection.MappingProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a>.  It can be specified using a number of
-different formats, as described <a class="reference internal" href="#mapping-matrix-specification"><span class="std std-ref">above</span></a>.</p>
-</li>
-</ul>
-<ul id="mapping-parameter-modulation-operation">
-<li><p class="first"><a class="reference internal" href="#MappingProjection.MappingProjection.param_modulation_operation" title="MappingProjection.MappingProjection.param_modulation_operation"><code class="xref any py py-attr docutils literal"><span class="pre">param_modulation_operation</span></code></a></p>
-<p>Used to determine how the value of any projections to the <a class="reference internal" href="ParameterState.html"><span class="doc">parameterState</span></a> for the
-<a class="reference internal" href="#MappingProjection.MappingProjection.matrix" title="MappingProjection.MappingProjection.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a> influence it.  For example, this is used for a <a class="reference internal" href="LearningProjection.html"><span class="doc">LearningProjection</span></a> to apply
-weight changes to the <a class="reference internal" href="#MappingProjection.MappingProjection.matrix" title="MappingProjection.MappingProjection.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a> during learning. The
-<code class="xref std std-keyword docutils literal"><span class="pre">param_modulation_operation</span></code> attribute must be assigned a value of <a class="reference internal" href="Utilities.html#Utilities.ModulationOperation" title="Utilities.ModulationOperation"><code class="xref any py py-class docutils literal"><span class="pre">ModulationOperation</span></code></a> and the operation
-is always applied in an element-wise (Hadamard) manner. The default operation is ADD.</p>
-</li>
-</ul>
-</div>
-<div class="section" id="execution">
-<span id="projection-execution"></span><h2>Execution<a class="headerlink" href="#execution" title="Permalink to this headline">¶</a></h2>
-<p>A MappingProjection uses its <code class="xref any docutils literal"><span class="pre">function</span></code> and <code class="xref any docutils literal"><span class="pre">matrix</span></code>, and assign this as the variable for its
-<code class="xref any docutils literal"><span class="pre">receiver</span></code>.  When executed, updating its parameterStates will cause in turn update
-the its <code class="xref any docutils literal"><span class="pre">matrix</span> <span class="pre">&lt;MappingProjetion.matrix&gt;</span> <span class="pre">parameter</span> <span class="pre">based</span> <span class="pre">on</span> <span class="pre">any</span> <span class="pre">projections</span> <span class="pre">it</span> <span class="pre">receives</span> <span class="pre">(e.g.,</span> <span class="pre">a</span> <span class="pre">`LearningProjection</span></code>).
-This will bring into effect any changes that occurred during the previous execution (e.g., due to learning).
-Because of <span class="xref std std-ref">Lazy Evaluation</span>, those changes will only take effect after the current execution (as a
-consequence, inspecting <a class="reference internal" href="#MappingProjection.MappingProjection.matrix" title="MappingProjection.MappingProjection.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a> will not show the effects of projections to its
-parameterState until the MappingProjection has been executed).</p>
-</div>
-<div class="section" id="class-reference">
-<span id="projection-class-reference"></span><h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalink to this headline">¶</a></h2>
-<dl class="class">
-<dt id="MappingProjection.MappingProjection">
-<em class="property">class </em><code class="descclassname">MappingProjection.</code><code class="descname">MappingProjection</code><span class="sig-paren">(</span><em>sender=None</em>, <em>receiver=None</em>, <em>matrix=DEFAULT_MATRIX</em>, <em>param_modulation_operation=ModulationOperation.ADD</em>, <em>params=None</em>, <em>name=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#MappingProjection.MappingProjection" title="Permalink to this definition">¶</a></dt>
-<dd><p>Implements a projection that transmits the output of one mechanism to the input of another.</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>sender</strong> (<em>Optional[OutputState or Mechanism]</em>) &#8211; specifies the source of the projection&#8217;s input. If a mechanism is specified, its
-<a class="reference internal" href="OutputState.html#outputstate-primary"><span class="std std-ref">primary outputState</span></a> will be used. If it is not specified, it will be assigned in
-the context in which the projection is used.</li>
-<li><strong>receiver</strong> (<em>Optional[InputState or Mechanism]</em>) &#8211; specifies the destination of the projection&#8217;s output.  If a mechanism is specified, its
-<a class="reference internal" href="Mechanism.html#mechanism-inputstates"><span class="std std-ref">primary inputState</span></a> will be used. If it is not specified, it will be assigned in
-the context in which the projection is used.</li>
-<li><strong>matrix</strong> (<em>list, np.ndarray, np.matrix, function or keyword : default DEFAULT_MATRIX</em>) &#8211; the matrix used by <code class="xref any docutils literal"><span class="pre">function</span></code> (default: <a class="reference internal" href="Function.html#Function.LinearCombination" title="Function.LinearCombination"><code class="xref any py py-class docutils literal"><span class="pre">LinearCombination</span></code></a>) to transform the
-value of the <a class="reference internal" href="#MappingProjection.MappingProjection.sender" title="MappingProjection.MappingProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a>.</li>
-<li><strong>param_modulation_operation</strong> (<em>ModulationOperation : default ModulationOperation.ADD</em>) &#8211; specifies the operation used to combine the value of any projections to the matrix&#8217;s parameterState with the
-<a class="reference internal" href="#MappingProjection.MappingProjection.matrix" title="MappingProjection.MappingProjection.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a> itself.  Most commonly used with <a class="reference internal" href="LearningProjection.html"><span class="doc">LearningProjections</span></a>.</li>
-<li><strong>params</strong> (<em>Optional[Dict[param keyword, param value]]</em>) &#8211; a <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> that can be used to specify the parameters for
-the projection, its function, and/or a custom function and its parameters. By default, it contains an entry for
-the projection&#8217;s default assignment (<a class="reference internal" href="Function.html#Function.LinearCombination" title="Function.LinearCombination"><code class="xref any py py-class docutils literal"><span class="pre">LinearCombination</span></code></a>).  Values specified for parameters in the dictionary
-override any assigned to those parameters in arguments of the constructor.</li>
-<li><strong>name</strong> (<em>str : default MappingProjection-&lt;index&gt;</em>) &#8211; a string used for the name of the MappingProjection.
-If not is specified, a default is assigned by <code class="xref any docutils literal"><span class="pre">ProjectionRegistry</span></code>
-(see <code class="xref any docutils literal"><span class="pre">Registry</span></code> for conventions used in naming, including for default and duplicate names).</li>
-<li><strong>prefs</strong> (<em>Optional[PreferenceSet or specification dict : Projection.classPreferences]</em>) &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for the MappingProjection.
-If it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
-(see <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for details).</li>
-</ul>
-</td>
-</tr>
-</tbody>
-</table>
-<dl class="attribute">
-<dt id="MappingProjection.MappingProjection.componentType">
-<code class="descname">componentType</code><a class="headerlink" href="#MappingProjection.MappingProjection.componentType" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>MAPPING_PROJECTION</em></p>
-</dd></dl>
-
-<dl class="attribute">
-<dt id="MappingProjection.MappingProjection.sender">
-<code class="descname">sender</code><a class="headerlink" href="#MappingProjection.MappingProjection.sender" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>OutputState</em> &#8211; identifies the source of the projection&#8217;s input.</p>
-</dd></dl>
-
-<dl class="attribute">
-<dt id="MappingProjection.MappingProjection.receiver">
-<code class="descname">receiver</code><a class="headerlink" href="#MappingProjection.MappingProjection.receiver" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>InputState</em> &#8211; identifies the destination of the projection.</p>
-</dd></dl>
-
-<dl class="attribute">
-<dt id="MappingProjection.MappingProjection.param_modulation_operation">
-<code class="descname">param_modulation_operation</code><a class="headerlink" href="#MappingProjection.MappingProjection.param_modulation_operation" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>ModulationOperation</em> &#8211; determines the operation used to combine the value of any projections to the matrix&#8217;s parameterState with the
-<a class="reference internal" href="#MappingProjection.MappingProjection.matrix" title="MappingProjection.MappingProjection.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a> itself.</p>
-</dd></dl>
-
-<dl class="attribute">
-<dt id="MappingProjection.MappingProjection.monitoringMechanism">
-<code class="descname">monitoringMechanism</code><a class="headerlink" href="#MappingProjection.MappingProjection.monitoringMechanism" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>MonitoringMechanism</em> &#8211; source of error signal for that determine changes to the <a class="reference internal" href="#MappingProjection.MappingProjection.matrix" title="MappingProjection.MappingProjection.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a> when
-<a class="reference internal" href="LearningProjection.html"><span class="doc">learning</span></a> is used.</p>
-</dd></dl>
-
-<dl class="attribute">
-<dt id="MappingProjection.MappingProjection.matrix">
-<code class="descname">matrix</code><a class="headerlink" href="#MappingProjection.MappingProjection.matrix" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>2d np.array</em> &#8211; matrix used by <code class="xref any docutils literal"><span class="pre">function</span></code> to transform input from the
-<a class="reference internal" href="#MappingProjection.MappingProjection.sender" title="MappingProjection.MappingProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a> to the value provided to the <a class="reference internal" href="#MappingProjection.MappingProjection.receiver" title="MappingProjection.MappingProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a>.</p>
-</dd></dl>
-
-<dl class="attribute">
-<dt id="MappingProjection.MappingProjection.has_learning_projection">
-<code class="descname">has_learning_projection</code><a class="headerlink" href="#MappingProjection.MappingProjection.has_learning_projection" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>bool : False</em> &#8211; identifies whether the MappingProjection&#8217;s <code class="xref any docutils literal"><span class="pre">MATRIX</span></code> <a class="reference internal" href="ParameterState.html"><span class="doc">parameterState</span></a> has been assigned a
-<a class="reference internal" href="LearningProjection.html"><span class="doc">LearningProjection</span></a>.</p>
-</dd></dl>
-
-<dl class="attribute">
-<dt id="MappingProjection.MappingProjection.name">
-<code class="descname">name</code><a class="headerlink" href="#MappingProjection.MappingProjection.name" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>str : default MappingProjection-&lt;index&gt;</em> &#8211; the name of the MappingProjection.
-Specified in the <a class="reference internal" href="#MappingProjection.MappingProjection.name" title="MappingProjection.MappingProjection.name"><code class="xref any py py-attr docutils literal"><span class="pre">name</span></code></a> argument of the constructor for the projection;
-if not is specified, a default is assigned by ProjectionRegistry
-(see <span class="xref doc">Registry</span> for conventions used in naming, including for default and duplicate names).</p>
-</dd></dl>
-
-<dl class="attribute">
-<dt id="MappingProjection.MappingProjection.prefs">
-<code class="descname">prefs</code><a class="headerlink" href="#MappingProjection.MappingProjection.prefs" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>PreferenceSet or specification dict : Projection.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for projection.
-Specified in the <a class="reference internal" href="#MappingProjection.MappingProjection.prefs" title="MappingProjection.MappingProjection.prefs"><code class="xref any py py-attr docutils literal"><span class="pre">prefs</span></code></a> argument of the constructor for the projection;
-if it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
-(see <span class="xref doc">PreferenceSet</span> for details).</p>
-</dd></dl>
-
-</dd></dl>
-
-</div>
+  <div class="section" id="mappingprojection">
+<h1>MappingProjection<a class="headerlink" href="#mappingprojection" title="Permalink to this headline">¶</a></h1>
 </div>
 
 
@@ -363,10 +168,10 @@
   
     <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
       
-        <a href="Run.html" class="btn btn-neutral float-right" title="Run" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
+        <a href="ModulatoryProjection.html" class="btn btn-neutral float-right" title="ModulatoryProjection" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
       
       
-        <a href="Projection.html" class="btn btn-neutral" title="Projection" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
+        <a href="PathwayProjection.html" class="btn btn-neutral" title="PathwayProjection" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
       
     </div>
   
diff --git a/docs/build/html/Mechanism.html b/docs/build/html/Mechanism.html
index 38344d7ef15..162483ec973 100644
--- a/docs/build/html/Mechanism.html
+++ b/docs/build/html/Mechanism.html
@@ -111,7 +111,7 @@
 <li class="toctree-l2"><a class="reference internal" href="#class-reference">Class Reference</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="State.html">States</a></li>
+<li class="toctree-l1"><a class="reference internal" href="State.html">State</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Projection.html">Projection</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Run.html">Run</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Component.html">Component</a></li>
@@ -172,31 +172,40 @@ <h1>Mechanism<a class="headerlink" href="#mechanism" title="Permalink to this he
 </div>
 <span class="target" id="module-Mechanism"></span><div class="section" id="overview">
 <span id="mechanism-overview"></span><h2>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">¶</a></h2>
-<p>A mechanism takes an input, transforms it in some way, and makes the result available as its output.  There are three
+<p>A mechanism takes an input, transforms it in some way, and makes the result available as its output.  There are two
 types of mechanisms in PsyNeuLink:</p>
 <blockquote>
 <div><ul class="simple">
-<li>A <code class="xref any docutils literal"><span class="pre">ProcessingMechanisms</span></code> aggregrates the input it receives from other mechanisms, and/or the input to the
-the <a class="reference internal" href="Process.html"><span class="doc">process</span></a> or <a class="reference internal" href="System.html"><span class="doc">system</span></a> to which it belongs, transforms it in some way, and provides the
-result as input to other mechanisms in the process or system, or as the output for the process or system
-itself.</li>
+<li><a class="reference internal" href="ProcessingMechanism.html"><span class="doc">ProcessingMechanisms</span></a> aggregrate the input they receive from other mechanisms, and/or the
+input to the the <a class="reference internal" href="Process.html"><span class="doc">process</span></a> or <a class="reference internal" href="System.html"><span class="doc">system</span></a> to which they belong, transform it in some way, and
+provide the result as input to other mechanisms in the process or system, or as the output for a process or
+system itself.  There are a variety of different types of ProcessingMechanism, that accept various forms of
+input and transform them in different ways (see <a class="reference internal" href="ProcessingMechanism.html"><span class="doc">ProcessingMechanisms</span></a> for a list).</li>
 </ul>
+<ul>
+<li><p class="first"><code class="xref any docutils literal"><span class="pre">AdpativeMechanisms</span></code> monitor the output of one or more other mechanisms, and use this
+to modulate the parameters of other mechanisms or projections.  There are three basic AdaptiveMechanisms:</p>
 <ul class="simple">
-<li>A <code class="xref any docutils literal"><span class="pre">MonitoringMechanism</span></code> monitors the output of one or more other mechanisms, receives training (target) values,
-and compares these to generate error signals used for <a class="reference internal" href="Process.html#process-learning"><span class="std std-ref">learning</span></a>).</li>
+<li><a class="reference internal" href="LearningMechanism.html"><span class="doc">LearningMechanisms</span></a> - these receive training (target) values, and compare them with the
+output of a mechanism to generate learning signals that are used to modify
+<code class="xref any docutils literal"><span class="pre">MappingProjection</span></code> (see <a class="reference internal" href="Process.html#process-learning"><span class="std std-ref">learning</span></a>).</li>
+<li><a class="reference internal" href="ControlMechanism.html"><span class="doc">ControlMechanisms</span></a> - these evaluate the output of a specified set of mechanisms, and
+generate control signals used to modify the parameters of those or other mechanisms.</li>
+<li><code class="xref any docutils literal"><span class="pre">GatingMechanisms</span></code> - these receive input, and use this to determine whether and how to
+modify the value of the inputState(s) and/or outputState(s) of other mechanisms.</li>
 </ul>
-<ul class="simple">
-<li>A <a class="reference internal" href="ControlMechanism.html"><span class="doc">Control Mechanism</span></a> evaluates the output of one or more other mechanisms in the system to which it belongs,
-and uses this to modify the parameters of those or other mechanisms in the system.</li>
+<p>Each type of AdaptiveMechanism is associated with a corresponding type of Projection (<a class="reference internal" href="LearningProjection.html"><span class="doc">LearningProjection</span></a>,
+<a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjection</span></a> and <a class="reference internal" href="GatingProjection.html"><span class="doc">GatingProjection</span></a>, respectively).</p>
+</li>
 </ul>
 </div></blockquote>
 <p>A mechanism is made up of four fundamental components: the function it uses to transform its input; and the states it
-uses to represent its input, processing parameters, and output.</p>
+uses to represent its input(s), parameter(s), and output(s).</p>
 </div>
 <div class="section" id="creating-a-mechanism">
 <span id="mechanism-creation"></span><h2>Creating a Mechanism<a class="headerlink" href="#creating-a-mechanism" title="Permalink to this headline">¶</a></h2>
 <p>Mechanisms can be created in several ways.  The simplest is to use the standard Python method of calling the
-constructor for the desired type of mechanism.  Alternatively, the <a class="reference internal" href="#Mechanism.mechanism" title="Mechanism.mechanism"><code class="xref py py-func docutils literal"><span class="pre">mechanism()</span></code></a> function can be used to
+constructor for the desired type of mechanism.  Alternatively, the <a class="reference internal" href="#Mechanism.mechanism" title="Mechanism.mechanism"><code class="xref any py py-func docutils literal"><span class="pre">mechanism()</span></code></a> function can be used to
 instantiate a specified type of mechanism or a default mechanism. Mechanisms can also be specified &#8220;in context,&#8221;
 for example in the <a class="reference internal" href="Process.html#Process.Process_Base.pathway" title="Process.Process_Base.pathway"><code class="xref any py py-attr docutils literal"><span class="pre">pathway</span></code></a> attribute of a process; the mechanism can be specified in either of the ways mentioned
 above, or using one of the following:</p>
@@ -244,11 +253,17 @@ <h1>Mechanism<a class="headerlink" href="#mechanism" title="Permalink to this he
 and to execute its <a class="reference internal" href="#mechanism-function"><span class="std std-ref">function</span></a>).  When a mechanism is created, it automatically creates the
 parameterStates it needs to represent its parameters, including those of its <a class="reference internal" href="#Mechanism.Mechanism_Base.function" title="Mechanism.Mechanism_Base.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>.
 It also creates any inputStates and outputStates required for the projections it has been assigned. InputStates and
-outputStates, and corresponding projections, can also be specified in the mechanism&#8217;s params dictionary, using entries
-with the keys INPUT_STATES and OUTPUT_STATES, respectively. The value of each entry can be the name of the state&#8217;s
-class (to create a default), an existing state, a specification dictionary for one, a value (used as the state&#8217;s
-<code class="docutils literal"><span class="pre">variable</span></code>) or a list containing of any of these to create multiple states (see <a class="reference internal" href="InputState.html#inputstate-creation"><span class="std std-ref">InputStates</span></a>
-and <a class="reference internal" href="OutputState.html#outputstates-creation"><span class="std std-ref">OutputStates</span></a> for details).</p>
+outputStates, and corresponding projections, can also be specified in <strong>input_states</strong> and <strong>output_state</strong> arguments
+of the mechanism&#8217;s constructor, or in its params dictionary using entries with the keys INPUT_STATES and OUTPUT_STATES,
+respectively. The value of each entry can be the name of an existing state, a specification dictionary for one, a value
+(used as the state&#8217;s <code class="docutils literal"><span class="pre">variable</span></code>), a string (used to create a default state with that name), or a list containing of
+any of these to create multiple states (see <a class="reference internal" href="InputState.html#inputstate-creation"><span class="std std-ref">InputStates</span></a> and
+<a class="reference internal" href="OutputState.html#outputstates-creation"><span class="std std-ref">OutputStates</span></a> for details).  The following is an example that creates an instance of a
+TransferMechanism with a default inputState named &#8220;MY_INPUT&#8221; and three
+<a class="reference internal" href="OutputState.html#outputstate-specification"><span class="std std-ref">pre-specified types of outputStates</span></a>:</p>
+<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">my_mech</span> <span class="o">=</span> <span class="n">TransferMechanism</span><span class="p">(</span><span class="n">input_states</span><span class="p">:[</span><span class="s1">&#39;MY_INPUT&#39;</span><span class="p">],</span> <span class="n">output_states</span><span class="p">:[</span><span class="n">RESULT</span><span class="p">,</span> <span class="n">MEAN</span><span class="p">,</span> <span class="n">VARIANCE</span><span class="p">])</span>
+</pre></div>
+</div>
 </div>
 <div class="section" id="structure">
 <span id="mechanism-structure"></span><h2>Structure<a class="headerlink" href="#structure" title="Permalink to this headline">¶</a></h2>
@@ -258,21 +273,21 @@ <h1>Mechanism<a class="headerlink" href="#mechanism" title="Permalink to this he
 specified by the mechanism&#8217;s <a class="reference internal" href="#Mechanism.Mechanism_Base.function" title="Mechanism.Mechanism_Base.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> attribute.  Every type of mechanism has at least one
 (primary) function, and some have additional (auxiliary) ones (for example, the <code class="xref any docutils literal"><span class="pre">EVCMechanism</span></code>).
 Mechanism functions are generally from the PsyNeuLink <a class="reference internal" href="Function.html"><span class="doc">Functions</span></a> class.  Most mechanisms allow their function to be
-specified, using the <a class="reference internal" href="Function.html#Function.BogaczEtAl.function" title="Function.BogaczEtAl.function"><code class="xref any py py-meth docutils literal"><span class="pre">function</span></code></a> argument of the mechanism&#8217;s contructor.  The function can be specified using the name of
+specified, using the <a class="reference internal" href="Function.html#Function.Reduce.function" title="Function.Reduce.function"><code class="xref any py py-meth docutils literal"><span class="pre">function</span></code></a> argument of the mechanism&#8217;s contructor.  The function can be specified using the name of
 <a class="reference internal" href="Function.html"><span class="doc">Functions</span></a> class, or its constructor (including arguments that specify its parameters).  For example, the
 <code class="xref std std-keyword docutils literal"><span class="pre">function</span></code> of a <a class="reference internal" href="TransferMechanism.html"><span class="doc">TransferMechanism</span></a> can be specified to be the <a class="reference internal" href="Function.html#Function.Logistic" title="Function.Logistic"><code class="xref any py py-class docutils literal"><span class="pre">Logistic</span></code></a> function as follows:</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">my_mechanism</span> <span class="o">=</span> <span class="n">TransferMechanism</span><span class="p">(</span><span class="n">function</span><span class="o">=</span><span class="n">Logistic</span><span class="p">(</span><span class="n">gain</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">bias</span><span class="o">=-</span><span class="mi">4</span><span class="p">))</span>
 </pre></div>
 </div>
-<p>Notice that the parameters of the <code class="xref std std-keyword docutils literal"><span class="pre">function</span></code> (in this case, <a class="reference internal" href="Function.html#Function.SoftMax.gain" title="Function.SoftMax.gain"><code class="xref any py py-attr docutils literal"><span class="pre">gain</span></code></a> and <a class="reference internal" href="Function.html#Function.BogaczEtAl.bias" title="Function.BogaczEtAl.bias"><code class="xref any py py-attr docutils literal"><span class="pre">bias</span></code></a>) can be specified by including
+<p>Notice that the parameters of the <code class="xref std std-keyword docutils literal"><span class="pre">function</span></code> (in this case, <a class="reference internal" href="Function.html#Function.Logistic.gain" title="Function.Logistic.gain"><code class="xref any py py-attr docutils literal"><span class="pre">gain</span></code></a> and <a class="reference internal" href="Function.html#Function.Logistic.bias" title="Function.Logistic.bias"><code class="xref any py py-attr docutils literal"><span class="pre">bias</span></code></a>) can be specified by including
 them in its constructor.  Some mechanisms support only a single function.  In that case, the <code class="xref std std-keyword docutils literal"><span class="pre">function</span></code>
 argument is not available in the mechanism&#8217;s constructor, but it does include arguments for the function&#8217;s
 parameters.  For example, the <code class="xref std std-keyword docutils literal"><span class="pre">function</span></code> of a <code class="xref any docutils literal"><span class="pre">ComparatorMechanism</span></code> is always the <a class="reference internal" href="Function.html#Function.LinearCombination" title="Function.LinearCombination"><code class="xref any py py-class docutils literal"><span class="pre">LinearCombination</span></code></a> function,
 so its constructor does not have a <code class="xref std std-keyword docutils literal"><span class="pre">function</span></code> argument.  However, it does have a <code class="xref any docutils literal"><span class="pre">comparison_operation</span></code>
-argument, that is used to set the LinearCombination function&#8217;s <a class="reference internal" href="Function.html#Function.LinearCombination.operation" title="Function.LinearCombination.operation"><code class="xref any py py-attr docutils literal"><span class="pre">operation</span></code></a> parameter.</p>
+argument, that is used to set the LinearCombination function&#8217;s <a class="reference internal" href="Function.html#Function.Reduce.operation" title="Function.Reduce.operation"><code class="xref any py py-attr docutils literal"><span class="pre">operation</span></code></a> parameter.</p>
 <p>For mechanisms that offer a selection of functions, if all of the functions use the same parameters then those
 parameters can also be specified as entries in a <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a>
-used for the <a class="reference internal" href="Function.html#Function.NormalDist.params" title="Function.NormalDist.params"><code class="xref any py py-attr docutils literal"><span class="pre">params</span></code></a> argument of the mechanism&#8217;s constructor;  in such cases, values specified in the parameter
+used for the <a class="reference internal" href="Function.html#Function.Distance.params" title="Function.Distance.params"><code class="xref any py py-attr docutils literal"><span class="pre">params</span></code></a> argument of the mechanism&#8217;s constructor;  in such cases, values specified in the parameter
 dictionary will override any specified within the constructor for the function itself (see <a class="reference internal" href="DDM.html#ddm-parameters"><span class="std std-ref">DDM Parameters</span></a> for an
 example). The parameters of a mechanism&#8217;s primary function (i.e., assigned to is <a class="reference internal" href="#Mechanism.Mechanism_Base.function" title="Mechanism.Mechanism_Base.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>
 attribute) are assigned to a dictionary in the mechanism&#8217;s <a class="reference internal" href="#Mechanism.Mechanism_Base.function_params" title="Mechanism.Mechanism_Base.function_params"><code class="xref any py py-attr docutils literal"><span class="pre">function_params</span></code></a>
@@ -284,11 +299,11 @@ <h1>Mechanism<a class="headerlink" href="#mechanism" title="Permalink to this he
 primary function, its <a class="reference internal" href="#Mechanism.Mechanism_Base.function" title="Mechanism.Mechanism_Base.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> attribute).</p>
 <p>The input to a mechanism&#8217;s <a class="reference internal" href="#Mechanism.Mechanism_Base.function" title="Mechanism.Mechanism_Base.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> is provided by the mechanism&#8217;s
 <a class="reference internal" href="#Mechanism.Mechanism_Base.variable" title="Mechanism.Mechanism_Base.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> attribute.  This is a 2d array with one item for each of the mechanism&#8217;s
-<code class="xref any docutils literal"><span class="pre">inputStates</span> <span class="pre">&lt;Mechanism_InputStates&gt;.</span>&#160; <span class="pre">The</span> <span class="pre">result</span> <span class="pre">of</span> <span class="pre">the</span> <span class="pre">:keyword:`function</span></code> is placed in the mechanism&#8217;s
+<code class="xref any docutils literal"><span class="pre">input_states</span> <span class="pre">&lt;Mechanism_InputStates&gt;.</span>&#160; <span class="pre">The</span> <span class="pre">result</span> <span class="pre">of</span> <span class="pre">the</span> <span class="pre">:keyword:`function</span></code> is placed in the mechanism&#8217;s
 <a class="reference internal" href="#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> attribute, which is also a 2d array with one or more items.  The
 mechanism&#8217;s <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code> is used by its <a class="reference internal" href="#mechanism-outputstates"><span class="std std-ref">outputStates</span></a> to generate their <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code>
 attributes, each of which is assigned as an item of the list in the mechanism&#8217;s
-<a class="reference internal" href="#Mechanism.Mechanism_Base.outputValue" title="Mechanism.Mechanism_Base.outputValue"><code class="xref any py py-attr docutils literal"><span class="pre">outputValue</span></code></a> attribute.</p>
+<a class="reference internal" href="#Mechanism.Mechanism_Base.output_values" title="Mechanism.Mechanism_Base.output_values"><code class="xref any py py-attr docutils literal"><span class="pre">output_values</span></code></a> attribute.</p>
 <div class="admonition note">
 <p class="first admonition-title">Note</p>
 <p class="last">The input to a mechanism is not necessarily the same as the input to its <a class="reference internal" href="#Mechanism.Mechanism_Base.function" title="Mechanism.Mechanism_Base.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>.
@@ -296,7 +311,7 @@ <h1>Mechanism<a class="headerlink" href="#mechanism" title="Permalink to this he
 <a class="reference internal" href="#Mechanism.Mechanism_Base" title="Mechanism.Mechanism_Base"><code class="xref any py py-class docutils literal"><span class="pre">variable</span></code></a> attribute, which is used as the input to its <a class="reference internal" href="#Mechanism.Mechanism_Base.function" title="Mechanism.Mechanism_Base.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>.
 Similarly, the result of a mechanism&#8217;s function is not necessarily the same as the mechanism&#8217;s output.  The result
 of the <a class="reference internal" href="#Mechanism.Mechanism_Base.function" title="Mechanism.Mechanism_Base.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> is assigned to the mechanism&#8217;s  <a class="reference internal" href="#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> attribute,
-which is then used by its outputStates to assign items to its <a class="reference internal" href="#Mechanism.Mechanism_Base.outputValue" title="Mechanism.Mechanism_Base.outputValue"><code class="xref any py py-attr docutils literal"><span class="pre">outputValue</span></code></a> attribute.</p>
+which is then used by its outputStates to assign items to its <a class="reference internal" href="#Mechanism.Mechanism_Base.output_values" title="Mechanism.Mechanism_Base.output_values"><code class="xref any py py-attr docutils literal"><span class="pre">output_values</span></code></a> attribute.</p>
 </div>
 </div>
 <div class="section" id="states">
@@ -310,61 +325,62 @@ <h1>Mechanism<a class="headerlink" href="#mechanism" title="Permalink to this he
 <span id="mechanism-inputstates"></span><h4>InputStates<a class="headerlink" href="#inputstates" title="Permalink to this headline">¶</a></h4>
 <p>These receive and represent the input to a mechanism. A mechanism usually has only one (<strong>primary</strong>) <code class="xref any docutils literal"><span class="pre">inputState</span></code> attribute.  However some mechanisms have
 more  than one inputState. For example, a <code class="xref any docutils literal"><span class="pre">ComparatorMechanism</span></code> has one inputState for its <code class="xref any docutils literal"><span class="pre">sample</span></code> and another for its
-<code class="xref any docutils literal"><span class="pre">target</span></code> input. If a mechanism has more than one inputState, they are identified in an OrderedDict in the mechanism&#8217;s
-<a class="reference internal" href="#Mechanism.Mechanism_Base.inputStates" title="Mechanism.Mechanism_Base.inputStates"><code class="xref any py py-attr docutils literal"><span class="pre">inputStates</span></code></a> attribute (note the plural).</p>
+<code class="xref any docutils literal"><span class="pre">target</span></code> input. If a mechanism has more than one inputState, they are identified in a ContentAddressableList in the
+mechanism&#8217;s <a class="reference internal" href="#Mechanism.Mechanism_Base.input_states" title="Mechanism.Mechanism_Base.input_states"><code class="xref any py py-attr docutils literal"><span class="pre">input_states</span></code></a> attribute (note the plural).  A specific inputState in the
+list can be accessed by using its name as the index for the list (e.g., my_mechanism[&#8216;input state name&#8217;].</p>
 <p>Each inputState of a mechanism can receive one or more projections from other mechanisms.  If the mechanism is an
-<a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> mechanism of a process, it also receives a projection from the <a class="reference internal" href="Process.html#process-input-and-ouput"><span class="std std-ref">ProcessInputState</span></a>
+<a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> mechanism of a process, it also receives a projection from the <code class="xref any docutils literal"><span class="pre">ProcessInputState</span></code>
 for that process. Each inputState&#8217;s <code class="xref std std-keyword docutils literal"><span class="pre">function</span></code> aggregates the values received
 from its projections (usually by summing them), and assigns the result to the inputState&#8217;s <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code> attribute.</p>
 <p id="mechanism-variable">The value of each inputState for the mechanism is assigned as the value of an item of the mechanism&#8217;s
 <a class="reference internal" href="#Mechanism.Mechanism_Base.variable" title="Mechanism.Mechanism_Base.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> attribute (a 2d np.array), as well as in a corresponding item of its
-<a class="reference internal" href="#Mechanism.Mechanism_Base.inputValue" title="Mechanism.Mechanism_Base.inputValue"><code class="xref any py py-attr docutils literal"><span class="pre">inputValue</span></code></a> attribute (a list).  The <code class="xref std std-keyword docutils literal"><span class="pre">variable</span></code> provides the input to the
-mechanism&#8217;s <a class="reference internal" href="#Mechanism.Mechanism_Base.function" title="Mechanism.Mechanism_Base.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>, while its <a href="#id1"><span class="problematic" id="id2">:kewyord:`inputValue`</span></a> provides a more convenient way
+<a class="reference internal" href="#Mechanism.Mechanism_Base.input_value" title="Mechanism.Mechanism_Base.input_value"><code class="xref any py py-attr docutils literal"><span class="pre">input_value</span></code></a> attribute (a list).  The <code class="xref std std-keyword docutils literal"><span class="pre">variable</span></code> provides the input to the
+mechanism&#8217;s <a class="reference internal" href="#Mechanism.Mechanism_Base.function" title="Mechanism.Mechanism_Base.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>, while its <a href="#id1"><span class="problematic" id="id2">:kewyord:`input_value`</span></a> provides a more convenient way
 of accessing its individual items.</p>
 </div>
 <div class="section" id="parameterstates">
 <span id="mechanism-parameterstates"></span><h4>ParameterStates<a class="headerlink" href="#parameterstates" title="Permalink to this headline">¶</a></h4>
-<p>These represent the parameters that control the operation of a mechanism, including the parameters of its
+<p>These represent the parameters that determine the operation of a mechanism, including the parameters of its
 <code class="xref std std-keyword docutils literal"><span class="pre">function</span></code>.  One <a class="reference internal" href="ParameterState.html"><span class="doc">parameterState</span></a> is assigned to each of the parameters of the mechanism
 and/or its <code class="xref std std-keyword docutils literal"><span class="pre">function</span></code> (these correspond to the arguments in their constructors).  Like other states,
 parameterStates can receive projections. Typically these are from the <a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjections</span></a>
 of a <a class="reference internal" href="ControlMechanism.html"><span class="doc">Control Mechanism</span></a> that is used to modify parameter values in response to the outcome(s) of
 processing.  A parameter value (and the value of its associated parameterState) can be specified when a mechanism or
 its function is first created  using the corresponding argument in the object&#8217;s constructor.  Parameter values can
-also be assigned later, using the mechanism&#8217;s <code class="xref any docutils literal"><span class="pre">assign_param</span></code> method (the safest means) or by direct assignment to the
-corresponding attribute (see <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">Specifying Parameters</span></a>).</p>
+also be assigned later, by direct assignment of a value to the corresponding attribute, or by using the mechanism&#8217;s
+<code class="xref any docutils literal"><span class="pre">assign_param</span></code> method (the safest means;  see <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">Specifying Parameters</span></a>).  All of the mechanism&#8217;s
+parameters are list in a dict in its <code class="xref any docutils literal"><span class="pre">user_params</span></code> attribute; the dict contains a <a class="reference internal" href="#Mechanism.Mechanism_Base.function_params" title="Mechanism.Mechanism_Base.function_params"><code class="xref any py py-attr docutils literal"><span class="pre">function_params</span></code></a> entry which
+in turn contains a dict of the parameters for the mechanism&#8217;s <code class="xref any docutils literal"><span class="pre">function</span></code>.</p>
 </div>
 <div class="section" id="outputstates">
 <span id="mechanism-outputstates"></span><h4>OutputStates<a class="headerlink" href="#outputstates" title="Permalink to this headline">¶</a></h4>
 <p>These represent the output(s) of a mechanism. A mechanism can have several <a class="reference internal" href="OutputState.html"><span class="doc">outputStates</span></a>, and each can
 send projections that transmit its value to other mechanisms and/or the output of the process or system to which the
-mechanism belongs. Similar to inputStates, the ** <em>primary</em> (first or only) outputState is assigned to the mechanism&#8217;s
-<a class="reference internal" href="#Mechanism.Mechanism_Base.outputState" title="Mechanism.Mechanism_Base.outputState"><code class="xref any py py-attr docutils literal"><span class="pre">outputState</span></code></a> attribute, while all of its outputStates (including the primary one) are
-identified in an OrderedDict in its <a class="reference internal" href="#Mechanism.Mechanism_Base.outputStates" title="Mechanism.Mechanism_Base.outputStates"><code class="xref any py py-attr docutils literal"><span class="pre">outputStates</span></code></a> attribute (note the plural).  The
-key for each entry in the <code class="xref std std-keyword docutils literal"><span class="pre">outputStates</span></code> dict is the name of an outputState, and the value is the outputState
-itself.  Every mechanism has at least one (&#8220;primary&#8221;) outputState, the <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code> of which is assigned the
-first item of the mechanism&#8217;s <a class="reference internal" href="#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> attribute (usually the direct output of the mechanism&#8217;s
-<a class="reference internal" href="#Mechanism.Mechanism_Base.function" title="Mechanism.Mechanism_Base.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>).  Other outputStates may be used for other purposes.  For example,
-some <a class="reference internal" href="ProcessingMechanism.html"><span class="doc">ProcessingMechanisms</span></a> (such as <a class="reference internal" href="TransferMechanism.html"><span class="doc">TransferMechanism</span></a>) use outputStates to represent
-values derived from the value of their <a class="reference internal" href="OutputState.html#outputstate-primary"><span class="std std-ref">primary outputState</span></a> (e.g., the mean and variance).
-<a class="reference internal" href="ControlMechanism.html"><span class="doc">ControlMechanisms</span></a> assign one outputState for each of their <a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjections</span></a>.  An outputState can be assigned to a particular item of the mechanism&#8217;s
-<a class="reference internal" href="#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> attribute using the outputState&#8217;s <a class="reference internal" href="index.html"><span class="doc">PsyNeuLink Documentation</span></a> parameter, and its <a class="reference internal" href="OutputState.html#OutputState.OutputState.calculate" title="OutputState.OutputState.calculate"><code class="xref any py py-attr docutils literal"><span class="pre">calculate</span></code></a> parameter can
-be used to modify that item before assigning it as the outputState&#8217;s <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code> (see <a class="reference internal" href="OutputState.html#outputstates-creation"><span class="std std-ref">Creating an OutputState</span></a>).
-The <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code> attributes of all of a mechanism&#8217;s outputStates  are assigned to the mechanism&#8217;s
-<a class="reference internal" href="#Mechanism.Mechanism_Base.outputValue" title="Mechanism.Mechanism_Base.outputValue"><code class="xref any py py-attr docutils literal"><span class="pre">outputValue</span></code></a> attribute (a list), in the same order in which they appear in mechanism&#8217;s
-<a class="reference internal" href="#Mechanism.Mechanism_Base.outputStates" title="Mechanism.Mechanism_Base.outputStates"><code class="xref any py py-attr docutils literal"><span class="pre">outputStates</span></code></a>  attribute.  Note that this is distinct from the mechanism&#8217;s <a class="reference internal" href="#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> attribute, which contains the full and unmodified results of its
-<a class="reference internal" href="#Mechanism.Mechanism_Base.function" title="Mechanism.Mechanism_Base.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>.</p>
+mechanism belongs.  Every mechanism has at least one outputState, referred to as its
+<a class="reference internal" href="OutputState.html#outputstate-primary"><span class="std std-ref">primary outputState</span></a>.  If outputStates are not explicitly specified for a mechanism, a primary
+outputState is automatically created and assigned to its <code class="xref any docutils literal"><span class="pre">outputState</span></code>
+attribute (note the singular), and also to the first entry of the mechanism&#8217;s <code class="xref any docutils literal"><span class="pre">outputStates</span></code> attribute (note the plural).  The <a class="reference internal" href="OutputState.html#OutputState.OutputState.value" title="OutputState.OutputState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> of the primary
+outputState is assigned as the first (and often only) item of the mechanism&#8217;s
+<code class="xref any docutils literal"><span class="pre">output_value</span></code>, which is the result of the mechanism`s
+<a class="reference internal" href="#Mechanism.Mechanism_Base.function" title="Mechanism.Mechanism_Base.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>.  Additional outputStates can be assigned to represent values derived
+from the result of the mechanism&#8217;s <code class="xref any docutils literal"><span class="pre">function</span></code>.  Standard outputStates are available for each
+type of mechanism, and custom ones can also be configured (see <a class="reference internal" href="OutputState.html#outputstate-specification"><span class="std std-ref">OutputState Specification</span></a>.
+These can be assigned in the <strong>output_states</strong> argument of the mechanism&#8217;s constructor.  All of the outputStates of a
+mechanism (including the primary one) are represented in its <code class="xref any docutils literal"><span class="pre">output_states</span></code> attribute
+(note the plural), that contains a ContentAddressableList of the outputStates.  A specific outputState in the list can
+be accessed by using its name as the index for the list (e.g., my_mechanism[&#8216;output state name&#8217;].  This can also be
+used to assign additional outputStates to the mechanism after it has been created.</p>
 </div>
 </div>
 <div class="section" id="mechanism-parameters">
 <span id="id3"></span><h3>Mechanism Parameters<a class="headerlink" href="#mechanism-parameters" title="Permalink to this headline">¶</a></h3>
 <p>Most mechanisms implement a standard set of parameters, that can be specified by direct reference to the corresponding
 attribute of the mechanisms (e.g., myMechanism.attribute), in a
-<a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> assigned to <a class="reference internal" href="Function.html#Function.NormalDist.params" title="Function.NormalDist.params"><code class="xref any py py-attr docutils literal"><span class="pre">params</span></code></a> argument in the mechanism&#8217;s
+<a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> assigned to <a class="reference internal" href="Function.html#Function.Distance.params" title="Function.Distance.params"><code class="xref any py py-attr docutils literal"><span class="pre">params</span></code></a> argument in the mechanism&#8217;s
 constructor, or with the mechanism&#8217;s <code class="xref any docutils literal"><span class="pre">assign_params</span></code> method, using the following keywords:</p>
 <blockquote>
 <div><ul class="simple">
-<li>INPUT_STATES - specifies specialized inputStates required by a mechanism subclass
+<li>INPUT_STATES - specifies specialized input_states required by a mechanism subclass
 (see <a class="reference internal" href="InputState.html#inputstate-creation"><span class="std std-ref">inputState specification</span></a> for details of specification).</li>
 </ul>
 <ul class="simple">
@@ -395,9 +411,8 @@ <h1>Mechanism<a class="headerlink" href="#mechanism" title="Permalink to this he
 <div class="line"><br /></div>
 </div>
 <ul class="simple">
-<li>a tuple with exactly two items: the parameter value and a projection type specifying either a
-<a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjection</span></a> or a <a class="reference internal" href="LearningProjection.html"><span class="doc">LearningProjection</span></a> (a <code class="xref any docutils literal"><span class="pre">ParamValueProjection</span></code> namedtuple can be used for
-clarity).</li>
+<li>a tuple with exactly two items: the parameter value and a projection type specifying a
+<a class="reference internal" href="LearningProjection.html"><span class="doc">LearningProjection</span></a>, <a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjection</span></a> or <a class="reference internal" href="GatingProjection.html"><span class="doc">GatingProjection</span></a>.</li>
 </ul>
 <div class="line-block">
 <div class="line"><br /></div>
@@ -416,7 +431,7 @@ <h1>Mechanism<a class="headerlink" href="#mechanism" title="Permalink to this he
 </ul>
 <ul class="simple">
 <li>MONITOR_FOR_CONTROL - specifies which of the mechanism&#8217;s outputStates is monitored by the <a class="reference internal" href="System.html#System.System_Base.controller" title="System.System_Base.controller"><code class="xref any py py-attr docutils literal"><span class="pre">controller</span></code></a>
-for the system to which the mechanism belongs (see <a class="reference internal" href="ControlMechanism.html#controlmechanism-monitored-outputstates"><span class="std std-ref">specifying monitored outputStates</span></a> for details of specification).</li>
+for the system to which the mechanism belongs (see <span class="xref std std-ref">specifying monitored outputStates</span> for details of specification).</li>
 </ul>
 <ul class="simple">
 <li>MONITOR_FOR_LEARNING - specifies which of the mechanism&#8217;s outputStates is used for learning
@@ -432,11 +447,12 @@ <h1>Mechanism<a class="headerlink" href="#mechanism" title="Permalink to this he
 <span id="mechanism-role-in-processes-and-systems"></span><h3>Role in Processes and Systems<a class="headerlink" href="#role-in-processes-and-systems" title="Permalink to this headline">¶</a></h3>
 <p>Mechanisms that are part of one or more processes are assigned designations that indicate the
 <a class="reference internal" href="Process.html#process-mechanisms"><span class="std std-ref">role</span></a> they play in those processes, and similarly for <a class="reference internal" href="System.html#system-mechanisms"><span class="std std-ref">role</span></a> they play in
-any systems to which they belong. These designations are listed in the mechanism&#8217;s <a class="reference internal" href="#Mechanism.Mechanism_Base.processes" title="Mechanism.Mechanism_Base.processes"><code class="xref any py py-attr docutils literal"><span class="pre">processes</span></code></a> and <a class="reference internal" href="#Mechanism.Mechanism_Base.systems" title="Mechanism.Mechanism_Base.systems"><code class="xref any py py-attr docutils literal"><span class="pre">systems</span></code></a>
-attributes, respectively.  Any mechanism designated as <a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> receives a projection to its primary inputState from
-the process(es) to which it belongs.  Accordingly, when the process (or system of which the process is a part) is
-executed, those mechainsms receive the input provided to the process (or system).  The <a class="reference internal" href="#Mechanism.Mechanism_Base.outputValue" title="Mechanism.Mechanism_Base.outputValue"><code class="xref any py py-attr docutils literal"><span class="pre">outputValue</span></code></a> of any mechanism designated as the <a class="reference internal" href="Keywords.html#Keywords.Keywords.TERMINAL" title="Keywords.Keywords.TERMINAL"><code class="xref any py py-attr docutils literal"><span class="pre">TERMINAL</span></code></a> mechanism for a process is assigned as
-the <a class="reference internal" href="Function.html#Function.SoftMax.output" title="Function.SoftMax.output"><code class="xref any py py-attr docutils literal"><span class="pre">output</span></code></a> of that process, and similarly for systems to which it belongs.</p>
+any systems to which they belong. These designations are listed in the mechanism&#8217;s <a class="reference internal" href="System.html#System.System_Base.processes" title="System.System_Base.processes"><code class="xref any py py-attr docutils literal"><span class="pre">processes</span></code></a> and <a class="reference internal" href="Process.html#Process.Process_Base.systems" title="Process.Process_Base.systems"><code class="xref any py py-attr docutils literal"><span class="pre">systems</span></code></a>
+attributes, respectively.  Any mechanism designated as <a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> receives a projection to its
+<a class="reference internal" href="#mechanism-inputstates"><span class="std std-ref">primary inputState</span></a> from the process(es) to which it belongs.  Accordingly, when the process
+(or system of which the process is a part) is executed, those mechainsms receive the input provided to the process
+(or system).  The <a class="reference internal" href="#Mechanism.Mechanism_Base.output_values" title="Mechanism.Mechanism_Base.output_values"><code class="xref any py py-attr docutils literal"><span class="pre">output_values</span></code></a> of any mechanism designated as the <a class="reference internal" href="Keywords.html#Keywords.Keywords.TERMINAL" title="Keywords.Keywords.TERMINAL"><code class="xref any py py-attr docutils literal"><span class="pre">TERMINAL</span></code></a>
+mechanism for a process is assigned as the <a class="reference internal" href="Function.html#Function.SoftMax.output" title="Function.SoftMax.output"><code class="xref any py py-attr docutils literal"><span class="pre">output</span></code></a> of that process, and similarly for systems to which it belongs.</p>
 <div class="admonition note">
 <p class="first admonition-title">Note</p>
 <p class="last">A mechanism can be the <a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> or <a class="reference internal" href="Keywords.html#Keywords.Keywords.TERMINAL" title="Keywords.Keywords.TERMINAL"><code class="xref any py py-attr docutils literal"><span class="pre">TERMINAL</span></code></a> of a process but not of a system to which that
@@ -476,8 +492,9 @@ <h1>Mechanism<a class="headerlink" href="#mechanism" title="Permalink to this he
 specification of parameters for the  state or its function use the  standard format for <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionaries</span></a>.  Parameters for all of the state&#8217;s projections can be specified in an entry
 with the key PROJECTION_PARAMS, and a subdictionary that contains the parameter specifications;  parameters for
 projections of a particular type can be placed in an entry with a key specifying the type (MAPPING_PROJECTION_PARAMS,
-CONTROL_PROJECTION_PARAMS, or LEARNING_PROJECTION_PARAMS; and parameters can for a specific projection can be placed
-in an entry with a key specifying the name of the project and a dictionary with the specifications.</p>
+LEARNING_PROJECTION_PARAMS, CONTROL_PROJECTION_PARAMS, or GATING_PROJECTION_PARAMS; and parameters can for a specific
+projection can be placed in an entry with a key specifying the name of the project and a dictionary with the
+specifications.</p>
 </div>
 </div>
 <div class="section" id="class-reference">
@@ -521,7 +538,7 @@ <h1>Mechanism<a class="headerlink" href="#mechanism" title="Permalink to this he
 
 <dl class="class">
 <dt id="Mechanism.Mechanism_Base">
-<em class="property">class </em><code class="descclassname">Mechanism.</code><code class="descname">Mechanism_Base</code><span class="sig-paren">(</span><em>variable=None</em>, <em>params=None</em>, <em>name=None</em>, <em>prefs=None</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Mechanism.Mechanism_Base" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">Mechanism.</code><code class="descname">Mechanism_Base</code><span class="sig-paren">(</span><em>variable=None</em>, <em>input_states=None</em>, <em>output_states=None</em>, <em>params=None</em>, <em>name=None</em>, <em>prefs=None</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Mechanism.Mechanism_Base" title="Permalink to this definition">¶</a></dt>
 <dd><p>Abstract class for Mechanism</p>
 <div class="admonition note">
 <p class="first admonition-title">Note</p>
@@ -539,27 +556,27 @@ <h1>Mechanism<a class="headerlink" href="#mechanism" title="Permalink to this he
 </dd></dl>
 
 <dl class="attribute">
-<dt id="Mechanism.Mechanism_Base.inputState">
-<code class="descname">inputState</code><a class="headerlink" href="#Mechanism.Mechanism_Base.inputState" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>InputState : default default InputState</em> &#8211; primary <a class="reference internal" href="#mechanism-inputstates"><span class="std std-ref">inputState</span></a> for the mechanism;  same as first entry of its <a class="reference internal" href="#Mechanism.Mechanism_Base.inputStates" title="Mechanism.Mechanism_Base.inputStates"><code class="xref any py py-attr docutils literal"><span class="pre">inputStates</span></code></a> attribute.</p>
+<dt id="Mechanism.Mechanism_Base.input_state">
+<code class="descname">input_state</code><a class="headerlink" href="#Mechanism.Mechanism_Base.input_state" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>InputState : default default InputState</em> &#8211; primary <a class="reference internal" href="#mechanism-inputstates"><span class="std std-ref">inputState</span></a> for the mechanism;  same as first entry of its <a class="reference internal" href="#Mechanism.Mechanism_Base.input_states" title="Mechanism.Mechanism_Base.input_states"><code class="xref any py py-attr docutils literal"><span class="pre">input_states</span></code></a> attribute.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="Mechanism.Mechanism_Base.inputStates">
-<code class="descname">inputStates</code><a class="headerlink" href="#Mechanism.Mechanism_Base.inputStates" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>OrderedDict[str, InputState]</em> &#8211; a dictionary of the mechanism&#8217;s <a class="reference internal" href="#mechanism-inputstates"><span class="std std-ref">inputStates</span></a>.
-The key of each entry is the name of an inputState, and its value is the inputState.
-There is always at least one entry, which identifies the mechanism&#8217;s primary inputState
-(i.e., the one in the its <a class="reference internal" href="#Mechanism.Mechanism_Base.inputState" title="Mechanism.Mechanism_Base.inputState"><code class="xref any py py-attr docutils literal"><span class="pre">inputState</span></code></a> attribute).</p>
+<dt id="Mechanism.Mechanism_Base.input_states">
+<code class="descname">input_states</code><a class="headerlink" href="#Mechanism.Mechanism_Base.input_states" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>OrderedDict[str, InputState]</em> &#8211; a dictionary of the mechanism&#8217;s <a class="reference internal" href="#mechanism-inputstates"><span class="std std-ref">input_states</span></a>.
+The key of each entry is the name of an inputState, and its value is the inputState.  There is always
+at least one entry, which identifies the mechanism&#8217;s <a class="reference internal" href="#mechanism-inputstates"><span class="std std-ref">primary inputState</span></a>
+(i.e., the one in the its <a class="reference internal" href="#Mechanism.Mechanism_Base.input_state" title="Mechanism.Mechanism_Base.input_state"><code class="xref any py py-attr docutils literal"><span class="pre">inputState</span></code></a> attribute).</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="Mechanism.Mechanism_Base.inputValue">
-<code class="descname">inputValue</code><a class="headerlink" href="#Mechanism.Mechanism_Base.inputValue" title="Permalink to this definition">¶</a></dt>
+<dt id="Mechanism.Mechanism_Base.input_value">
+<code class="descname">input_value</code><a class="headerlink" href="#Mechanism.Mechanism_Base.input_value" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>List[List or 1d np.array] : default variableInstanceDefault</em> &#8211; a list of values, one for each <a class="reference internal" href="#mechanism-inputstates"><span class="std std-ref">inputState</span></a> in the mechanism&#8217;s
-<a class="reference internal" href="#Mechanism.Mechanism_Base.inputStates" title="Mechanism.Mechanism_Base.inputStates"><code class="xref any py py-attr docutils literal"><span class="pre">inputStates</span></code></a> attribute.  The value of each item is the same as the corresponding
+<a class="reference internal" href="#Mechanism.Mechanism_Base.input_states" title="Mechanism.Mechanism_Base.input_states"><code class="xref any py py-attr docutils literal"><span class="pre">input_states</span></code></a> attribute.  The value of each item is the same as the corresponding
 item in the mechanism&#8217;s <a class="reference internal" href="#Mechanism.Mechanism_Base.variable" title="Mechanism.Mechanism_Base.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> attribute.  The latter is a 2d np.array;
-the :keyword:<a href="#id4"><span class="problematic" id="id5">`</span></a>inputValue attribute provides this information in a simpler list format.</p>
+the :keyword:<a href="#id4"><span class="problematic" id="id5">`</span></a>input_value attribute provides this information in a simpler list format.</p>
 </dd></dl>
 
 <dl class="attribute">
@@ -594,39 +611,39 @@ <h1>Mechanism<a class="headerlink" href="#mechanism" title="Permalink to this he
 <dt id="Mechanism.Mechanism_Base.value">
 <code class="descname">value</code><a class="headerlink" href="#Mechanism.Mechanism_Base.value" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>2d np.array : default None</em> &#8211; output of the mechanism&#8217;s <a class="reference internal" href="#Mechanism.Mechanism_Base.function" title="Mechanism.Mechanism_Base.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>.
-Note: this is not necessarily the same as the mechanism&#8217;s <a class="reference internal" href="#Mechanism.Mechanism_Base.outputValue" title="Mechanism.Mechanism_Base.outputValue"><code class="xref any py py-attr docutils literal"><span class="pre">outputValue</span></code></a> attribute,
-which lists the values of its <a class="reference internal" href="#Mechanism.Mechanism_Base.outputStates" title="Mechanism.Mechanism_Base.outputStates"><code class="xref any py py-attr docutils literal"><span class="pre">outputStates</span></code></a>.
+Note: this is not necessarily the same as the mechanism&#8217;s <a class="reference internal" href="#Mechanism.Mechanism_Base.output_values" title="Mechanism.Mechanism_Base.output_values"><code class="xref any py py-attr docutils literal"><span class="pre">output_values</span></code></a> attribute,
+which lists the values of its <code class="xref any docutils literal"><span class="pre">outputStates</span></code>.
 The keyword:<a class="reference internal" href="#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> is <code class="xref any docutils literal"><span class="pre">None</span></code> until the mechanism has been executed at least once.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="Mechanism.Mechanism_Base.outputState">
-<code class="descname">outputState</code><a class="headerlink" href="#Mechanism.Mechanism_Base.outputState" title="Permalink to this definition">¶</a></dt>
+<dt id="Mechanism.Mechanism_Base.output_state">
+<code class="descname">output_state</code><a class="headerlink" href="#Mechanism.Mechanism_Base.output_state" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>OutputState : default default OutputState</em> &#8211; primary <a class="reference internal" href="#mechanism-outputstates"><span class="std std-ref">outputState</span></a> for the mechanism;  same as first entry of its
-<a class="reference internal" href="#Mechanism.Mechanism_Base.outputStates" title="Mechanism.Mechanism_Base.outputStates"><code class="xref any py py-attr docutils literal"><span class="pre">outputStates</span></code></a> attribute.</p>
+<code class="xref any docutils literal"><span class="pre">outputStates</span></code> attribute.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="Mechanism.Mechanism_Base.outputStates">
-<code class="descname">outputStates</code><a class="headerlink" href="#Mechanism.Mechanism_Base.outputStates" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>OrderedDict[str, InputState]</em> &#8211; a dictionary of the mechanism&#8217;s <a class="reference internal" href="#mechanism-outputstates"><span class="std std-ref">outputStates</span></a>.
+<dt id="Mechanism.Mechanism_Base.output_states">
+<code class="descname">output_states</code><a class="headerlink" href="#Mechanism.Mechanism_Base.output_states" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>OrderedDict[str, OutputState]</em> &#8211; a dictionary of the mechanism&#8217;s <a class="reference internal" href="#mechanism-outputstates"><span class="std std-ref">outputStates</span></a>.
 The key of each entry is the name of an outputState, and its value is the outputState.  There is always
 at least one entry, which identifies the mechanism&#8217;s <a class="reference internal" href="OutputState.html#outputstate-primary"><span class="std std-ref">primary outputState</span></a>.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="Mechanism.Mechanism_Base.outputValue">
-<code class="descname">outputValue</code><a class="headerlink" href="#Mechanism.Mechanism_Base.outputValue" title="Permalink to this definition">¶</a></dt>
+<dt id="Mechanism.Mechanism_Base.output_values">
+<code class="descname">output_values</code><a class="headerlink" href="#Mechanism.Mechanism_Base.output_values" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>List[value] : default mechanism.function(variableInstanceDefault)</em> &#8211; a list of values, one for each <a class="reference internal" href="#mechanism-outputstates"><span class="std std-ref">outputState</span></a> in the mechanism&#8217;s
 <code class="xref std std-keyword docutils literal"><span class="pre">outputStates</span></code> attribute.</p>
 <div class="admonition note">
 <p class="first admonition-title">Note</p>
-<p class="last">The <code class="xref std std-keyword docutils literal"><span class="pre">outputValue</span></code> of a mechanism is not necessarily the same as its
+<p class="last">The <code class="xref std std-keyword docutils literal"><span class="pre">output_values</span></code> of a mechanism is not necessarily the same as its
 <a class="reference internal" href="#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> attribute, since the outputState&#8217;s
 <a class="reference internal" href="OutputState.html#OutputState.OutputState.function" title="OutputState.OutputState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> and/or its <code class="xref any docutils literal"><span class="pre">calculate</span></code>
 attribute may use the mechanism&#8217;s <a class="reference internal" href="#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> to generate a derived quantity for
 the <a class="reference internal" href="OutputState.html#OutputState.OutputState.value" title="OutputState.OutputState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> of the outputState and its corresponding item in the
-the mechanism&#8217;s <code class="xref std std-keyword docutils literal"><span class="pre">outputValue</span></code> attribute.</p>
+the mechanism&#8217;s <code class="xref std std-keyword docutils literal"><span class="pre">output_values</span></code> attribute.</p>
 </div>
 </dd></dl>
 
@@ -666,7 +683,7 @@ <h1>Mechanism<a class="headerlink" href="#mechanism" title="Permalink to this he
 <dt id="Mechanism.Mechanism_Base.name">
 <code class="descname">name</code><a class="headerlink" href="#Mechanism.Mechanism_Base.name" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>str : default &lt;Mechanism subclass&gt;-&lt;index&gt;</em> &#8211; the name of the mechanism.
-Specified in the <a class="reference internal" href="#Mechanism.Mechanism_Base.name" title="Mechanism.Mechanism_Base.name"><code class="xref any py py-attr docutils literal"><span class="pre">name</span></code></a> argument of the constructor for the mechanism;  if not is specified,
+Specified in the <strong>name</strong> argument of the constructor for the mechanism;  if not is specified,
 a default is assigned by <code class="xref any docutils literal"><span class="pre">MechanismRegistry</span></code> based on the mechanism&#8217;s subclass
 (see <code class="xref any docutils literal"><span class="pre">Registry</span></code> for conventions used in naming, including for default and duplicate names).</p>
 </dd></dl>
@@ -675,42 +692,14 @@ <h1>Mechanism<a class="headerlink" href="#mechanism" title="Permalink to this he
 <dt id="Mechanism.Mechanism_Base.prefs">
 <code class="descname">prefs</code><a class="headerlink" href="#Mechanism.Mechanism_Base.prefs" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>PreferenceSet or specification dict : Mechanism.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for the mechanism.
-Specified in the <a class="reference internal" href="#Mechanism.Mechanism_Base.prefs" title="Mechanism.Mechanism_Base.prefs"><code class="xref any py py-attr docutils literal"><span class="pre">prefs</span></code></a> argument of the constructor for the mechanism;
+Specified in the <strong>prefs</strong> argument of the constructor for the mechanism;
 if it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
 (see <span class="xref doc">PreferenceSet</span> for details).</p>
 </dd></dl>
 
-<dl class="method">
-<dt id="Mechanism.Mechanism_Base.plot">
-<code class="descname">plot</code><span class="sig-paren">(</span><em>x_range=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Mechanism.Mechanism_Base.plot" title="Permalink to this definition">¶</a></dt>
-<dd><p>Generate a plot of the mechanism&#8217;s function using the specified parameter values. See (see
-<a class="reference internal" href="DDM.html#DDM.DDM.plot" title="DDM.DDM.plot"><code class="xref any py py-meth docutils literal"><span class="pre">DDM.plot</span></code></a> for details of the animated DDM plot).</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>x_range</strong> (<em>List</em>) &#8211; <dl class="docutils">
-<dt>specify the range over which the function should be plotted. x_range must be provides as a list containing</dt>
-<dd>two floats: lowest value of x and highest value of x.  Default values depend on the mechanism&#8217;s function.</dd>
-</dl>
-<ul class="simple">
-<li>Logistic Function: default x_range = [-5.0, 5.0]</li>
-<li>Exponential Function: default x_range = [0.1, 5.0]</li>
-<li>All Other Functions: default x_range = [-10.0, 10.0]</li>
-</ul>
-</td>
-</tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>mechanism&#8217;s function plot</strong> &#8211; Matplotlib window of the mechanism&#8217;s function plotted with specified parameters over the specified x_range</td>
-</tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">Matplotlib window</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
 <dl class="method">
 <dt id="Mechanism.Mechanism_Base.execute">
-<code class="descname">execute</code><span class="sig-paren">(</span><em>input=None</em>, <em>runtime_params=None</em>, <em>clock=&lt;PsyNeuLink.Globals.TimeScale.Clock object&gt;</em>, <em>time_scale=&lt;TimeScale.TRIAL: 1&gt;</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Mechanism.Mechanism_Base.execute" title="Permalink to this definition">¶</a></dt>
+<code class="descname">execute</code><span class="sig-paren">(</span><em>input=None</em>, <em>runtime_params=None</em>, <em>clock=&lt;PsyNeuLink.Globals.TimeScale.Clock object&gt;</em>, <em>time_scale=&lt;TimeScale.TRIAL: 2&gt;</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Mechanism.Mechanism_Base.execute" title="Permalink to this definition">¶</a></dt>
 <dd><p>Carry out a single execution of the mechanism.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
@@ -720,7 +709,7 @@ <h1>Mechanism<a class="headerlink" href="#mechanism" title="Permalink to this he
 <li><strong>input</strong> (<em>List[value] or ndarray : default variableInstanceDefault</em>) &#8211; input to use for execution of the mechanism.
 This must be consistent with the format of the mechanism&#8217;s inputState(s):
 the number of items in the  outermost level of the list, or axis 0 of the ndarray, must equal the number
-of the mechanism&#8217;s <a class="reference internal" href="#Mechanism.Mechanism_Base.inputStates" title="Mechanism.Mechanism_Base.inputStates"><code class="xref any py py-attr docutils literal"><span class="pre">inputStates</span></code></a>, and each item must be compatible with the
+of the mechanism&#8217;s <a class="reference internal" href="#Mechanism.Mechanism_Base.input_states" title="Mechanism.Mechanism_Base.input_states"><code class="xref any py py-attr docutils literal"><span class="pre">input_states</span></code></a>, and each item must be compatible with the
 format (number and type of elements) of the corresponding inputState&#8217;s
 <a class="reference internal" href="InputState.html#InputState.InputState.variable" title="InputState.InputState.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> (see <a class="reference internal" href="Run.html#run-inputs"><span class="std std-ref">Run Inputs</span></a> for details of input
 specification formats).</li>
@@ -734,7 +723,7 @@ <h1>Mechanism<a class="headerlink" href="#mechanism" title="Permalink to this he
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>mechanism&#8217;s outputValue</strong> &#8211; list of the <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code> of each of the mechanism&#8217;s <a class="reference internal" href="#mechanism-outputstates"><span class="std std-ref">outputStates</span></a> after
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>mechanism&#8217;s output_values</strong> &#8211; list of the <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code> of each of the mechanism&#8217;s <a class="reference internal" href="#mechanism-outputstates"><span class="std std-ref">outputStates</span></a> after
 either one time_step or a trial.</p>
 </td>
 </tr>
@@ -762,7 +751,7 @@ <h1>Mechanism<a class="headerlink" href="#mechanism" title="Permalink to this he
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>mechanism&#8217;s outputValue</strong> &#8211; list of the <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code> of each of the mechanism&#8217;s <a class="reference internal" href="#mechanism-outputstates"><span class="std std-ref">outputStates</span></a> for
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>mechanism&#8217;s output_values</strong> &#8211; list of the <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code> of each of the mechanism&#8217;s <a class="reference internal" href="#mechanism-outputstates"><span class="std std-ref">outputStates</span></a> for
 each execution of the mechanism.</p>
 </td>
 </tr>
@@ -777,7 +766,7 @@ <h1>Mechanism<a class="headerlink" href="#mechanism" title="Permalink to this he
 <dt id="Mechanism.Mechanism_Base.initialize">
 <code class="descname">initialize</code><span class="sig-paren">(</span><em>value</em><span class="sig-paren">)</span><a class="headerlink" href="#Mechanism.Mechanism_Base.initialize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Assign an initial value to the mechanism&#8217;s <a class="reference internal" href="#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> attribute and update its
-<a class="reference internal" href="#Mechanism.Mechanism_Base.outputStates" title="Mechanism.Mechanism_Base.outputStates"><code class="xref any py py-attr docutils literal"><span class="pre">outputStates</span></code></a>.</p>
+<code class="xref any docutils literal"><span class="pre">outputStates</span></code>.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
@@ -788,6 +777,34 @@ <h1>Mechanism<a class="headerlink" href="#mechanism" title="Permalink to this he
 </table>
 </dd></dl>
 
+<dl class="method">
+<dt id="Mechanism.Mechanism_Base.plot">
+<code class="descname">plot</code><span class="sig-paren">(</span><em>x_range=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Mechanism.Mechanism_Base.plot" title="Permalink to this definition">¶</a></dt>
+<dd><p>Generate a plot of the mechanism&#8217;s function using the specified parameter values. See (see
+<a class="reference internal" href="DDM.html#DDM.DDM.plot" title="DDM.DDM.plot"><code class="xref any py py-meth docutils literal"><span class="pre">DDM.plot</span></code></a> for details of the animated DDM plot).</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>x_range</strong> (<em>List</em>) &#8211; <dl class="docutils">
+<dt>specify the range over which the function should be plotted. x_range must be provides as a list containing</dt>
+<dd>two floats: lowest value of x and highest value of x.  Default values depend on the mechanism&#8217;s function.</dd>
+</dl>
+<ul class="simple">
+<li>Logistic Function: default x_range = [-5.0, 5.0]</li>
+<li>Exponential Function: default x_range = [0.1, 5.0]</li>
+<li>All Other Functions: default x_range = [-10.0, 10.0]</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>mechanism&#8217;s function plot</strong> &#8211; Matplotlib window of the mechanism&#8217;s function plotted with specified parameters over the specified x_range</td>
+</tr>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">Matplotlib window</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
 </dd></dl>
 
 </div>
diff --git a/docs/build/html/OutputState.html b/docs/build/html/OutputState.html
index 2253bb8e0ac..841d9826f68 100644
--- a/docs/build/html/OutputState.html
+++ b/docs/build/html/OutputState.html
@@ -34,8 +34,8 @@
               href="genindex.html"/>
         <link rel="search" title="Search" href="search.html"/>
     <link rel="top" title="PsyNeuLink 0.1 documentation" href="index.html"/>
-        <link rel="up" title="States" href="State.html"/>
-        <link rel="next" title="Projection" href="Projection.html"/>
+        <link rel="up" title="State" href="State.html"/>
+        <link rel="next" title="ModulatorySignal" href="ModulatorySignal.html"/>
         <link rel="prev" title="ParameterState" href="ParameterState.html"/> 
 
   
@@ -89,17 +89,26 @@
 <li class="toctree-l1"><a class="reference internal" href="System.html">System</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Process.html">Process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Mechanism.html">Mechanism</a></li>
-<li class="toctree-l1 current"><a class="reference internal" href="State.html">States</a><ul class="current">
+<li class="toctree-l1 current"><a class="reference internal" href="State.html">State</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="InputState.html">InputState</a></li>
 <li class="toctree-l2"><a class="reference internal" href="ParameterState.html">ParameterState</a></li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">OutputState</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#overview">Overview</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#creating-an-outputstate">Creating an OutputState</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#structure">Structure</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#creating-an-outputstate">Creating an OutputState</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#primary-outputstate">Primary OutputState</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#outputstate-specification">OutputState Specification</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#outputstate-customization">OutputState Customization</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#structure">Structure</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#additional-attributes">Additional Attributes</a></li>
+</ul>
+</li>
 <li class="toctree-l3"><a class="reference internal" href="#execution">Execution</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#class-reference">Class Reference</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="ModulatorySignal.html">ModulatorySignal</a></li>
 <li class="toctree-l2"><a class="reference internal" href="State.html#overview">Overview</a></li>
 <li class="toctree-l2"><a class="reference internal" href="State.html#creating-a-state">Creating a State</a></li>
 <li class="toctree-l2"><a class="reference internal" href="State.html#structure">Structure</a></li>
@@ -141,7 +150,7 @@
   <ul class="wy-breadcrumbs">
     <li><a href="index.html">Docs</a> &raquo;</li>
       
-          <li><a href="State.html">States</a> &raquo;</li>
+          <li><a href="State.html">State</a> &raquo;</li>
       
     <li>OutputState</li>
       <li class="wy-breadcrumbs-aside">
@@ -163,44 +172,51 @@
 <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">¶</a></h2>
 <p>OutputState(s) represent the result(s) of executing a mechanism.  This may be the result(s) of its
 <a class="reference internal" href="#OutputState.OutputState.function" title="OutputState.OutputState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> and/or values derived from that result.  The full set of results are stored in the
-mechanism&#8217;s <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> attribute.  OutputStates are used to represent individual items
-of the mechanism&#8217;s <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>, and/or useful quantities derived from them.  For example,
-the <a class="reference internal" href="TransferMechanism.html#TransferMechanism.TransferMechanism.function" title="TransferMechanism.TransferMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> of a <a class="reference internal" href="TransferMechanism.html"><span class="doc">TransferMechanism</span></a> generates a single result (the
-transformed value of its input);  however, a TransferMechanism has outputStates that represent not only that
-result, but also its mean and variance (if it is an array).  In contrast, the <a class="reference internal" href="DDM.html#DDM.DDM.function" title="DDM.DDM.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> of a
-<a class="reference internal" href="DDM.html"><span class="doc">DDM</span></a> mechanism generates several results (such as decision accuracy and response time), each of which is assigned as
-the <a class="reference internal" href="#OutputState.OutputState.value" title="OutputState.OutputState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> of a different outputState.  The outputState(s) of a mechanism can serve as the input to
-other  mechanisms (by way of <code class="xref any docutils literal"><span class="pre">projections</span></code>), or as the output of a process and/or system.  The
-outputState&#8217;s <a class="reference internal" href="#OutputState.OutputState.sendsToProjections" title="OutputState.OutputState.sendsToProjections"><code class="xref any py py-attr docutils literal"><span class="pre">sendsToProjections</span></code></a> attribute lists all of its outgoing projections.</p>
+mechanism&#8217;s <code class="xref any docutils literal"><span class="pre">output_value</span></code> attribute.  OutputStates are used to represent
+individual items of the mechanism&#8217;s <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>, and/or useful quantities derived from
+them.  For example, the <a class="reference internal" href="TransferMechanism.html#ProcessingMechanisms.TransferMechanism.TransferMechanism.function" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> of a <a class="reference internal" href="TransferMechanism.html"><span class="doc">TransferMechanism</span></a> generates
+a single result (the transformed value of its input);  however, a TransferMechanism can also be assigned outputStates
+that represent its mean, variance or other derived values.  In contrast, the <a class="reference internal" href="DDM.html#DDM.DDM.function" title="DDM.DDM.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>
+of a <a class="reference internal" href="DDM.html"><span class="doc">DDM</span></a> mechanism generates several results (such as decision accuracy and response time), each of which can be
+assigned as the <a class="reference internal" href="#OutputState.OutputState.value" title="OutputState.OutputState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> of a different outputState.  The outputState(s) of a mechanism can serve
+as the input to other  mechanisms (by way of <code class="xref any docutils literal"><span class="pre">projections</span></code>), or as the output of a process and/or
+system.  The outputState&#8217;s <a class="reference internal" href="#OutputState.OutputState.efferents" title="OutputState.OutputState.efferents"><code class="xref any py py-attr docutils literal"><span class="pre">efferents</span></code></a> attribute lists all of its outgoing
+projections.</p>
 </div>
 <div class="section" id="creating-an-outputstate">
 <span id="outputstates-creation"></span><h2>Creating an OutputState<a class="headerlink" href="#creating-an-outputstate" title="Permalink to this headline">¶</a></h2>
-<p>An outputState can be created by calling its constructor. However, in general this is not necessary as a mechanism
-usually automatically creates the outputState(s) it needs when it is created.  For example, if the mechanism is
-created within the <a class="reference internal" href="Process.html#Process.Process_Base.pathway" title="Process.Process_Base.pathway"><code class="xref any py py-attr docutils literal"><span class="pre">pathway</span></code></a> of a <a class="reference internal" href="Process.html"><span class="doc">process</span></a>, an outputState will be created and assigned as the
-<a class="reference internal" href="MappingProjection.html#MappingProjection.MappingProjection.sender" title="MappingProjection.MappingProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a> of a <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a> to the next mechanism in the pathway,
-or to the process&#8217;s <a class="reference internal" href="Process.html#process-input-and-ouput"><span class="std std-ref">output</span></a> if the mechanism is a <a class="reference internal" href="Keywords.html#Keywords.Keywords.TERMINAL" title="Keywords.Keywords.TERMINAL"><code class="xref any py py-attr docutils literal"><span class="pre">TERMINAL</span></code></a> mechanism for that process.</p>
+<p>An outputState can be created by calling its constructor. However, in general this is not necessary, as a mechanism
+automatically creates a default outputState if none is explicitly specified, that contains the primary result of its
+<a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.function" title="Mechanism.Mechanism_Base.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>.  For example, if the mechanism is created within the <a class="reference internal" href="Process.html#Process.Process_Base.pathway" title="Process.Process_Base.pathway"><code class="xref any py py-attr docutils literal"><span class="pre">pathway</span></code></a> of a
+<a class="reference internal" href="Process.html"><span class="doc">process</span></a>, an outputState will be created and assigned as the
+<code class="xref any docutils literal"><span class="pre">sender</span></code> of a <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a> to the next mechanism in the pathway,
+or to the process&#8217;s <code class="xref any docutils literal"><span class="pre">output</span></code> if the mechanism is a <a class="reference internal" href="Keywords.html#Keywords.Keywords.TERMINAL" title="Keywords.Keywords.TERMINAL"><code class="xref any py py-attr docutils literal"><span class="pre">TERMINAL</span></code></a> mechanism for that process.
+Process_Input_And_Ouput.  Other configurations can also easily be specified using a mechanism&#8217;s
+<a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.output_states" title="Mechanism.Mechanism_Base.output_states"><code class="xref any py py-attr docutils literal"><span class="pre">output_states</span></code></a> attribute (see <a class="reference internal" href="#outputstate-specification"><span class="std std-ref">OutputState Specification</span></a> below).</p>
 <p>An outputState must be owned by a mechanism. Therefore, if the outputState is created explicitly, the mechanism to
 which it belongs must be specified in the <strong>owner</strong> argument of its constructor; if the outputState is specified
 in the OUTPUT_STATES entry of the <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> for a
 mechanism, then the owner is inferred from the context.</p>
-<p id="outputstate-primary">Every mechanism has at least one outputState, referred to as its <em>primary outputState</em>, that is automatically created
-and assigned to the mechanism&#8217;s <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.outputState" title="Mechanism.Mechanism_Base.outputState"><code class="xref any py py-attr docutils literal"><span class="pre">outputState</span></code></a> attribute (note the singular),
-and also as the first entry in the OrderedDictionary of the mechanism&#8217;s
-<a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.outputStates" title="Mechanism.Mechanism_Base.outputStates"><code class="xref any py py-attr docutils literal"><span class="pre">outputStates</span></code></a> attribute (note the plural).  The <a class="reference internal" href="#OutputState.OutputState.value" title="OutputState.OutputState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> of
-the primary outputState is assigned as the first (and often only) item of the mechanism&#8217;s
-<a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>, which is the result of the mechanism`s
-<a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.function" title="Mechanism.Mechanism_Base.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>.  In addition to the primary outputState, many mechanisms also assign
-an outputState for each additional item of their <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>, and some assign
-additional outputStates that calculate values derived from one or more of those items.</p>
-<p id="outputstate-specification">If one or more custom outputStates need to be added when a mechanism is created, or added to an existing
-mechanism, they can be specified in an entry of the mechanism&#8217;s
-<a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a>, using the key OUTPUT_STATES.  For a single
-outputState, the value of the entry can be any of the specifications in the the list below.  To create multiple
-outputStates, the value can be either: a list, each item of which can be any of the specifications below; or
-it can be an OrderedDict in which the key for each entry is a string  specifying the name for the outputState to be
-created, and its value is one of the specifications below.  Each outputState to be created can be specified using any
-of the following formats:</p>
+<div class="section" id="primary-outputstate">
+<span id="outputstate-primary"></span><h3>Primary OutputState<a class="headerlink" href="#primary-outputstate" title="Permalink to this headline">¶</a></h3>
+<p>Every mechanism has at least one outputState, referred to as its <em>primary outputState</em>.  If outputStates are not
+<a class="reference internal" href="#outputstate-specification"><span class="std std-ref">explicitly specified</span></a> for a mechanism, a primary outputState is automatically created
+and assigned to its <code class="xref any docutils literal"><span class="pre">outputState</span></code> attribute (note the singular),
+and also to the first entry of the mechanism&#8217;s <code class="xref any docutils literal"><span class="pre">outputStates</span></code> attribute
+(note the plural).  The <a class="reference internal" href="#OutputState.OutputState.value" title="OutputState.OutputState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> of the primary outputState is assigned as the first (and often
+only) item of the mechanism&#8217;s <code class="xref any docutils literal"><span class="pre">output_value</span></code>, which is the result of the
+mechanism`s <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.function" title="Mechanism.Mechanism_Base.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>.</p>
+</div>
+<div class="section" id="outputstate-specification">
+<span id="id1"></span><h3>OutputState Specification<a class="headerlink" href="#outputstate-specification" title="Permalink to this headline">¶</a></h3>
+<p>The primary outputState of a mechanism can be supplemented or replaced using the <strong>output_states</strong> argument of a
+mechanism&#8217;s constructor &lt;Mechanism_OutputStates&gt;`.  If any are specified there, <em>all</em>  of the outputStates desired
+must be specified there;  that is, explicitly specifying <em>any</em> outputStates in an <strong>output_states</strong> argument
+replaces any defaults.  Therefore, if the default outputState &#8211; that usually contains the result of the mechanism&#8217;s
+function &#8211; is to be retained, it too must be specified along with any additional ones desired.  OutputStates are
+specified in a list, either in the <strong>output_states</strong> argument of a <a class="reference internal" href="Mechanism.html#mechanism-creation"><span class="std std-ref">mechanism&#8217;s constructor</span></a>, or
+by direct assignment to its <code class="xref any docutils literal"><span class="pre">output_states</span></code> attribute.  The specification of each
+outputState in a list can be any of the following:</p>
 <blockquote>
 <div><ul class="simple">
 <li>A reference to an <strong>existing outputState</strong>.  Its <a class="reference internal" href="#OutputState.OutputState.variable" title="OutputState.OutputState.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> must match (in the
@@ -208,25 +224,22 @@ <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this head
 which the outputState is assigned (designated by its <a class="reference internal" href="#outputstate-index"><span class="std std-ref">index</span></a> attribute).</li>
 </ul>
 <ul class="simple">
-<li>A reference to the <strong>OutputState class</strong> or a ** name string**.  This creates a default outputState that is
-assigned the first item of the owner mechanism&#8217;s <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> as its value.  If the
-name of the OutputState class or its keyword (OUTPUTSTATE) are used, a default name is assigned to the
-outputState;  if a string is used, it is assigned as the name of the outputState
-(see <span class="xref std std-ref">naming conventions</span>).</li>
-</ul>
-<ul class="simple">
-<li>A <strong>specification dictionary</strong>.  This can include entries, each of which uses the name of an argument for
+<li>A <strong>specification dictionary</strong>.  Each entry should use the name of an argument for
 an outputState parameter (used in the outputState constructor) as its key, and the value of that
 parameter as its value.  By default, the outputState is assigned to the first item of the owner mechanism&#8217;s
-<a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>.  However, <code class="xref std std-keyword docutils literal"><span class="pre">INDEX</span></code> can be used to
-specify the outputState&#8217;s  <a class="reference internal" href="#outputstate-index"><span class="std std-ref">index</span></a> attribute and assign it to different item of the
-mechanism&#8217;s <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>.</li>
+<a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>.  However, an entry with <code class="xref std std-keyword docutils literal"><span class="pre">INDEX</span></code> as its
+key can be used to assign it to different item of the mechanism&#8217;s <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>.</li>
+</ul>
+<ul class="simple">
+<li>A <strong>string</strong>.  This creates a default outputState using that string as its name, and is assigned as the
+<a class="reference internal" href="#outputstate-primary"><span class="std std-ref">primary outputState</span></a> for the mechanism.</li>
 </ul>
 <ul class="simple">
 <li>A <strong>value</strong>.  This creates a default outputState using the specified value as the outputState&#8217;s
 <a class="reference internal" href="#OutputState.OutputState.value" title="OutputState.OutputState.value"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>.  This must be compatible with (have the same number and type of elements as) the
 item of the owner mechanism&#8217;s <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> to which the outputState will be assigned
-(its primary outputState by default, or designated by its <a class="reference internal" href="#OutputState.OutputState.index" title="OutputState.OutputState.index"><code class="xref any py py-attr docutils literal"><span class="pre">index</span></code></a> attribute).</li>
+(its primary outputState by default, or designated by its <a class="reference internal" href="#OutputState.OutputState.index" title="OutputState.OutputState.index"><code class="xref any py py-attr docutils literal"><span class="pre">index</span></code></a> attribute).  A default
+name will be assigned based on the name of the mechanism (see <span class="xref std std-ref">naming conventions</span>).</li>
 </ul>
 <div class="admonition note">
 <p class="first admonition-title">Note</p>
@@ -235,28 +248,78 @@ <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this head
 assigned.</p>
 </div>
 </div></blockquote>
-<p id="outputstate-names">Assigning outputStates explicitly (i.e., including an OUTPUT_STATES entry in the mechanism&#8217;s
-<a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a>) adds them to any that are automatically generated for
-that mechanism.  If the name of an explicitly specified outputState is the same as one that was created automatically
-(or another one that was created explicitly), its name will be suffixed with a numerical index (incremented for each
-outputState with that name), and the outputState will be added to the list (that is, it will <em>not</em> replace ones that
-were already created).</p>
-<p id="outputstate-index-and-calculate">By default, an explicitly specified outputState will use the first item of the owner mechanism&#8217;s
-<a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>.  However, it can be assigned to a different item by specifying its
-<a class="reference internal" href="#OutputState.OutputState.index" title="OutputState.OutputState.index"><code class="xref any py py-attr docutils literal"><span class="pre">index</span></code></a> attribute.  The <a class="reference internal" href="#OutputState.OutputState.variable" title="OutputState.OutputState.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> of
-an outputState must match (in the number and type of its elements) the item of the mechanism&#8217;s
-<a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> to which it is assigned. An outputState can also be configured to transform
-the value of the item, by specifying a function for its <a class="reference internal" href="#OutputState.OutputState.calculate" title="OutputState.OutputState.calculate"><code class="xref any py py-attr docutils literal"><span class="pre">calculate</span></code></a> attribute;  the function
-must be able to take as input a value that is compatible with the item of the Mechahnism&#8217;s
-<a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> to which the outputState is assigned.</p>
+<p>The list of outputStates for a mechanism can be accessed using its <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.output_states" title="Mechanism.Mechanism_Base.output_states"><code class="xref any py py-attr docutils literal"><span class="pre">output_states</span></code></a>
+attribute, and new ones can be added to an existing mechanism by appending a list (containing specifications in the
+form listed above) to the output_states list. Assigning without appending will replace any existing outputStates.
+If the name of an explicitly specified outputState is the same as one that was created automatically (or another one
+that was created explicitly), its name will be suffixed with a numerical index (incremented for each outputState with
+that name), and the outputState will be added to the list (that is, it will <em>not</em> replace ones that were already
+created).</p>
+<p>The values of a mechanism&#8217;s outputStats will be assigned as items in its <code class="xref any docutils literal"><span class="pre">output_value</span></code>
+attribute, in the order in which they are assigned in the <strong>output_states</strong> argument of its constructor, and listed
+in its <code class="xref any docutils literal"><span class="pre">output_states</span></code> attribute.  Note that the <code class="xref any docutils literal"><span class="pre">output_value</span></code>
+of a mechanism is distinct from its <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> attribute, which contains the full and unmodified
+results of its <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.function" title="Mechanism.Mechanism_Base.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>.</p>
+<div class="section" id="standard-outputstates">
+<span id="outputstate-standard"></span><h4>Standard OutputStates<a class="headerlink" href="#standard-outputstates" title="Permalink to this headline">¶</a></h4>
+<p>Most types of mechanisms have a <code class="xref any docutils literal"><span class="pre">standard_output_states</span></code> class attribute, that contains a list of predefined
+outputStates relevant to that type of mechanism (for example, the TransferMechanism class has outputStates for
+calculating the mean, median, variance, and standard deviation of its result).  The names of these are listed as
+attributes of a class with the name &lt;ABBREVIATED_CLASS_NAME&gt;_OUTPUT&gt;.  For example, the TransferMechanism class
+defines <code class="xref any docutils literal"><span class="pre">TRANSFER_OUTPUT</span></code>, with attributes MEAN, MEDIAN, VARIANCE and STANDARD_DEV that are the names of
+predefined outputStates in its <code class="xref any docutils literal"><span class="pre">standard_output_states</span></code> attribute.
+These can be used in the list of outputStates specified for a TransferMechanism object, as in the example below:</p>
+<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">my_mech</span> <span class="o">=</span> <span class="n">TransferMechanism</span><span class="p">[</span><span class="n">default_input_value</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span>
+                            <span class="n">function</span><span class="o">=</span><span class="n">Logistic</span><span class="p">(),</span>
+                            <span class="n">output_states</span><span class="o">=</span><span class="p">[</span><span class="n">TRANSFER_OUTPUT</span><span class="o">.</span><span class="n">RESULT</span><span class="p">,</span>
+                                           <span class="n">TRANSFER_OUTPUT</span><span class="o">.</span><span class="n">MEAN</span><span class="p">,</span>
+                                           <span class="n">TRANSFER_OUTPUT</span><span class="o">.</span><span class="n">VARIANCE</span><span class="p">]</span>
+</pre></div>
+</div>
+<p>In this example, <code class="docutils literal"><span class="pre">my_mech</span></code> is configured with three outputStates;  the first will be named <em>RESULT</em> and will
+represent logistic transform of the 2-element input vector;  the second will be named  <em>MEAN</em> and will represent mean
+of the result (i.e., of its two elements); and the third will be named <em>VARIANCE</em> and contain the variance of the
+result.</p>
+</div>
+</div>
+<div class="section" id="outputstate-customization">
+<span id="id2"></span><h3>OutputState Customization<a class="headerlink" href="#outputstate-customization" title="Permalink to this headline">¶</a></h3>
+<p>The default outputState uses the first (and usually only) item of the owner mechanism&#8217;s
+<a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> as its value.  However, this can be modified in two ways, using the
+outputState&#8217;s <a class="reference internal" href="#OutputState.OutputState.index" title="OutputState.OutputState.index"><code class="xref any py py-attr docutils literal"><span class="pre">index</span></code></a> and <a class="reference internal" href="#OutputState.OutputState.calculate" title="OutputState.OutputState.calculate"><code class="xref any py py-attr docutils literal"><span class="pre">calculate</span></code></a> attributes (see
+<a class="reference internal" href="#outputstate-attributes"><span class="std std-ref">Additional Attributes</span></a> below). If the mechanism&#8217;s <code class="xref any docutils literal"><span class="pre">function</span></code> returns a value with more than one
+item (i.e., a list of lists, or a 2d np.array), then an outputState can be assigned to any of those items by specifying
+its <a class="reference internal" href="#OutputState.OutputState.index" title="OutputState.OutputState.index"><code class="xref any py py-attr docutils literal"><span class="pre">index</span></code></a> attribute. An outputState can also be configured to transform
+the value of the item, by specifying a function for its <a class="reference internal" href="#OutputState.OutputState.calculate" title="OutputState.OutputState.calculate"><code class="xref any py py-attr docutils literal"><span class="pre">calculate</span></code></a> attribute; the result
+will then be assigned as the outputState&#8217;s <a class="reference internal" href="#OutputState.OutputState.value" title="OutputState.OutputState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>.  An outputState&#8217;s <a class="reference internal" href="#OutputState.OutputState.index" title="OutputState.OutputState.index"><code class="xref any py py-attr docutils literal"><span class="pre">index</span></code></a>
+and <a class="reference internal" href="#OutputState.OutputState.calculate" title="OutputState.OutputState.calculate"><code class="xref any py py-attr docutils literal"><span class="pre">calculate</span></code></a> attributes can be assigned when the outputState is assigned to a mechanism,
+by including <em>INDEX</em> and <em>CALCULATE</em> entries in a  <a class="reference internal" href="#outputstate-specification"><span class="std std-ref">specification dictionary</span></a> for the
+outputState, as in the following example:</p>
+<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">my_mech</span> <span class="o">=</span> <span class="n">DDM</span><span class="p">(</span><span class="n">function</span><span class="o">=</span><span class="n">BogaczEtAl</span><span class="p">(),</span>
+              <span class="n">output_states</span><span class="o">=</span><span class="p">[</span> <span class="n">DDM</span><span class="o">.</span><span class="n">DECISION_VARIABLE</span><span class="p">,</span>
+                              <span class="n">DDM</span><span class="o">.</span><span class="n">PROB_UPPER_THRESHOLD</span><span class="p">,</span>
+                              <span class="p">{</span> <span class="n">NAME</span><span class="p">:</span> <span class="s1">&#39;DECISION ENTROPY&#39;</span><span class="p">,</span>
+                                <span class="n">INDEX</span><span class="p">:</span> <span class="mi">2</span><span class="p">},</span>
+                                <span class="n">CALCULATE</span><span class="p">:</span> <span class="n">Entropy</span><span class="p">()</span><span class="o">.</span><span class="n">function</span> <span class="p">}</span> <span class="p">]</span> <span class="p">)</span>
+</pre></div>
+</div>
+<p>In this example, <code class="docutils literal"><span class="pre">my_mech</span></code> is configured with three outputStates.  The first two are standard outputStates that
+represent the decision variable of the DDM and the probability of it crossing of the upper (vs. lower) threshold.  the
+third is a custom outputState, that computes the entropy of the probability of crossing the upper threshold.  It uses
+the <code class="xref any docutils literal"><span class="pre">Entropy</span></code> Function for its <a class="reference internal" href="#OutputState.OutputState.calculate" title="OutputState.OutputState.calculate"><code class="xref any py py-attr docutils literal"><span class="pre">calculate</span></code></a> attribute, and INDEX is assigned <code class="docutils literal"><span class="pre">2</span></code> to reference
+the third item of the DDM&#8217;s <a class="reference internal" href="DDM.html#DDM.DDM.value" title="DDM.DDM.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> attribute, which contains the probability of crossing the upper
+threshold.  The three outputStates will be assigned to the <code class="xref any docutils literal"><span class="pre">output_states</span></code> attribute of
+<code class="docutils literal"><span class="pre">my_mech</span></code>, and their values will be assigned as items in its <code class="xref any docutils literal"><span class="pre">output_value</span></code> attribute,
+each in the order in which it is listed in the <strong>output_states</strong> argument of the constructor for <code class="docutils literal"><span class="pre">my_mech</span></code>.</p>
+</div>
 </div>
 <div class="section" id="structure">
 <span id="outputstate-structure"></span><h2>Structure<a class="headerlink" href="#structure" title="Permalink to this headline">¶</a></h2>
 <p>Every outputState is owned by a <a class="reference internal" href="Mechanism.html"><span class="doc">mechanism</span></a>. It can send one or more
 <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjections</span></a> to other mechanisms.  If its owner is a <a class="reference internal" href="Keywords.html#Keywords.Keywords.TERMINAL" title="Keywords.Keywords.TERMINAL"><code class="xref any py py-attr docutils literal"><span class="pre">TERMINAL</span></code></a> mechanism of a process
-and/or system, then the outputState will also be treated as the output of that <a class="reference internal" href="Process.html#process-input-and-ouput"><span class="std std-ref">process</span></a>
+and/or system, then the outputState will also be treated as the output of that <code class="xref any docutils literal"><span class="pre">process</span></code>
 and/or of a system.  The projections that the outputState sends are listed in its
-<a class="reference internal" href="#OutputState.OutputState.sendsToProjections" title="OutputState.OutputState.sendsToProjections"><code class="xref any py py-attr docutils literal"><span class="pre">sendsToProjections</span></code></a> attribute.</p>
+<a class="reference internal" href="#OutputState.OutputState.efferents" title="OutputState.OutputState.efferents"><code class="xref any py py-attr docutils literal"><span class="pre">efferents</span></code></a> attribute.</p>
 <p>Like all PsyNeuLink components, an outputState has the three following core attributes:</p>
 <ul class="simple">
 <li><a class="reference internal" href="#OutputState.OutputState.variable" title="OutputState.OutputState.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>:  this must match (both in number and types of elements) the
@@ -280,26 +343,34 @@ <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this head
 <a class="reference internal" href="#OutputState.OutputState.function" title="OutputState.OutputState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> (though see <a class="reference internal" href="#outputstate-function-note-2"><span class="std std-ref">note</span></a>).  It is used as the input to any
 projections that the outputStatue sends.</li>
 </ul>
-<p id="outputstate-attributes">An outputState also has two additional attributes that determine its operation:</p>
+<div class="section" id="additional-attributes">
+<span id="outputstate-attributes"></span><h3>Additional Attributes<a class="headerlink" href="#additional-attributes" title="Permalink to this headline">¶</a></h3>
+<p>An outputState also has two additional attributes that determine its operation, and can be used to further
+<a class="reference internal" href="#outputstate-customization"><span class="std std-ref">customize the outputState</span></a>:</p>
 <ul class="simple" id="outputstate-index">
 <li><a class="reference internal" href="#OutputState.OutputState.index" title="OutputState.OutputState.index"><code class="xref any py py-attr docutils literal"><span class="pre">index</span></code></a>: this determines the item of its owner mechanism&#8217;s
 <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> to which it is assigned.  By default, this is set to 0, which assigns it to
-the first item of the mechanism&#8217;s <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>.</li>
+the first item of the mechanism&#8217;s <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>.  Its value must be equal to or less than
+one minus the number of outputStates in the mechanism&#8217;s <code class="xref any docutils literal"><span class="pre">output_states</span></code> attribute.  In
+addition, the <a class="reference internal" href="#OutputState.OutputState.variable" title="OutputState.OutputState.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> of an outputState must match (in the number and type of its elements)
+the item of the mechanism&#8217;s <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> to which the index refers.</li>
 </ul>
 <ul class="simple" id="outputstate-calculate">
-<li><a class="reference internal" href="#OutputState.OutputState.calculate" title="OutputState.OutputState.calculate"><code class="xref any py py-attr docutils literal"><span class="pre">calculate</span></code></a>:  this specifies the function used to convert the item of the owner mechanism&#8217;s
+<li><a class="reference internal" href="#OutputState.OutputState.calculate" title="OutputState.OutputState.calculate"><code class="xref any py py-attr docutils literal"><span class="pre">calculate</span></code></a>:  this specifies a function used to convert the item of the owner mechanism&#8217;s
 <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> (designated by the outputState&#8217;s <a class="reference internal" href="#OutputState.OutputState.index" title="OutputState.OutputState.index"><code class="xref any py py-attr docutils literal"><span class="pre">index</span></code></a> attribute),
-before assigning it as the outputState&#8217;s <code class="xref any docutils literal"><span class="pre">value</span></code>.  The result is combined with the result of the
-outputState&#8217;s <a class="reference internal" href="#OutputState.OutputState.function" title="OutputState.OutputState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> attribute (which aggregates the value of its projections), to
-determine the outputState&#8217;s <a class="reference internal" href="#OutputState.OutputState.value" title="OutputState.OutputState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> (though see <code class="xref any docutils literal"><span class="pre">note</span></code>). The
-default for <a class="reference internal" href="#OutputState.OutputState.calculate" title="OutputState.OutputState.calculate"><code class="xref any py py-attr docutils literal"><span class="pre">calculate</span></code></a> is an identity function (<a class="reference internal" href="Function.html#Function.Linear" title="Function.Linear"><code class="xref any py py-class docutils literal"><span class="pre">Linear</span></code></a> with <strong>slope</strong>=1 and
-<strong>intercept</strong>=0), which simply assigns the specified item of the mechanism&#8217;s
-<a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> unmodified as the <a class="reference internal" href="#OutputState.OutputState.value" title="OutputState.OutputState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> of the outputState. However,
-<a class="reference internal" href="#OutputState.OutputState.calculate" title="OutputState.OutputState.calculate"><code class="xref any py py-attr docutils literal"><span class="pre">calculate</span></code></a> can be assigned any function that can take as its input the designated item
-of the owner mechanism&#8217;s <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>, and the result of which can be combined with the
-result of the outputState&#8217;s <a class="reference internal" href="#OutputState.OutputState.function" title="OutputState.OutputState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>.</li>
+before assigning it as the outputState&#8217;s <code class="xref any docutils literal"><span class="pre">value</span></code>.  The result is used as the input to the
+outputState&#8217;s <a class="reference internal" href="#OutputState.OutputState.function" title="OutputState.OutputState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> attribute (which implements the effects of any
+<code class="xref any docutils literal"><span class="pre">GatingProjections</span></code> to the outputState), the result of whch is assigned as the outputState&#8217;s
+<a class="reference internal" href="#OutputState.OutputState.value" title="OutputState.OutputState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> (though see <code class="xref any docutils literal"><span class="pre">note</span></code>). The
+<a class="reference internal" href="#OutputState.OutputState.calculate" title="OutputState.OutputState.calculate"><code class="xref any py py-attr docutils literal"><span class="pre">calculate</span></code></a> attribute can be assigned any function that can take as its input the
+item of the owner mechanism&#8217;s <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> designated by the outputState&#8217;s
+<a class="reference internal" href="#OutputState.OutputState.index" title="OutputState.OutputState.index"><code class="xref any py py-attr docutils literal"><span class="pre">index</span></code></a> attribute, and the result of which can be used as the variable for the outputState&#8217;s
+<a class="reference internal" href="#OutputState.OutputState.function" title="OutputState.OutputState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>.  The default is an identity function (<a class="reference internal" href="Function.html#Function.Linear" title="Function.Linear"><code class="xref any py py-class docutils literal"><span class="pre">Linear</span></code></a> with <strong>slope</strong>=1 and
+<strong>intercept</strong>=0), that simply assigns the specified item of the mechanism&#8217;s <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>
+unmodified as the variable for outputState&#8217;s <a class="reference internal" href="#OutputState.OutputState.function" title="OutputState.OutputState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>.</li>
 </ul>
 </div>
+</div>
 <div class="section" id="execution">
 <span id="outputstate-execution"></span><h2>Execution<a class="headerlink" href="#execution" title="Permalink to this headline">¶</a></h2>
 <p>An outputState cannot be executed directly.  It is executed when the mechanism to which it belongs is executed.
@@ -310,15 +381,15 @@ <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this head
 <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> as its input.  This is possibly modified by the result of the outputState&#8217;s
 <a class="reference internal" href="#OutputState.OutputState.function" title="OutputState.OutputState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> (though see <a class="reference internal" href="#outputstate-function-note-2"><span class="std std-ref">note</span></a>).  The final result is assigned as
 the outputState&#8217;s <a class="reference internal" href="#OutputState.OutputState.value" title="OutputState.OutputState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>, as well as to a corresponding item of the mechanism&#8217;s
-<a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.outputValue" title="Mechanism.Mechanism_Base.outputValue"><code class="xref any py py-attr docutils literal"><span class="pre">outputValue</span></code></a> attribute. It is also used as the input to any projections for
+<a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.output_values" title="Mechanism.Mechanism_Base.output_values"><code class="xref any py py-attr docutils literal"><span class="pre">output_values</span></code></a> attribute. It is also used as the input to any projections for
 which the outputState is the <code class="xref any docutils literal"><span class="pre">sender</span></code>.</p>
 </div>
 <div class="section" id="class-reference">
 <span id="outputstate-class-reference"></span><h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalink to this headline">¶</a></h2>
 <dl class="class">
 <dt id="OutputState.OutputState">
-<em class="property">class </em><code class="descclassname">OutputState.</code><code class="descname">OutputState</code><span class="sig-paren">(</span><em>owner</em>, <em>value=None</em>, <em>index=PRIMARY_OUTPUT_STATE</em>, <em>calculate=Linear</em>, <em>function=LinearCombination(operation=SUM)</em>, <em>params=None</em>, <em>name=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#OutputState.OutputState" title="Permalink to this definition">¶</a></dt>
-<dd><p>Implements a subclass of <a class="reference internal" href="State.html"><span class="doc">States</span></a> that represents an output of a mechanism.</p>
+<em class="property">class </em><code class="descclassname">OutputState.</code><code class="descname">OutputState</code><span class="sig-paren">(</span><em>owner</em>, <em>reference_value</em>, <em>variable=None</em>, <em>function=LinearCombination(operation=SUM)</em>, <em>index=PRIMARY_OUTPUT_STATE</em>, <em>calculate=Linear</em>, <em>params=None</em>, <em>name=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#OutputState.OutputState" title="Permalink to this definition">¶</a></dt>
+<dd><p>Implements a subclass of <a class="reference internal" href="State.html"><span class="doc">State</span></a> that represents an output of a mechanism.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
@@ -328,18 +399,10 @@ <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this head
 context in which the outputState is created.</li>
 <li><strong>reference_value</strong> (<em>number, list or np.ndarray</em>) &#8211; a template that specifies the format of the item of the owner mechanism&#8217;s
 <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> attribute to which the outputState will be assigned (specified by
-the <code class="xref std std-keyword docutils literal"><span class="pre">index</span></code> argument).  This must match (in number and type of elements) the outputState&#8217;s
-<a class="reference internal" href="#OutputState.OutputState.variable" title="OutputState.OutputState.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> argument.  It is used to insure the compatibility of the source of the
+the <strong>index</strong> argument).  This must match (in number and type of elements) the outputState&#8217;s
+<strong>variable</strong> argument.  It is used to insure the compatibility of the source of the
 input for the outputState with its <a class="reference internal" href="#OutputState.OutputState.variable" title="OutputState.OutputState.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>.</li>
-<li><strong>value</strong> (<em>number, list or np.ndarray</em>) &#8211; specifies the template for the outputState&#8217;s <a class="reference internal" href="#OutputState.OutputState.value" title="OutputState.OutputState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>.</li>
-<li><strong>index</strong> (<em>int : default PRIMARY_OUTPUT_STATE</em>) &#8211; specifies the item of the owner mechanism&#8217;s <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> used as input for the
-function specified by the outputState&#8217;s <a class="reference internal" href="#OutputState.OutputState.calculate" title="OutputState.OutputState.calculate"><code class="xref any py py-attr docutils literal"><span class="pre">calculate</span></code></a> attribute, to determine the
-outputState&#8217;s <a class="reference internal" href="#OutputState.OutputState.value" title="OutputState.OutputState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>.</li>
-<li><strong>calculate</strong> (<em>Function, function, or method : default Linear</em>) &#8211; specifies the function used to convert the designated item of the owner mechanism&#8217;s
-<a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> (specified by the outputState&#8217;s <code class="xref std std-keyword docutils literal"><span class="pre">index</span></code> attribute),
-before it is assigned as the outputState&#8217;s <a class="reference internal" href="#OutputState.OutputState.value" title="OutputState.OutputState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>.  The function must accept a value that
-has the same format (number and type of elements) as the item of the mechanism&#8217;s
-<a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>.</li>
+<li><strong>variable</strong> (<em>number, list or np.ndarray</em>) &#8211; specifies the template for the outputState&#8217;s <a class="reference internal" href="#OutputState.OutputState.variable" title="OutputState.OutputState.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>.</li>
 <li><strong>function</strong> (<em>Function, function, or method : default LinearCombination(operation=SUM)</em>) &#8211; <p>function used to aggregate the values of the projections received by the outputState.
 It must produce a result that has the same format (number and type of elements) as the item of the mechanism&#8217;s
 <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> to which the outputState is assigned (specified by its
@@ -349,6 +412,14 @@ <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this head
 <p class="last">This is not used a present (see <a class="reference internal" href="#outputstate-function-note-2"><span class="std std-ref">note</span></a> for additonal details).</p>
 </div>
 </li>
+<li><strong>index</strong> (<em>int : default PRIMARY_OUTPUT_STATE</em>) &#8211; specifies the item of the owner mechanism&#8217;s <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> used as input for the
+function specified by the outputState&#8217;s <a class="reference internal" href="#OutputState.OutputState.calculate" title="OutputState.OutputState.calculate"><code class="xref any py py-attr docutils literal"><span class="pre">calculate</span></code></a> attribute, to determine the
+outputState&#8217;s <a class="reference internal" href="#OutputState.OutputState.value" title="OutputState.OutputState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>.</li>
+<li><strong>calculate</strong> (<em>Function, function, or method : default Linear</em>) &#8211; specifies the function used to convert the designated item of the owner mechanism&#8217;s
+<a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> (specified by the outputState&#8217;s <code class="xref std std-keyword docutils literal"><span class="pre">index</span></code> attribute),
+before it is assigned as the outputState&#8217;s <a class="reference internal" href="#OutputState.OutputState.value" title="OutputState.OutputState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>.  The function must accept a value that
+has the same format (number and type of elements) as the item of the mechanism&#8217;s
+<a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.value" title="Mechanism.Mechanism_Base.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>.</li>
 <li><strong>params</strong> (<em>Optional[Dict[param keyword, param value]]</em>) &#8211; a <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> that can be used to specify the parameters for
 the outputState, its function, and/or a custom function and its parameters. Values specified for parameters
 in the dictionary override any assigned to those parameters in arguments of the constructor.</li>
@@ -369,13 +440,6 @@ <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this head
 <dd><p><em>Mechanism</em> &#8211; the mechanism to which the outputState belongs.</p>
 </dd></dl>
 
-<dl class="attribute">
-<dt id="OutputState.OutputState.sendsToProjections">
-<code class="descname">sendsToProjections</code><a class="headerlink" href="#OutputState.OutputState.sendsToProjections" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>Optional[List[Projection]]</em> &#8211; a list of the projections sent by the outputState (i.e., for which the outputState is a
-<code class="xref any docutils literal"><span class="pre">sender</span></code>).</p>
-</dd></dl>
-
 <dl class="attribute">
 <dt id="OutputState.OutputState.variable">
 <code class="descname">variable</code><a class="headerlink" href="#OutputState.OutputState.variable" title="Permalink to this definition">¶</a></dt>
@@ -397,7 +461,7 @@ <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this head
 the outputState&#8217;s <a class="reference internal" href="#OutputState.OutputState.index" title="OutputState.OutputState.index"><code class="xref any py py-attr docutils literal"><span class="pre">index</span></code></a> attribute.  The result is combined with the result of the
 outputState&#8217;s <a class="reference internal" href="#OutputState.OutputState.function" title="OutputState.OutputState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> ((though see <a class="reference internal" href="#outputstate-function-note-2"><span class="std std-ref">note below</span></a>)
 to determine both the <a class="reference internal" href="#OutputState.OutputState.value" title="OutputState.OutputState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> of the outputState, as well as the value of the
-corresponding item of the owner mechanism&#8217;s <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.outputValue" title="Mechanism.Mechanism_Base.outputValue"><code class="xref any py py-attr docutils literal"><span class="pre">outputValue</span></code></a>.
+corresponding item of the owner mechanism&#8217;s <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.output_values" title="Mechanism.Mechanism_Base.output_values"><code class="xref any py py-attr docutils literal"><span class="pre">output_values</span></code></a>.
 The default (<a class="reference internal" href="Function.html#Function.Linear" title="Function.Linear"><code class="xref any py py-class docutils literal"><span class="pre">Linear</span></code></a>) transfers the value unmodified.</p>
 </dd></dl>
 
@@ -407,10 +471,10 @@ <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this head
 <dd><p><em>CombinationFunction : default LinearCombination(operation=SUM))</em> &#8211; performs an element-wise (Hadamard) aggregation  of the values of the projections received by the
 outputState.  The result is combined with the result of the function specified by
 <a class="reference internal" href="#OutputState.OutputState.calculate" title="OutputState.OutputState.calculate"><code class="xref any py py-attr docutils literal"><span class="pre">calculate</span></code></a>, and assigned as both the outputState&#8217;s <a class="reference internal" href="#OutputState.OutputState.value" title="OutputState.OutputState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>
-and the corresponding item of the owner&#8217;s <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.outputValue" title="Mechanism.Mechanism_Base.outputValue"><code class="xref any py py-attr docutils literal"><span class="pre">outputValue</span></code></a>.</p>
+and the corresponding item of the owner&#8217;s <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.output_values" title="Mechanism.Mechanism_Base.output_values"><code class="xref any py py-attr docutils literal"><span class="pre">output_values</span></code></a>.</p>
 <div class="admonition note" id="outputstate-function-note-2">
 <p class="first admonition-title">Note</p>
-<p class="last">Currently PsyNeuLink does not currently support projections to outputStates.  Therefore, the
+<p class="last">PsyNeuLink does not currently support projections to outputStates.  Therefore, the
 <code class="xref std std-keyword docutils literal"><span class="pre">function</span></code> attribute is not used.  It is implemented strictly for consistency with other
 state classes, and for potential future use.</p>
 </div>
@@ -421,14 +485,21 @@ <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this head
 <code class="descname">value</code><a class="headerlink" href="#OutputState.OutputState.value" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>number, list or np.ndarray</em> &#8211; assigned the result of <a class="reference internal" href="#OutputState.OutputState.function" title="OutputState.OutputState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>
 (though see note under <code class="xref any docutils literal"><span class="pre">function</span></code>;  the same value is assigned to the corresponding item of the owner
-mechanism&#8217;s <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.outputValue" title="Mechanism.Mechanism_Base.outputValue"><code class="xref any py py-attr docutils literal"><span class="pre">outputValue</span></code></a>.</p>
+mechanism&#8217;s <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.output_values" title="Mechanism.Mechanism_Base.output_values"><code class="xref any py py-attr docutils literal"><span class="pre">output_values</span></code></a>.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="OutputState.OutputState.efferents">
+<code class="descname">efferents</code><a class="headerlink" href="#OutputState.OutputState.efferents" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>Optional[List[Projection]]</em> &#8211; a list of the projections sent by the outputState (i.e., for which the outputState is a
+<code class="xref any docutils literal"><span class="pre">sender</span></code>).</p>
 </dd></dl>
 
 <dl class="attribute">
 <dt id="OutputState.OutputState.name">
 <code class="descname">name</code><a class="headerlink" href="#OutputState.OutputState.name" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>str : default &lt;State subclass&gt;-&lt;index&gt;</em> &#8211; name of the outputState.
-Specified in the <a class="reference internal" href="#OutputState.OutputState.name" title="OutputState.OutputState.name"><code class="xref any py py-attr docutils literal"><span class="pre">name</span></code></a> argument of the constructor for the outputState.  If not is specified, a default is
+Specified in the <strong>name</strong> argument of the constructor for the outputState.  If not is specified, a default is
 assigned by the StateRegistry of the mechanism to which the outputState belongs
 (see <span class="xref doc">Registry</span> for conventions used in naming, including for default and duplicate names).</p>
 <div class="admonition note">
@@ -444,13 +515,47 @@ <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this head
 <dt id="OutputState.OutputState.prefs">
 <code class="descname">prefs</code><a class="headerlink" href="#OutputState.OutputState.prefs" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>PreferenceSet or specification dict : State.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for the outputState.
-Specified in the <a class="reference internal" href="#OutputState.OutputState.prefs" title="OutputState.OutputState.prefs"><code class="xref any py py-attr docutils literal"><span class="pre">prefs</span></code></a> argument of the constructor for the projection;  if it is not specified, a default is
+Specified in the <strong>prefs</strong> argument of the constructor for the projection;  if it is not specified, a default is
 assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
 (see <span class="xref doc">PreferenceSet</span> for details).</p>
 </dd></dl>
 
 </dd></dl>
 
+<dl class="class">
+<dt id="OutputState.StandardOutputStates">
+<em class="property">class </em><code class="descclassname">OutputState.</code><code class="descname">StandardOutputStates</code><span class="sig-paren">(</span><em>owner: PsyNeuLink.Components.Component.Component</em>, <em>output_state_dicts: list</em>, <em>indices: &lt;typecheck.framework.optional object at 0x10e5519e8&gt; = None</em><span class="sig-paren">)</span><a class="headerlink" href="#OutputState.StandardOutputStates" title="Permalink to this definition">¶</a></dt>
+<dd><p>Collection of OutputState specification dictionaries for standard_output_states of a class</p>
+<p>data attribute contains dictionary of OutputState specification dictionaries
+indices contains a list of the default index for each OutputState specified
+names containes a list of the default name of each OutputState</p>
+<dl class="docutils">
+<dt><strong>indices</strong> arg of constructore specifies how to assign the INDEX entry for each dict listed in output_state_dicts;</dt>
+<dd><dl class="first docutils">
+<dt>the arg can be:</dt>
+<dd><blockquote class="first">
+<div><ul class="simple">
+<li>PRIMARY_OUTPUT_STATES keyword - assigns the INDEX for the owner&#8217;s primary outputState to all indices;</li>
+<li>SEQUENTIAL keyword - assigns sequentially incremented int to each INDEX entry</li>
+</ul>
+</div></blockquote>
+<ul class="last simple">
+<li>a list of ints - assigns each int to the corresponding entry in output_state_dicts</li>
+</ul>
+</dd>
+</dl>
+<p class="last">if it is not specified, assigns <code class="xref any docutils literal"><span class="pre">None</span></code> to each INDEX entry.</p>
+</dd>
+</dl>
+<p><code class="xref any docutils literal"><span class="pre">get_dict</span></code> method takes a name as its arg and returns a copy of the designated OutputState specification dictionary</p>
+</dd></dl>
+
+<dl class="function">
+<dt id="OutputState.make_readonly_property">
+<code class="descclassname">OutputState.</code><code class="descname">make_readonly_property</code><span class="sig-paren">(</span><em>val</em><span class="sig-paren">)</span><a class="headerlink" href="#OutputState.make_readonly_property" title="Permalink to this definition">¶</a></dt>
+<dd><p>Return property that provides read-only access to its value</p>
+</dd></dl>
+
 </div>
 </div>
 
@@ -461,7 +566,7 @@ <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this head
   
     <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
       
-        <a href="Projection.html" class="btn btn-neutral float-right" title="Projection" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
+        <a href="ModulatorySignal.html" class="btn btn-neutral float-right" title="ModulatorySignal" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
       
       
         <a href="ParameterState.html" class="btn btn-neutral" title="ParameterState" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
diff --git a/docs/build/html/ParameterState.html b/docs/build/html/ParameterState.html
index 89e1467fe5e..9a0bf3b4eea 100644
--- a/docs/build/html/ParameterState.html
+++ b/docs/build/html/ParameterState.html
@@ -34,7 +34,7 @@
               href="genindex.html"/>
         <link rel="search" title="Search" href="search.html"/>
     <link rel="top" title="PsyNeuLink 0.1 documentation" href="index.html"/>
-        <link rel="up" title="States" href="State.html"/>
+        <link rel="up" title="State" href="State.html"/>
         <link rel="next" title="OutputState" href="OutputState.html"/>
         <link rel="prev" title="InputState" href="InputState.html"/> 
 
@@ -89,7 +89,7 @@
 <li class="toctree-l1"><a class="reference internal" href="System.html">System</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Process.html">Process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Mechanism.html">Mechanism</a></li>
-<li class="toctree-l1"><a class="reference internal" href="State.html">States</a></li>
+<li class="toctree-l1"><a class="reference internal" href="State.html">State</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Projection.html">Projection</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Run.html">Run</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Component.html">Component</a></li>
@@ -124,7 +124,7 @@
   <ul class="wy-breadcrumbs">
     <li><a href="index.html">Docs</a> &raquo;</li>
       
-          <li><a href="State.html">States</a> &raquo;</li>
+          <li><a href="State.html">State</a> &raquo;</li>
       
     <li>ParameterState</li>
       <li class="wy-breadcrumbs-aside">
@@ -151,10 +151,7 @@ <h1>ParameterState<a class="headerlink" href="#parameterstate" title="Permalink
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="#structure">Structure</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#execution">Execution</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#runtime-specification-of-parameters">Runtime Specification of Parameters</a></li>
-</ul>
-</li>
+<li class="toctree-l2"><a class="reference internal" href="#execution">Execution</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#class-reference">Class Reference</a></li>
 </ul>
 </li>
@@ -163,71 +160,75 @@ <h1>ParameterState<a class="headerlink" href="#parameterstate" title="Permalink
 <span class="target" id="module-ParameterState"></span><div class="section" id="overview">
 <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">¶</a></h2>
 <p>A parameterState belongs to either a <a class="reference internal" href="Mechanism.html"><span class="doc">mechanism</span></a> or a <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a>, and is used to represent and
-possibly modify the value of a parameter of its owner or it owner&#8217;s function.  It can receive one or more
-<a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjections</span></a> and/or <a class="reference internal" href="LearningProjection.html"><span class="doc">LearningProjections</span></a> that modify that
-parameter.   The projections received by a parameterState are listed in its
-<a class="reference internal" href="#ParameterState.ParameterState.receivesFromProjections" title="ParameterState.ParameterState.receivesFromProjections"><code class="xref any py py-attr docutils literal"><span class="pre">receivesFromProjections</span></code></a> attribute.
-Its <a class="reference internal" href="#ParameterState.ParameterState.function" title="ParameterState.ParameterState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> combines the values of these inputs, and uses the result to modify the value
-of the parameter for which it is responsible.</p>
+possibly modify the value of the parameter used by its owner or owner&#8217;s <code class="xref any docutils literal"><span class="pre">function</span></code>.  It can
+receive one or more <a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjections</span></a> and/or <a class="reference internal" href="LearningProjection.html"><span class="doc">LearningProjections</span></a> that
+modify the value of the parameter. The projections received by a parameterState are listed in its
+<code class="xref any docutils literal"><span class="pre">path_afferents</span></code> attribute. Its <a class="reference internal" href="#ParameterState.ParameterState.function" title="ParameterState.ParameterState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> combines the
+values of those projections, and uses the result to modify the value of the parameter that is used by the
+owner or its <code class="xref any docutils literal"><span class="pre">function</span></code>.</p>
 </div>
 <div class="section" id="creating-a-parameterstate">
 <span id="parameterstate-creation"></span><h2>Creating a ParameterState<a class="headerlink" href="#creating-a-parameterstate" title="Permalink to this headline">¶</a></h2>
-<p>A parameterState can be created by calling its constructor, but in general this is not necessary or advisable as
-parameterStates are created automatically when the mechanism or projection to which they belong is created.  The
-<code class="xref any docutils literal"><span class="pre">owner</span></code> of a parameterState must be a <a class="reference internal" href="Mechanism.html"><span class="doc">mechanism</span></a> or <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a>.  If the
-<code class="xref any docutils literal"><span class="pre">owner</span></code> is not explicitly specified, and can&#8217;t be determined by context, the parameterState
-will be assigned to the <span class="xref std std-ref">DefaultProcessingMechanism</span>.  One parameterState is created for each configurable
-parameter of its owner, as well as for each parameter that has been specified for that component&#8217;s <code class="xref std std-keyword docutils literal"><span class="pre">function</span></code>.
-Each parameterState is created using the specification of the parameter for which it is responsible, as described below.</p>
+<p>A ParameterState can be created by calling its constructor, but in general this is not necessary or advisable as
+ParameterStates are created automatically when the Mechanism or Projection to which they belong is created.  The
+<code class="xref any docutils literal"><span class="pre">owner</span></code> of a ParameterState must be a <a class="reference internal" href="Mechanism.html"><span class="doc">Mechanism</span></a> or <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a>.  One
+ParameterState is created for each configurable parameter of its owner, as well as for each configurable parameter
+of the owner&#8217;s <code class="xref any docutils literal"><span class="pre">function</span></code>.  Each ParameterState is created using the value specified for the
+corresponding parameter, as described below.</p>
 <div class="section" id="specifying-parameters">
 <span id="parameterstate-specifying-parameters"></span><h3>Specifying Parameters<a class="headerlink" href="#specifying-parameters" title="Permalink to this headline">¶</a></h3>
 <p>Parameters can be specified in one of several places:</p>
 <blockquote>
 <div><ul class="simple">
-<li>In the <strong>argument for the parameter</strong> of the constructor for the <a class="reference internal" href="Component.html"><span class="doc">component</span></a> to which the parameter
+<li>In the <strong>argument for the parameter</strong> of the constructor for the <a class="reference internal" href="Component.html"><span class="doc">Component</span></a> to which the parameter
 belongs (see <span class="xref std std-ref">Component_Specifying_Functions_and_Parameters</span> for additional details).</li>
 </ul>
 <ul class="simple">
-<li>In a <strong>parameter specification dictionary</strong> assigned to the <strong>params</strong> argument in the constructor for the
-component to which the parameter belongs. The entry for each parameter must use the name of the parameter
+<li>In a <em>parameter specification dictionary</em> assigned to the <strong>params</strong> argument in the constructor for the
+Component to which the parameter belongs. The entry for each parameter must use the name of the parameter
 (or a corresponding keyword) as its key, and the parameter&#8217;s specification as its value (see
-<a class="reference internal" href="#parameterstate-specification-examples"><span class="std std-ref">examples</span></a> below). Parameters for a component&#8217;s <code class="xref std std-keyword docutils literal"><span class="pre">function</span></code>
-must be specified in an entry with the key FUNCTION_PARAMS, the value of which is a parameter dictionary
-containing an entry for each of the function&#8217;s parameters to be specified.  When a value is assigned to a
-parameter in a parameter dictionary, it overrides any value assigned to the argument for the parameter in the
-component&#8217;s constructor.</li>
+<a class="reference internal" href="#parameterstate-specification-examples"><span class="std std-ref">examples</span></a> below). Parameters for a Component&#8217;s
+<code class="xref any docutils literal"><span class="pre">function</span></code> can be specified in an entry with the key <em>FUNCTION_PARAMS</em>,
+a value that is itself a parameter specification dictionary containing an entry for each of the
+function&#8217;s parameters to be specified.  When a value is assigned to a parameter in a specification dictionary,
+it overrides any value assigned to the argument for the parameter in the Component&#8217;s constructor.</li>
 </ul>
 <ul class="simple">
-<li>By direct assignment to the corresponding attribute of the component to which the parameter belongs.  The
-attribute always has the same name as the parameter and can be referenced using standard python attribute
-notation (e.g., myComponent.paramter_name).</li>
+<li>By direct assignment to the Component&#8217;s attribute for the parameter (see below).</li>
 </ul>
 <ul class="simple">
-<li>In the <code class="xref any docutils literal"><span class="pre">assign_params</span></code> method for the component.</li>
+<li>In the <code class="xref any docutils literal"><span class="pre">assign_params</span></code> method for the Component.</li>
 </ul>
 <ul class="simple">
-<li>When the component is executed, in the <strong>runtime_params</strong> argument of a call to component&#8217;s
-<a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.execute" title="Mechanism.Mechanism_Base.execute"><code class="xref any py py-meth docutils literal"><span class="pre">execute</span></code></a>
-method (only for a mechanism), or in a tuple with the mechanism where it is specified as part of the
-<a class="reference internal" href="Process.html#Process.Process_Base.pathway" title="Process.Process_Base.pathway"><code class="xref any py py-attr docutils literal"><span class="pre">pathway</span></code></a> for a process (see <a class="reference internal" href="#parameterstate-runtime-parameters"><span class="std std-ref">Runtime Specification</span></a> below).</li>
+<li>When the Component is executed, in the <strong>runtime_params</strong> argument of a call to component&#8217;s
+<a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.execute" title="Mechanism.Mechanism_Base.execute"><code class="xref any py py-meth docutils literal"><span class="pre">execute</span></code></a> method.</li>
 </ul>
 </div></blockquote>
-<p>The value specified for the parameter (either explicitly or by default) is assigned as the parameterState&#8217;s
-<a class="reference internal" href="#ParameterState.ParameterState.baseValue" title="ParameterState.ParameterState.baseValue"><code class="xref any py py-attr docutils literal"><span class="pre">baseValue</span></code></a>, and any projections assigned to it are added to its
-<a class="reference internal" href="#ParameterState.ParameterState.receivesFromProjections" title="ParameterState.ParameterState.receivesFromProjections"><code class="xref any py py-attr docutils literal"><span class="pre">receiveFromProjections</span></code></a> attribute. When the parameterState&#8217;s owner is
-executed, the parameterState&#8217;s <a class="reference internal" href="#ParameterState.ParameterState.baseValue" title="ParameterState.ParameterState.baseValue"><code class="xref any py py-attr docutils literal"><span class="pre">baseValue</span></code></a> is combined with the value of the projections
-it receives to determine the value of the parameter for which the parameterState is responsible
-(see <a class="reference internal" href="#parameterstate-execution"><span class="std std-ref">Execution</span></a> for details).</p>
-<p>The specification of a parameter can take any of the following forms:</p>
+<p>The value specified for a parameter (either explicitly or by default) is assigned to an attribute of the
+Component or its <code class="xref any docutils literal"><span class="pre">function</span></code> to which the parameter belongs.  The attribute has the same
+name as the parameter, and can be referenced using standard python attribute (&#8220;dot&#8221;) notation;  for example,
+the value of a parameter named <em>param</em> is assigned to an attribute named <code class="docutils literal"><span class="pre">param</span></code> that can be referenced as
+<code class="docutils literal"><span class="pre">my_component.param</span></code>).</p>
+<p>When the Component is executed, it updates the ParameterState by calling the ParameterState&#8217;s
+<a class="reference internal" href="#ParameterState.ParameterState.function" title="ParameterState.ParameterState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> with the attribute&#8217;s value for the parameter.  The result is
+assigned as the ParameterState&#8217;s <a class="reference internal" href="#ParameterState.ParameterState.value" title="ParameterState.ParameterState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>, which is used by the owner
+of the ParameterState as the value of the correspondign parameter of its own <code class="xref any docutils literal"><span class="pre">function</span></code>.</p>
+<div class="admonition note">
+<p class="first admonition-title">Note</p>
+<p class="last">It is important to note the distinction between the <a class="reference internal" href="#ParameterState.ParameterState.function" title="ParameterState.ParameterState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> of a ParameterState,
+and the <code class="xref any docutils literal"><span class="pre">function</span></code> of its owner.**  The former is used to determine the value of a parameter
+used by the latter (see <code class="xref any docutils literal"><span class="pre">figure</span></code>, and <a class="reference internal" href="State.html#state-execution"><span class="std std-ref">Execution</span></a> for additional details).</p>
+</div>
+<p>The specification of the initial value of a parameter can take any of the following forms:</p>
 <blockquote>
 <div><ul class="simple">
-<li>A <strong>value</strong>.  This must be a valid value for the parameter.  it creates a default parameterState,
-assigns the parameter&#8217;s default value as the parameterState&#8217;s <a class="reference internal" href="#ParameterState.ParameterState.baseValue" title="ParameterState.ParameterState.baseValue"><code class="xref any py py-attr docutils literal"><span class="pre">baseValue</span></code></a>,
-and assigns the parameter&#8217;s name as the name of the parameterState.</li>
+<li>A <strong>value</strong>.  This must be a valid value for the parameter.  it creates a default ParameterState,
+assigns the parameter&#8217;s default value as the ParameterState&#8217;s <a class="reference internal" href="#ParameterState.ParameterState.value" title="ParameterState.ParameterState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>,
+and assigns the parameter&#8217;s name as the name of the ParameterState.</li>
 </ul>
 <ul>
-<li><p class="first">A reference to an existing <strong>parameterState</strong> object.
-Its value must be a valid one for the parameter.</p>
+<li><p class="first">A reference to an existing <strong>ParameterState</strong> object.  It&#8217;s name must be the name of a parameter of the
+owner or its <code class="xref any docutils literal"><span class="pre">function</span></code>, and its value must be a valid one for the parameter.</p>
 <div class="admonition note">
 <p class="first admonition-title">Note</p>
 <p class="last">This capability is provided for generality and potential
@@ -236,272 +237,160 @@ <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this head
 </li>
 </ul>
 <ul class="simple">
-<li>A <a class="reference internal" href="Projection.html#projection-in-context-specification"><span class="std std-ref">projection specification</span></a>.  This creates a default parameterState,
-assigns the parameter&#8217;s default value as the parameterState&#8217;s <a class="reference internal" href="#ParameterState.ParameterState.baseValue" title="ParameterState.ParameterState.baseValue"><code class="xref any py py-attr docutils literal"><span class="pre">baseValue</span></code></a>,
-and assigns the parameter&#8217;s name as the name of the parameterState.  It also creates and/or assigns the
-specified projection, and assigns the parameterState as the projection&#8217;s
-<code class="xref any docutils literal"><span class="pre">receiver</span></code>.  The projection must be a <a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjection</span></a> or
-<a class="reference internal" href="LearningProjection.html"><span class="doc">LearningProjection</span></a>, and its value must be a valid one for the parameter.</li>
+<li>A Modulatory specification.  This can be an existing <a class="reference internal" href="ControlSignal.html"><span class="doc">ControlSignal</span></a> or <a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjection</span></a> object, a
+<a class="reference internal" href="LearningSignal.html"><span class="doc">LearningSignal</span></a> or <a class="reference internal" href="LearningProjection.html"><span class="doc">LearningProjection</span></a> object, a constructor or the class name for any of these, or the
+keywords <em>CONTROL</em> or <em>LEARNING</em>.  Any of these create a default ParameterState, assign the parameter&#8217;s default
+value as the ParameterState&#8217;s <a class="reference internal" href="#ParameterState.ParameterState.value" title="ParameterState.ParameterState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>, and assign the parameter&#8217;s name as the name of the
+ParameterState.  They also create and/or assign the corresponding ModulatorySignal and ModulatoryProjection,
+and assign the ParameterState as the ModulatoryProjection&#8217;s <code class="xref any docutils literal"><span class="pre">receiver</span></code>.
+If the ModulatorySignal and/or ModulatoryProjection already exist, their value(s) must be valid one(s) for the
+parameter.  Note that only Control and Learning Modulatory components can be assigned to a ParameterState
+(Gating components cannot &#8211; they can only be assgined to InputStates or OutputStates).</li>
 </ul>
 <ul>
-<li><p class="first">A <code class="xref any docutils literal"><span class="pre">ParamValueProjection</span></code> or 2-item (value, projection specification) <strong>tuple</strong>.  This creates a default
+<li><p class="first">A 2-item (value, Modulatory specification) <strong>tuple</strong>.  This creates a default
 parameterState, uses the value (1st) item of the tuple as parameterState&#8217;s
-<a class="reference internal" href="#ParameterState.ParameterState.baseValue" title="ParameterState.ParameterState.baseValue"><code class="xref any py py-attr docutils literal"><span class="pre">baseValue</span></code></a>, and assigns the parameter&#8217;s name as the name of the parameterState.
+<code class="xref any docutils literal"><span class="pre">base_value</span></code>, and assigns the parameter&#8217;s name as the name of the parameterState.
 The projection (2nd) item of the tuple is used to create and/or assign the specified projection, that is assigned
 the parameterState as its <code class="xref any docutils literal"><span class="pre">receiver</span></code>.  The projection must be a
 <a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjection</span></a> or <a class="reference internal" href="LearningProjection.html"><span class="doc">LearningProjection</span></a>, and its value must be a valid one for the parameter.</p>
 <div class="admonition note">
 <p class="first admonition-title">Note</p>
-<p class="last">Currently, the <code class="xref std std-keyword docutils literal"><span class="pre">function</span></code> of a component, although it can be specified a parameter value,
-cannot be assigned
-a ControlProjection or a LearningProjection. This may change in the future.</p>
+<p class="last">Currently, the <code class="xref any docutils literal"><span class="pre">function</span></code> of a Component, although it can be specified as a
+parameter value, cannot be assigned a <a class="reference internal" href="ModulatorySignal.html"><span class="doc">ModulatorySignal</span></a> or modified in the <strong>runtime_params</strong> argument of
+a call to a Mechanism&#8217;s <code class="xref any docutils literal"><span class="pre">execute</span></code> method. This may change in the future.</p>
 </div>
 </li>
 </ul>
 </div></blockquote>
-<p>The <strong>default value</strong> assigned to a parameterState is the default value of the argument for the parameter in the
-constructor for the parameter&#8217;s owner.  If the value of a parameter is specified as <code class="xref any docutils literal"><span class="pre">None</span></code>, <code class="xref any docutils literal"><span class="pre">NotImplemented</span></code>,
-or any other non-numeric value that is not one of those listed above, then no parameter state is created and the
-parameter cannot be modified by a <a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjection</span></a>, &#8216;LearningProjection&#8217;, or
-<a class="reference internal" href="#parameterstate-runtime-parameters"><span class="std std-ref">runtime specification</span></a>.</p>
-<p id="parameterstate-specification-examples"><strong>Examples</strong></p>
-<p>In the following example, a mechanism is created with a function that has four parameters,
-each of which is specified using a different format:</p>
-<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">my_mechanism</span> <span class="o">=</span> <span class="n">SomeMechanism</span><span class="p">(</span><span class="n">function</span><span class="o">=</span><span class="n">SomeFunction</span><span class="p">(</span><span class="n">param_a</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
-                                                   <span class="n">param_b</span><span class="o">=</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">ControlProjection</span><span class="p">),</span>
-                                                   <span class="n">param_c</span><span class="o">=</span><span class="p">(</span><span class="mi">36</span><span class="p">,</span> <span class="n">ControlProjection</span><span class="p">(</span><span class="n">function</span><span class="o">=</span><span class="n">Logistic</span><span class="p">),</span>
-                                                   <span class="n">param_d</span><span class="o">=</span><span class="n">ControlProjection</span><span class="p">)))</span>
+<p>The <strong>default value</strong> assigned to a ParameterState is the default value of the argument for the corresponding parameter
+in the constructor for the parameter&#8217;s owner.  If the value of a parameter is specified as <code class="xref any docutils literal"><span class="pre">None</span></code>, <code class="xref any docutils literal"><span class="pre">NotImplemented</span></code>,
+or any other non-numeric value that is not one of those listed above, then no ParameterState is created and the
+parameter cannot be modified by a <a class="reference internal" href="ModulatorySignal.html"><span class="doc">ModulatorySignal</span></a> or in the <strong>runtime_params</strong> argument of a call to a
+Mechanism&#8217;s <code class="xref any docutils literal"><span class="pre">execute</span></code> method.</p>
+<p class="rubric" id="parameterstate-specification-examples">Examples</p>
+<p>In the following example, a mechanism is created by specifying two of its parameters, as well as its
+<code class="xref any docutils literal"><span class="pre">function</span></code> and two of that function&#8217;s parameters, each using a different specification format:</p>
+<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">my_mechanism</span> <span class="o">=</span> <span class="n">RecurrentTransferMechanism</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">5</span>
+                                          <span class="n">noise</span><span class="o">=</span><span class="n">ControlSignal</span><span class="p">),</span>
+                                          <span class="n">function</span><span class="o">=</span><span class="n">Logistic</span><span class="p">(</span><span class="n">gain</span><span class="o">=</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">ControlSignal</span><span class="p">),</span>
+                                                            <span class="n">bias</span><span class="o">=</span><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">ControlSignal</span><span class="p">(</span>
+                                                                          <span class="n">modulation</span><span class="o">=</span><span class="n">ModulationParam</span><span class="o">.</span><span class="n">ADDITIVE</span><span class="p">))))</span>
 </pre></div>
 </div>
-<p>The first parameter of the mechanism&#8217;s function (<code class="docutils literal"><span class="pre">param_a</span></code>) is assigned a value directly; the second (<code class="docutils literal"><span class="pre">param_b</span></code>) is
-assigned a value and a ControlProjection; the third (<code class="docutils literal"><span class="pre">param_c</span></code>) is assigned a value and a
-<a class="reference internal" href="ControlProjection.html#controlprojection-structure"><span class="std std-ref">ControlProjection with a specified function</span></a>; and the fourth (<code class="docutils literal"><span class="pre">param_d</span></code>) is
-assigned just a <a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjection</span></a> (the default value for the parameter will be used).</p>
+<p>The first argument of the constructor for the Mechanism specifies its <code class="xref any docutils literal"><span class="pre">size</span></code> parameter by
+directly assigning a value to it.  The second specifies the <a class="reference internal" href="RecurrentTransferMechanism.html#ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism.noise" title="ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism.noise"><code class="xref any py py-attr docutils literal"><span class="pre">noise</span></code></a> parameter
+by assigning a default <a class="reference internal" href="ControlSignal.html"><span class="doc">ControlSignal</span></a>;  this will use the default value of the
+<a class="reference internal" href="RecurrentTransferMechanism.html#ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism.noise" title="ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism.noise"><code class="xref any py py-attr docutils literal"><span class="pre">noise</span></code></a> attribute.  The <strong>function</strong> argument is specified using the constructor for
+a <a class="reference internal" href="Function.html#Function.Logistic" title="Function.Logistic"><code class="xref any py py-class docutils literal"><span class="pre">Logistic</span></code></a> function, that specifies two of its parameters.  The <a class="reference internal" href="Function.html#Function.Logistic.gain" title="Function.Logistic.gain"><code class="xref any py py-attr docutils literal"><span class="pre">gain</span></code></a> parameter
+is specified using a tuple, the first item of which is the value to be assigned, and the second that specifies
+a default <a class="reference internal" href="ControlSignal.html"><span class="doc">ControlSignal</span></a>.  The <a class="reference internal" href="Function.html#Function.Logistic.bias" title="Function.Logistic.bias"><code class="xref any py py-attr docutils literal"><span class="pre">bias</span></code></a> parameter is also specified using a tuple,
+in this case with a constructor for the ControlSignal that specifies its <code class="xref any docutils literal"><span class="pre">modulation</span></code>
+parameter.</p>
 <p>In the following example, a <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a> is created, and its
-<a class="reference internal" href="MappingProjection.html#MappingProjection.MappingProjection.matrix" title="MappingProjection.MappingProjection.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a> parameter is assigned a random weight matrix (using a
-<a class="reference internal" href="MappingProjection.html#matrix-keywords"><span class="std std-ref">matrix keyword</span></a>) and <a class="reference internal" href="LearningProjection.html"><span class="doc">LearningProjection</span></a>:</p>
+<code class="xref any docutils literal"><span class="pre">matrix</span></code> parameter is assigned a random weight matrix (using a
+<code class="xref any docutils literal"><span class="pre">matrix</span> <span class="pre">keyword</span></code>) and <a class="reference internal" href="LearningSignal.html"><span class="doc">LearningSignal</span></a>:</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">my_mapping_projection</span> <span class="o">=</span> <span class="n">MappingProjection</span><span class="p">(</span><span class="n">sender</span><span class="o">=</span><span class="n">my_input_mechanism</span><span class="p">,</span>
                                           <span class="n">receiver</span><span class="o">=</span><span class="n">my_output_mechanism</span><span class="p">,</span>
-                                          <span class="n">matrix</span><span class="o">=</span><span class="p">(</span><span class="n">RANDOM_CONNECTIVITY_MATRIX</span><span class="p">,</span> <span class="n">LearningProjection</span><span class="p">))</span>
+                                          <span class="n">matrix</span><span class="o">=</span><span class="p">(</span><span class="n">RANDOM_CONNECTIVITY_MATRIX</span><span class="p">,</span> <span class="n">LearningSignal</span><span class="p">))</span>
 </pre></div>
 </div>
 <div class="admonition note">
 <p class="first admonition-title">Note</p>
-<p class="last">the <a class="reference internal" href="MappingProjection.html#MappingProjection.MappingProjection.matrix" title="MappingProjection.MappingProjection.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a> parameter belongs to the MappingProjection&#8217;s
+<p class="last">the <code class="xref any docutils literal"><span class="pre">matrix</span></code> parameter belongs to the MappingProjection&#8217;s
 <code class="xref any docutils literal"><span class="pre">function</span></code>;  however, since it has only one standard function,
 its arguments are available in the constructor for the projection (see
 <code class="xref any docutils literal"><span class="pre">Component_Specifying_Functions_and_Parameters</span></code> for a more detailed explanation).</p>
 </div>
+<p>The example below shows how to specify the parameters in the first example using a parameter specification dictionary:</p>
+<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">my_mechanism</span> <span class="o">=</span> <span class="n">RecurrentTransferMechanism</span><span class="p">(</span>
+                          <span class="n">size</span><span class="o">=</span><span class="mi">5</span>
+                          <span class="n">params</span><span class="o">=</span><span class="p">{</span><span class="n">SIZE</span><span class="p">:</span><span class="mi">5</span><span class="p">,</span>
+                                  <span class="s1">&#39;size&#39;</span><span class="p">:</span><span class="n">ControlSignal</span><span class="p">,</span>
+                                  <span class="n">FUNCTION</span><span class="p">:</span><span class="n">Logistic</span><span class="p">,</span>
+                                  <span class="n">FUNCTION_PARAMS</span><span class="p">:{</span><span class="n">GAIN</span><span class="p">:(</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">ControlSignal</span><span class="p">),</span>
+                                                   <span class="n">BIAS</span><span class="p">:(</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">ControlSignal</span><span class="p">(</span><span class="n">modulation</span><span class="o">=</span><span class="n">ModulationParam</span><span class="o">.</span><span class="n">ADDITIVE</span><span class="p">))))</span>
+</pre></div>
+</div>
+<p>There are several things to note here.  First, the parameter specification dictionary must be assigned to the
+<strong>params</strong> argument of the constructor.  Second, both methods for specifying a parameter &#8211; directly in an argument
+for the parameter, or in an entry of a parameter specification dictionary &#8211; can be used within the same constructor.
+If a particular parameter is specified in both ways (as is the case for <strong>size</strong> in the example), the value in the
+parameter specification dictionary takes priority (i.e., it is the value that will be assigned to the parameter).  If
+the parameter is specified in a parameter specification dictionary, the key for the parameter must be a string that is
+the same as the name of parameter (i.e., identical to how it appears as an arg in the constructor; as is shown
+for <strong>size</strong> in the example), or using a keyword that resolves to such a string (as shown for <em>NOISE</em> in the
+example).  Finally, the keyword <em>FUNCTION_PARAMS</em> can be used in a parameter specification dictionary to specify
+parameters of the Component&#8217;s <code class="xref any docutils literal"><span class="pre">function</span></code>, as shown for the <strong>gain</strong> and <strong>bias</strong> parameters of
+the Logistic function in the example.</p>
 </div>
 </div>
 <div class="section" id="structure">
 <span id="parameterstate-structure"></span><h2>Structure<a class="headerlink" href="#structure" title="Permalink to this headline">¶</a></h2>
-<p>Every parameterState is owned by a <a class="reference internal" href="Mechanism.html"><span class="doc">mechanism</span></a> or <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a>. It can receive one or more
-<a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjections</span></a> or <a class="reference internal" href="LearningProjection.html"><span class="doc">LearningProjections</span></a>.  However, the format (the
-number and type of its elements) of each must match the value of the parameter for which the parameterState is
-responsible.  When the parameterState is updated (i.e., the owner is executed) the values of its projections are
-combined (using the  parameterState&#8217;s <a class="reference internal" href="#ParameterState.ParameterState.function" title="ParameterState.ParameterState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>) and the result is used to modify the
-parameter for which the parameterState is responsible (see <a class="reference internal" href="#parameterstate-execution"><span class="std std-ref">Execution</span></a> below).  The
-projections received by a parameterState are listed in its <a class="reference internal" href="#ParameterState.ParameterState.receivesFromProjections" title="ParameterState.ParameterState.receivesFromProjections"><code class="xref any py py-attr docutils literal"><span class="pre">receiveFromProjections</span></code></a> attribute. Like all PsyNeuLink components, it has the three following core
-attributes:</p>
+<p>Every ParameterState is owned by a <a class="reference internal" href="Mechanism.html"><span class="doc">Mechanism</span></a> or <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a>. It can receive one or more
+<a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjections</span></a> or <a class="reference internal" href="LearningProjection.html"><span class="doc">LearningProjections</span></a>, that are listed in its
+<a class="reference internal" href="#ParameterState.ParameterState.mod_afferents" title="ParameterState.ParameterState.mod_afferents"><code class="xref any py py-attr docutils literal"><span class="pre">mod_afferents</span></code></a> attribute.  However, the <code class="xref any docutils literal"><span class="pre">value</span></code> of each
+must be compatible with (i.e., have the number and type of elements as) the value of the parameter for which the
+ParameterState is responsible.  A ParameterState cannot receive <a class="reference internal" href="PathwayProjection.html"><span class="doc">PathwayProjections</span></a> or
+<a class="reference internal" href="GatingProjection.html"><span class="doc">GatingProjections</span></a>.  When the ParameterState is updated (i.e., its owner is executed), it uses
+the values of its ControlProjections and LearningProjections to determine whether and how to modify its parameter&#8217;s
+attribute value, which is then assigned as the ParameterState&#8217;s <a class="reference internal" href="#ParameterState.ParameterState.value" title="ParameterState.ParameterState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>
+(see <a class="reference internal" href="#parameterstate-execution"><span class="std std-ref">Execution</span></a> for addition details). ParameterStates have the following core attributes:</p>
 <ul class="simple">
-<li><a class="reference internal" href="#ParameterState.ParameterState.variable" title="ParameterState.ParameterState.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>:  this serves as a template for the <code class="xref any docutils literal"><span class="pre">value</span></code> of
-each projection that the parameterState receives.  It must match the format (the number and type of elements) of the
-parameter for which the parameterState is responsible. Any projections the parameterState receives must, it turn,
-match the format of <code class="xref std std-keyword docutils literal"><span class="pre">variable</span></code>.</li>
-<li><a class="reference internal" href="#ParameterState.ParameterState.function" title="ParameterState.ParameterState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>:  this performs an elementwise (Hadamard) aggregation  of the values of the
-projections received by the parameterState.  The default function is <a class="reference internal" href="Function.html#Function.LinearCombination" title="Function.LinearCombination"><code class="xref any py py-class docutils literal"><span class="pre">LinearCombination</span></code></a> that multiplies the
-values. A custom function can be specified (e.g., to perform a Hadamard sum, or to handle non-numeric values in
-some way), so long as it generates a result that is compatible with the <a class="reference internal" href="#ParameterState.ParameterState.value" title="ParameterState.ParameterState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> of the
-parameterState.</li>
-<li><a class="reference internal" href="#ParameterState.ParameterState.value" title="ParameterState.ParameterState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>:  this is the value assigned to the parameter for which the parameterState is
-responsible.  It is the <a class="reference internal" href="#ParameterState.ParameterState.baseValue" title="ParameterState.ParameterState.baseValue"><code class="xref any py py-attr docutils literal"><span class="pre">baseValue</span></code></a> of the parameterState, modified by
-aggregated value of the projections received by the parameterState returned by the
-<a href="#id1"><span class="problematic" id="id2">`</span></a>function &lt;ParameterState.function&gt;.</li>
+<li><a class="reference internal" href="#ParameterState.ParameterState.variable" title="ParameterState.ParameterState.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>:  the parameter&#8217;s attribute value &#8211; that is, the value assigned to the
+attribute of the ParameterState&#8217;s owner for the parameter;  it can be thought of as the parameter&#8217;s &#8220;base&#8221; value.
+It is used by <a class="reference internal" href="#ParameterState.ParameterState.function" title="ParameterState.ParameterState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> to determine the <em>ParameterState&#8217;s</em> <a class="reference internal" href="#ParameterState.ParameterState.value" title="ParameterState.ParameterState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>.
+It must match the format (the number and type of elements) of the parameter&#8217;s attribute value.</li>
+<li><a class="reference internal" href="#ParameterState.ParameterState.mod_afferents" title="ParameterState.ParameterState.mod_afferents"><code class="xref any py py-attr docutils literal"><span class="pre">mod_afferents</span></code></a>: lists the <code class="xref any docutils literal"><span class="pre">ModulatoryProjections</span></code> that
+project to the ParameterState.  These specify either the value of the ParameterState&#8217;s
+<code class="xref any docutils literal"><span class="pre">function</span></code>, the <a class="reference internal" href="#ParameterState.ParameterState.value" title="ParameterState.ParameterState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> of the ParameterState itself
+(see <a href="#id1"><span class="problematic" id="id2">`</span></a>ModulatorySignals_Modulation).</li>
+<li><a class="reference internal" href="#ParameterState.ParameterState.function" title="ParameterState.ParameterState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>:  takes <code class="xref any docutils literal"><span class="pre">variable</span></code> as its input, and the values
+specified for the function&#8217;s parameters by any <code class="xref any docutils literal"><span class="pre">ModulatoryProjections</span></code> (listed in <a class="reference internal" href="#ParameterState.ParameterState.mod_afferents" title="ParameterState.ParameterState.mod_afferents"><code class="xref any py py-attr docutils literal"><span class="pre">mod_afferents</span></code></a>, and assigns its result to the ParameterState&#8217;s <a class="reference internal" href="#ParameterState.ParameterState.value" title="ParameterState.ParameterState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>.</li>
+<li><a class="reference internal" href="#ParameterState.ParameterState.value" title="ParameterState.ParameterState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>: the result of <a class="reference internal" href="#ParameterState.ParameterState.function" title="ParameterState.ParameterState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>; used by the ParameterState&#8217;s
+owner as the value of the parameter of its <code class="xref any docutils literal"><span class="pre">function</span></code> for which the the ParameterState is
+responsble when the owner executes.</li>
 </ul>
-<p>In addition, a parameterState has two other attributes that are used to determine the value it assigns to the
-parameter for which it is responsible (as shown in the <a class="reference internal" href="#parameterstate-figure"><span class="std std-ref">figure</span></a> below):</p>
-<ul class="simple">
-<li><a class="reference internal" href="#ParameterState.ParameterState.baseValue" title="ParameterState.ParameterState.baseValue"><code class="xref any py py-attr docutils literal"><span class="pre">baseValue</span></code></a>:  this is the default value of the parameter for which the
-parameterState is responsible.  It is combined with the result of the parameterState&#8217;s
-<a class="reference internal" href="#ParameterState.ParameterState.function" title="ParameterState.ParameterState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> to determine the value of the parameter for which the parameterState is
-responsible.</li>
-</ul>
-<ul class="simple">
-<li><a class="reference internal" href="#ParameterState.ParameterState.parameterModulationOperation" title="ParameterState.ParameterState.parameterModulationOperation"><code class="xref any py py-attr docutils literal"><span class="pre">parameterModulationOperation</span></code></a>: this determines how the
-result of the parameterState&#8217;s <a class="reference internal" href="#ParameterState.ParameterState.function" title="ParameterState.ParameterState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> (the aggregated values of the projections it
-receives) is combined with its <a class="reference internal" href="#ParameterState.ParameterState.baseValue" title="ParameterState.ParameterState.baseValue"><code class="xref any py py-attr docutils literal"><span class="pre">baseValue</span></code></a> to generate the value of the parameter
-for which it is responsible.  This must be a value of <a class="reference internal" href="Utilities.html#Utilities.ModulationOperation" title="Utilities.ModulationOperation"><code class="xref any py py-class docutils literal"><span class="pre">ModulationOperation</span></code></a>.  It can be specified in either
-the <strong>parameter_modulation_operation</strong> argument of the parameterState&#8217;s constructor, or in a
-PARAMETER_MODULATION_OPERATION entry of a <a class="reference internal" href="#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> in
-either the <strong>params</strong> argument of the parameterState&#8217;s constructor or within a PARAMETER_STATE_PARAMS
-dictionary in a <a class="reference internal" href="#parameterstate-runtime-parameters"><span class="std std-ref">runtime specification</span></a>. The default is value is
-<code class="xref any docutils literal"><span class="pre">ModulationOperation.PRODUCT</span></code>, which multiples the parameterState&#8217;s <a class="reference internal" href="#ParameterState.ParameterState.baseValue" title="ParameterState.ParameterState.baseValue"><code class="xref any py py-attr docutils literal"><span class="pre">baseValue</span></code></a> by the
-aggregated value of the result of the parameterState&#8217;s <a class="reference internal" href="#ParameterState.ParameterState.function" title="ParameterState.ParameterState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> to determine the value
-of the parameter.</li>
-</ul>
-<p>All of the user-modifiable parameters of a component are listed in its <code class="xref any docutils literal"><span class="pre">user_params</span></code> attribute,
-which is a read-only dictionary with an entry for each parameter.  The parameters of a component can be
-modified individually by assigning a value to the corresponding attribute, or in groups using the component&#8217;s
-<code class="xref any docutils literal"><span class="pre">assign_params</span></code> method.  The parameters for a component&#8217;s <code class="xref any docutils literal"><span class="pre">function</span></code>
+<p>All of the user-modifiable parameters of a Component are listed in its <code class="xref any docutils literal"><span class="pre">user_params</span></code> attribute,
+which is a read-only dictionary with an entry for each parameter.  The parameters of a Component can be
+modified individually by assigning a value to the corresponding attribute, or in groups using the Component&#8217;s
+<code class="xref any docutils literal"><span class="pre">assign_params</span></code> method.  The parameters for a Component&#8217;s <code class="xref any docutils literal"><span class="pre">function</span></code>
 are listed in its <code class="xref any docutils literal"><span class="pre">function_params</span></code> attribute, which is a read-only dictionary with an
-entry for each of its function&#8217;s parameter.  The parameters of a component&#8217;s function can be modified by
-assigning a value to the corresponding attribute of the component&#8217;s <code class="xref any docutils literal"><span class="pre">function_object</span></code>
-attribute (e.g., myMechanism.function_object.my_parameter), or in a FUNCTION_PARAMS dict in <code class="xref any docutils literal"><span class="pre">assign_params</span></code>.</p>
-<p id="parameterstate-figure">The figure below shows how the specifications for a parameter are combined by its parameterState to determine the
-parameter&#8217;s value.</p>
-<blockquote>
-<div><p><strong>How a ParameterState Determines the Value of a Parameter</strong></p>
-<div class="figure">
-<a class="reference internal image-reference" href="_images/ParameterState_fig_without_runtime_params.pdf"><img alt="ParameterState" src="_images/ParameterState_fig_without_runtime_params.pdf" /></a>
-<div class="legend">
-<table border="1" class="docutils">
-<colgroup>
-<col width="8%" />
-<col width="92%" />
-</colgroup>
-<thead valign="bottom">
-<tr class="row-odd"><th class="head">Component</th>
-<th class="head">Impact of ParameterState on Parameter Value</th>
-</tr>
-</thead>
-<tbody valign="top">
-<tr class="row-even"><td>A (brown)</td>
-<td><a class="reference internal" href="#ParameterState.ParameterState.baseValue" title="ParameterState.ParameterState.baseValue"><code class="xref any py py-attr docutils literal"><span class="pre">baseValue</span></code></a> (default value of the parameter)</td>
-</tr>
-<tr class="row-odd"><td>B (blue)</td>
-<td>parameterState&#8217;s <a class="reference internal" href="#ParameterState.ParameterState.function" title="ParameterState.ParameterState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> combines <code class="xref any docutils literal"><span class="pre">value</span></code> of projections</td>
-</tr>
-<tr class="row-even"><td>C (green)</td>
-<td>parameterState&#8217;s <a class="reference internal" href="#ParameterState.ParameterState.parameterModulationOperation" title="ParameterState.ParameterState.parameterModulationOperation"><code class="xref any py py-attr docutils literal"><span class="pre">parameterModulationOperation</span></code></a> combines projections and <a class="reference internal" href="#ParameterState.ParameterState.baseValue" title="ParameterState.ParameterState.baseValue"><code class="xref any py py-attr docutils literal"><span class="pre">baseValue</span></code></a></td>
-</tr>
-</tbody>
-</table>
-<p>In example, the values for the parameters (shown in brown) &#8211;&nbsp;<code class="docutils literal"><span class="pre">param_x</span></code> for the mechanism, and <code class="docutils literal"><span class="pre">param_y</span></code>
-for its <code class="docutils literal"><span class="pre">function</span></code> &#8211; specify the <a class="reference internal" href="#ParameterState.ParameterState.baseValue" title="ParameterState.ParameterState.baseValue"><code class="xref py py-data docutils literal"><span class="pre">baseValue</span></code></a> of the paramterState for
-each parameter (labeled &#8220;A&#8221; in the figure).  These are the values that will be used for
-those parameters absent any other influences. However, <code class="docutils literal"><span class="pre">param_y</span></code> is assigned a ControlProjection, so its value
-will also be determined by the value of the ControlProjection to its parameterState;  that will be combined
-with its <a class="reference internal" href="#ParameterState.ParameterState.baseValue" title="ParameterState.ParameterState.baseValue"><code class="xref any py py-attr docutils literal"><span class="pre">baseValue</span></code></a> using the
-<a class="reference internal" href="#ParameterState.ParameterState.parameterModulationOperation" title="ParameterState.ParameterState.parameterModulationOperation"><code class="xref any py py-attr docutils literal"><span class="pre">parameterModulationOperation</span></code></a>
-specified for the mechanism (<code class="docutils literal"><span class="pre">ModulationOperation.SUM</span></code>, shown in green, and labeled &#8220;C&#8221; in the figure). If
-there had been more than one ControlProjection specified, their values would have been combined using the
-parameterState&#8217;s <a class="reference internal" href="#ParameterState.ParameterState.function" title="ParameterState.ParameterState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> (lableled &#8220;B&#8221; in the figure), before combining the result
-with the
-baseValue.</p>
-</div>
-</div>
-</div></blockquote>
+entry for each of its function&#8217;s parameter.  The parameters of a Component&#8217;s function can be modified by
+assigning a value to the corresponding attribute of the Component&#8217;s <code class="xref any docutils literal"><span class="pre">function_object</span></code>
+attribute (e.g., myMechanism.function_object.my_parameter), or in FUNCTION_PARAMS dict in a
+parameter specification dictionary assigned to the <strong>params</strong> arg of a Component&#8217;s constructor or its
+<code class="xref any docutils literal"><span class="pre">assign_params</span></code> method.</p>
 </div>
 <div class="section" id="execution">
 <span id="parameterstate-execution"></span><h2>Execution<a class="headerlink" href="#execution" title="Permalink to this headline">¶</a></h2>
-<p>A parameterState cannot be executed directly.  It is executed when the mechanism to which it belongs is executed.
-When this occurs, the parameterState executes any <code class="xref any docutils literal"><span class="pre">ControlProjections</span></code> and/or <code class="xref any docutils literal"><span class="pre">LearningProjections</span></code> it receives, and
-calls its <a class="reference internal" href="#ParameterState.ParameterState.function" title="ParameterState.ParameterState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> to aggregate their values.  It then combines the result with the
-parameterState&#8217;s <a class="reference internal" href="#ParameterState.ParameterState.baseValue" title="ParameterState.ParameterState.baseValue"><code class="xref any py py-attr docutils literal"><span class="pre">baseValue</span></code></a> using its
-<a class="reference internal" href="#ParameterState.ParameterState.parameterModulationOperation" title="ParameterState.ParameterState.parameterModulationOperation"><code class="xref any py py-attr docutils literal"><span class="pre">parameterModulationOperation</span></code></a> attribute, combines the result with any
-<a class="reference internal" href="#parameterstate-runtime-parameters"><span class="std std-ref">runtime specification</span></a> for the parameter using the <a class="reference internal" href="Utilities.html#Utilities.ModulationOperation" title="Utilities.ModulationOperation"><code class="xref any py py-class docutils literal"><span class="pre">ModulationOperation</span></code></a>
-specified for runtime parameters, and finally assigns the result as the <a class="reference internal" href="#ParameterState.ParameterState.value" title="ParameterState.ParameterState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> of the
-parameterState.  This is used as the value of the parameter for which the parameterState is responsible.</p>
-<div class="section" id="runtime-specification-of-parameters">
-<span id="parameterstate-runtime-parameters"></span><h3>Runtime Specification of Parameters<a class="headerlink" href="#runtime-specification-of-parameters" title="Permalink to this headline">¶</a></h3>
-<div class="admonition note">
-<p class="first admonition-title">Note</p>
-<p class="last">This is an advanced feature, that is generally not required for most applications.</p>
-</div>
-<p>In general, it should not be necessary to modify parameters programmatically each time a process or system is
-executed or run; ordinarily, this should be done using <a class="reference internal" href="ControlProjection.html"><span class="doc">control projections</span></a> and/or
-<a class="reference internal" href="LearningProjection.html"><span class="doc">learning projections</span></a>.  However, if necessary, it is possible to modify parameters
-&#8220;on-the-fly&#8221; in two ways:  by specifying runtime parameters for a mechanism as part of a tuple where it is
-specified in the <a class="reference internal" href="Process.html#Process.Process_Base.pathway" title="Process.Process_Base.pathway"><code class="xref any py py-attr docutils literal"><span class="pre">pathway</span></code></a> of a process, or in the
-<a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.execute" title="Mechanism.Mechanism_Base.execute"><code class="xref any py py-meth docutils literal"><span class="pre">execute</span></code></a>
-method for a mechanism, process or system (see <a class="reference internal" href="Mechanism.html#mechanism-runtime-parameters"><span class="std std-ref">Runtime Parameters</span></a>).  By default, runtime assignment of
-a parameter value is <em>one-time</em>:  that is, it applies only for the round of execution in which it is specified,
-and the parameter&#8217;s value returns to the default for the instance of the component to which it belongs after
-execution.  The <code class="xref any docutils literal"><span class="pre">runtimeParamsStickyAssignmentPref</span></code> can be used to specify persistent assignment of a runtime value,
-however in general it is better to modify a parameter&#8217;s value permantently by assigning the value directly its
-corresponding attribute, or using the <code class="xref any docutils literal"><span class="pre">assign_params</span></code> method of its component.</p>
-<div class="admonition note">
-<p class="first admonition-title">Note</p>
-<p class="last">At this time, runtime specification can be used only  for the parameters of a mechanism or of its <code class="docutils literal"><span class="pre">function</span></code>.
-Since the function itself is not currently assigned a parameterState, it cannot be modified at runtime;  nor is
-there currently a method for runtime specification for the parameters of a MappingProjection.  These may be
-supported in the future.</p>
-</div>
-<p id="parameterstate-runtime-figure">The figure below shows how runtime paramter specification combines the others ways to specify a parameter&#8217;s value:</p>
-<blockquote>
-<div><div class="figure">
-<a class="reference internal image-reference" href="_images/ParameterState_fig_with_runtime_params.pdf"><img alt="ParameterState" src="_images/ParameterState_fig_with_runtime_params.pdf" /></a>
-<div class="legend">
-<table border="1" class="docutils">
-<colgroup>
-<col width="17%" />
-<col width="83%" />
-</colgroup>
-<thead valign="bottom">
-<tr class="row-odd"><th class="head">Component</th>
-<th class="head">Impact of ParameterState on Parameter Value (including at runtime)</th>
-</tr>
-</thead>
-<tbody valign="top">
-<tr class="row-even"><td>A (brown)</td>
-<td><code class="docutils literal"><span class="pre">baseValue</span></code> (default value of parameter``)</td>
-</tr>
-<tr class="row-odd"><td>B (blue)</td>
-<td>parameterState&#8217;s <code class="docutils literal"><span class="pre">function</span></code> combines <code class="docutils literal"><span class="pre">value</span></code> of  projections</td>
-</tr>
-<tr class="row-even"><td>C (green)</td>
-<td>runtime parameter influences projection-modulated <code class="docutils literal"><span class="pre">baseValue</span></code></td>
-</tr>
-<tr class="row-odd"><td>D (violet)</td>
-<td>runtime specification of parameter value</td>
-</tr>
-<tr class="row-even"><td>E (red)</td>
-<td>combined projection values modulate <code class="docutils literal"><span class="pre">baseValue</span></code></td>
-</tr>
-</tbody>
-</table>
-<ul class="simple">
-<li>1st example:  param_x is given a runtime value (violet) but no runtime ModulationOperation;
-param_y is given a runtime value (violet) and also a runtime ModulationOperation (red);</li>
-</ul>
-<blockquote>
-<div>the parameterState&#8217;s parameterModulationOperation is set to MULTIPLY (green).</div></blockquote>
-<ul class="simple">
-<li>2nd example:  param_x is given a runtime value (violet) and also a runtime ModulationOperation (red);
-param_y is given a runtime value (violet) but no runtime ModulationOperation;
-the parameterState&#8217;s parameterModulationOperation is set to SUM (green)</li>
-</ul>
-</div>
-</div>
-</div></blockquote>
-</div>
+<p>A ParameterState cannot be executed directly.  It is executed when the Mechanism to which it belongs is executed.
+When this occurs, the ParameterState executes any <code class="xref any docutils literal"><span class="pre">ModulatoryProjections</span></code> it receives, the values of which
+modulate parameters of the ParameterState&#8217;s <a class="reference internal" href="#ParameterState.ParameterState.function" title="ParameterState.ParameterState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>.  The ParameterState then calls
+its <a class="reference internal" href="#ParameterState.ParameterState.function" title="ParameterState.ParameterState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> and the result is used as the value of the parameter of its owner&#8217;s function
+when that executes.</p>
 </div>
 <div class="section" id="class-reference">
 <span id="parameterstate-class-reference"></span><h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalink to this headline">¶</a></h2>
 <dl class="class">
 <dt id="ParameterState.ParameterState">
-<em class="property">class </em><code class="descclassname">ParameterState.</code><code class="descname">ParameterState</code><span class="sig-paren">(</span><em>owner</em>, <em>reference_value=None                                             value=None</em>, <em>function=LinearCombination(operation=PRODUCT)</em>, <em>parameter_modulation_operation=ModulationOperation.MULTIPLY</em>, <em>params=None</em>, <em>name=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#ParameterState.ParameterState" title="Permalink to this definition">¶</a></dt>
-<dd><p>Implements a subclass of <a class="reference internal" href="State.html"><span class="doc">States</span></a> that represents and possibly modifies the value of a parameter for a mechanism,
+<em class="property">class </em><code class="descclassname">ParameterState.</code><code class="descname">ParameterState</code><span class="sig-paren">(</span><em>owner</em>, <em>reference_value=None                                             function=LinearCombination(operation=PRODUCT)</em>, <em>variable=None</em>, <em>parameter_modulation_operation=Modulation.MULTIPLY</em>, <em>params=None</em>, <em>name=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#ParameterState.ParameterState" title="Permalink to this definition">¶</a></dt>
+<dd><p>Implements a subclass of <a class="reference internal" href="State.html"><span class="doc">State</span></a> that represents and possibly modifies the value of a parameter for a mechanism,
 projection, or function.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
-<li><strong>owner</strong> (<em>Mechanism or Projection</em>) &#8211; the <a class="reference internal" href="Mechanism.html"><span class="doc">mechanism</span></a> or <a class="reference internal" href="Projection.html"><span class="doc">projection</span></a> to which to which the parameterState belongs; it must
-be specified or determinable from the context in which the parameterState is created. The owner of a
-parameterState for the parameter of a <code class="xref std std-keyword docutils literal"><span class="pre">function</span></code> should be specified as the mechanism or projection
+<li><strong>owner</strong> (<em>Mechanism or MappingProjection</em>) &#8211; the <a class="reference internal" href="Mechanism.html"><span class="doc">Mechanism</span></a> or <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a> to which to which the ParameterState belongs; it must be specified or
+determinable from the context in which the ParameterState is created. The owner of a ParameterState for the
+parameter of a <code class="xref any docutils literal"><span class="pre">function</span></code> should be specified as the Mechanism or Projection
 to which the function belongs.</li>
-<li><strong>reference_value</strong> (<em>number, list or np.ndarray</em>) &#8211; specifies the default value of the parameter for which the parameterState is responsible.</li>
-<li><strong>value</strong> (<em>number, list or np.ndarray</em>) &#8211; specifies the template for the parametersState&#8217;s <a class="reference internal" href="#ParameterState.ParameterState.variable" title="ParameterState.ParameterState.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> (since a parameterState&#8217;s
-<a class="reference internal" href="#ParameterState.ParameterState.variable" title="ParameterState.ParameterState.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> and <a class="reference internal" href="#ParameterState.ParameterState.value" title="ParameterState.ParameterState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> attributes must have the same format
-(number and type of elements).</li>
-<li><strong>function</strong> (<em>Function or method : default LinearCombination(operation=SUM)</em>) &#8211; specifies the function used to aggregate the values of the projections received by the parameterState.
-It must produce a result that has the same format (number and type of elements) as its input.</li>
-<li><strong>parameter_modulation_operation</strong> (<em>ModulationOperation : default ModulationOperation.MULTIPLY</em>) &#8211; specifies the operation by which the values of the projections received by the parameterState are used
-to modify its <a class="reference internal" href="#ParameterState.ParameterState.baseValue" title="ParameterState.ParameterState.baseValue"><code class="xref any py py-attr docutils literal"><span class="pre">baseValue</span></code></a> before assigning it as the value of the parameter for
-which the parameterState is responsible.</li>
+<li><strong>reference_value</strong> (<em>number, list or np.ndarray</em>) &#8211; specifies the default value of the parameter for which the ParameterState is responsible.</li>
+<li><strong>variable</strong> (<em>number, list or np.ndarray</em>) &#8211; specifies the parameter&#8217;s initial value and attribute value —&nbsp;that is, the value of the attribute of the
+ParameterState&#8217;s owner or its <code class="xref any docutils literal"><span class="pre">function</span></code> assigned to the parameter.</li>
+<li><strong>function</strong> (<em>Function or method : default LinearCombination(operation=SUM)</em>) &#8211; specifies the function used to convert the parameter&#8217;s attribute value (same as the ParameterState&#8217;s
+<a class="reference internal" href="#ParameterState.ParameterState.variable" title="ParameterState.ParameterState.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>) to the ParameterState&#8217;s <a class="reference internal" href="#ParameterState.ParameterState.value" title="ParameterState.ParameterState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>.</li>
 <li><strong>params</strong> (<em>Optional[Dict[param keyword, param value]]</em>) &#8211; a <a class="reference internal" href="#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> that can be used to specify the parameters for
 the parameterState or its function, and/or a custom function and its parameters.  Values specified for
 parameters in the dictionary override any assigned to those parameters in arguments of the constructor.</li>
@@ -519,61 +408,53 @@ <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this head
 <dl class="attribute">
 <dt id="ParameterState.ParameterState.owner">
 <code class="descname">owner</code><a class="headerlink" href="#ParameterState.ParameterState.owner" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>Mechanism</em> &#8211; the mechanism to which the parameterState belongs.</p>
-</dd></dl>
-
-<dl class="attribute">
-<dt id="ParameterState.ParameterState.receivesFromProjections">
-<code class="descname">receivesFromProjections</code><a class="headerlink" href="#ParameterState.ParameterState.receivesFromProjections" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>Optional[List[Projection]]</em> &#8211; a list of the projections received by the parameterState (i.e., for which it is a
-<code class="xref any docutils literal"><span class="pre">receiver</span></code>); generally these are <a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjection(s)</span></a>
-and/or <a class="reference internal" href="LearningProjection.html"><span class="doc">LearningProjection(s)</span></a>.</p>
+<dd><p><em>Mechanism or MappingProjection</em> &#8211; the <a class="reference internal" href="Mechanism.html"><span class="doc">Mechanism</span></a> or <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a> to which the parameterState belongs.</p>
 </dd></dl>
 
 <dl class="attribute">
 <dt id="ParameterState.ParameterState.variable">
 <code class="descname">variable</code><a class="headerlink" href="#ParameterState.ParameterState.variable" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>number, list or np.ndarray</em> &#8211; the template for the <code class="xref any docutils literal"><span class="pre">value</span></code> of each projection that the parameterState receives,
-each of which must match the format (number and types of elements) of the parameterState&#8217;s <code class="xref std std-keyword docutils literal"><span class="pre">variable</span></code>.</p>
+<dd><p><em>number, list or np.ndarray</em> &#8211; the parameter&#8217;s attribute value —&nbsp;that is, the value of the attribute of the
+ParameterState&#8217;s owner or its <code class="xref any docutils literal"><span class="pre">function</span></code> assigned to the parameter.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="ParameterState.ParameterState.function">
-<code class="descname">function</code><a class="headerlink" href="#ParameterState.ParameterState.function" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>CombinationFunction : default LinearCombination(operation=PRODUCT))</em> &#8211; performs an element-wise (Hadamard) aggregation  of the <code class="xref any docutils literal"><span class="pre">value</span></code> of each
-projection received by the parameterState.</p>
+<dt id="ParameterState.ParameterState.mod_afferents">
+<code class="descname">mod_afferents</code><a class="headerlink" href="#ParameterState.ParameterState.mod_afferents" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>Optional[List[Projection]]</em> &#8211; a list of the <a class="reference internal" href="ModulatoryProjection.html"><span class="doc">ModulatoryProjection</span></a> that project to the ParameterState (i.e.,
+for which it is a <code class="xref any docutils literal"><span class="pre">receiver</span></code>); these can be
+<a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjection(s)</span></a> and/or <a class="reference internal" href="LearningProjection.html"><span class="doc">LearningProjection(s)</span></a>,
+but not <a class="reference internal" href="GatingProjection.html"><span class="doc">GatingProjection</span></a>.  The <code class="xref any docutils literal"><span class="pre">value</span></code> of each
+must match the format (number and types of elements) of the ParameterState&#8217;s
+<a class="reference internal" href="#ParameterState.ParameterState.variable" title="ParameterState.ParameterState.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="ParameterState.ParameterState.baseValue">
-<code class="descname">baseValue</code><a class="headerlink" href="#ParameterState.ParameterState.baseValue" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>number, list or np.ndarray</em> &#8211; the default value for the parameterState.  It is combined with the aggregated value of any projections it
-receives using its <a class="reference internal" href="#ParameterState.ParameterState.parameterModulationOperation" title="ParameterState.ParameterState.parameterModulationOperation"><code class="xref any py py-attr docutils literal"><span class="pre">parameterModulationOperation</span></code></a>
-and then assigned to <a class="reference internal" href="#ParameterState.ParameterState.value" title="ParameterState.ParameterState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>.</p>
-</dd></dl>
-
-<dl class="attribute">
-<dt id="ParameterState.ParameterState.parameterModulationOperation">
-<code class="descname">parameterModulationOperation</code><a class="headerlink" href="#ParameterState.ParameterState.parameterModulationOperation" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>ModulationOperation : default ModulationOperation.PRODUCT</em> &#8211; the arithmetic operation used to combine the aggregated value of any projections is receives
-(the result of the parameterState&#8217;s <a class="reference internal" href="#ParameterState.ParameterState.function" title="ParameterState.ParameterState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>) with its
-<a class="reference internal" href="#ParameterState.ParameterState.baseValue" title="ParameterState.ParameterState.baseValue"><code class="xref any py py-attr docutils literal"><span class="pre">baseValue</span></code></a>, the result of which is assigned to <a class="reference internal" href="#ParameterState.ParameterState.value" title="ParameterState.ParameterState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>.</p>
+<dt id="ParameterState.ParameterState.function">
+<code class="descname">function</code><a class="headerlink" href="#ParameterState.ParameterState.function" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>Function : default Linear</em> &#8211; converts the parameter&#8217;s attribute value (same as the ParameterState&#8217;s <a class="reference internal" href="#ParameterState.ParameterState.variable" title="ParameterState.ParameterState.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>)
+to the ParameterState&#8217;s <a class="reference internal" href="#ParameterState.ParameterState.value" title="ParameterState.ParameterState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a>, under the influence of any
+<a class="reference internal" href="ModulatoryProjection.html"><span class="doc">ModulatoryProjections</span></a> received by the ParameterState (and listed in its
+<a class="reference internal" href="#ParameterState.ParameterState.mod_afferents" title="ParameterState.ParameterState.mod_afferents"><code class="xref any py py-attr docutils literal"><span class="pre">mod_afferents</span></code></a> attribute.  The result is assigned as the ParameterState&#8217;s
+<a class="reference internal" href="#"><span class="doc">value</span></a>.</p>
 </dd></dl>
 
 <dl class="attribute">
 <dt id="ParameterState.ParameterState.value">
 <code class="descname">value</code><a class="headerlink" href="#ParameterState.ParameterState.value" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>number, list or np.ndarray</em> &#8211; the aggregated value of the projections received by the ParameterState, combined with the
-<a class="reference internal" href="#ParameterState.ParameterState.baseValue" title="ParameterState.ParameterState.baseValue"><code class="xref any py py-attr docutils literal"><span class="pre">baseValue</span></code></a> using its
-<a class="reference internal" href="#ParameterState.ParameterState.parameterModulationOperation" title="ParameterState.ParameterState.parameterModulationOperation"><code class="xref any py py-attr docutils literal"><span class="pre">parameterModulationOperation</span></code></a>
-.  This is the value assigned to the parameter for which the parameterState is responsible.</p>
+<dd><p><em>number, list or np.ndarray</em> &#8211; the result returned by the ParameterState&#8217;s <a class="reference internal" href="#ParameterState.ParameterState.function" title="ParameterState.ParameterState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>, and used by the
+ParameterState&#8217;s owner or its <code class="xref any docutils literal"><span class="pre">function</span></code> as the value of the parameter for which the
+ParmeterState is responsible.  Note that this is not necessarily the same as the parameter&#8217;s attribute value
+(that is, the value of the owner&#8217;s attribute for the parameter), since the ParameterState&#8217;s
+<a class="reference internal" href="#ParameterState.ParameterState.function" title="ParameterState.ParameterState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> may modify the latter under the influence of its
+<a class="reference internal" href="#ParameterState.ParameterState.mod_afferents" title="ParameterState.ParameterState.mod_afferents"><code class="xref any py py-attr docutils literal"><span class="pre">mod_afferents</span></code></a>.</p>
 </dd></dl>
 
 <dl class="attribute">
 <dt id="ParameterState.ParameterState.name">
 <code class="descname">name</code><a class="headerlink" href="#ParameterState.ParameterState.name" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>str : default &lt;State subclass&gt;-&lt;index&gt;</em> &#8211; the name of the inputState.
-Specified in the <a class="reference internal" href="#ParameterState.ParameterState.name" title="ParameterState.ParameterState.name"><code class="xref any py py-attr docutils literal"><span class="pre">name</span></code></a> argument of the constructor for the outputState.  If not is specified, a default is
+Specified in the <strong>name</strong> argument of the constructor for the outputState.  If not is specified, a default is
 assigned by the StateRegistry of the mechanism to which the outputState belongs
 (see <span class="xref doc">Registry</span> for conventions used in naming, including for default and duplicate names).</p>
 <div class="admonition note">
@@ -589,7 +470,7 @@ <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this head
 <dt id="ParameterState.ParameterState.prefs">
 <code class="descname">prefs</code><a class="headerlink" href="#ParameterState.ParameterState.prefs" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>PreferenceSet or specification dict : State.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for the inputState.
-Specified in the <a class="reference internal" href="#ParameterState.ParameterState.prefs" title="ParameterState.ParameterState.prefs"><code class="xref any py py-attr docutils literal"><span class="pre">prefs</span></code></a> argument of the constructor for the projection;  if it is not specified, a default is
+Specified in the <strong>prefs</strong> argument of the constructor for the projection;  if it is not specified, a default is
 assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
 (see <span class="xref doc">PreferenceSet</span> for details).</p>
 </dd></dl>
diff --git a/docs/build/html/Preferences.html b/docs/build/html/Preferences.html
index 82e4161d37f..57b34fe4b9f 100644
--- a/docs/build/html/Preferences.html
+++ b/docs/build/html/Preferences.html
@@ -86,7 +86,7 @@
 <li class="toctree-l1"><a class="reference internal" href="System.html">System</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Process.html">Process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Mechanism.html">Mechanism</a></li>
-<li class="toctree-l1"><a class="reference internal" href="State.html">States</a></li>
+<li class="toctree-l1"><a class="reference internal" href="State.html">State</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Projection.html">Projection</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Run.html">Run</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Component.html">Component</a></li>
diff --git a/docs/build/html/Process.html b/docs/build/html/Process.html
index 792eec85bd5..cc96f6f8186 100644
--- a/docs/build/html/Process.html
+++ b/docs/build/html/Process.html
@@ -102,7 +102,7 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="Mechanism.html">Mechanism</a></li>
-<li class="toctree-l1"><a class="reference internal" href="State.html">States</a></li>
+<li class="toctree-l1"><a class="reference internal" href="State.html">State</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Projection.html">Projection</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Run.html">Run</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Component.html">Component</a></li>
@@ -202,8 +202,8 @@ <h1>Process<a class="headerlink" href="#process" title="Permalink to this headli
 patterns;  that requires the use of a  <a class="reference internal" href="System.html"><span class="doc">System</span></a>.  The mechanisms specified in the <a class="reference internal" href="#Process.Process_Base.pathway" title="Process.Process_Base.pathway"><code class="xref any py py-attr docutils literal"><span class="pre">pathway</span></code></a> for a process are generally
 <a class="reference internal" href="ProcessingMechanism.html"><span class="doc">ProcessingMechanisms</span></a>.  The projections between mechanisms in a process must be
 <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjections</span></a>.  These transmit the output of a mechanism (the projection&#8217;s
-<a class="reference internal" href="MappingProjection.html#MappingProjection.MappingProjection.sender" title="MappingProjection.MappingProjection.sender"><code class="xref any py py-attr docutils literal"><span class="pre">sender</span></code></a>) to the input of another mechanism (the projection&#8217;s
-<a class="reference internal" href="MappingProjection.html#MappingProjection.MappingProjection.receiver" title="MappingProjection.MappingProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a>). Specification of a <a class="reference internal" href="#Process.Process_Base.pathway" title="Process.Process_Base.pathway"><code class="xref any py py-attr docutils literal"><span class="pre">pathway</span></code></a> requires, at the least, a list
+<code class="xref any docutils literal"><span class="pre">sender</span></code>) to the input of another mechanism (the projection&#8217;s
+<code class="xref any docutils literal"><span class="pre">receiver</span></code>). Specification of a <a class="reference internal" href="#Process.Process_Base.pathway" title="Process.Process_Base.pathway"><code class="xref any py py-attr docutils literal"><span class="pre">pathway</span></code></a> requires, at the least, a list
 of mechanisms.  These can be specified directly, or in a <strong>tuple</strong> that also contains a set of
 <a class="reference internal" href="Mechanism.html#mechanism-runtime-parameters"><span class="std std-ref">runtime parameters</span></a> and/or a <a class="reference internal" href="System.html#system-execution-phase"><span class="std std-ref">phase</span></a> specification (see
 <a class="reference internal" href="#process-mechanism-specification"><span class="std std-ref">below</span></a>). Projections between a pair of mechanisms can be specified by
@@ -250,7 +250,7 @@ <h1>Process<a class="headerlink" href="#process" title="Permalink to this headli
 <dd><p class="first last">Projection specifications can be interposed between any two mechanisms in the <a class="reference internal" href="#Process.Process_Base.pathway" title="Process.Process_Base.pathway"><code class="xref any py py-attr docutils literal"><span class="pre">pathway</span></code></a> list.  This creates a
 projection from the preceding mechanism in the list to the one that follows it.  The projection specification can
 be an instance of a <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a>, the class name <code class="xref std std-keyword docutils literal"><span class="pre">MappingProjection</span></code>, a
-<a class="reference internal" href="MappingProjection.html#matrix-keywords"><span class="std std-ref">matrix keyword</span></a> for a type of MappingProjection (<a class="reference internal" href="Keywords.html#Keywords.MatrixKeywords.IDENTITY_MATRIX" title="Keywords.MatrixKeywords.IDENTITY_MATRIX"><code class="xref any py py-attr docutils literal"><span class="pre">IDENTITY_MATRIX</span></code></a>, <a class="reference internal" href="Keywords.html#Keywords.MatrixKeywords.FULL_CONNECTIVITY_MATRIX" title="Keywords.MatrixKeywords.FULL_CONNECTIVITY_MATRIX"><code class="xref any py py-attr docutils literal"><span class="pre">FULL_CONNECTIVITY_MATRIX</span></code></a>,
+<code class="xref any docutils literal"><span class="pre">matrix</span> <span class="pre">keyword</span></code> for a type of MappingProjection (<a class="reference internal" href="Keywords.html#Keywords.MatrixKeywords.IDENTITY_MATRIX" title="Keywords.MatrixKeywords.IDENTITY_MATRIX"><code class="xref any py py-attr docutils literal"><span class="pre">IDENTITY_MATRIX</span></code></a>, <a class="reference internal" href="Keywords.html#Keywords.MatrixKeywords.FULL_CONNECTIVITY_MATRIX" title="Keywords.MatrixKeywords.FULL_CONNECTIVITY_MATRIX"><code class="xref any py py-attr docutils literal"><span class="pre">FULL_CONNECTIVITY_MATRIX</span></code></a>,
 or <a class="reference internal" href="Keywords.html#Keywords.MatrixKeywords.RANDOM_CONNECTIVITY_MATRIX" title="Keywords.MatrixKeywords.RANDOM_CONNECTIVITY_MATRIX"><code class="xref any py py-attr docutils literal"><span class="pre">RANDOM_CONNECTIVITY_MATRIX</span></code></a>), or a dictionary with <a class="reference internal" href="Projection.html#projection-creation"><span class="std std-ref">specifications for the projection</span></a>.</p>
 </dd>
 </dl>
@@ -274,7 +274,7 @@ <h1>Process<a class="headerlink" href="#process" title="Permalink to this headli
 <a class="reference internal" href="Keywords.html#Keywords.MatrixKeywords.IDENTITY_MATRIX" title="Keywords.MatrixKeywords.IDENTITY_MATRIX"><code class="xref any py py-attr docutils literal"><span class="pre">IDENTITY_MATRIX</span></code></a> is used for the projection;  if the formats do not match, or
 <a class="reference internal" href="#process-learning"><span class="std std-ref">learning has been specified</span></a> either for the projection or the process,
 then a <a class="reference internal" href="Keywords.html#Keywords.MatrixKeywords.FULL_CONNECTIVITY_MATRIX" title="Keywords.MatrixKeywords.FULL_CONNECTIVITY_MATRIX"><code class="xref any py py-attr docutils literal"><span class="pre">FULL_CONNECTIVITY_MATRIX</span></code></a> is used. If the mechanism is the <a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> mechanism (i.e., first in the
-<a class="reference internal" href="#Process.Process_Base.pathway" title="Process.Process_Base.pathway"><code class="xref any py py-attr docutils literal"><span class="pre">pathway</span></code></a>), a <a class="reference internal" href="#process-input-and-ouput"><span class="std std-ref">ProcessInputState</span></a> will be used as the sender,
+<a class="reference internal" href="#Process.Process_Base.pathway" title="Process.Process_Base.pathway"><code class="xref any py py-attr docutils literal"><span class="pre">pathway</span></code></a>), a <code class="xref any docutils literal"><span class="pre">ProcessInputState</span></code> will be used as the sender,
 and an <a class="reference internal" href="Keywords.html#Keywords.MatrixKeywords.IDENTITY_MATRIX" title="Keywords.MatrixKeywords.IDENTITY_MATRIX"><code class="xref any py py-attr docutils literal"><span class="pre">IDENTITY_MATRIX</span></code></a> is used for the projection.</p>
 </dd>
 </dl>
@@ -288,14 +288,14 @@ <h1>Process<a class="headerlink" href="#process" title="Permalink to this headli
 When a process is created, a set of <a class="reference internal" href="#Process.Process_Base.processInputStates" title="Process.Process_Base.processInputStates"><code class="xref any py py-attr docutils literal"><span class="pre">ProcessInputStates</span></code></a> and <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjections</span></a> are automatically generated to transmit the process&#8217; input to its <a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> mechanism, as follows:</p>
 <ul>
 <li><dl class="first docutils">
-<dt>if the number of items in the <a class="reference internal" href="#Process.Process_Base.input" title="Process.Process_Base.input"><code class="xref any py py-attr docutils literal"><span class="pre">input</span></code></a> is the same as the number of <a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> inputStates:</dt>
+<dt>if the number of items in the <a class="reference internal" href="#Process.Process_Base.input" title="Process.Process_Base.input"><code class="xref any py py-attr docutils literal"><span class="pre">input</span></code></a> is the same as the number of <a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> input_states:</dt>
 <dd><p class="first last">a MappingProjection is created for each value of the input to an inputState of the <a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> mechanism;</p>
 </dd>
 </dl>
 </li>
 <li><dl class="first docutils">
 <dt>if the <a class="reference internal" href="#Process.Process_Base.input" title="Process.Process_Base.input"><code class="xref any py py-attr docutils literal"><span class="pre">input</span></code></a> has only one item but the <a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> mechanism has more than one inputState:</dt>
-<dd><p class="first last">a single ProcessInputState is created with projections to each of the <a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> mechanism inputStates;</p>
+<dd><p class="first last">a single ProcessInputState is created with projections to each of the <a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> mechanism input_states;</p>
 </dd>
 </dl>
 </li>
@@ -308,7 +308,7 @@ <h1>Process<a class="headerlink" href="#process" title="Permalink to this headli
 <li><dl class="first docutils">
 <dt>otherwise, if both the <a class="reference internal" href="#Process.Process_Base.input" title="Process.Process_Base.input"><code class="xref any py py-attr docutils literal"><span class="pre">input</span></code></a> and <a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> mechanism have more than one inputState, but the numbers are not equal:</dt>
 <dd><p class="first last">an error message is generated indicating that the there is an ambiguous mapping from the Process&#8217;
-input value to <a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> mechanism&#8217;s inputStates.</p>
+input value to <a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> mechanism&#8217;s input_states.</p>
 </dd>
 </dl>
 </li>
@@ -322,7 +322,7 @@ <h1>Process<a class="headerlink" href="#process" title="Permalink to this headli
 <code class="xref any docutils literal"><span class="pre">receiver</span></code>.  Learning occurs when a projection or process for which learning has been
 specified is executed.  Learning can be specified for a particular projection in a process, or for the entire
 process. It is specified for a particular projection by including a <a class="reference internal" href="LearningProjection.html#learningprojection-creation"><span class="std std-ref">LearningProjection specification</span></a> in the specification for the projection.  It is specified for the entire process by
-assigning either a <a class="reference internal" href="LearningProjection.html"><span class="doc">LearningProjection</span></a> specification, or the keyword <code class="xref any docutils literal"><span class="pre">LEARNING</span></code> to the <a class="reference internal" href="System.html#System.System_Base.learning" title="System.System_Base.learning"><code class="xref any py py-attr docutils literal"><span class="pre">learning</span></code></a> argument of the
+assigning either a <a class="reference internal" href="LearningProjection.html"><span class="doc">LearningProjection</span></a> specification, or the keyword <code class="xref any docutils literal"><span class="pre">LEARNING</span></code> to the <a class="reference internal" href="#Process.Process_Base.learning" title="Process.Process_Base.learning"><code class="xref any py py-attr docutils literal"><span class="pre">learning</span></code></a> argument of the
 process` constructor.  Specifying learning for a process will implement it for all eligible projections in the
 process (i.e., all <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjections</span></a>, excluding projections from
 the process&#8217; inputState to its <a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> mechanism, and projections from the <a class="reference internal" href="Keywords.html#Keywords.Keywords.TERMINAL" title="Keywords.Keywords.TERMINAL"><code class="xref any py py-attr docutils literal"><span class="pre">TERMINAL</span></code></a> mechanism to
@@ -401,7 +401,7 @@ <h1>Process<a class="headerlink" href="#process" title="Permalink to this headli
 <span class="n">my_process</span> <span class="o">=</span> <span class="n">process</span><span class="p">(</span><span class="n">pathway</span><span class="o">=</span><span class="p">[</span><span class="n">mechanism_1</span><span class="p">,</span> <span class="n">projection_A</span><span class="p">,</span> <span class="n">mechanism_2</span><span class="p">,</span> <span class="n">mechanism_3</span><span class="p">])</span>
 </pre></div>
 </div>
-<p><em>Inline projection specification using a keyword:</em>  In this <a class="reference internal" href="#Process.Process_Base.pathway" title="Process.Process_Base.pathway"><code class="xref any py py-attr docutils literal"><span class="pre">pathway</span></code></a>, a <a class="reference internal" href="MappingProjection.html#matrix-keywords"><span class="std std-ref">RANDOM_CONNECTIVITY_MATRIX</span></a>
+<p><em>Inline projection specification using a keyword:</em>  In this <a class="reference internal" href="#Process.Process_Base.pathway" title="Process.Process_Base.pathway"><code class="xref any py py-attr docutils literal"><span class="pre">pathway</span></code></a>, a <code class="xref any docutils literal"><span class="pre">RANDOM_CONNECTIVITY_MATRIX</span></code>
 is assigned as the projection between the first and second mechanisms:</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">my_process</span> <span class="o">=</span> <span class="n">process</span><span class="p">(</span><span class="n">pathway</span><span class="o">=</span><span class="p">[</span><span class="n">mechanism_1</span><span class="p">,</span> <span class="n">RANDOM_CONNECTIVITY_MATRIX</span><span class="p">,</span> <span class="n">mechanism_2</span><span class="p">,</span> <span class="n">mechanism_3</span><span class="p">])</span>
 </pre></div>
@@ -466,9 +466,9 @@ <h1>Process<a class="headerlink" href="#process" title="Permalink to this headli
 </ul>
 </div></blockquote>
 </li>
-<li><strong>default_projection_matrix</strong> (<em>keyword, list or ndarray : default DEFAULT_PROJECTION_MATRIX,</em>) &#8211; the type of matrix used for default projections (see <a class="reference internal" href="MappingProjection.html#MappingProjection.MappingProjection.matrix" title="MappingProjection.MappingProjection.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a> parameter for <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a>).</li>
+<li><strong>default_projection_matrix</strong> (<em>keyword, list or ndarray : default DEFAULT_PROJECTION_MATRIX,</em>) &#8211; the type of matrix used for default projections (see <a class="reference internal" href="Function.html#Function.LinearMatrix.matrix" title="Function.LinearMatrix.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a> parameter for <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a>).</li>
 <li><strong>learning</strong> (<em>Optional[LearningProjection spec]</em>) &#8211; implements <code class="xref any docutils literal"><span class="pre">learning</span></code> for all eligible projections in the process.</li>
-<li><strong>learning_rate</strong> (<em>float : None</em>) &#8211; set the learning rate for all mechanisms in the process (see <a class="reference internal" href="LearningProjection.html#LearningProjection.LearningProjection.learning_rate" title="LearningProjection.LearningProjection.learning_rate"><code class="xref any py py-attr docutils literal"><span class="pre">learning_rate</span></code></a> attribute for additional
+<li><strong>learning_rate</strong> (<em>float : None</em>) &#8211; set the learning rate for all mechanisms in the process (see <a class="reference internal" href="System.html#System.System_Base.learning_rate" title="System.System_Base.learning_rate"><code class="xref any py py-attr docutils literal"><span class="pre">learning_rate</span></code></a> attribute for additional
 information).</li>
 <li><strong>target</strong> (<em>List or ndarray : default ndarray of zeroes</em>) &#8211; the value assigned as the TARGET input for the <a class="reference internal" href="ObjectiveMechanism.html"><span class="doc">ObjectiveMechanism</span></a> to which the <a class="reference internal" href="Keywords.html#Keywords.Keywords.TERMINAL" title="Keywords.Keywords.TERMINAL"><code class="xref any py py-attr docutils literal"><span class="pre">TERMINAL</span></code></a> mechanism of the
 process projects (and assigned as its <a class="reference internal" href="Keywords.html#Keywords.Keywords.TARGET" title="Keywords.Keywords.TARGET"><code class="xref any py py-attr docutils literal"><span class="pre">TARGET</span></code></a> mechanism; used for <a class="reference internal" href="#process-learning"><span class="std std-ref">learning</span></a>).
@@ -535,7 +535,7 @@ <h1>Process<a class="headerlink" href="#process" title="Permalink to this headli
 <dt id="Process.Process_Base.processInputStates">
 <code class="descname">processInputStates</code><a class="headerlink" href="#Process.Process_Base.processInputStates" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>Optional[List[ProcessInputState]]</em> &#8211; used to represent the input to the process, and transmit this to the inputState(s) of its <a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a>
-mechanism.  Each processInputState sends a MappingProjection to one or more inputStates of the
+mechanism.  Each processInputState sends a MappingProjection to one or more input_states of the
 <a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> mechanism.</p>
 </dd></dl>
 
@@ -559,8 +559,8 @@ <h1>Process<a class="headerlink" href="#process" title="Permalink to this headli
 </dd></dl>
 
 <dl class="attribute">
-<dt id="Process.Process_Base.inputValue">
-<code class="descname">inputValue</code><a class="headerlink" href="#Process.Process_Base.inputValue" title="Permalink to this definition">¶</a></dt>
+<dt id="Process.Process_Base.input_value">
+<code class="descname">input_value</code><a class="headerlink" href="#Process.Process_Base.input_value" title="Permalink to this definition">¶</a></dt>
 <dd><p>2d np.array : default <code class="docutils literal"><span class="pre">variableInstanceDefault</span></code> &#8211; same as the <code class="xref std std-keyword docutils literal"><span class="pre">variable</span></code> attribute of the process; contains the values of the ProcessInputStates in its
 <a class="reference internal" href="#Process.Process_Base.processInputStates" title="Process.Process_Base.processInputStates"><code class="xref any py py-attr docutils literal"><span class="pre">processInputStates</span></code></a> attribute.</p>
 </dd></dl>
@@ -704,7 +704,7 @@ <h1>Process<a class="headerlink" href="#process" title="Permalink to this headli
 <dt id="Process.Process_Base.name">
 <code class="descname">name</code><a class="headerlink" href="#Process.Process_Base.name" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>str : default Process-&lt;index&gt;</em> &#8211; the name of the process.
-Specified in the <a class="reference internal" href="#Process.Process_Base.name" title="Process.Process_Base.name"><code class="xref any py py-attr docutils literal"><span class="pre">name</span></code></a> argument of the constructor for the process;
+Specified in the <strong>name</strong> argument of the constructor for the process;
 if not is specified, a default is assigned by ProcessRegistry
 (see <span class="xref std std-ref">Registry</span> for conventions used in naming, including for default and duplicate names).</p>
 </dd></dl>
@@ -713,7 +713,7 @@ <h1>Process<a class="headerlink" href="#process" title="Permalink to this headli
 <dt id="Process.Process_Base.prefs">
 <code class="descname">prefs</code><a class="headerlink" href="#Process.Process_Base.prefs" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>PreferenceSet or specification dict : Process.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for the process.
-Specified in the <a class="reference internal" href="#Process.Process_Base.prefs" title="Process.Process_Base.prefs"><code class="xref any py py-attr docutils literal"><span class="pre">prefs</span></code></a> argument of the constructor for the process;  if it is not specified, a default is
+Specified in the <strong>prefs</strong> argument of the constructor for the process;  if it is not specified, a default is
 assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py (see <span class="xref std std-ref">PreferenceSet</span> for details).</p>
 </dd></dl>
 
@@ -725,7 +725,7 @@ <h1>Process<a class="headerlink" href="#process" title="Permalink to this headli
 
 <dl class="method">
 <dt id="Process.Process_Base.execute">
-<code class="descname">execute</code><span class="sig-paren">(</span><em>input=None</em>, <em>target=None</em>, <em>execution_id=None</em>, <em>clock=&lt;PsyNeuLink.Globals.TimeScale.Clock object&gt;</em>, <em>time_scale=None</em>, <em>runtime_params=None</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Process.Process_Base.execute" title="Permalink to this definition">¶</a></dt>
+<code class="descname">execute</code><span class="sig-paren">(</span><em>input=None</em>, <em>target=None</em>, <em>execution_id=None</em>, <em>clock=&lt;PsyNeuLink.Globals.TimeScale.Clock object&gt;</em>, <em>time_scale=None</em>, <em>runtime_params=None</em>, <em>termination_processing=None</em>, <em>termination_learning=None</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Process.Process_Base.execute" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute the mechanisms specified in the process` <a class="reference internal" href="#Process.Process_Base.pathway" title="Process.Process_Base.pathway"><code class="xref any py py-attr docutils literal"><span class="pre">pathway</span></code></a> attribute.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
diff --git a/docs/build/html/ProcessingMechanism.html b/docs/build/html/ProcessingMechanism.html
index 6093555a8ad..b8d7160f483 100644
--- a/docs/build/html/ProcessingMechanism.html
+++ b/docs/build/html/ProcessingMechanism.html
@@ -35,7 +35,7 @@
         <link rel="search" title="Search" href="search.html"/>
     <link rel="top" title="PsyNeuLink 0.1 documentation" href="index.html"/>
         <link rel="up" title="Mechanism" href="Mechanism.html"/>
-        <link rel="next" title="TransferMechanism" href="TransferMechanism.html"/>
+        <link rel="next" title="IntegratorMechanism" href="IntegratorMechanism.html"/>
         <link rel="prev" title="Mechanism" href="Mechanism.html"/> 
 
   
@@ -90,14 +90,12 @@
 <li class="toctree-l1"><a class="reference internal" href="Process.html">Process</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="Mechanism.html">Mechanism</a><ul class="current">
 <li class="toctree-l2 current"><a class="current reference internal" href="#">Processing Mechanism</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="TransferMechanism.html">TransferMechanism</a></li>
 <li class="toctree-l3"><a class="reference internal" href="IntegratorMechanism.html">IntegratorMechanism</a></li>
 <li class="toctree-l3"><a class="reference internal" href="DDM.html">DDM</a></li>
+<li class="toctree-l3"><a class="reference internal" href="TransferMechanism.html">TransferMechanism</a></li>
+<li class="toctree-l3"><a class="reference internal" href="RecurrentTransferMechanism.html">RecurrentTransferMechanism</a></li>
+<li class="toctree-l3"><a class="reference internal" href="LCA.html">LCA</a></li>
 <li class="toctree-l3"><a class="reference internal" href="ObjectiveMechanism.html">ObjectiveMechanism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#overview">Overview</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#creating-a-processingmechanism">Creating a ProcessingMechanism</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#structure">Structure</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#execution">Execution</a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="AdaptiveMechanism.html">Adaptive Mechanism</a></li>
@@ -108,7 +106,7 @@
 <li class="toctree-l2"><a class="reference internal" href="Mechanism.html#class-reference">Class Reference</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="State.html">States</a></li>
+<li class="toctree-l1"><a class="reference internal" href="State.html">State</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Projection.html">Projection</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Run.html">Run</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Component.html">Component</a></li>
@@ -163,38 +161,14 @@
 <h1>Processing Mechanism<a class="headerlink" href="#processing-mechanism" title="Permalink to this headline">¶</a></h1>
 <div class="toctree-wrapper compound">
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="TransferMechanism.html">TransferMechanism</a></li>
 <li class="toctree-l1"><a class="reference internal" href="IntegratorMechanism.html">IntegratorMechanism</a></li>
 <li class="toctree-l1"><a class="reference internal" href="DDM.html">DDM</a></li>
+<li class="toctree-l1"><a class="reference internal" href="TransferMechanism.html">TransferMechanism</a></li>
+<li class="toctree-l1"><a class="reference internal" href="RecurrentTransferMechanism.html">RecurrentTransferMechanism</a></li>
+<li class="toctree-l1"><a class="reference internal" href="LCA.html">LCA</a></li>
 <li class="toctree-l1"><a class="reference internal" href="ObjectiveMechanism.html">ObjectiveMechanism</a></li>
 </ul>
 </div>
-<span class="target" id="module-ProcessingMechanism"></span><div class="section" id="overview">
-<h2>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">¶</a></h2>
-<p>A ProcessingMechanism is a type of <a class="reference internal" href="Mechanism.html"><span class="doc">Mechanism</span></a> that transforms its input in some way.  A
-ProcessingMechanism always receives its input either from another ProcessingMechanism, or from the input to a <a class="reference internal" href="Process.html"><span class="doc">process</span></a> or <a class="reference internal" href="System.html"><span class="doc">system</span></a> when it is executed.  Similarly, its output is generally conveyed to another
-ProcessingMechanism or used as the ouput for a process or system.  However, the output of a ProcessingMechanism may
-also be used by an <a class="reference internal" href="AdaptiveMechanism.html"><span class="doc">Adaptive Mechanism</span></a> to modify the parameters of other components (or its own).
-ProcessingMechanisms are always executed before all AdpativeMechanisms in the process and/or system to which they
-belong, so that any modificatons made by the AdpativeMechanism are available to all ProcessingMechanisms in the next
-round of execution.</p>
-</div>
-<div class="section" id="creating-a-processingmechanism">
-<span id="processingmechanism-creation"></span><h2>Creating a ProcessingMechanism<a class="headerlink" href="#creating-a-processingmechanism" title="Permalink to this headline">¶</a></h2>
-<p>A ProcessingMechanism can be created by using the standard Python method of calling the constructor for the desired
-type. Some types of ProcessingMechanism (for example, <a class="reference internal" href="ObjectiveMechanism.html"><span class="doc">ObjectiveMechanisms</span></a>) are also created
-when a system or process is created, if <code class="xref any docutils literal"><span class="pre">learning</span></code> and/or <code class="xref any docutils literal"><span class="pre">control</span></code> have been specified for it.</p>
-</div>
-<div class="section" id="structure">
-<span id="adaptivemechanism-structure"></span><h2>Structure<a class="headerlink" href="#structure" title="Permalink to this headline">¶</a></h2>
-<p>A ProcessingMechanism has the same basic structure as a <code class="xref any docutils literal"><span class="pre">Mechanism</span></code>.  See the documentation for
-individual subtypes of ProcessingMechanism for more specific information about their structure.</p>
-</div>
-<div class="section" id="execution">
-<span id="comparator-execution"></span><h2>Execution<a class="headerlink" href="#execution" title="Permalink to this headline">¶</a></h2>
-<p>A ProcessingMechanism always executes before any <a class="reference internal" href="AdaptiveMechanism.html"><span class="doc">AdaptiveMechanisms</span></a> in the process or
-system to which it belongs.</p>
-</div>
 </div>
 
 
@@ -204,7 +178,7 @@ <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this head
   
     <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
       
-        <a href="TransferMechanism.html" class="btn btn-neutral float-right" title="TransferMechanism" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
+        <a href="IntegratorMechanism.html" class="btn btn-neutral float-right" title="IntegratorMechanism" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
       
       
         <a href="Mechanism.html" class="btn btn-neutral" title="Mechanism" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
diff --git a/docs/build/html/Projection.html b/docs/build/html/Projection.html
index 185e0144b3e..20b7713e2fb 100644
--- a/docs/build/html/Projection.html
+++ b/docs/build/html/Projection.html
@@ -34,8 +34,8 @@
               href="genindex.html"/>
         <link rel="search" title="Search" href="search.html"/>
     <link rel="top" title="PsyNeuLink 0.1 documentation" href="index.html"/>
-        <link rel="next" title="MappingProjection" href="MappingProjection.html"/>
-        <link rel="prev" title="OutputState" href="OutputState.html"/> 
+        <link rel="next" title="PathwayProjection" href="PathwayProjection.html"/>
+        <link rel="prev" title="GatingSignal" href="GatingSignal.html"/> 
 
   
   <script src="_static/js/modernizr.min.js"></script>
@@ -88,11 +88,10 @@
 <li class="toctree-l1"><a class="reference internal" href="System.html">System</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Process.html">Process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Mechanism.html">Mechanism</a></li>
-<li class="toctree-l1"><a class="reference internal" href="State.html">States</a></li>
+<li class="toctree-l1"><a class="reference internal" href="State.html">State</a></li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Projection</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="MappingProjection.html">MappingProjection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="LearningProjection.html">LearningProjection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ControlProjection.html">ControlProjection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="PathwayProjection.html">PathwayProjection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ModulatoryProjection.html">ModulatoryProjection</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#overview">Overview</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#creating-a-projection">Creating a Projection</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#in-context-specification">In Context Specification</a></li>
@@ -158,11 +157,12 @@
 <h1>Projection<a class="headerlink" href="#projection" title="Permalink to this headline">¶</a></h1>
 <div class="toctree-wrapper compound">
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="MappingProjection.html">MappingProjection</a></li>
-<li class="toctree-l1"><a class="reference internal" href="LearningProjection.html">LearningProjection</a></li>
-<li class="toctree-l1"><a class="reference internal" href="ControlProjection.html">ControlProjection</a></li>
+<li class="toctree-l1"><a class="reference internal" href="PathwayProjection.html">PathwayProjection</a></li>
+<li class="toctree-l1"><a class="reference internal" href="ModulatoryProjection.html">ModulatoryProjection</a></li>
 </ul>
 </div>
+<div class="toctree-wrapper compound">
+</div>
 <span class="target" id="module-Projection"></span><div class="section" id="overview">
 <span id="projection-overview"></span><h2>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">¶</a></h2>
 <p>Projections allow information to be passed between mechanisms.  A projection takes its input from the
@@ -173,7 +173,7 @@ <h1>Projection<a class="headerlink" href="#projection" title="Permalink to this
 <li><dl class="first docutils">
 <dt><a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a></dt>
 <dd><p class="first last">These take the output of a <a class="reference internal" href="ProcessingMechanism.html"><span class="doc">ProcessingMechanism</span></a>, convert this by convolving it with
-the projection&#8217;s <a class="reference internal" href="MappingProjection.html#MappingProjection.MappingProjection.matrix" title="MappingProjection.MappingProjection.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a> parameter, and transmit the result as
+the projection&#8217;s <code class="xref any docutils literal"><span class="pre">matrix</span></code> parameter, and transmit the result as
 input to another ProcessingMechanism.  Typically, MappingProjections are used to connect mechanisms in the
 <a class="reference internal" href="Process.html#Process.Process_Base.pathway" title="Process.Process_Base.pathway"><code class="xref any py py-attr docutils literal"><span class="pre">pathway</span></code></a> of a <a class="reference internal" href="Process.html"><span class="doc">Process</span></a>.</p>
 </dd>
@@ -183,7 +183,7 @@ <h1>Projection<a class="headerlink" href="#projection" title="Permalink to this
 <ul>
 <li><dl class="first docutils">
 <dt><a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjection</span></a></dt>
-<dd><p class="first last">These take an <a class="reference internal" href="ControlProjection.html#ControlProjection.ControlProjection.allocation" title="ControlProjection.ControlProjection.allocation"><code class="xref any py py-attr docutils literal"><span class="pre">allocation</span></code></a> specification, usually the ouptput
+<dd><p class="first last">These take an <a class="reference internal" href="ControlProjection.html#ModulatoryProjections.ControlProjection.ControlProjection.allocation" title="ModulatoryProjections.ControlProjection.ControlProjection.allocation"><code class="xref any py py-attr docutils literal"><span class="pre">allocation</span></code></a> specification, usually the ouptput
 of a <a class="reference internal" href="ControlMechanism.html"><span class="doc">ControlMechanism</span></a>, and transmit this to the <a class="reference internal" href="ParameterState.html"><span class="doc">parameterState</span></a> of
 a ProcessingMechanism which uses this to modulate a parameter of the mechanism or its function.
 ControlProjections are typically used in the context of a <a class="reference internal" href="System.html"><span class="doc">System</span></a>.</p>
@@ -196,7 +196,7 @@ <h1>Projection<a class="headerlink" href="#projection" title="Permalink to this
 <dt><a class="reference internal" href="LearningProjection.html"><span class="doc">LearningProjection</span></a></dt>
 <dd><p class="first last">These take an <code class="xref any docutils literal"><span class="pre">error_signal</span></code>,&nbsp;usually the output of a
 <code class="xref any docutils literal"><span class="pre">MonitoringMechanism</span></code>, and transmit this to the <a class="reference internal" href="ParameterState.html"><span class="doc">parameterState</span></a> of a
-<a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a> which uses this to modify its <a class="reference internal" href="MappingProjection.html#MappingProjection.MappingProjection.matrix" title="MappingProjection.MappingProjection.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a>
+<a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a> which uses this to modify its <code class="xref any docutils literal"><span class="pre">matrix</span></code>
 parameter. LearningProjections are used when learning has been specified for a <a class="reference internal" href="Process.html#process-learning"><span class="std std-ref">process</span></a>
 or <a class="reference internal" href="System.html#system-execution-learning"><span class="std std-ref">system</span></a>.</p>
 </dd>
@@ -238,8 +238,14 @@ <h1>Projection<a class="headerlink" href="#projection" title="Permalink to this
 <div class="line"><br /></div>
 </div>
 <ul class="simple">
+<li>GATING_PROJECTION &#8211; a <a class="reference internal" href="GatingProjection.html"><span class="doc">GatingProjection</span></a> with the <code class="xref any docutils literal"><span class="pre">DefaultGatingMechanism</span></code> as its <code class="xref std std-keyword docutils literal"><span class="pre">sender</span></code>.</li>
+</ul>
+<div class="line-block">
+<div class="line"><br /></div>
+</div>
+<ul class="simple">
 <li>LEARNING_PROJECTION &#8211; a <a class="reference internal" href="LearningProjection.html"><span class="doc">LearningProjection</span></a>.  At present, this can only be used together with the
-specification of a MappingProjection (see <a class="reference internal" href="MappingProjection.html#mapping-matrix-specification"><span class="std std-ref">tuple</span></a> format).  If the
+specification of a MappingProjection (see <code class="xref any docutils literal"><span class="pre">tuple</span></code> format).  If the
 <code class="xref std std-keyword docutils literal"><span class="pre">receiver</span></code> of the MappingProjection projects to a <code class="xref any docutils literal"><span class="pre">MonitoringMechanism</span></code>,
 the latter will be used as the <code class="xref std std-keyword docutils literal"><span class="pre">sender</span></code> for the LearningProjection.  Otherwise,
 a MonitoringMechanism will be created for it
@@ -279,7 +285,7 @@ <h1>Projection<a class="headerlink" href="#projection" title="Permalink to this
 <span id="projection-automatic-creation"></span><h3>Automatic creation<a class="headerlink" href="#automatic-creation" title="Permalink to this headline">¶</a></h3>
 <p>Under some circumstances PsyNeuLink will automatically create a projection. For example, a <a class="reference internal" href="Process.html"><span class="doc">process</span></a>
 automatically generates a <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a> between adjacent mechanisms in its <a class="reference internal" href="Process.html#Process.Process_Base.pathway" title="Process.Process_Base.pathway"><code class="xref any py py-attr docutils literal"><span class="pre">pathway</span></code></a> if none is specified;
-and <a class="reference internal" href="LearningProjection.html"><span class="doc">LearningProjections</span></a> are automatically generated when <a class="reference internal" href="System.html#System.System_Base.learning" title="System.System_Base.learning"><code class="xref any py py-attr docutils literal"><span class="pre">learning</span></code></a> is specified for a
+and <a class="reference internal" href="LearningProjection.html"><span class="doc">LearningProjections</span></a> are automatically generated when <a class="reference internal" href="Process.html#Process.Process_Base.learning" title="Process.Process_Base.learning"><code class="xref any py py-attr docutils literal"><span class="pre">learning</span></code></a> is specified for a
 <a class="reference internal" href="Process.html#process-learning"><span class="std std-ref">process</span></a> or <a class="reference internal" href="System.html#system-execution-learning"><span class="std std-ref">system</span></a>.  Creating a <a class="reference internal" href="State.html"><span class="doc">state</span></a> will also
 automatically generate a projection and a mechanism as the projection&#8217;s <code class="xref any docutils literal"><span class="pre">sender</span></code> if none is
 specified in the constructor for the state (the type of projection and mechanism depend on the type of state &#8211; see
@@ -292,7 +298,7 @@ <h1>Projection<a class="headerlink" href="#projection" title="Permalink to this
 <div class="section" id="sender">
 <span id="projection-sender"></span><h3>Sender<a class="headerlink" href="#sender" title="Permalink to this headline">¶</a></h3>
 <p>This must be an <a class="reference internal" href="OutputState.html"><span class="doc">OutputState</span></a>.  The projection is assigned to the outputState&#8217;s
-<a class="reference internal" href="State.html#State.State_Base.sendsToProjections" title="State.State_Base.sendsToProjections"><code class="xref any py py-attr docutils literal"><span class="pre">sendsToProjections</span></code></a> list, and outputState&#8217;s <a class="reference internal" href="OutputState.html#OutputState.OutputState.value" title="OutputState.OutputState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> is used as the <code class="xref std std-keyword docutils literal"><span class="pre">variable</span></code> for projection&#8217;s <code class="xref any docutils literal"><span class="pre">function</span></code>.
+<a class="reference internal" href="State.html#State.State_Base.efferents" title="State.State_Base.efferents"><code class="xref any py py-attr docutils literal"><span class="pre">efferents</span></code></a> list, and outputState&#8217;s <a class="reference internal" href="OutputState.html#OutputState.OutputState.value" title="OutputState.OutputState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> is used as the <code class="xref std std-keyword docutils literal"><span class="pre">variable</span></code> for projection&#8217;s <code class="xref any docutils literal"><span class="pre">function</span></code>.
 A sender can be specified as:</p>
 <blockquote>
 <div><ul class="simple">
@@ -316,9 +322,9 @@ <h1>Projection<a class="headerlink" href="#projection" title="Permalink to this
 </ul>
 <ul class="simple">
 <li><a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjection</span></a>: if the projection&#8217;s <code class="xref any docutils literal"><span class="pre">receiver</span></code> belongs to a system, then the system&#8217;s
-<a class="reference internal" href="System.html#System.System_Base.controller" title="System.System_Base.controller"><code class="xref any py py-attr docutils literal"><span class="pre">controller</span></code></a> is used as the mechanism for the <code class="xref any docutils literal"><span class="pre">sender</span> <span class="pre">&lt;Projection.sender&gt;.</span>&#160; <span class="pre">Otherwise,</span> <span class="pre">the</span> <span class="pre">`DefaultControlMechanism</span></code>
-is used.  In either case, an outputState is added to the ControlMechanism and assigned as the
-projection&#8217;s <code class="xref any docutils literal"><span class="pre">sender</span></code>.</li>
+<a class="reference internal" href="System.html#System.System_Base.controller" title="System.System_Base.controller"><code class="xref any py py-attr docutils literal"><span class="pre">controller</span></code></a> is used as the mechanism for the <code class="xref any docutils literal"><span class="pre">sender</span></code>, an outputState is added to the
+ControlMechanism, and assigned as the projection&#8217;s <code class="xref any docutils literal"><span class="pre">sender</span></code>.  If the receiver does not
+belong to a system, the ControlProjection will be ignored.</li>
 </ul>
 <ul class="simple">
 <li><a class="reference internal" href="LearningProjection.html"><span class="doc">LearningProjection</span></a>: if it is to a MappingProjection that projects to the <a class="reference internal" href="Keywords.html#Keywords.Keywords.TERMINAL" title="Keywords.Keywords.TERMINAL"><code class="xref any py py-attr docutils literal"><span class="pre">TERMINAL</span></code></a> mechanism of a process,
@@ -331,8 +337,9 @@ <h1>Projection<a class="headerlink" href="#projection" title="Permalink to this
 <div class="section" id="receiver">
 <span id="projection-receiver"></span><h3>Receiver<a class="headerlink" href="#receiver" title="Permalink to this headline">¶</a></h3>
 <p>This must be an <a class="reference internal" href="InputState.html"><span class="doc">InputState</span></a> or a <a class="reference internal" href="ParameterState.html"><span class="doc">ParameterState</span></a>.  The projection is assigned to the receiver&#8217;s
-<a class="reference internal" href="State.html#State.State_Base.receivesFromProjections" title="State.State_Base.receivesFromProjections"><code class="xref any py py-attr docutils literal"><span class="pre">receivesFromProjections</span></code></a> list, and the output of the projection&#8217;s
-<code class="xref any docutils literal"><span class="pre">function</span></code> is transmitted to its receiver.  A <code class="xref any docutils literal"><span class="pre">receiver</span></code> can be specified as:</p>
+<a class="reference internal" href="State.html#State.State_Base.path_afferents" title="State.State_Base.path_afferents"><code class="xref any py py-attr docutils literal"><span class="pre">path_afferents</span></code></a> list, and the output of the projection&#8217;s
+<code class="xref any docutils literal"><span class="pre">function</span></code> is transmitted to its receiver.  A <code class="xref any docutils literal"><span class="pre">receiver</span></code>
+can be specified as:</p>
 <blockquote>
 <div><ul class="simple">
 <li>an existing <strong>inputState</strong>;</li>
@@ -371,10 +378,10 @@ <h1>Projection<a class="headerlink" href="#projection" title="Permalink to this
   
     <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
       
-        <a href="MappingProjection.html" class="btn btn-neutral float-right" title="MappingProjection" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
+        <a href="PathwayProjection.html" class="btn btn-neutral float-right" title="PathwayProjection" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
       
       
-        <a href="OutputState.html" class="btn btn-neutral" title="OutputState" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
+        <a href="GatingSignal.html" class="btn btn-neutral" title="GatingSignal" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
       
     </div>
   
diff --git a/docs/build/html/Run.html b/docs/build/html/Run.html
index 934f3222ef9..311b7afaca5 100644
--- a/docs/build/html/Run.html
+++ b/docs/build/html/Run.html
@@ -35,7 +35,7 @@
         <link rel="search" title="Search" href="search.html"/>
     <link rel="top" title="PsyNeuLink 0.1 documentation" href="index.html"/>
         <link rel="next" title="Component" href="Component.html"/>
-        <link rel="prev" title="MappingProjection" href="MappingProjection.html"/> 
+        <link rel="prev" title="GatingProjection" href="GatingProjection.html"/> 
 
   
   <script src="_static/js/modernizr.min.js"></script>
@@ -88,7 +88,7 @@
 <li class="toctree-l1"><a class="reference internal" href="System.html">System</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Process.html">Process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Mechanism.html">Mechanism</a></li>
-<li class="toctree-l1"><a class="reference internal" href="State.html">States</a></li>
+<li class="toctree-l1"><a class="reference internal" href="State.html">State</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Projection.html">Projection</a></li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Run</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="#overview">Overview</a><ul>
@@ -240,15 +240,15 @@ <h3>Scope of Execution<a class="headerlink" href="#scope-of-execution" title="Pe
 </div>
 <div class="section" id="inputs">
 <span id="run-inputs"></span><h3>Inputs<a class="headerlink" href="#inputs" title="Permalink to this headline">¶</a></h3>
-<p>The <code class="xref std std-keyword docutils literal"><span class="pre">run</span></code> function presents the inputs for each round of execution to the inputStates of the relevant
+<p>The <code class="xref std std-keyword docutils literal"><span class="pre">run</span></code> function presents the inputs for each round of execution to the input_states of the relevant
 mechanisms. These are specified in the <strong>inputs</strong> argument of the <code class="xref std std-keyword docutils literal"><span class="pre">execute</span></code> or <code class="xref std std-keyword docutils literal"><span class="pre">run</span></code> method.
-For a mechanism, they comprise the input value for each of the mechanism&#8217;s <a class="reference internal" href="InputState.html"><span class="doc">inputStates</span></a>.  For a process
+For a mechanism, they comprise the input value for each of the mechanism&#8217;s <a class="reference internal" href="InputState.html"><span class="doc">input_states</span></a>.  For a process
 or system, they comprise the input values for the inputState(s) of the <a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> mechanism(s).  Input values can be
 specified in one of two ways: <a class="reference internal" href="#run-inputs-sequence-format"><span class="std std-ref">sequence format</span></a> and <code class="xref any docutils literal"><span class="pre">mechanism</span> <span class="pre">format</span></code>.
 Sequence format is more complex, but does not require the specification of mechanisms by name, and thus may better
 suited for automated means of generating inputs.  Mechanism format requires that inputs be assigned to mechanisms by
 name, but is easier to use (as the order in which the mechanisms are specified does not matter).  Both formats require
-that inputs be specified as nested lists or ndarrays, that define the number of executions, mechanisms, inputStates
+that inputs be specified as nested lists or ndarrays, that define the number of executions, mechanisms, input_states
 and elements of each input value.  These factors determine the levels of nesting required for a list, or
 the dimensionality (number of axes) for an ndarray.  They are described below, followed by a description of the two
 formats.</p>
@@ -268,10 +268,10 @@ <h3>Scope of Execution<a class="headerlink" href="#scope-of-execution" title="Pe
 since that separates the inputs for each mechanism into separate entries of a dictionary.</li>
 </ul>
 <ul class="simple">
-<li><strong>Number of inputStates.</strong> In general, mechanisms have a single (&#8220;primary&#8221;) inputState; however, some types of
+<li><strong>Number of input_states.</strong> In general, mechanisms have a single (&#8220;primary&#8221;) inputState; however, some types of
 mechanisms can have more than one (see <a class="reference internal" href="Mechanism.html#mechanism-inputstates"><span class="std std-ref">InputStates</span></a>).  If any <a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> mechanism in a process or
 system has more than one inputState, then the next level of nesting of a list, or next higher axis of an ndarray,
-is used for the set of inputStates for each mechanism.</li>
+is used for the set of input_states for each mechanism.</li>
 </ul>
 <ul class="simple">
 <li><strong>Number of elements for the value of an inputState.</strong> The input for an inputState can be a single element (e.g.,
@@ -300,7 +300,7 @@ <h3>Scope of Execution<a class="headerlink" href="#scope-of-execution" title="Pe
 <div><p><em>Lists:</em> if there is more than one round, then the outermost level of the list is used for the sequence of
 executions.  If there is only one <a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> mechanism and it has only one inputState (the most common
 case), then a single sublist is used for the input of each round.  If the <a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> mechanism has more
-than one inputState, then the entry for each round is a sublist of the inputStates, each entry of which is a
+than one inputState, then the entry for each round is a sublist of the input_states, each entry of which is a
 sublist containing the input for that inputState.  If there is more than one mechanism, but none have more than
 one inputState, then a sublist is used for each mechanism in each round, within which a sublist is used for the
 input for that mechanism.  If there is more than one mechanism, and any have more than one inputState,
@@ -311,10 +311,10 @@ <h3>Scope of Execution<a class="headerlink" href="#scope-of-execution" title="Pe
 more than one round, only one <a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> mechanism, and that has only one inputState (the most common case),
 then axis 0 is used for round, and axis 1 for inputs per round.  In the extreme, if there are multiple rounds,
 more than one <a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> mechanism, and more than one inputState for one or more of the <a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> mechanisms,
-then axis 0 is used for rounds, axis 1 for mechanisms within round, axis 2 for inputStates of each mechanism, and
+then axis 0 is used for rounds, axis 1 for mechanisms within round, axis 2 for input_states of each mechanism, and
 axis 3 for the input to each inputState of a mechanism.  Note that if <em>any</em> mechanism being run (directly, or as
 one of the <a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> mechanisms of a process or system) has more than one inputState, then an axis must be
-committed to inputStates, and the input to every inputState of every mechanism must be specified in that axis
+committed to input_states, and the input to every inputState of every mechanism must be specified in that axis
 (i.e., even for those mechanisms that have a single inputState).</p>
 <div class="figure align-center" id="id5">
 <span id="run-sequence-format-fig"></span><a class="reference internal image-reference" href="_images/Sequence_format_input_specs_fig.png"><img alt="Example input specifications in sequence format" src="_images/Sequence_format_input_specs_fig.png" style="width: 1553.25px; height: 1622.25px;" /></a>
@@ -332,9 +332,9 @@ <h3>Scope of Execution<a class="headerlink" href="#scope-of-execution" title="Pe
 process or system being run.  The key for each entry is the <a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> mechanism, and the value contains either a list
 or ndarray specifying the sequence of inputs for that mechanism, one for each round of execution.  If a list is used,
 and the mechanism has more than one inputState, then a sublist is used in each item of the list to specify the inputs
-for each of the mechanism&#8217;s inputStates for that round.  If an ndarray is used, axis 0 is used for the sequence of
+for each of the mechanism&#8217;s input_states for that round.  If an ndarray is used, axis 0 is used for the sequence of
 rounds. If the mechanism has a single inputState, then axis 1 is used for the input for each round.  If the mechanism
-has multiple inputStates, then axis 1 is used for the inputStates, and axis 2 is used for the input to each
+has multiple input_states, then axis 1 is used for the input_states, and axis 2 is used for the input to each
 inputState for each round.</p>
 <blockquote>
 <div><div class="figure align-center" id="id6">
@@ -352,7 +352,7 @@ <h3>Scope of Execution<a class="headerlink" href="#scope-of-execution" title="Pe
 specified in the <code class="xref std std-keyword docutils literal"><span class="pre">initial_values</span></code> argument of <code class="xref std std-keyword docutils literal"><span class="pre">run</span></code>, as a dictionary. The key for each entry must
 be a mechanism designated as <a class="reference internal" href="Keywords.html#Keywords.Keywords.INITIALIZE_CYCLE" title="Keywords.Keywords.INITIALIZE_CYCLE"><code class="xref any py py-attr docutils literal"><span class="pre">INITIALIZE_CYCLE</span></code></a>, and its value an input for the mechanism to be used as its initial
 value.  The size of the input (length of the outermost level if it is a list, or axis 0 if it is an np.ndarray),
-must equal the number of inputStates of the mechanism, and the size of each value must match (in number and type of
+must equal the number of input_states of the mechanism, and the size of each value must match (in number and type of
 elements) that of the <a class="reference internal" href="InputState.html#InputState.InputState.variable" title="InputState.InputState.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> for the corresponding inputState.</p>
 </div>
 <div class="section" id="targets">
@@ -363,7 +363,7 @@ <h3>Scope of Execution<a class="headerlink" href="#scope-of-execution" title="Pe
 any of three formats.  The two formats used for <code class="xref std std-keyword docutils literal"><span class="pre">inputs</span></code> (<a class="reference internal" href="#run-inputs-sequence-format"><span class="std std-ref">sequence</span></a> and
 <a class="reference internal" href="#run-inputs-mechanism-format"><span class="std std-ref">mechanism</span></a> format) can also be used for targets.  However, the format of the lists or
 ndarrays is simpler, since each <a class="reference internal" href="Keywords.html#Keywords.Keywords.TARGET" title="Keywords.Keywords.TARGET"><code class="xref any py py-attr docutils literal"><span class="pre">TARGET</span></code></a> mechanism is assigned only a single target value, so there is never the need
-for the extra level of nesting (or dimension of ndarray) used for inputStates in the specification of <code class="xref std std-keyword docutils literal"><span class="pre">inputs</span></code>.
+for the extra level of nesting (or dimension of ndarray) used for input_states in the specification of <code class="xref std std-keyword docutils literal"><span class="pre">inputs</span></code>.
 Details concerning the use of the <a class="reference internal" href="#run-targets-sequence-format"><span class="std std-ref">sequence</span></a>  and
 <a class="reference internal" href="#run-targets-mechanism-format"><span class="std std-ref">mechanism</span></a> formats for targets is described below. Targets can also be specified
 as a <a class="reference internal" href="#run-targets-function-format"><span class="std std-ref">function</span></a> (for example, to allow the target to depend on the outcome of processing).</p>
@@ -381,7 +381,7 @@ <h3>Scope of Execution<a class="headerlink" href="#scope-of-execution" title="Pe
 the <a class="reference internal" href="Keywords.html#Keywords.Keywords.TERMINAL" title="Keywords.Keywords.TERMINAL"><code class="xref any py py-attr docutils literal"><span class="pre">TERMINAL</span></code></a> mechanism with which the target is compared (that is, the mechanism that provides the
 <code class="xref any docutils literal"><span class="pre">ComparatorMechanism's</span></code> <code class="xref any docutils literal"><span class="pre">sample</span></code>
 value, then the target must be within that range (for example, if the <a class="reference internal" href="Keywords.html#Keywords.Keywords.TERMINAL" title="Keywords.Keywords.TERMINAL"><code class="xref any py py-attr docutils literal"><span class="pre">TERMINAL</span></code></a> mechanism is a
-<a class="reference internal" href="TransferMechanism.html"><span class="doc">TransferMechanism</span></a> that uses a <a class="reference internal" href="Function.html#Function.Logistic" title="Function.Logistic"><code class="xref any py py-class docutils literal"><span class="pre">Logistic</span></code></a> function, it&#8217;s <a class="reference internal" href="TransferMechanism.html#TransferMechanism.TransferMechanism.range" title="TransferMechanism.TransferMechanism.range"><code class="xref any py py-attr docutils literal"><span class="pre">range</span></code></a> is
+<a class="reference internal" href="TransferMechanism.html"><span class="doc">TransferMechanism</span></a> that uses a <a class="reference internal" href="Function.html#Function.Logistic" title="Function.Logistic"><code class="xref any py py-class docutils literal"><span class="pre">Logistic</span></code></a> function, it&#8217;s <a class="reference internal" href="TransferMechanism.html#ProcessingMechanisms.TransferMechanism.TransferMechanism.range" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.range"><code class="xref any py py-attr docutils literal"><span class="pre">range</span></code></a> is
 [0,1], so the target must be within that range).</p>
 <div class="section" id="run-targets-sequence-format">
 <span id="id1"></span><h4>Sequence Format<a class="headerlink" href="#run-targets-sequence-format" title="Permalink to this headline">¶</a></h4>
@@ -466,7 +466,7 @@ <h3>Scope of Execution<a class="headerlink" href="#scope-of-execution" title="Pe
         <a href="Component.html" class="btn btn-neutral float-right" title="Component" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
       
       
-        <a href="MappingProjection.html" class="btn btn-neutral" title="MappingProjection" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
+        <a href="GatingProjection.html" class="btn btn-neutral" title="GatingProjection" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
       
     </div>
   
diff --git a/docs/build/html/State.html b/docs/build/html/State.html
index 4d2dbcd1d4d..957578d4a4a 100644
--- a/docs/build/html/State.html
+++ b/docs/build/html/State.html
@@ -8,7 +8,7 @@
   
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
   
-  <title>States &mdash; PsyNeuLink 0.1 documentation</title>
+  <title>State &mdash; PsyNeuLink 0.1 documentation</title>
   
 
   
@@ -88,13 +88,17 @@
 <li class="toctree-l1"><a class="reference internal" href="System.html">System</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Process.html">Process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Mechanism.html">Mechanism</a></li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">States</a><ul>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">State</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="InputState.html">InputState</a></li>
 <li class="toctree-l2"><a class="reference internal" href="ParameterState.html">ParameterState</a></li>
 <li class="toctree-l2"><a class="reference internal" href="OutputState.html">OutputState</a></li>
+<li class="toctree-l2"><a class="reference internal" href="ModulatorySignal.html">ModulatorySignal</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#overview">Overview</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#creating-a-state">Creating a State</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#structure">Structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#structure">Structure</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#modulation">Modulation</a></li>
+</ul>
+</li>
 <li class="toctree-l2"><a class="reference internal" href="#execution">Execution</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#class-reference">Class Reference</a></li>
 </ul>
@@ -133,7 +137,7 @@
   <ul class="wy-breadcrumbs">
     <li><a href="index.html">Docs</a> &raquo;</li>
       
-    <li>States</li>
+    <li>State</li>
       <li class="wy-breadcrumbs-aside">
         
           
@@ -147,49 +151,63 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
             
-  <div class="section" id="states">
-<h1>States<a class="headerlink" href="#states" title="Permalink to this headline">¶</a></h1>
+  <div class="section" id="state">
+<h1>State<a class="headerlink" href="#state" title="Permalink to this headline">¶</a></h1>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="InputState.html">InputState</a></li>
 <li class="toctree-l1"><a class="reference internal" href="ParameterState.html">ParameterState</a></li>
 <li class="toctree-l1"><a class="reference internal" href="OutputState.html">OutputState</a></li>
+<li class="toctree-l1"><a class="reference internal" href="ModulatorySignal.html">ModulatorySignal</a></li>
 </ul>
 </div>
 <div class="toctree-wrapper compound">
 </div>
 <span class="target" id="module-State"></span><div class="section" id="overview">
 <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">¶</a></h2>
-<p>A State provides an interface to a <a class="reference internal" href="Projection.html"><span class="doc">projection</span></a>, and represents the <a class="reference internal" href="Projection.html"><span class="doc">value</span></a>
-associated with that projection.  There are three types of states, all of which are used by
-<a class="reference internal" href="Mechanism.html"><span class="doc">mechanisms</span></a> and one of which is used by <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjections</span></a>, as summarized below:</p>
+<p>A State provides an interface to one or more <a class="reference internal" href="Projection.html"><span class="doc">projections</span></a>, and receives the <a class="reference internal" href="Projection.html"><span class="doc">value(s)</span></a>
+provide by them.  The value of a state can be modulated by a <a class="reference internal" href="ModulatoryProjection.html"><span class="doc">ModulatoryProjection</span></a>. There are three primary types of
+states, all of which are used by <a class="reference internal" href="Mechanism.html"><span class="doc">Mechanisms</span></a>, one of which is used by
+<a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjections</span></a>, and all of which are subject to modulation by
+<a class="reference internal" href="ModulatorySignal.html"><span class="doc">ModulatorySignals</span></a>, as summarized below:</p>
 <ul>
 <li><dl class="first docutils">
-<dt><strong>InputState</strong>:</dt>
-<dd><p class="first last">used by a mechanism to represent its input, and as the <a class="reference internal" href="LearningProjection.html#LearningProjection.LearningProjection.receiver" title="LearningProjection.LearningProjection.receiver"><code class="xref any py py-attr docutils literal"><span class="pre">receiver</span></code></a> of any incoming
-<a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjections</span></a>.</p>
+<dt><a class="reference internal" href="InputState.html"><span class="doc">InputState</span></a>:</dt>
+<dd><p class="first last">used by a mechanism to receive input from <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjections</span></a>;
+its value can be modulated by a <a class="reference internal" href="GatingSignal.html"><span class="doc">GatingSignal</span></a>.</p>
 </dd>
 </dl>
 </li>
 <li><dl class="first docutils">
-<dt><strong>ParameterState</strong>:</dt>
-<dd><ul class="first simple">
-<li>used by a mechanism to represent the value of one of its parameters, or a parameter of its <code class="xref std std-keyword docutils literal"><span class="pre">function</span></code>,
-possibly regulated by a <a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjection</span></a>;</li>
-<li>used by a <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a> to represent the value of its <a class="reference internal" href="MappingProjection.html#MappingProjection.MappingProjection.matrix" title="MappingProjection.MappingProjection.matrix"><code class="xref any py py-attr docutils literal"><span class="pre">matrix</span></code></a></li>
+<dt><a class="reference internal" href="ParameterState.html"><span class="doc">ParameterState</span></a>:</dt>
+<dd><ul class="first last simple">
+<li>used by a mechanism to represent the value of one of its parameters, or a parameter of its
+<code class="xref any docutils literal"><span class="pre">function</span></code>, that can be modulated by a <a class="reference internal" href="ControlSignal.html"><span class="doc">ControlSignal</span></a>;</li>
+<li>used by a <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a> to represent the value of its <code class="xref any docutils literal"><span class="pre">matrix</span></code>
+parameter, that can be modulated by a <a class="reference internal" href="LearningSignal.html"><span class="doc">LearningSignal</span></a>.</li>
 </ul>
-<p class="last">parameter, possibly regulated by a <a class="reference internal" href="LearningProjection.html"><span class="doc">LearningProjection</span></a>.</p>
 </dd>
 </dl>
 </li>
 <li><dl class="first docutils">
-<dt><strong>OutputState</strong>:</dt>
-<dd><ul class="first last simple">
-<li>used by a mechanism to represent its output, and as the <a class="reference internal" href="Projection.html"><span class="doc">sender</span></a> of any outgoing projection(s):
-* <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a> for a <a class="reference internal" href="ProcessingMechanism.html"><span class="doc">ProcessingMechanism</span></a>;
-* <a class="reference internal" href="ControlProjection.html"><span class="doc">ControlProjection</span></a> for a <a class="reference internal" href="ControlMechanism.html"><span class="doc">ControlMechanism</span></a>;
-* <a class="reference internal" href="LearningProjection.html"><span class="doc">LearningProjection</span></a> for a <code class="xref any docutils literal"><span class="pre">MonitoringMechanism</span></code>.</li>
-</ul>
+<dt><a class="reference internal" href="OutputState.html"><span class="doc">OutputState</span></a>:</dt>
+<dd><p class="first last">used by a mechanism to send its value to any efferent projections.  For
+<a class="reference internal" href="ProcessingMechanism.html"><span class="doc">ProcessingMechanisms</span></a> these are <a class="reference internal" href="PathwayProjection.html"><span class="doc">PathwayProjections</span></a>, most commonly
+<a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a>.  For <code class="xref any docutils literal"><span class="pre">ModulatoryMechanisms</span></code>, these
+<a class="reference internal" href="ModulatoryProjection.html"><span class="doc">ModulatoryProjectios</span></a> as described below. The <code class="xref any docutils literal"><span class="pre">value</span> <span class="pre">&lt;OutputState.value&gt;</span> <span class="pre">of</span> <span class="pre">an</span>
+<span class="pre">OutputState</span> <span class="pre">can</span> <span class="pre">be</span> <span class="pre">modulated</span> <span class="pre">by</span> <span class="pre">a</span> <span class="pre">`GatingSignal</span></code>.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="first docutils">
+<dt><a class="reference internal" href="ModulatorySignal.html"><span class="doc">ModulatorySignal</span></a>:</dt>
+<dd><p class="first last">used by an <a class="reference internal" href="AdaptiveMechanism.html"><span class="doc">Adaptive Mechanism</span></a> to modulate the value of the primary types of states listed above.
+There are three types of ModulatorySignals:
+* <a class="reference internal" href="LearningSignal.html"><span class="doc">LearningSignal</span></a>, used by a <a class="reference internal" href="LearningMechanism.html"><span class="doc">Learning Mechanism</span></a> to modulate the <em>MATRIX</em> ParameterState of a <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a>;
+* <a class="reference internal" href="ControlSignal.html"><span class="doc">ControlSignal</span></a>, used by a <a class="reference internal" href="ControlMechanism.html"><span class="doc">Control Mechanism</span></a> to modulate the <a class="reference internal" href="ParameterState.html"><span class="doc">ParameterState</span></a> of a <a class="reference internal" href="Mechanism.html"><span class="doc">Mechanism</span></a>;
+* <a class="reference internal" href="GatingSignal.html"><span class="doc">GatingSignal</span></a>, used by a <code class="xref any docutils literal"><span class="pre">GatingMechanism</span></code> to modulate the <a class="reference internal" href="InputState.html"><span class="doc">InputState</span></a> or <a class="reference internal" href="OutputState.html"><span class="doc">OutputState</span></a> of a <a class="reference internal" href="Mechanism.html"><span class="doc">Mechanism</span></a>.
+Modulation is discussed further <a class="reference internal" href="#state-modulation"><span class="std std-ref">below</span></a>, and described in detail under
+<a class="reference internal" href="ModulatorySignal.html#modulatorysignal-modulation"><span class="std std-ref">ModulatorySignals</span></a>.</p>
 </dd>
 </dl>
 </li>
@@ -198,51 +216,117 @@ <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this head
 <div class="section" id="creating-a-state">
 <span id="state-creation"></span><h2>Creating a State<a class="headerlink" href="#creating-a-state" title="Permalink to this headline">¶</a></h2>
 <p>States can be created using the constructor for one of the subclasses.  However, in general, they are created
-automatically by the objects to which they belong, and/or through specification in context (e.g., when
-<a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">specifying the parameters</span></a> of a mechanism or its function to be controlled,
-or of a <a href="#id1"><span class="problematic" id="id2">`</span></a>MappingProjections to be learned &lt;MappingProjection_Tuple_Specification&gt;).</p>
+automatically by the objects to which they belong, or by specifying the state in the constructor for the object
+to which it belongs.  For example, <a class="reference internal" href="InputState.html"><span class="doc">InputStates</span></a> and <code class="xref any docutils literal"><span class="pre">OutputStates</span></code> can be specified,
+in the <strong>input_states</strong> and <strong>output_states</strong> arguments, respectively, of the constructor for a <a class="reference internal" href="Mechanism.html"><span class="doc">Mechanism</span></a>;
+and a <a class="reference internal" href="ParameterState.html"><span class="doc">ParameterState</span></a> can be specified in the argument of the constructor for a function of a Mechanism or Projection,
+where its parameters are specified.  A state can be specified in those cases in any of the following forms:</p>
+<blockquote>
+<div><ul class="simple">
+<li>an existing <strong>State</strong> object;</li>
+</ul>
+<ul class="simple">
+<li>the name of a <strong>state subclass</strong> (<a class="reference internal" href="InputState.html"><span class="doc">InputState</span></a>, <code class="xref any docutils literal"><span class="pre">ParmeterState</span></code>, or <a class="reference internal" href="OutputState.html"><span class="doc">OutputState</span></a> - a default state of the
+corresponding type will be created, using a default value for the state that is determined by the context
+in which it is specified.</li>
+</ul>
+<ul class="simple">
+<li>a <strong>value</strong>.  This creates a default State using the specified value as its default <code class="xref any docutils literal"><span class="pre">value</span></code>.</li>
+</ul>
+<ul>
+<li><p class="first">a <strong>state specification dictionary</strong>; every state specification can contain the following <em>KEY</em>:&lt;value&gt;
+entries, in addition to those specific to a particular state subtype (see subtype documentation):
+..
+* <em>NAME</em>:&lt;str&gt; - the string is used as the name of the state;
+..
+* <em>VALUE</em>:&lt;value&gt; - the value is used as the default value of the state;
+..
+* <em>str</em>:&lt;List&gt; - the key is used as the name of the state, and the list must contain specifications for</p>
+<blockquote>
+<div><p>one or more <a class="reference internal" href="Projection.html#projection-in-context-specification"><span class="std std-ref">projections</span></a> to or from the state,
+depending on the type of state and the context in which it is specified;
+..</p>
+</div></blockquote>
+</li>
+<li><p class="first">a <strong>2-item tuple</strong> - the first item must be a value, used as the default value for the state,
+and the second item must be a specification for a <a class="reference internal" href="Projection.html#projection-in-context-specification"><span class="std std-ref">projection</span></a>
+to or from the state, depending on the type of state and the context in which it is specified;</p>
+</li>
+</ul>
+</div></blockquote>
 </div>
 <div class="section" id="structure">
-<h2>Structure<a class="headerlink" href="#structure" title="Permalink to this headline">¶</a></h2>
-<p>Every state is owned by either a <a class="reference internal" href="Mechanism.html"><span class="doc">mechanism</span></a> or a <a class="reference internal" href="Projection.html"><span class="doc">projection</span></a>. Like all PsyNeuLink
-components, a state has the three following core attributes:</p>
+<span id="state-structure"></span><h2>Structure<a class="headerlink" href="#structure" title="Permalink to this headline">¶</a></h2>
+<p>Every State is owned by either a <a class="reference internal" href="Mechanism.html"><span class="doc">Mechanism</span></a> or a <a class="reference internal" href="Projection.html"><span class="doc">Projection</span></a>. Like all PsyNeuLink
+components, a State has the three following core attributes:</p>
 <blockquote>
 <div><ul class="simple">
-<li><code class="xref any docutils literal"><span class="pre">variable</span></code>:  for an <a class="reference internal" href="InputState.html"><span class="doc">inputState</span></a> and <a class="reference internal" href="ParameterState.html"><span class="doc">parameterState</span></a>,
+<li><code class="xref any docutils literal"><span class="pre">variable</span></code>:  for an <a class="reference internal" href="InputState.html"><span class="doc">InputState</span></a> and <a class="reference internal" href="ParameterState.html"><span class="doc">ParameterState</span></a>,
 the value of this is determined by the  value(s) of the projection(s) that it receives (and that are listed in
-its <code class="xref any docutils literal"><span class="pre">receivesFromProjections</span></code> attribute).  For an <a class="reference internal" href="OutputState.html"><span class="doc">outputState</span></a>,
-it is the item of the owner mechanism&#8217;s <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code> to which the outputState is assigned (specified by the
-outputStates <a class="reference internal" href="OutputState.html#outputstate-index"><span class="std std-ref">index</span></a> attribute.</li>
+its <code class="xref any docutils literal"><span class="pre">path_afferents</span></code> attribute).  For an <a class="reference internal" href="OutputState.html"><span class="doc">OutputState</span></a>, it is the item of the owner
+mechanism&#8217;s <code class="xref any docutils literal"><span class="pre">value</span></code> to which the OutputState is assigned (specified by the OutputState&#8217;s
+<a class="reference internal" href="OutputState.html#outputstate-index"><span class="std std-ref">index</span></a> attribute.</li>
 </ul>
 <ul class="simple">
-<li><code class="xref any docutils literal"><span class="pre">function</span></code>:  for an <a class="reference internal" href="InputState.html"><span class="doc">inputState</span></a> and <a class="reference internal" href="ParameterState.html"><span class="doc">parameterState</span></a>, this
-aggregates the values of the projections that the state receives (the default is <a class="reference internal" href="Function.html#Function.LinearCombination" title="Function.LinearCombination"><code class="xref any py py-class docutils literal"><span class="pre">LinearCombination</span></code></a> that sums
-the values).  At present, the <a class="reference internal" href="OutputState.html#OutputState.OutputState.function" title="OutputState.OutputState.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> of an outputState is not
-actively used;  it simply returns the item of the owner mechanism&#8217;s <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code> to which the outputState is
-assigned.</li>
+<li><code class="xref any docutils literal"><span class="pre">function</span></code>:  for an <a class="reference internal" href="InputState.html"><span class="doc">InputState</span></a> this aggregates the values of the projections that the state
+receives (the default is <a class="reference internal" href="Function.html#Function.LinearCombination" title="Function.LinearCombination"><code class="xref any py py-class docutils literal"><span class="pre">LinearCombination</span></code></a> that sums the values), under the potential influence of a
+<a class="reference internal" href="GatingSignal.html"><span class="doc">GatingSignal</span></a>;  for a <a class="reference internal" href="ParameterState.html"><span class="doc">ParameterState</span></a>, it determines the value of the associated parameter, under the
+potential influence of a <a class="reference internal" href="ControlSignal.html"><span class="doc">ControlSignal</span></a> (for a <a class="reference internal" href="Mechanism.html"><span class="doc">Mechanism</span></a>) or a <a class="reference internal" href="LearningSignal.html"><span class="doc">LearningSignal</span></a> (for a <a class="reference internal" href="MappingProjection.html"><span class="doc">MappingProjection</span></a>);
+for an OutputState, it conveys the result  of the Mechanism&#8217;s function to its
+<code class="xref any docutils literal"><span class="pre">output_values</span> <span class="pre">&lt;Mechanism.output_values&gt;</span> <span class="pre">attribute,</span> <span class="pre">under</span> <span class="pre">the</span> <span class="pre">potential</span> <span class="pre">influence</span> <span class="pre">of</span> <span class="pre">a</span> <span class="pre">`GatingSignal</span></code>.
+See  <a class="reference internal" href="ModulatorySignal.html#modulatorysignal-structure"><span class="std std-ref">ModulatorySignals</span></a> and the <a class="reference internal" href="AdaptiveMechanism.html"><span class="doc">AdaptiveMechanism</span></a> associated
+with each type for a description of how they can be used to modulate the <a href="#id1"><span class="problematic" id="id2">`</span></a>function &lt;State.function&gt; of a State.</li>
 </ul>
 <ul class="simple">
-<li><code class="xref any docutils literal"><span class="pre">value</span></code>:  for an <a class="reference internal" href="InputState.html"><span class="doc">inputState</span></a> and <a class="reference internal" href="ParameterState.html"><span class="doc">parameterState</span></a>, this is the
-aggregated value of the projections it receives.  For an <a class="reference internal" href="OutputState.html"><span class="doc">outputState</span></a>, this is th item of the
-owner mechanism&#8217;s <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code> to which the outputState is assigned, possibly modified by a function
-assigned to its <a class="reference internal" href="OutputState.html#outputstate-calculate"><span class="std std-ref">calculate</span></a> attribute.</li>
+<li><code class="xref any docutils literal"><span class="pre">value</span></code>:  for an <a class="reference internal" href="InputState.html"><span class="doc">InputState</span></a> this is the aggregated value of the <code class="xref any docutils literal"><span class="pre">PathwayProjections</span></code> it
+receives;  for a <a class="reference internal" href="ParameterState.html"><span class="doc">ParameterState</span></a>, this represents the value of the parameter that will be used by the
+State&#8217;s owner or its <code class="xref any docutils literal"><span class="pre">function</span></code>; for an <a class="reference internal" href="OutputState.html"><span class="doc">OutputState</span></a>, it is the item of the  owner
+Mechanism&#8217;s <code class="xref any docutils literal"><span class="pre">value</span></code> to which the OutputState is assigned, possibly modified by its
+<a class="reference internal" href="OutputState.html#outputstate-calculate"><span class="std std-ref">calculate</span></a> attribute and/or a <a class="reference internal" href="GatingSignal.html"><span class="doc">GatingSignal</span></a>, and used as the
+<code class="xref any docutils literal"><span class="pre">value</span></code> of the projections listed in its <code class="xref any docutils literal"><span class="pre">efferents</span></code> attribute.</li>
 </ul>
 </div></blockquote>
+<div class="section" id="modulation">
+<span id="state-modulation"></span><h3>Modulation<a class="headerlink" href="#modulation" title="Permalink to this headline">¶</a></h3>
+<p>Every type of State has a <code class="xref any docutils literal"><span class="pre">mod_afferents</span></code> attribute, that lists the
+<a class="reference internal" href="ModulatoryProjection.html"><span class="doc">ModulatoryProjections</span></a> it receives.  Each ModulatoryProjection comes from a <a class="reference internal" href="ModulatorySignal.html"><span class="doc">ModulatorySignal</span></a>
+that specifies how it should modulate the State&#8217;s <code class="xref any docutils literal"><span class="pre">value</span></code> when the State is updated (see
+<a class="reference internal" href="ModulatorySignal.html#modulatorysignal-modulation"><span class="std std-ref">Modulation</span></a> and <code class="xref any docutils literal"><span class="pre">ModulatorySignal_Anatomy_Figure).</span>&#160; <span class="pre">In</span> <span class="pre">most</span> <span class="pre">cases,</span> <span class="pre">a</span> <span class="pre">ModulatorySignal</span> <span class="pre">uses</span> <span class="pre">the</span>
+<span class="pre">State's</span> <span class="pre">`function</span></code> to modulate its <code class="xref any docutils literal"><span class="pre">value</span></code>.  The function of every State assigns one
+of its parameters as its <em>MULTIPLICATIVE_PARAM</em> and another as its <em>MULTIPLICATIVE_PARAM</em>. The
+<code class="xref any docutils literal"><span class="pre">modulation</span></code> attribute of a ModulatorySignal determines which of these to modify when
+the State uses it <code class="xref any docutils literal"><span class="pre">function</span></code> to calculate its <code class="xref any docutils literal"><span class="pre">value</span></code>.  However, the ModulatorySignal
+can also be configured to override the State&#8217;s <code class="xref any docutils literal"><span class="pre">value</span></code> (i.e., assign it directly), or to disable
+modulation, using one of the values of &#8216;ModulationParm` for its <a class="reference internal" href="ModulatorySignal.html#ModulatorySignals.ModulatorySignal.ModulatorySignal.modulation" title="ModulatorySignals.ModulatorySignal.ModulatorySignal.modulation"><code class="xref any py py-attr docutils literal"><span class="pre">modulation</span></code></a> attribute
+(see <a class="reference internal" href="ModulatorySignal.html#modulatorysignal-modulation"><span class="std std-ref">Modulation</span></a> for a more detailed discussion).</p>
+</div>
 </div>
 <div class="section" id="execution">
-<h2>Execution<a class="headerlink" href="#execution" title="Permalink to this headline">¶</a></h2>
-<p>A state cannot be executed directly.  It is executed when the component to which it belongs is executed.
-When this occurs, each inputState and parameterState belonging to the component executes any projections it receives,
-calls its <code class="xref std std-keyword docutils literal"><span class="pre">function</span></code> to aggregate their values, and then assigns this as that state&#8217;s <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code> &#8211;
-this conforms to a &#8220;lazy evaluation&#8221; protocol (see <span class="xref std std-ref">Lazy Evaluation</span> for a more detailed discussion).
-The <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code> of an outputState is assigned after the <code class="xref std std-keyword docutils literal"><span class="pre">function</span></code> of the mechanism that owns it has
-been called (see <a class="reference internal" href="Mechanism.html#mechanism-outputstates"><span class="std std-ref">Mechanism OutputStates</span></a>).</p>
+<span id="state-execution"></span><h2>Execution<a class="headerlink" href="#execution" title="Permalink to this headline">¶</a></h2>
+<p>States cannot be executed.  They are updated when the component to which they belong is executed.  InputStates and
+ParameterStates belonging to a Mechanism are updated before the Mechanism&#8217;s function is called.  OutputStates
+are updated after the Mechanism&#8217;s function is called.  When a State is updated, it executes any Projections that
+project to it (listed in its <code class="xref any docutils literal"><span class="pre">all_afferents</span></code> attribute.  It uses the values it receives from any
+<code class="xref any docutils literal"><span class="pre">PathWayProjections</span></code> (listed in its <a class="reference internal" href="#State.State_Base.path_afferents" title="State.State_Base.path_afferents"><code class="xref any py py-attr docutils literal"><span class="pre">path_afferents</span></code></a> attribute) as the variable for its <code class="xref any docutils literal"><span class="pre">function</span></code>,
+It then executes all of the ModulatoryProjections it receives.  Different ModulatorySignals may call for different
+forms of modulation (see <a class="reference internal" href="ModulatorySignal.html#modulatorysignal-modulation"><span class="std std-ref">Modulation</span></a>).  Accordingly, it separately sums the values specified
+by any ModulatorySignals for the <em>MULTIPLICATIVE_PARAM</em> of its <code class="xref any docutils literal"><span class="pre">function</span></code>, and similarly for the
+<em>ADDITIVE_PARAM</em>.  It then applies the summed value for each to the corresponding parameter of its
+<code class="xref any docutils literal"><span class="pre">function</span></code>.  If any of the ModulatorySignals specifies <em>OVERRIDE</em>, then the value of that
+ModulatorySignal is used as the State&#8217;s <code class="xref any docutils literal"><span class="pre">value</span></code>. Finally, the State calls its
+<code class="xref any docutils literal"><span class="pre">function</span></code> to determine its <code class="xref any docutils literal"><span class="pre">value</span></code>.</p>
+<div class="admonition note">
+<p class="first admonition-title">Note</p>
+<p class="last">The change in the value of a <a class="reference internal" href="#"><span class="doc">State</span></a> does not occur until the Mechanism to which the state belongs is next
+executed; This conforms to a &#8220;lazy evaluation&#8221; protocol (see <span class="xref std std-ref">Lazy Evaluation</span> for an explanation
+of &#8220;lazy&#8221; updating).</p>
+</div>
 </div>
 <div class="section" id="class-reference">
 <span id="state-class-reference"></span><h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalink to this headline">¶</a></h2>
 <dl class="class">
 <dt id="State.State_Base">
-<em class="property">class </em><code class="descclassname">State.</code><code class="descname">State_Base</code><span class="sig-paren">(</span><em>owner            value=None</em>, <em>params=None</em>, <em>name=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#State.State_Base" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">State.</code><code class="descname">State_Base</code><span class="sig-paren">(</span><em>owner</em>, <em>params=None</em>, <em>name=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#State.State_Base" title="Permalink to this definition">¶</a></dt>
 <dd><p>Abstract class for State.</p>
 <div class="admonition note">
 <p class="first admonition-title">Note</p>
@@ -257,21 +341,43 @@ <h2>Execution<a class="headerlink" href="#execution" title="Permalink to this he
 </dd></dl>
 
 <dl class="attribute">
-<dt id="State.State_Base.baseValue">
-<code class="descname">baseValue</code><a class="headerlink" href="#State.State_Base.baseValue" title="Permalink to this definition">¶</a></dt>
+<dt id="State.State_Base.base_value">
+<code class="descname">base_value</code><a class="headerlink" href="#State.State_Base.base_value" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>number, list or np.ndarray</em> &#8211; value with which the state was initialized.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="State.State_Base.receivesFromProjections">
-<code class="descname">receivesFromProjections</code><a class="headerlink" href="#State.State_Base.receivesFromProjections" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>Optional[List[Projection]]</em> &#8211; list of projections for which the state is a <code class="xref std std-keyword docutils literal"><span class="pre">receiver</span></code>.</p>
+<dt id="State.State_Base.all_afferents">
+<code class="descname">all_afferents</code><a class="headerlink" href="#State.State_Base.all_afferents" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>Optional[List[Projection]]</em> &#8211; list of all Projections received by the State (i.e., for which it is a <code class="xref any docutils literal"><span class="pre">receiver</span></code>.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="State.State_Base.path_afferents">
+<code class="descname">path_afferents</code><a class="headerlink" href="#State.State_Base.path_afferents" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>Optional[List[Projection]]</em> &#8211; list all <a class="reference internal" href="PathwayProjection.html"><span class="doc">PathwayProjections</span></a> received by the State.
+(note:  only <a href="#id3"><span class="problematic" id="id4">`</span></a>InputStates &lt;InputState&gt; have path_efferents;  the list is empty for other types of States).</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="State.State_Base.mod_afferents">
+<code class="descname">mod_afferents</code><a class="headerlink" href="#State.State_Base.mod_afferents" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>Optional[List[GatingProjection]]</em> &#8211; list of all <a class="reference internal" href="ModulatoryProjection.html"><span class="doc">ModulatoryProjections</span></a> received by the State.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="State.State_Base.efferents">
+<code class="descname">efferents</code><a class="headerlink" href="#State.State_Base.efferents" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>Optional[List[Projection]]</em> &#8211; list of outoging Projections from the State (i.e., for which is a <code class="xref any docutils literal"><span class="pre">sender</span></code>
+(note:  only <a href="#id5"><span class="problematic" id="id6">`</span></a>OutputStates &lt;OutputState&gt; have efferents;  the list is empty for other types of States).</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="State.State_Base.sendsToProjections">
-<code class="descname">sendsToProjections</code><a class="headerlink" href="#State.State_Base.sendsToProjections" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>Optional[List[Projection]]</em> &#8211; list of projections for which the state is a <code class="xref std std-keyword docutils literal"><span class="pre">sender</span></code>.</p>
+<dt id="State.State_Base.function">
+<code class="descname">function</code><a class="headerlink" href="#State.State_Base.function" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>TransferFunction : default determined by type</em> &#8211; used to determine the state&#8217;s own value from the value of the projection(s) it receives;  the parameters that
+the TrasnferFunction identifies as ADDITIVE and MULTIPLICATIVE are subject to modulation by a
+<a class="reference internal" href="ModulatoryProjection.html#modulatoryprojection-structure"><span class="std std-ref">ModualtoryProjection</span></a>.</p>
 </dd></dl>
 
 <dl class="attribute">
@@ -284,7 +390,7 @@ <h2>Execution<a class="headerlink" href="#execution" title="Permalink to this he
 <dt id="State.State_Base.name">
 <code class="descname">name</code><a class="headerlink" href="#State.State_Base.name" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>str : default &lt;State subclass&gt;-&lt;index&gt;</em> &#8211; the name of the state.
-Specified in the <a class="reference internal" href="#State.State_Base.name" title="State.State_Base.name"><code class="xref any py py-attr docutils literal"><span class="pre">name</span></code></a> argument of the constructor for the state;  if not is specified,
+Specified in the <strong>name</strong> argument of the constructor for the state;  if not is specified,
 a default is assigned by StateRegistry based on the states&#8217;s subclass
 (see <span class="xref doc">Registry</span> for conventions used in naming, including for default and duplicate names).</p>
 <div class="admonition note">
@@ -300,7 +406,7 @@ <h2>Execution<a class="headerlink" href="#execution" title="Permalink to this he
 <dt id="State.State_Base.prefs">
 <code class="descname">prefs</code><a class="headerlink" href="#State.State_Base.prefs" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>PreferenceSet or specification dict : State.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for the state.
-Specified in the <a class="reference internal" href="#State.State_Base.prefs" title="State.State_Base.prefs"><code class="xref any py py-attr docutils literal"><span class="pre">prefs</span></code></a> argument of the constructor for the projection;  if it is not specified, a default is
+Specified in the <strong>prefs</strong> argument of the constructor for the projection;  if it is not specified, a default is
 assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
 (see <span class="xref doc">PreferenceSet</span> for details).</p>
 </dd></dl>
diff --git a/docs/build/html/System.html b/docs/build/html/System.html
index 2607363a5be..b8e2365c4c3 100644
--- a/docs/build/html/System.html
+++ b/docs/build/html/System.html
@@ -106,7 +106,7 @@
 </li>
 <li class="toctree-l1"><a class="reference internal" href="Process.html">Process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Mechanism.html">Mechanism</a></li>
-<li class="toctree-l1"><a class="reference internal" href="State.html">States</a></li>
+<li class="toctree-l1"><a class="reference internal" href="State.html">State</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Projection.html">Projection</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Run.html">Run</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Component.html">Component</a></li>
@@ -315,7 +315,7 @@ <h1>System<a class="headerlink" href="#system" title="Permalink to this headline
 </div>
 <div class="section" id="learning">
 <span id="system-execution-learning"></span><h3>Learning<a class="headerlink" href="#learning" title="Permalink to this headline">¶</a></h3>
-<p>The system will execute learning if it is specified for any process in the system.  The system&#8217;s <a class="reference internal" href="#System.System_Base.learning" title="System.System_Base.learning"><code class="xref any py py-attr docutils literal"><span class="pre">learning</span></code></a> attribute
+<p>The system will execute learning if it is specified for any process in the system.  The system&#8217;s <a class="reference internal" href="Process.html#Process.Process_Base.learning" title="Process.Process_Base.learning"><code class="xref any py py-attr docutils literal"><span class="pre">learning</span></code></a> attribute
 indicates whether learning is enabled for the system. Learning is executed for any components (individual projections
 or processes) for which it is specified after all processing mechanisms in the
 system have been executed, but before the controller is executed (see below).  The learning components of a system
@@ -338,7 +338,7 @@ <h1>System<a class="headerlink" href="#system" title="Permalink to this headline
 <p>Every system is associated with a single <a class="reference internal" href="#System.System_Base.controller" title="System.System_Base.controller"><code class="xref any py py-attr docutils literal"><span class="pre">controller</span></code></a>.  The controller uses an <a class="reference internal" href="ObjectiveMechanism.html"><span class="doc">ObjectiveMechanism</span></a> to monitor
 the outputState(s) of one or more mechanisms in the system (listed in its <a class="reference internal" href="EVCMechanism.html#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitored_output_states" title="AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitored_output_states"><code class="xref any py py-attr docutils literal"><span class="pre">monitored_output_states</span></code></a> attribute),
 and uses that information to set the value of parameters for those or other mechanisms in the system, or their functions
-(see <a class="reference internal" href="ControlMechanism.html#controlmechanism-monitored-outputstates"><span class="std std-ref">Monitored OutputStates</span></a> for a description of how to specify which outputStates are
+(see <span class="xref std std-ref">ControlMechanism_Monitored_OutputStates</span> for a description of how to specify which outputStates are
 monitored, and <a class="reference internal" href="ControlProjection.html#controlprojection-creation"><span class="std std-ref">Creating a ControlProjection</span></a> for specifying parameters to be controlled).  The control components
 of a system can be displayed using the system&#8217;s <a class="reference internal" href="#System.System_Base.show_graph" title="System.System_Base.show_graph"><code class="xref any py py-meth docutils literal"><span class="pre">show_graph</span></code></a> method with its <strong>show_control</strong> argument assigned
 :keyword:<a href="#id1"><span class="problematic" id="id2">``</span></a>True`. The controller is executed after all other mechanisms in the system are executed, and sets the
@@ -349,7 +349,7 @@ <h1>System<a class="headerlink" href="#system" title="Permalink to this headline
 <span id="system-class-reference"></span><h2>Class Reference<a class="headerlink" href="#class-reference" title="Permalink to this headline">¶</a></h2>
 <dl class="function">
 <dt id="System.system">
-<code class="descclassname">System.</code><code class="descname">system</code><span class="sig-paren">(</span><em>default_input_value=None</em>, <em>processes=None</em>, <em>initial_values=None</em>, <em>controller=SystemDefaultControlMechanism</em>, <em>enable_controller=:keyword:`False`</em>, <em>monitor_for_control=`None`</em>, <em>learning_rate=None</em>, <em>targets=None</em>, <em>params=None</em>, <em>name=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#System.system" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">System.</code><code class="descname">system</code><span class="sig-paren">(</span><em>default_input_value=None</em>, <em>processes=None</em>, <em>initial_values=None</em>, <em>controller=SystemDefaultControlMechanism</em>, <em>enable_controller=:keyword:False</em>, <em>monitor_for_control=None</em>, <em>control_signals=None</em>, <em>learning_rate=None</em>, <em>targets=None</em>, <em>params=None</em>, <em>name=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#System.system" title="Permalink to this definition">¶</a></dt>
 <dd><p>Factory method for System: returns instance of System.</p>
 <p>If called with no arguments, returns an instance of System with a single default process and mechanism;
 if called with a name string, that is used as the name of the instance of System returned;
@@ -370,12 +370,12 @@ <h1>System<a class="headerlink" href="#system" title="Permalink to this headline
 <a class="reference internal" href="Keywords.html#Keywords.Keywords.INITIALIZE_CYCLE" title="Keywords.Keywords.INITIALIZE_CYCLE"><code class="xref any py py-attr docutils literal"><span class="pre">INITIALIZE_CYCLE</span></code></a>). The key for each entry is a mechanism object, and the value is a number,
 list or 1d np.array that must be compatible with the format of the first item of the mechanism&#8217;s value
 (i.e., mechanism.value[0]).</li>
-<li><strong>controller</strong> (<em>ControlMechanism : default DefaultController</em>) &#8211; specifies the <a class="reference internal" href="ControlMechanism.html"><span class="doc">Control Mechanism</span></a> used to monitor the value of the outputState(s) for mechanisms specified in
+<li><strong>controller</strong> (<em>ControlMechanism : default SystemDefaultControlMechanism</em>) &#8211; specifies the <a class="reference internal" href="ControlMechanism.html"><span class="doc">Control Mechanism</span></a> used to monitor the value of the outputState(s) for mechanisms specified in
 <code class="xref any docutils literal"><span class="pre">monitor_for_control</span></code>, and that specify the value of <code class="xref any docutils literal"><span class="pre">ControlProjections</span></code> in the system.</li>
 <li><strong>enable_controller</strong> (bool : default <code class="xref std std-keyword docutils literal"><span class="pre">False</span></code>) &#8211; specifies whether the <a class="reference internal" href="#System.System_Base.controller" title="System.System_Base.controller"><code class="xref any py py-attr docutils literal"><span class="pre">controller</span></code></a> is executed during system execution.</li>
 <li><strong>monitor_for_control</strong> (<em>list of OutputState objects or specifications : default None</em>) &#8211; specifies the outputStates of the <a class="reference internal" href="Keywords.html#Keywords.Keywords.TERMINAL" title="Keywords.Keywords.TERMINAL"><code class="xref any py py-attr docutils literal"><span class="pre">TERMINAL</span></code></a> mechanisms in the system to be monitored by its <a class="reference internal" href="#System.System_Base.controller" title="System.System_Base.controller"><code class="xref any py py-attr docutils literal"><span class="pre">controller</span></code></a>
-(see <a class="reference internal" href="ControlMechanism.html#controlmechanism-monitored-outputstates"><span class="std std-ref">Monitored OutputStates</span></a> for specifying the <code class="xref any docutils literal"><span class="pre">monitor_for_control</span></code> argument).</li>
-<li><strong>learning_rate</strong> (<em>float : None</em>) &#8211; set the learning rate for all mechanisms in the system (see <a class="reference internal" href="LearningProjection.html#LearningProjection.LearningProjection.learning_rate" title="LearningProjection.LearningProjection.learning_rate"><code class="xref any py py-attr docutils literal"><span class="pre">learning_rate</span></code></a> attribute for additional
+(see <code class="xref any docutils literal"><span class="pre">ControlMechanism_Monitored_OutputStates</span></code> for specifying the <code class="xref any docutils literal"><span class="pre">monitor_for_control</span></code> argument).</li>
+<li><strong>learning_rate</strong> (<em>float : None</em>) &#8211; set the learning rate for all mechanisms in the system (see <a class="reference internal" href="#System.System_Base.learning_rate" title="System.System_Base.learning_rate"><code class="xref any py py-attr docutils literal"><span class="pre">learning_rate</span></code></a> attribute for additional
 information).</li>
 <li><strong>targets</strong> (<em>Optional[List[List]], 2d np.ndarray] : default ndarrays of zeroes</em>) &#8211; the values assigned to the TARGET input of each <a class="reference internal" href="Keywords.html#Keywords.Keywords.TARGET" title="Keywords.Keywords.TARGET"><code class="xref any py py-attr docutils literal"><span class="pre">TARGET</span></code></a> mechanism in the system (listed in its
 <a class="reference internal" href="Process.html#Process.Process_Base.targetMechanisms" title="Process.Process_Base.targetMechanisms"><code class="xref any py py-attr docutils literal"><span class="pre">targetMechanisms</span></code></a> attribute).  There must be the same number of items as there are <a class="reference internal" href="Process.html#Process.Process_Base.targetMechanisms" title="Process.Process_Base.targetMechanisms"><code class="xref any py py-attr docutils literal"><span class="pre">targetMechanisms</span></code></a>,
@@ -402,8 +402,9 @@ <h1>System<a class="headerlink" href="#system" title="Permalink to this headline
 
 <dl class="class">
 <dt id="System.System_Base">
-<em class="property">class </em><code class="descclassname">System.</code><code class="descname">System_Base</code><span class="sig-paren">(</span><em>default_input_value=None</em>, <em>processes=None</em>, <em>initial_values=None</em>, <em>controller=SystemDefaultControlMechanism</em>, <em>enable_controller=:keyword:`False`</em>, <em>monitor_for_control=`None`</em>, <em>learning_rate=None</em>, <em>targets=None</em>, <em>params=None</em>, <em>name=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#System.System_Base" title="Permalink to this definition">¶</a></dt>
-<dd><p>Abstract class for System.</p>
+<em class="property">class </em><code class="descclassname">System.</code><code class="descname">System_Base</code><span class="sig-paren">(</span><em>default_input_value=None</em>, <em>processes=None</em>, <em>initial_values=None</em>, <em>controller=&lt;class 'PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.DefaultControlMechanism.DefaultControlMechanism'&gt;</em>, <em>enable_controller=False</em>, <em>monitor_for_control=None</em>, <em>control_signals=None</em>, <em>learning_rate=None</em>, <em>targets=None</em>, <em>params=None</em>, <em>name=None</em>, <em>scheduler=None</em>, <em>prefs: &lt;function is_pref_set at 0x10c1702f0&gt; = None</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#System.System_Base" title="Permalink to this definition">¶</a></dt>
+<dd><p>System_Base(                                  default_input_value=None,                     processes=None,                               initial_values=None,                          controller=SystemDefaultControlMechanism,     enable_controller=:keyword:<code class="xref any docutils literal"><span class="pre">False</span></code>,           monitor_for_control=None,                     control_signals=None,                         learning_rate=None,                       targets=None,                                 params=None,                                  name=None,                                    prefs=None)</p>
+<p>Abstract class for System.</p>
 <div class="admonition note">
 <p class="first admonition-title">Note</p>
 <p class="last">Systems should NEVER be instantiated by a direct call to the base class.
@@ -424,7 +425,7 @@ <h1>System<a class="headerlink" href="#system" title="Permalink to this headline
 <dl class="attribute">
 <dt id="System.System_Base.controller">
 <code class="descname">controller</code><a class="headerlink" href="#System.System_Base.controller" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>ControlMechanism : default DefaultController</em> &#8211; the ControlMechanism used to monitor the value of the outputState(s) for mechanisms specified in
+<dd><p><em>ControlMechanism : default SystemDefaultControlMechanism</em> &#8211; the ControlMechanism used to monitor the value of the outputState(s) for mechanisms specified in
 <code class="docutils literal"><span class="pre">monitor_for_control</span></code> argument, and specify the value of ControlProjections in the system.</p>
 </dd></dl>
 
@@ -463,8 +464,8 @@ <h1>System<a class="headerlink" href="#system" title="Permalink to this headline
 <code class="descname">graph</code><a class="headerlink" href="#System.System_Base.graph" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>OrderedDict</em> &#8211; contains a graph of all of the mechanisms in the system.
 Each entry specifies a set of &lt;Receiver&gt;: {sender, sender...} dependencies.
-The key of each entry is a receiver mech_tuple, and
-the value is a set of mech_tuples that send projections to that receiver.
+The key of each entry is a receiver object_item, and
+the value is a set of mechs that send projections to that receiver.
 If a key (receiver) has no dependents, its value is an empty set.</p>
 </dd></dl>
 
@@ -552,7 +553,7 @@ <h1>System<a class="headerlink" href="#system" title="Permalink to this headline
 <dl class="attribute">
 <dt id="System.System_Base.value">
 <code class="descname">value</code><a class="headerlink" href="#System.System_Base.value" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>3D ndarray</em> &#8211; contains an array of 2D arrays, each of which is the <code class="xref any docutils literal"><span class="pre">outputValue</span> <span class="pre">`of</span> <span class="pre">a</span> <span class="pre">`TERMINAL</span></code> mechanism in the system.</p>
+<dd><p><em>3D ndarray</em> &#8211; contains an array of 2D arrays, each of which is the <code class="xref any docutils literal"><span class="pre">output_values</span> <span class="pre">`of</span> <span class="pre">a</span> <span class="pre">`TERMINAL</span></code> mechanism in the system.</p>
 </dd></dl>
 
 <dl class="attribute">
@@ -585,7 +586,7 @@ <h1>System<a class="headerlink" href="#system" title="Permalink to this headline
 <dt id="System.System_Base.name">
 <code class="descname">name</code><a class="headerlink" href="#System.System_Base.name" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>str : default System-&lt;index&gt;</em> &#8211; the name of the system;
-Specified in the <a class="reference internal" href="#System.System_Base.name" title="System.System_Base.name"><code class="xref any py py-attr docutils literal"><span class="pre">name</span></code></a> argument of the constructor for the system;
+Specified in the <strong>name</strong> argument of the constructor for the system;
 if not is specified, a default is assigned by SystemRegistry
 (see <span class="xref doc">Registry</span> for conventions used in naming, including for default and duplicate names).</p>
 </dd></dl>
@@ -593,7 +594,7 @@ <h1>System<a class="headerlink" href="#system" title="Permalink to this headline
 <dl class="docutils">
 <dt>prefs</dt>
  <span class="classifier-delimiter">:</span> <span class="classifier">PreferenceSet or specification dict</span> <span class="classifier-delimiter">:</span> <span class="classifier">System.classPreferences</span><dd>the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for system.
-Specified in the <a class="reference internal" href="Function.html#Function.UserDefinedFunction.prefs" title="Function.UserDefinedFunction.prefs"><code class="xref any py py-attr docutils literal"><span class="pre">prefs</span></code></a> argument of the constructor for the system;  if it is not specified, a default is
+Specified in the <strong>prefs</strong> argument of the constructor for the system;  if it is not specified, a default is
 assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
 (see <span class="xref std std-ref">PreferenceSet</span> for details).</dd>
 </dl>
@@ -605,7 +606,7 @@ <h1>System<a class="headerlink" href="#system" title="Permalink to this headline
 
 <dl class="method">
 <dt id="System.System_Base.execute">
-<code class="descname">execute</code><span class="sig-paren">(</span><em>input=None</em>, <em>target=None</em>, <em>execution_id=None</em>, <em>clock=&lt;PsyNeuLink.Globals.TimeScale.Clock object&gt;</em>, <em>time_scale=None</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#System.System_Base.execute" title="Permalink to this definition">¶</a></dt>
+<code class="descname">execute</code><span class="sig-paren">(</span><em>input=None</em>, <em>target=None</em>, <em>execution_id=None</em>, <em>clock=&lt;PsyNeuLink.Globals.TimeScale.Clock object&gt;</em>, <em>time_scale=None</em>, <em>termination_processing=None</em>, <em>termination_learning=None</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#System.System_Base.execute" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute mechanisms in system at specified <a class="reference internal" href="#system-execution-phase"><span class="std std-ref">phases</span></a> in order         specified by the <a class="reference internal" href="#System.System_Base.executionGraph" title="System.System_Base.executionGraph"><code class="xref py py-data docutils literal"><span class="pre">executionGraph</span></code></a> attribute.</p>
 <p>Assign items of input to <a class="reference internal" href="Keywords.html#Keywords.Keywords.ORIGIN" title="Keywords.Keywords.ORIGIN"><code class="xref any py py-attr docutils literal"><span class="pre">ORIGIN</span></code></a> mechanisms</p>
 <p>Execute mechanisms in the order specified in executionList and with phases equal to
@@ -629,7 +630,7 @@ <h1>System<a class="headerlink" href="#system" title="Permalink to this headline
 
 <dl class="method">
 <dt id="System.System_Base.run">
-<code class="descname">run</code><span class="sig-paren">(</span><em>inputs</em>, <em>num_executions=None</em>, <em>reset_clock=True</em>, <em>initialize=False</em>, <em>targets=None</em>, <em>learning=None</em>, <em>call_before_trial=None</em>, <em>call_after_trial=None</em>, <em>call_before_time_step=None</em>, <em>call_after_time_step=None</em>, <em>clock=&lt;PsyNeuLink.Globals.TimeScale.Clock object&gt;</em>, <em>time_scale=None</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#System.System_Base.run" title="Permalink to this definition">¶</a></dt>
+<code class="descname">run</code><span class="sig-paren">(</span><em>inputs</em>, <em>num_executions=None</em>, <em>reset_clock=True</em>, <em>initialize=False</em>, <em>targets=None</em>, <em>learning=None</em>, <em>call_before_trial=None</em>, <em>call_after_trial=None</em>, <em>call_before_time_step=None</em>, <em>call_after_time_step=None</em>, <em>clock=&lt;PsyNeuLink.Globals.TimeScale.Clock object&gt;</em>, <em>time_scale=None</em>, <em>termination_processing=None</em>, <em>termination_learning=None</em>, <em>context=None</em><span class="sig-paren">)</span><a class="headerlink" href="#System.System_Base.run" title="Permalink to this definition">¶</a></dt>
 <dd><p>Run a sequence of executions</p>
 <p>Call execute method for each execution in a sequence specified by inputs.  See <a class="reference internal" href="Run.html"><span class="doc">Run</span></a> for details of
 formatting input specifications.</p>
@@ -733,22 +734,6 @@ <h1>System<a class="headerlink" href="#system" title="Permalink to this headline
 </table>
 </dd></dl>
 
-<dl class="attribute">
-<dt id="System.System_Base.inputValue">
-<code class="descname">inputValue</code><a class="headerlink" href="#System.System_Base.inputValue" title="Permalink to this definition">¶</a></dt>
-<dd><p>Value of input to system</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>value of input to system</strong></td>
-</tr>
-<tr class="field-even field"><th class="field-name">Return type:</th><td class="field-body">ndarray</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
 <dl class="attribute">
 <dt>
 <code class="descname">numPhases</code></dt>
@@ -766,20 +751,6 @@ <h1>System<a class="headerlink" href="#system" title="Permalink to this headline
 </table>
 </dd></dl>
 
-<dl class="attribute">
-<dt id="System.System_Base.execution_graph_mechs">
-<code class="descname">execution_graph_mechs</code><a class="headerlink" href="#System.System_Base.execution_graph_mechs" title="Permalink to this definition">¶</a></dt>
-<dd><p>Mechanisms whose mech_tuples appear as keys in self.executionGraph</p>
-<table class="docutils field-list" frame="void" rules="none">
-<col class="field-name" />
-<col class="field-body" />
-<tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">list of Mechanism objects</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
 <dl class="method">
 <dt id="System.System_Base.show_graph">
 <code class="descname">show_graph</code><span class="sig-paren">(</span><em>direction='BT'</em>, <em>show_learning=False</em>, <em>show_control=False</em>, <em>learning_color='green'</em>, <em>control_color='blue'</em>, <em>output_fmt='pdf'</em><span class="sig-paren">)</span><a class="headerlink" href="#System.System_Base.show_graph" title="Permalink to this definition">¶</a></dt>
diff --git a/docs/build/html/TimeScale.html b/docs/build/html/TimeScale.html
index ebb6ac3f286..a69c708a949 100644
--- a/docs/build/html/TimeScale.html
+++ b/docs/build/html/TimeScale.html
@@ -86,7 +86,7 @@
 <li class="toctree-l1"><a class="reference internal" href="System.html">System</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Process.html">Process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Mechanism.html">Mechanism</a></li>
-<li class="toctree-l1"><a class="reference internal" href="State.html">States</a></li>
+<li class="toctree-l1"><a class="reference internal" href="State.html">State</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Projection.html">Projection</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Run.html">Run</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Component.html">Component</a></li>
diff --git a/docs/build/html/TransferMechanism.html b/docs/build/html/TransferMechanism.html
index 9fd5a8f502b..d58a4b42e13 100644
--- a/docs/build/html/TransferMechanism.html
+++ b/docs/build/html/TransferMechanism.html
@@ -35,8 +35,8 @@
         <link rel="search" title="Search" href="search.html"/>
     <link rel="top" title="PsyNeuLink 0.1 documentation" href="index.html"/>
         <link rel="up" title="Processing Mechanism" href="ProcessingMechanism.html"/>
-        <link rel="next" title="IntegratorMechanism" href="IntegratorMechanism.html"/>
-        <link rel="prev" title="Processing Mechanism" href="ProcessingMechanism.html"/> 
+        <link rel="next" title="RecurrentTransferMechanism" href="RecurrentTransferMechanism.html"/>
+        <link rel="prev" title="DDM" href="DDM.html"/> 
 
   
   <script src="_static/js/modernizr.min.js"></script>
@@ -85,11 +85,34 @@
           
             
             
-                <ul>
+                <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="System.html">System</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Process.html">Process</a></li>
-<li class="toctree-l1"><a class="reference internal" href="Mechanism.html">Mechanism</a></li>
-<li class="toctree-l1"><a class="reference internal" href="State.html">States</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="Mechanism.html">Mechanism</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="ProcessingMechanism.html">Processing Mechanism</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="IntegratorMechanism.html">IntegratorMechanism</a></li>
+<li class="toctree-l3"><a class="reference internal" href="DDM.html">DDM</a></li>
+<li class="toctree-l3 current"><a class="current reference internal" href="#">TransferMechanism</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#overview">Overview</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#creating-a-transfermechanism">Creating a TransferMechanism</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#structure">Structure</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#execution">Execution</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="RecurrentTransferMechanism.html">RecurrentTransferMechanism</a></li>
+<li class="toctree-l3"><a class="reference internal" href="LCA.html">LCA</a></li>
+<li class="toctree-l3"><a class="reference internal" href="ObjectiveMechanism.html">ObjectiveMechanism</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="AdaptiveMechanism.html">Adaptive Mechanism</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Mechanism.html#overview">Overview</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Mechanism.html#creating-a-mechanism">Creating a Mechanism</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Mechanism.html#structure">Structure</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Mechanism.html#execution">Execution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Mechanism.html#class-reference">Class Reference</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="State.html">State</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Projection.html">Projection</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Run.html">Run</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Component.html">Component</a></li>
@@ -155,7 +178,7 @@ <h1>TransferMechanism<a class="headerlink" href="#transfermechanism" title="Perm
 </li>
 </ul>
 </div>
-<span class="target" id="module-TransferMechanism"></span><div class="section" id="overview">
+<span class="target" id="module-ProcessingMechanisms.TransferMechanism"></span><div class="section" id="overview">
 <span id="transfer-overview"></span><h2>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">¶</a></h2>
 <p>A TransferMechanism transforms its input using a simple mathematical function.  The input can be a single scalar
 value or an an array of scalars (list or 1d np.array).  The function used can be selected from a standard set
@@ -163,21 +186,21 @@ <h1>TransferMechanism<a class="headerlink" href="#transfermechanism" title="Perm
 </div>
 <div class="section" id="creating-a-transfermechanism">
 <span id="transfer-creation"></span><h2>Creating a TransferMechanism<a class="headerlink" href="#creating-a-transfermechanism" title="Permalink to this headline">¶</a></h2>
-<p>A TransferMechanism can be created directly by calling its constructor, or using the <code class="xref py py-func docutils literal"><span class="pre">mechanism()</span></code>
-function and specifying <code class="xref any docutils literal"><span class="pre">TRANSFER_MECHANISM</span></code> as its <code class="xref any docutils literal"><span class="pre">mech_spec</span></code> argument.  Its function is specified in the
-<code class="xref std std-keyword docutils literal"><span class="pre">function</span></code> argument, which can be simply the name of the class (first example below), or a call to its
+<p>A TransferMechanism can be created directly by calling its constructor, or using the <a class="reference internal" href="Mechanism.html#Mechanism.mechanism" title="Mechanism.mechanism"><code class="xref any py py-func docutils literal"><span class="pre">mechanism()</span></code></a>
+function and specifying TRANSFER_MECHANISM as its <strong>mech_spec</strong> argument.  Its function is specified in the
+<strong>function</strong> argument, which can be simply the name of the class (first example below), or a call to its
 constructor which can include arguments specifying the function&#8217;s parameters (second example):</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">my_linear_transfer_mechanism</span> <span class="o">=</span> <span class="n">TransferMechanism</span><span class="p">(</span><span class="n">function</span><span class="o">=</span><span class="n">Linear</span><span class="p">)</span>
 <span class="n">my_logistic_transfer_mechanism</span> <span class="o">=</span> <span class="n">TransferMechanism</span><span class="p">(</span><span class="n">function</span><span class="o">=</span><span class="n">Logistic</span><span class="p">(</span><span class="n">gain</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">bias</span><span class="o">=-</span><span class="mi">4</span><span class="p">)</span>
 </pre></div>
 </div>
-<p>In addition to function-specific parameters, <a class="reference internal" href="#TransferMechanism.TransferMechanism.noise" title="TransferMechanism.TransferMechanism.noise"><code class="xref any py py-attr docutils literal"><span class="pre">noise</span></code></a> and <a class="reference internal" href="#TransferMechanism.TransferMechanism.time_constant" title="TransferMechanism.TransferMechanism.time_constant"><code class="xref any py py-attr docutils literal"><span class="pre">time_constant</span></code></a>
+<p>In addition to function-specific parameters, <a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.noise" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.noise"><code class="xref any py py-attr docutils literal"><span class="pre">noise</span></code></a> and <a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.time_constant" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.time_constant"><code class="xref any py py-attr docutils literal"><span class="pre">time_constant</span></code></a>
 parameters can be specified (see <code class="xref any docutils literal"><span class="pre">Execution</span></code> below).</p>
 </div>
 <div class="section" id="structure">
 <span id="transfer-structure"></span><h2>Structure<a class="headerlink" href="#structure" title="Permalink to this headline">¶</a></h2>
 <p>A TransferMechanism has a single <a class="reference internal" href="InputState.html"><span class="doc">inputState</span></a>, the <a class="reference internal" href="InputState.html#InputState.InputState.value" title="InputState.InputState.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> of which is
-used as the <a class="reference internal" href="#TransferMechanism.TransferMechanism.variable" title="TransferMechanism.TransferMechanism.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> for its <a class="reference internal" href="#TransferMechanism.TransferMechanism.function" title="TransferMechanism.TransferMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>. The
+used as the <a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.variable" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> for its <a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.function" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>. The
 <code class="xref std std-keyword docutils literal"><span class="pre">function</span></code> can be selected from one of three standard PsyNeuLink <code class="xref any docutils literal"><span class="pre">Function</span></code>: <a class="reference internal" href="Function.html#Function.Linear" title="Function.Linear"><code class="xref any py py-class docutils literal"><span class="pre">Linear</span></code></a>,
 <a class="reference internal" href="Function.html#Function.Logistic" title="Function.Logistic"><code class="xref any py py-class docutils literal"><span class="pre">Logistic</span></code></a> or <a class="reference internal" href="Function.html#Function.Exponential" title="Function.Exponential"><code class="xref any py py-class docutils literal"><span class="pre">Exponential</span></code></a>; or a custom function can be specified, so long as it returns a numeric value or
 list or np.ndarray of numeric values.  A TransferMechanism has three <code class="xref any docutils literal"><span class="pre">outputStates</span> <span class="pre">&lt;OutputStates&gt;,</span> <span class="pre">described</span> <span class="pre">under</span>
@@ -189,62 +212,37 @@ <h1>TransferMechanism<a class="headerlink" href="#transfermechanism" title="Perm
 parameters (in addition to those specified for the function):</p>
 <blockquote>
 <div><ul class="simple">
-<li><a class="reference internal" href="#TransferMechanism.TransferMechanism.noise" title="TransferMechanism.TransferMechanism.noise"><code class="xref any py py-attr docutils literal"><span class="pre">noise</span></code></a>: applied element-wise to the input before transforming it.</li>
+<li><a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.noise" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.noise"><code class="xref any py py-attr docutils literal"><span class="pre">noise</span></code></a>: applied element-wise to the input before transforming it.</li>
 </ul>
 <ul class="simple">
-<li><a class="reference internal" href="#TransferMechanism.TransferMechanism.time_constant" title="TransferMechanism.TransferMechanism.time_constant"><code class="xref any py py-attr docutils literal"><span class="pre">time_constant</span></code></a>: if <a class="reference internal" href="#TransferMechanism.TransferMechanism.time_scale" title="TransferMechanism.TransferMechanism.time_scale"><code class="xref any py py-attr docutils literal"><span class="pre">time_scale</span></code></a> is <code class="xref std std-keyword docutils literal"><span class="pre">TimeScale.TIME_STEP</span></code>,
+<li><a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.time_constant" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.time_constant"><code class="xref any py py-attr docutils literal"><span class="pre">time_constant</span></code></a>: if <a class="reference internal" href="DDM.html#DDM.DDM.time_scale" title="DDM.DDM.time_scale"><code class="xref any py py-attr docutils literal"><span class="pre">time_scale</span></code></a> is <code class="xref std std-keyword docutils literal"><span class="pre">TimeScale.TIME_STEP</span></code>,
 the input is exponentially time-averaged before transforming it (higher value specifies faster rate);
-if <a class="reference internal" href="#TransferMechanism.TransferMechanism.time_scale" title="TransferMechanism.TransferMechanism.time_scale"><code class="xref any py py-attr docutils literal"><span class="pre">time_scale</span></code></a> is <code class="xref std std-keyword docutils literal"><span class="pre">TimeScale.TRIAL</span></code>, <a class="reference internal" href="#TransferMechanism.TransferMechanism.time_constant" title="TransferMechanism.TransferMechanism.time_constant"><code class="xref any py py-attr docutils literal"><span class="pre">time_constant</span></code></a> is ignored.</li>
+if <a class="reference internal" href="DDM.html#DDM.DDM.time_scale" title="DDM.DDM.time_scale"><code class="xref any py py-attr docutils literal"><span class="pre">time_scale</span></code></a> is <code class="xref std std-keyword docutils literal"><span class="pre">TimeScale.TRIAL</span></code>, <a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.time_constant" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.time_constant"><code class="xref any py py-attr docutils literal"><span class="pre">time_constant</span></code></a> is ignored.</li>
 </ul>
 <ul class="simple">
-<li><a class="reference internal" href="#TransferMechanism.TransferMechanism.range" title="TransferMechanism.TransferMechanism.range"><code class="xref any py py-attr docutils literal"><span class="pre">range</span></code></a>: caps all elements of the <a class="reference internal" href="#TransferMechanism.TransferMechanism.function" title="TransferMechanism.TransferMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> result by
+<li><a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.range" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.range"><code class="xref any py py-attr docutils literal"><span class="pre">range</span></code></a>: caps all elements of the <a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.function" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> result by
 the lower and upper values specified by range.</li>
 </ul>
 </div></blockquote>
 <p>After each execution of the mechanism:</p>
 <blockquote id="transfer-results">
 <div><ul class="simple">
-<li><strong>result</strong> of <a class="reference internal" href="#TransferMechanism.TransferMechanism.function" title="TransferMechanism.TransferMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> is assigned to the mechanism&#8217;s
-<a class="reference internal" href="#TransferMechanism.TransferMechanism.value" title="TransferMechanism.TransferMechanism.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> attribute, the <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code> of its <code class="xref any docutils literal"><span class="pre">TRANSFER_RESULT</span></code> outputState,
-and to the 1st item of the mechanism&#8217;s <a class="reference internal" href="#TransferMechanism.TransferMechanism.outputValue" title="TransferMechanism.TransferMechanism.outputValue"><code class="xref any py py-attr docutils literal"><span class="pre">outputValue</span></code></a> attribute;</li>
+<li><strong>result</strong> of <a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.function" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a> is assigned to the mechanism&#8217;s
+<a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.value" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.value"><code class="xref any py py-attr docutils literal"><span class="pre">value</span></code></a> attribute, the <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code> of its TRANSFER_RESULT outputState,
+and to the 1st item of the mechanism&#8217;s <a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.output_values" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.output_values"><code class="xref any py py-attr docutils literal"><span class="pre">output_values</span></code></a> attribute;</li>
 </ul>
 <ul class="simple">
-<li><strong>mean</strong> of the result is assigned to the the <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code> of the mechanism&#8217;s <code class="xref any docutils literal"><span class="pre">TRANSFER_MEAN</span></code> outputState,
-and to the 2nd item of its <a class="reference internal" href="#TransferMechanism.TransferMechanism.outputValue" title="TransferMechanism.TransferMechanism.outputValue"><code class="xref any py py-attr docutils literal"><span class="pre">outputValue</span></code></a> attribute;</li>
+<li><strong>mean</strong> of the result is assigned to the the <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code> of the mechanism&#8217;s TRANSFER_MEAN outputState,
+and to the 2nd item of its <a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.output_values" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.output_values"><code class="xref any py py-attr docutils literal"><span class="pre">output_values</span></code></a> attribute;</li>
 </ul>
 <ul class="simple">
-<li><strong>variance</strong> of the result is assigned to the <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code> of the mechanism&#8217;s <code class="xref any docutils literal"><span class="pre">TRANSFER_VARIANCE</span></code> outputState,
-and to the 3rd item of its <a class="reference internal" href="#TransferMechanism.TransferMechanism.outputValue" title="TransferMechanism.TransferMechanism.outputValue"><code class="xref any py py-attr docutils literal"><span class="pre">outputValue</span></code></a> attribute.</li>
+<li><strong>variance</strong> of the result is assigned to the <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code> of the mechanism&#8217;s TRANSFER_VARIANCE outputState,
+and to the 3rd item of its <a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.output_values" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.output_values"><code class="xref any py py-attr docutils literal"><span class="pre">output_values</span></code></a> attribute.</li>
 </ul>
 </div></blockquote>
 <dl class="class">
-<dt id="TransferMechanism.Transfer_Output">
-<em class="property">class </em><code class="descclassname">TransferMechanism.</code><code class="descname">Transfer_Output</code><a class="headerlink" href="#TransferMechanism.Transfer_Output" title="Permalink to this definition">¶</a></dt>
-<dd><p>Indices of the <a class="reference internal" href="#TransferMechanism.TransferMechanism.outputValue" title="TransferMechanism.TransferMechanism.outputValue"><code class="xref any py py-attr docutils literal"><span class="pre">outputValue</span></code></a> attribute of the TransferMechanism containing the
-values described below.</p>
-<dl class="attribute">
-<dt id="TransferMechanism.Transfer_Output.RESULT">
-<code class="descname">RESULT</code><em class="property"> = &lt;Transfer_Output.RESULT: 0&gt;</em><a class="headerlink" href="#TransferMechanism.Transfer_Output.RESULT" title="Permalink to this definition">¶</a></dt>
-<dd><p>Result of the TransferMechanism&#8217;s <a class="reference internal" href="#TransferMechanism.TransferMechanism.function" title="TransferMechanism.TransferMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>.</p>
-</dd></dl>
-
-<dl class="attribute">
-<dt id="TransferMechanism.Transfer_Output.MEAN">
-<code class="descname">MEAN</code><em class="property"> = &lt;Transfer_Output.MEAN: 1&gt;</em><a class="headerlink" href="#TransferMechanism.Transfer_Output.MEAN" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>Mean of the elements in the</em> &#8211; keyword`value` of the RESULT outputState.</p>
-</dd></dl>
-
-<dl class="attribute">
-<dt id="TransferMechanism.Transfer_Output.VARIANCE">
-<code class="descname">VARIANCE</code><em class="property"> = &lt;Transfer_Output.VARIANCE: 2&gt;</em><a class="headerlink" href="#TransferMechanism.Transfer_Output.VARIANCE" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>Variance of the elements in the</em> &#8211; keyword`value` of the RESULT outputState.</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="class">
-<dt id="TransferMechanism.TransferMechanism">
-<em class="property">class </em><code class="descclassname">TransferMechanism.</code><code class="descname">TransferMechanism</code><span class="sig-paren">(</span><em>default_input_value=None</em>, <em>function=Linear</em>, <em>initial_value=None</em>, <em>noise=0.0</em>, <em>time_constant=1.0</em>, <em>range=(float:min</em>, <em>float:max)</em>, <em>time_scale=TimeScale.TRIAL</em>, <em>params=None</em>, <em>name=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#TransferMechanism.TransferMechanism" title="Permalink to this definition">¶</a></dt>
+<dt id="ProcessingMechanisms.TransferMechanism.TransferMechanism">
+<em class="property">class </em><code class="descclassname">ProcessingMechanisms.TransferMechanism.</code><code class="descname">TransferMechanism</code><span class="sig-paren">(</span><em>default_input_value=None</em>, <em>function=Linear</em>, <em>initial_value=None</em>, <em>noise=0.0</em>, <em>time_constant=1.0</em>, <em>range=(float:min</em>, <em>float:max)</em>, <em>time_scale=TimeScale.TRIAL</em>, <em>params=None</em>, <em>name=None</em>, <em>prefs=None</em><span class="sig-paren">)</span><a class="headerlink" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements TransferMechanism subclass of <a class="reference internal" href="Mechanism.html"><span class="doc">Mechanism</span></a>.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
@@ -253,32 +251,32 @@ <h1>TransferMechanism<a class="headerlink" href="#transfermechanism" title="Perm
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
 <li><strong>default_input_value</strong> (<em>number, list or np.ndarray : default Transfer_DEFAULT_BIAS</em>) &#8211; specifies the input to the mechanism to use if none is provided in a call to its
 <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.execute" title="Mechanism.Mechanism_Base.execute"><code class="xref any py py-meth docutils literal"><span class="pre">execute</span></code></a> or <a class="reference internal" href="Mechanism.html#Mechanism.Mechanism_Base.run" title="Mechanism.Mechanism_Base.run"><code class="xref any py py-meth docutils literal"><span class="pre">run</span></code></a> method;
-also serves as a template to specify the length of <a class="reference internal" href="#TransferMechanism.TransferMechanism.variable" title="TransferMechanism.TransferMechanism.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> for
-<a class="reference internal" href="#TransferMechanism.TransferMechanism.function" title="TransferMechanism.TransferMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>, and the <a class="reference internal" href="OutputState.html#outputstate-primary"><span class="std std-ref">primary outputState</span></a>
+also serves as a template to specify the length of <a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.variable" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> for
+<a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.function" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>, and the <a class="reference internal" href="OutputState.html#outputstate-primary"><span class="std std-ref">primary outputState</span></a>
 of the mechanism.</li>
 <li><strong>function</strong> (<em>TransferFunction : default Linear</em>) &#8211; specifies the function used to transform the input;  can be <a class="reference internal" href="Function.html#Function.Linear" title="Function.Linear"><code class="xref any py py-class docutils literal"><span class="pre">Linear</span></code></a>, <a class="reference internal" href="Function.html#Function.Logistic" title="Function.Logistic"><code class="xref any py py-class docutils literal"><span class="pre">Logistic</span></code></a>, <a class="reference internal" href="Function.html#Function.Exponential" title="Function.Exponential"><code class="xref any py py-class docutils literal"><span class="pre">Exponential</span></code></a>,
 or a custom function.</li>
 <li><strong>initial_value</strong> (<em>value, list or np.ndarray : default Transfer_DEFAULT_BIAS</em>) &#8211; specifies the starting value for time-averaged input (only relevant if
-<a class="reference internal" href="#TransferMechanism.TransferMechanism.time_constant" title="TransferMechanism.TransferMechanism.time_constant"><code class="xref any py py-attr docutils literal"><span class="pre">time_constant</span></code></a> is not 1.0).
+<a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.time_constant" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.time_constant"><code class="xref any py py-attr docutils literal"><span class="pre">time_constant</span></code></a> is not 1.0).
 <code class="xref py py-data docutils literal"><span class="pre">Transfer_DEFAULT_BIAS</span></code></li>
-<li><strong>noise</strong> (<em>float or function : default 0.0</em>) &#8211; a stochastically-sampled value added to the result of the <a class="reference internal" href="#TransferMechanism.TransferMechanism.function" title="TransferMechanism.TransferMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>:
+<li><strong>noise</strong> (<em>float or function : default 0.0</em>) &#8211; a stochastically-sampled value added to the result of the <a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.function" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>:
 if it is a float, it must be in the interval [0,1] and is used to scale the variance of a zero-mean Gaussian;
 if it is a function, it must return a scalar value.</li>
-<li><strong>time_constant</strong> (<em>float : default 1.0</em>) &#8211; <p>the time constant for exponential time averaging of input when the mechanism is executed with <a class="reference internal" href="#TransferMechanism.TransferMechanism.time_scale" title="TransferMechanism.TransferMechanism.time_scale"><code class="xref any py py-attr docutils literal"><span class="pre">time_scale</span></code></a>
+<li><strong>time_constant</strong> (<em>float : default 1.0</em>) &#8211; <p>the time constant for exponential time averaging of input when the mechanism is executed with <a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.time_scale" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.time_scale"><code class="xref any py py-attr docutils literal"><span class="pre">time_scale</span></code></a>
 set to <code class="xref any docutils literal"><span class="pre">TimeScale.TIME_STEP</span></code>:</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">result</span> <span class="o">=</span> <span class="p">(</span><span class="n">time_constant</span> <span class="o">*</span> <span class="n">current</span> <span class="nb">input</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">time_constant</span> <span class="o">*</span> <span class="n">result</span> <span class="n">on</span> <span class="n">previous</span> <span class="n">time_step</span><span class="p">)</span>
 </pre></div>
 </div>
 </li>
-<li><strong>range</strong> (<em>Optional[Tuple[float, float]]</em>) &#8211; specifies the allowable range for the result of <a class="reference internal" href="#TransferMechanism.TransferMechanism.function" title="TransferMechanism.TransferMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>:
+<li><strong>range</strong> (<em>Optional[Tuple[float, float]]</em>) &#8211; specifies the allowable range for the result of <a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.function" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>:
 the first item specifies the minimum allowable value of the result, and the second its maximum allowable value;
 any element of the result that exceeds the specified minimum or maximum value is set to the value of
-<a class="reference internal" href="#TransferMechanism.TransferMechanism.range" title="TransferMechanism.TransferMechanism.range"><code class="xref any py py-attr docutils literal"><span class="pre">range</span></code></a> that it exceeds.</li>
+<a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.range" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.range"><code class="xref any py py-attr docutils literal"><span class="pre">range</span></code></a> that it exceeds.</li>
 <li><strong>params</strong> (<em>Optional[Dict[param keyword, param value]]</em>) &#8211; a <a class="reference internal" href="ParameterState.html#parameterstate-specifying-parameters"><span class="std std-ref">parameter dictionary</span></a> that can be used to specify the parameters for
 the mechanism, its function, and/or a custom function and its parameters.  Values specified for parameters in
 the dictionary override any assigned to those parameters in arguments of the constructor.</li>
 <li><strong>time_scale</strong> (<em>TimeScale : TimeScale.TRIAL</em>) &#8211; specifies whether the mechanism is executed using the <code class="xref any docutils literal"><span class="pre">TIME_STEP</span></code> or <code class="xref any docutils literal"><span class="pre">TRIAL</span></code> <a class="reference internal" href="TimeScale.html"><span class="doc">TimeScale</span></a>.
-This must be set to <code class="xref any docutils literal"><span class="pre">TimeScale.TIME_STEP</span></code> for the <a class="reference internal" href="#TransferMechanism.TransferMechanism.time_constant" title="TransferMechanism.TransferMechanism.time_constant"><code class="xref any py py-attr docutils literal"><span class="pre">time_constant</span></code></a>
+This must be set to <code class="xref any docutils literal"><span class="pre">TimeScale.TIME_STEP</span></code> for the <a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.time_constant" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.time_constant"><code class="xref any py py-attr docutils literal"><span class="pre">time_constant</span></code></a>
 parameter to have an effect.</li>
 <li><strong>name</strong> (<em>str : default TransferMechanism-&lt;index&gt;</em>) &#8211; a string used for the name of the mechanism.
 If not is specified, a default is assigned by <code class="xref any docutils literal"><span class="pre">MechanismRegistry</span></code>
@@ -297,42 +295,49 @@ <h1>TransferMechanism<a class="headerlink" href="#transfermechanism" title="Perm
 <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>instance of TransferMechanism</strong></p>
 </td>
 </tr>
-<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#module-TransferMechanism" title="TransferMechanism">TransferMechanism</a></p>
+<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last"><a class="reference internal" href="#module-ProcessingMechanisms.TransferMechanism" title="ProcessingMechanisms.TransferMechanism">TransferMechanism</a></p>
 </td>
 </tr>
 </tbody>
 </table>
 <dl class="attribute">
-<dt id="TransferMechanism.TransferMechanism.variable">
-<code class="descname">variable</code><a class="headerlink" href="#TransferMechanism.TransferMechanism.variable" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>value: default Transfer_DEFAULT_BIAS</em> &#8211; the input to mechanism&#8217;s <code class="docutils literal"><span class="pre">function</span></code>.  <code class="xref py py-data docutils literal"><span class="pre">Transfer_DEFAULT_BIAS</span></code></p>
+<dt id="ProcessingMechanisms.TransferMechanism.TransferMechanism.variable">
+<code class="descname">variable</code><a class="headerlink" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.variable" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>value: default Transfer_DEFAULT_BIAS</em> &#8211; the input to mechanism&#8217;s <a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.function" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="TransferMechanism.TransferMechanism.function">
-<code class="descname">function</code><a class="headerlink" href="#TransferMechanism.TransferMechanism.function" title="Permalink to this definition">¶</a></dt>
+<dt id="ProcessingMechanisms.TransferMechanism.TransferMechanism.size">
+<code class="descname">size</code><a class="headerlink" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.size" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>int</em> &#8211; length of the first (and only) item in <a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.variable" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a>
+(i.e., the vector transformed by <a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.function" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="ProcessingMechanisms.TransferMechanism.TransferMechanism.function">
+<code class="descname">function</code><a class="headerlink" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.function" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>Function :  default Linear</em> &#8211; the function used to transform the input.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="TransferMechanism.TransferMechanism.initial_value">
-<code class="descname">initial_value</code><a class="headerlink" href="#TransferMechanism.TransferMechanism.initial_value" title="Permalink to this definition">¶</a></dt>
+<dt id="ProcessingMechanisms.TransferMechanism.TransferMechanism.initial_value">
+<code class="descname">initial_value</code><a class="headerlink" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.initial_value" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>value, list or np.ndarray : Transfer_DEFAULT_BIAS</em> &#8211; determines the starting value for time-averaged input
-(only relevant if <a class="reference internal" href="#TransferMechanism.TransferMechanism.time_constant" title="TransferMechanism.TransferMechanism.time_constant"><code class="xref any py py-attr docutils literal"><span class="pre">time_constant</span></code></a> parameter is not 1.0).
+(only relevant if <a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.time_constant" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.time_constant"><code class="xref any py py-attr docutils literal"><span class="pre">time_constant</span></code></a> parameter is not 1.0).
 <code class="xref py py-data docutils literal"><span class="pre">Transfer_DEFAULT_BIAS</span></code></p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="TransferMechanism.TransferMechanism.noise">
-<code class="descname">noise</code><a class="headerlink" href="#TransferMechanism.TransferMechanism.noise" title="Permalink to this definition">¶</a></dt>
+<dt id="ProcessingMechanisms.TransferMechanism.TransferMechanism.noise">
+<code class="descname">noise</code><a class="headerlink" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.noise" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>float or function : default 0.0</em> &#8211; a stochastically-sampled value added to the output of the <code class="xref any docutils literal"><span class="pre">function</span></code>:
 if it is a float, it must be in the interval [0,1] and is used to scale the variance of a zero-mean Gaussian;
 if it is a function, it must return a scalar value.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="TransferMechanism.TransferMechanism.time_constant">
-<code class="descname">time_constant</code><a class="headerlink" href="#TransferMechanism.TransferMechanism.time_constant" title="Permalink to this definition">¶</a></dt>
+<dt id="ProcessingMechanisms.TransferMechanism.TransferMechanism.time_constant">
+<code class="descname">time_constant</code><a class="headerlink" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.time_constant" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>float : default 1.0</em> &#8211; the time constant for exponential time averaging of input
 when the mechanism is executed using the <code class="xref any docutils literal"><span class="pre">TIME_STEP</span></code> <a class="reference internal" href="TimeScale.html"><span class="doc">TimeScale</span></a>:</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">result</span> <span class="o">=</span> <span class="p">(</span><span class="n">time_constant</span> <span class="o">*</span> <span class="n">current</span> <span class="nb">input</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">time_constant</span> <span class="o">*</span> <span class="n">result</span> <span class="n">on</span> <span class="n">previous</span> <span class="n">time_step</span><span class="p">)</span>
@@ -341,59 +346,65 @@ <h1>TransferMechanism<a class="headerlink" href="#transfermechanism" title="Perm
 </dd></dl>
 
 <dl class="attribute">
-<dt id="TransferMechanism.TransferMechanism.range">
-<code class="descname">range</code><a class="headerlink" href="#TransferMechanism.TransferMechanism.range" title="Permalink to this definition">¶</a></dt>
+<dt id="ProcessingMechanisms.TransferMechanism.TransferMechanism.range">
+<code class="descname">range</code><a class="headerlink" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.range" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>Optional[Tuple[float, float]]</em> &#8211; determines the allowable range of the result: the first value specifies the minimum allowable value
 and the second the maximum allowable value;  any element of the result that exceeds minimum or maximum
-is set to the value of <a class="reference internal" href="#TransferMechanism.TransferMechanism.range" title="TransferMechanism.TransferMechanism.range"><code class="xref any py py-attr docutils literal"><span class="pre">range</span></code></a> it exceeds.  If <a class="reference internal" href="#TransferMechanism.TransferMechanism.function" title="TransferMechanism.TransferMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>
-is <a class="reference internal" href="Function.html#Function.Logistic" title="Function.Logistic"><code class="xref any py py-class docutils literal"><span class="pre">Logistic</span></code></a>, <a class="reference internal" href="#TransferMechanism.TransferMechanism.range" title="TransferMechanism.TransferMechanism.range"><code class="xref any py py-attr docutils literal"><span class="pre">range</span></code></a> is set by default to (0,1).</p>
+is set to the value of <a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.range" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.range"><code class="xref any py py-attr docutils literal"><span class="pre">range</span></code></a> it exceeds.  If <a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.function" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>
+is <a class="reference internal" href="Function.html#Function.Logistic" title="Function.Logistic"><code class="xref any py py-class docutils literal"><span class="pre">Logistic</span></code></a>, <a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.range" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.range"><code class="xref any py py-attr docutils literal"><span class="pre">range</span></code></a> is set by default to (0,1).</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="ProcessingMechanisms.TransferMechanism.TransferMechanism.previous_input">
+<code class="descname">previous_input</code><a class="headerlink" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.previous_input" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>float</em> &#8211; the value of the <a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.variable" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.variable"><code class="xref any py py-attr docutils literal"><span class="pre">variable</span></code></a> on the <code class="xref any docutils literal"><span class="pre">previous</span> <span class="pre">round</span> <span class="pre">of</span> <span class="pre">execution</span></code>.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="TransferMechanism.TransferMechanism.value">
-<code class="descname">value</code><a class="headerlink" href="#TransferMechanism.TransferMechanism.value" title="Permalink to this definition">¶</a></dt>
-<dd><p><em>2d np.array [array(float64)]</em> &#8211; result of executing <a class="reference internal" href="#TransferMechanism.TransferMechanism.function" title="TransferMechanism.TransferMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>; same value as fist item of
-<a class="reference internal" href="#TransferMechanism.TransferMechanism.outputValue" title="TransferMechanism.TransferMechanism.outputValue"><code class="xref any py py-attr docutils literal"><span class="pre">outputValue</span></code></a>.</p>
+<dt id="ProcessingMechanisms.TransferMechanism.TransferMechanism.value">
+<code class="descname">value</code><a class="headerlink" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.value" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>2d np.array [array(float64)]</em> &#8211; result of executing <a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.function" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>; same value as fist item of
+<a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.output_values" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.output_values"><code class="xref any py py-attr docutils literal"><span class="pre">output_values</span></code></a>.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="TransferMechanism.TransferMechanism.outputStates">
-<code class="descname">outputStates</code><a class="headerlink" href="#TransferMechanism.TransferMechanism.outputStates" title="Permalink to this definition">¶</a></dt>
+<dt id="ProcessingMechanisms.TransferMechanism.TransferMechanism.outputStates">
+<code class="descname">outputStates</code><a class="headerlink" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.outputStates" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>Dict[str, OutputState]</em> &#8211; an OrderedDict with three <a class="reference internal" href="OutputState.html"><span class="doc">outputStates</span></a>:
-* <code class="xref any docutils literal"><span class="pre">TRANSFER_RESULT</span></code>, the <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code> of which is the <strong>result</strong> of <a class="reference internal" href="#TransferMechanism.TransferMechanism.function" title="TransferMechanism.TransferMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>;
+* <code class="xref any docutils literal"><span class="pre">TRANSFER_RESULT</span></code>, the <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code> of which is the <strong>result</strong> of <a class="reference internal" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.function" title="ProcessingMechanisms.TransferMechanism.TransferMechanism.function"><code class="xref any py py-attr docutils literal"><span class="pre">function</span></code></a>;
 * <code class="xref any docutils literal"><span class="pre">TRANSFER_MEAN</span></code>, the <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code> of which is the mean of the result;
 * <code class="xref any docutils literal"><span class="pre">TRANSFER_VARIANCE</span></code>, the <code class="xref std std-keyword docutils literal"><span class="pre">value</span></code> of which is the variance of the result;</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="TransferMechanism.TransferMechanism.outputValue">
-<code class="descname">outputValue</code><a class="headerlink" href="#TransferMechanism.TransferMechanism.outputValue" title="Permalink to this definition">¶</a></dt>
+<dt id="ProcessingMechanisms.TransferMechanism.TransferMechanism.output_values">
+<code class="descname">output_values</code><a class="headerlink" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.output_values" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>List[array(float64), float, float]</em> &#8211; a list with the following items:
-* <strong>result</strong> of the <code class="docutils literal"><span class="pre">function</span></code> calculation (value of <code class="xref any docutils literal"><span class="pre">TRANSFER_RESULT</span></code> outputState);
-* <strong>mean</strong> of the result (<code class="docutils literal"><span class="pre">value</span></code> of <code class="xref any docutils literal"><span class="pre">TRANSFER_MEAN</span></code> outputState)
-* <strong>variance</strong> of the result (<code class="docutils literal"><span class="pre">value</span></code> of <code class="xref any docutils literal"><span class="pre">TRANSFER_VARIANCE</span></code> outputState)</p>
+* <strong>result</strong> of the <code class="docutils literal"><span class="pre">function</span></code> calculation (value of TRANSFER_RESULT outputState);
+* <strong>mean</strong> of the result (<code class="docutils literal"><span class="pre">value</span></code> of TRANSFER_MEAN outputState)
+* <strong>variance</strong> of the result (<code class="docutils literal"><span class="pre">value</span></code> of TRANSFER_VARIANCE outputState)</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="TransferMechanism.TransferMechanism.time_scale">
-<code class="descname">time_scale</code><a class="headerlink" href="#TransferMechanism.TransferMechanism.time_scale" title="Permalink to this definition">¶</a></dt>
+<dt id="ProcessingMechanisms.TransferMechanism.TransferMechanism.time_scale">
+<code class="descname">time_scale</code><a class="headerlink" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.time_scale" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>TimeScale : defaul tTimeScale.TRIAL</em> &#8211; specifies whether the mechanism is executed using the <code class="xref any docutils literal"><span class="pre">TIME_STEP</span></code> or <code class="xref any docutils literal"><span class="pre">TRIAL</span></code> <a class="reference internal" href="TimeScale.html"><span class="doc">TimeScale</span></a>.</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="TransferMechanism.TransferMechanism.name">
-<code class="descname">name</code><a class="headerlink" href="#TransferMechanism.TransferMechanism.name" title="Permalink to this definition">¶</a></dt>
+<dt id="ProcessingMechanisms.TransferMechanism.TransferMechanism.name">
+<code class="descname">name</code><a class="headerlink" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.name" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>str : default TransferMechanism-&lt;index&gt;</em> &#8211; the name of the mechanism.
-Specified in the <a class="reference internal" href="#TransferMechanism.TransferMechanism.name" title="TransferMechanism.TransferMechanism.name"><code class="xref any py py-attr docutils literal"><span class="pre">name</span></code></a> argument of the constructor for the projection;
+Specified in the <strong>name</strong> argument of the constructor for the projection;
 if not is specified, a default is assigned by <code class="xref any docutils literal"><span class="pre">MechanismRegistry</span></code>
 (see <span class="xref doc">Registry</span> for conventions used in naming, including for default and duplicate names).</p>
 </dd></dl>
 
 <dl class="attribute">
-<dt id="TransferMechanism.TransferMechanism.prefs">
-<code class="descname">prefs</code><a class="headerlink" href="#TransferMechanism.TransferMechanism.prefs" title="Permalink to this definition">¶</a></dt>
+<dt id="ProcessingMechanisms.TransferMechanism.TransferMechanism.prefs">
+<code class="descname">prefs</code><a class="headerlink" href="#ProcessingMechanisms.TransferMechanism.TransferMechanism.prefs" title="Permalink to this definition">¶</a></dt>
 <dd><p><em>PreferenceSet or specification dict : Mechanism.classPreferences</em> &#8211; the <code class="xref any docutils literal"><span class="pre">PreferenceSet</span></code> for mechanism.
-Specified in the <a class="reference internal" href="#TransferMechanism.TransferMechanism.prefs" title="TransferMechanism.TransferMechanism.prefs"><code class="xref any py py-attr docutils literal"><span class="pre">prefs</span></code></a> argument of the constructor for the mechanism;
+Specified in the <strong>prefs</strong> argument of the constructor for the mechanism;
 if it is not specified, a default is assigned using <code class="xref any docutils literal"><span class="pre">classPreferences</span></code> defined in __init__.py
 (see <span class="xref doc">PreferenceSet</span> for details).</p>
 </dd></dl>
@@ -410,10 +421,10 @@ <h1>TransferMechanism<a class="headerlink" href="#transfermechanism" title="Perm
   
     <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
       
-        <a href="IntegratorMechanism.html" class="btn btn-neutral float-right" title="IntegratorMechanism" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
+        <a href="RecurrentTransferMechanism.html" class="btn btn-neutral float-right" title="RecurrentTransferMechanism" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
       
       
-        <a href="ProcessingMechanism.html" class="btn btn-neutral" title="Processing Mechanism" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
+        <a href="DDM.html" class="btn btn-neutral" title="DDM" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
       
     </div>
   
diff --git a/docs/build/html/Utilities.html b/docs/build/html/Utilities.html
index 68782951fe8..7b7bff9c56e 100644
--- a/docs/build/html/Utilities.html
+++ b/docs/build/html/Utilities.html
@@ -86,7 +86,7 @@
 <li class="toctree-l1"><a class="reference internal" href="System.html">System</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Process.html">Process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Mechanism.html">Mechanism</a></li>
-<li class="toctree-l1"><a class="reference internal" href="State.html">States</a></li>
+<li class="toctree-l1"><a class="reference internal" href="State.html">State</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Projection.html">Projection</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Run.html">Run</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Component.html">Component</a></li>
@@ -162,16 +162,23 @@ <h3>TYPE CHECKING VALUE COMPARISON<a class="headerlink" href="#type-checking-val
 <p class="last">PsyNeuLink-specific typechecking functions are in the <a class="reference internal" href="Component.html"><span class="doc">Component</span></a> module</p>
 </div>
 <ul class="simple">
-<li><code class="xref any docutils literal"><span class="pre">is_numeric_or_none</span></code></li>
+<li><a class="reference internal" href="#Utilities.parameter_spec" title="Utilities.parameter_spec"><code class="xref any py py-func docutils literal"><span class="pre">parameter_spec</span></code></a></li>
+<li><a class="reference internal" href="#Utilities.optional_parameter_spec" title="Utilities.optional_parameter_spec"><code class="xref any py py-func docutils literal"><span class="pre">optional_parameter_spec</span></code></a></li>
+<li><a href="#id1"><span class="problematic" id="id2">`</span></a>is_matrix</li>
+<li><code class="xref any docutils literal"><span class="pre">is_matrix_spec</span></code></li>
 <li><code class="xref any docutils literal"><span class="pre">is_numeric</span></code></li>
+<li><code class="xref any docutils literal"><span class="pre">is_numeric_or_none</span></code></li>
 <li><a class="reference internal" href="#Utilities.iscompatible" title="Utilities.iscompatible"><code class="xref any py py-func docutils literal"><span class="pre">iscompatible</span></code></a></li>
+<li><code class="xref any docutils literal"><span class="pre">is_value_spec</span></code></li>
+<li><code class="xref any docutils literal"><span class="pre">is_unit_interval</span></code></li>
+<li><a class="reference internal" href="#Utilities.is_same_function_spec" title="Utilities.is_same_function_spec"><code class="xref any py py-func docutils literal"><span class="pre">is_same_function_spec</span></code></a></li>
 </ul>
 </div>
 <div class="section" id="enum">
 <h3>ENUM<a class="headerlink" href="#enum" title="Permalink to this headline">¶</a></h3>
 <ul class="simple">
 <li><code class="xref any docutils literal"><span class="pre">Autonumber</span></code></li>
-<li><a class="reference internal" href="#Utilities.ModulationOperation" title="Utilities.ModulationOperation"><code class="xref any py py-class docutils literal"><span class="pre">ModulationOperation</span></code></a></li>
+<li><a class="reference internal" href="#Utilities.Modulation" title="Utilities.Modulation"><code class="xref any py py-class docutils literal"><span class="pre">Modulation</span></code></a></li>
 <li><code class="xref any docutils literal"><span class="pre">get_modulationOperation_name</span></code></li>
 </ul>
 </div>
@@ -198,10 +205,13 @@ <h3>OTHER<a class="headerlink" href="#other" title="Permalink to this headline">
 <li><code class="xref any docutils literal"><span class="pre">underscore_to_camelCase</span></code></li>
 <li><code class="xref any docutils literal"><span class="pre">append_type_to_name</span></code></li>
 <li><code class="xref any docutils literal"><span class="pre">ReadOnlyOrderedDict</span></code></li>
+<li><a class="reference internal" href="#Utilities.ContentAddressableList" title="Utilities.ContentAddressableList"><code class="xref any py py-class docutils literal"><span class="pre">ContentAddressableList</span></code></a></li>
+<li><a class="reference internal" href="#Utilities.make_readonly_property" title="Utilities.make_readonly_property"><code class="xref any py py-func docutils literal"><span class="pre">make_readonly_property</span></code></a></li>
+<li><code class="xref any docutils literal"><span class="pre">get_class_attributes</span></code></li>
 </ul>
 <dl class="class">
-<dt id="Utilities.ModulationOperation">
-<em class="property">class </em><code class="descclassname">Utilities.</code><code class="descname">ModulationOperation</code><a class="headerlink" href="#Utilities.ModulationOperation" title="Permalink to this definition">¶</a></dt>
+<dt id="Utilities.Modulation">
+<em class="property">class </em><code class="descclassname">Utilities.</code><code class="descname">Modulation</code><a class="headerlink" href="#Utilities.Modulation" title="Permalink to this definition">¶</a></dt>
 <dd><p>An enumeration.</p>
 </dd></dl>
 
@@ -229,6 +239,45 @@ <h3>OTHER<a class="headerlink" href="#other" title="Permalink to this headline">
 * obj based on int rather than object</p>
 </dd></dl>
 
+<dl class="function">
+<dt id="Utilities.optional_parameter_spec">
+<code class="descclassname">Utilities.</code><code class="descname">optional_parameter_spec</code><span class="sig-paren">(</span><em>param</em><span class="sig-paren">)</span><a class="headerlink" href="#Utilities.optional_parameter_spec" title="Permalink to this definition">¶</a></dt>
+<dd><p>Test whether param is a legal PsyNeuLink parameter specification or <code class="xref any docutils literal"><span class="pre">None</span></code></p>
+<p>Calls parameter_spec if param is not <code class="xref any docutils literal"><span class="pre">None</span></code>
+Used with typecheck</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><ul class="simple">
+<li><code class="xref any docutils literal"><span class="pre">True</span></code> if it is a legal parameter or <code class="xref any docutils literal"><span class="pre">None</span></code>.</li>
+<li><code class="xref any docutils literal"><span class="pre">False</span></code> if it is neither.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="function">
+<dt id="Utilities.parameter_spec">
+<code class="descclassname">Utilities.</code><code class="descname">parameter_spec</code><span class="sig-paren">(</span><em>param</em><span class="sig-paren">)</span><a class="headerlink" href="#Utilities.parameter_spec" title="Permalink to this definition">¶</a></dt>
+<dd><p>Test whether param is a legal PsyNeuLink parameter specification</p>
+<p>Used with typecheck</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><ul class="simple">
+<li><code class="xref any docutils literal"><span class="pre">True</span></code> if it is a legal parameter.</li>
+<li><code class="xref any docutils literal"><span class="pre">False</span></code> if it is not.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
 <dl class="function">
 <dt id="Utilities.iscompatible">
 <code class="descclassname">Utilities.</code><code class="descname">iscompatible</code><span class="sig-paren">(</span><em>candidate</em>, <em>reference=None</em>, <em>**kargs</em><span class="sig-paren">)</span><a class="headerlink" href="#Utilities.iscompatible" title="Permalink to this definition">¶</a></dt>
@@ -423,6 +472,176 @@ <h3>OTHER<a class="headerlink" href="#other" title="Permalink to this headline">
 </table>
 </dd></dl>
 
+<dl class="class">
+<dt id="Utilities.ContentAddressableList">
+<em class="property">class </em><code class="descclassname">Utilities.</code><code class="descname">ContentAddressableList</code><span class="sig-paren">(</span><em>component_type</em>, <em>key=None</em>, <em>list=None</em><span class="sig-paren">)</span><a class="headerlink" href="#Utilities.ContentAddressableList" title="Permalink to this definition">¶</a></dt>
+<dd><p>Implements dict-like list, that can be keyed by the names of the <a class="reference internal" href="Component.html"><span class="doc">compoments</span></a> in its entries.</p>
+<dl class="docutils">
+<dt>Supports:</dt>
+<dd><ul class="first last">
+<li><p class="first">getting and setting entries in the list using keys (string), in addition to numeric indices.
+the key to use is specified by the <strong>key</strong> arg of the constructor, and must be a string attribute;
+* for getting an entry:</p>
+<blockquote>
+<div><p>the key must match the keyed attribute of a component in the list; otherwise an exception is raised;</p>
+</div></blockquote>
+<ul>
+<li><dl class="first docutils">
+<dt>for setting an entry:</dt>
+<dd><ul class="first last simple">
+<li>the key must match the key of the component being assigned;</li>
+<li>if there is already a component in the list the keyed vaue of which matches the key, it is replaced;</li>
+<li>if there is no component in the list the keyed attribute of which matches the key,
+the component is appended to the list;</li>
+</ul>
+</dd>
+</dl>
+</li>
+<li><dl class="first docutils">
+<dt>for getting lists of the names, values of the keyed attributes, and values of the <code class="xref any docutils literal"><span class="pre">value</span></code></dt>
+<dd><p class="first last">attributes of components in the list.</p>
+</dd>
+</dl>
+</li>
+</ul>
+</li>
+</ul>
+</dd>
+<dt>IMPLEMENTATION NOTE:</dt>
+<dd><p class="first">This class allows components to be maintained in lists, while providing ordered storage
+and the convenience access and assignment by name (e.g., akin to a dict).
+Lists are used (instead of a dict or OrderedDict) since:</p>
+<blockquote class="last">
+<div><ul>
+<li><dl class="first docutils">
+<dt>ordering is in many instances convenient, and in some critical (e.g., for consistent mapping from</dt>
+<dd><p class="first last">collections of states to other variables, such as lists of their values);</p>
+</dd>
+</dl>
+</li>
+<li><dl class="first docutils">
+<dt>they are most commonly accessed either exhaustively (e.g., in looping through them during execution),</dt>
+<dd><p class="first last">or by key (e.g., to get the first, &#8220;primary&#8221; one), which makes the efficiencies of a dict for
+accessing by key/name less critical;</p>
+</dd>
+</dl>
+</li>
+<li><dl class="first docutils">
+<dt>the number of states in a collection for a given mechanism is likely to be small so that, even when</dt>
+<dd><p class="first last">accessed by key/name, the inefficiencies of searching a list are likely to be inconsequential.</p>
+</dd>
+</dl>
+</li>
+</ul>
+</div></blockquote>
+</dd>
+</dl>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
+<li><strong>component_type</strong> (<em>Class</em>) &#8211; specifies the class of the items in the list.</li>
+<li><strong>name</strong> (<em>str : 'ContentAddressableList'</em>) &#8211; name to use for ContentAddressableList</li>
+<li><strong>key</strong> (str : default <a class="reference internal" href="#Utilities.ContentAddressableList.name" title="Utilities.ContentAddressableList.name"><code class="xref any py py-attr docutils literal"><span class="pre">name</span></code></a>) &#8211; specifies the attribute of <strong>component_type</strong> used to key items in the list by content;
+<strong>component_type</strong> must have this attribute or, if it is not provided, an attribute with the name &#8216;name&#8217;.</li>
+<li><strong>list</strong> (<em>List : default None</em>) &#8211; specifies a list used to initialize the list;
+all of the items must be of type <strong>component_type</strong> and have the <strong>key</strong> attribute.</li>
+</ul>
+</td>
+</tr>
+</tbody>
+</table>
+<dl class="attribute">
+<dt id="Utilities.ContentAddressableList.component_type">
+<code class="descname">component_type</code><a class="headerlink" href="#Utilities.ContentAddressableList.component_type" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>Class</em> &#8211; the class of the items in the list.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Utilities.ContentAddressableList.name">
+<code class="descname">name</code><a class="headerlink" href="#Utilities.ContentAddressableList.name" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>str</em> &#8211; name if provided as arg, else name of of ContentAddressableList class</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Utilities.ContentAddressableList.key">
+<code class="descname">key</code><a class="headerlink" href="#Utilities.ContentAddressableList.key" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>str</em> &#8211; the attribute of <a class="reference internal" href="#Utilities.ContentAddressableList.component_type" title="Utilities.ContentAddressableList.component_type"><code class="xref any py py-attr docutils literal"><span class="pre">component_type</span></code></a> used to key items in the list by content;</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Utilities.ContentAddressableList.data">
+<code class="descname">data</code><a class="headerlink" href="#Utilities.ContentAddressableList.data" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>List (property)</em> &#8211; the actual list of items.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Utilities.ContentAddressableList.names">
+<code class="descname">names</code><a class="headerlink" href="#Utilities.ContentAddressableList.names" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>List (property)</em> &#8211; values of the <a class="reference internal" href="Component.html"><span class="doc">name</span></a> attribute of components in the list.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Utilities.ContentAddressableList.key_values">
+<code class="descname">key_values</code><a class="headerlink" href="#Utilities.ContentAddressableList.key_values" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>List (property)</em> &#8211; values of the keyed attribute of each component in the list.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Utilities.ContentAddressableList.values">
+<code class="descname">values</code><a class="headerlink" href="#Utilities.ContentAddressableList.values" title="Permalink to this definition">¶</a></dt>
+<dd><p><em>List (property)</em> &#8211; values of the <a class="reference internal" href="Component.html"><span class="doc">value</span></a> attribute of components in the list.</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt>
+<code class="descname">names</code></dt>
+<dd><p>Return list of <code class="xref any docutils literal"><span class="pre">values</span></code> of the name attribute of components in the list.
+:returns: <strong>names</strong> &#8211; list of the values of the <code class="xref any docutils literal"><span class="pre">name</span></code> attributes of components in the list.
+:rtype: list</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt>
+<code class="descname">key_values</code></dt>
+<dd><p>Return list of <code class="xref any docutils literal"><span class="pre">values</span></code> of the keyed attribute of components in the list.
+:returns: <strong>key_values</strong> &#8211; list of the values of the <code class="xref any docutils literal"><span class="pre">keyed</span></code> attributes of components in the list.
+:rtype: list</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt>
+<code class="descname">values</code></dt>
+<dd><p>Return list of values of components in the list.
+:returns: <strong>values</strong> &#8211; list of the values of the <code class="xref any docutils literal"><span class="pre">value</span></code> attributes of components in the list.
+:rtype: list</p>
+</dd></dl>
+
+<dl class="attribute">
+<dt id="Utilities.ContentAddressableList.values_as_lists">
+<code class="descname">values_as_lists</code><a class="headerlink" href="#Utilities.ContentAddressableList.values_as_lists" title="Permalink to this definition">¶</a></dt>
+<dd><p>Return list of values of components in the list, each converted to a list.
+:returns: <strong>values</strong> &#8211; list of list values of the <code class="xref any docutils literal"><span class="pre">value</span></code> attributes of components in the list,
+:rtype: list</p>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="function">
+<dt id="Utilities.is_same_function_spec">
+<code class="descclassname">Utilities.</code><code class="descname">is_same_function_spec</code><span class="sig-paren">(</span><em>fct_spec_1</em>, <em>fct_spec_2</em><span class="sig-paren">)</span><a class="headerlink" href="#Utilities.is_same_function_spec" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compare two function specs and return True if both are for the same class of Function</p>
+<p>Arguments can be either a class or instance of a PsyNeuLink Function, or any other callable method or function
+Return True only if both are instances or classes of a PsyNeuLink Function;  otherwise, return False</p>
+</dd></dl>
+
+<dl class="function">
+<dt id="Utilities.make_readonly_property">
+<code class="descclassname">Utilities.</code><code class="descname">make_readonly_property</code><span class="sig-paren">(</span><em>val</em><span class="sig-paren">)</span><a class="headerlink" href="#Utilities.make_readonly_property" title="Permalink to this definition">¶</a></dt>
+<dd><p>Return property that provides read-only access to its value</p>
+</dd></dl>
+
 </div>
 </div>
 </div>
diff --git a/docs/build/html/_sources/ControlProjection.txt b/docs/build/html/_sources/ControlProjection.txt
index ea003d39a88..d5463fa3cf7 100644
--- a/docs/build/html/_sources/ControlProjection.txt
+++ b/docs/build/html/_sources/ControlProjection.txt
@@ -1,9 +1,6 @@
 ControlProjection
 =================
 
-.. toctree::
-   :maxdepth: 3
-
-.. automodule:: ControlProjection
+.. automodule:: ModulatoryProjections.ControlProjection
    :members:
    :exclude-members: random, execute
\ No newline at end of file
diff --git a/docs/build/html/_sources/Function.txt b/docs/build/html/_sources/Function.txt
index d6c36033f26..2da15c59a2a 100644
--- a/docs/build/html/_sources/Function.txt
+++ b/docs/build/html/_sources/Function.txt
@@ -5,26 +5,29 @@ Functions
    :maxdepth: 3
 
 .. automodule:: Function
-	:members: Function_Base,
-	          ArgumentTherapy,
-	          UserDefinedFunction,
-              Reduce,
-	          LinearCombination,
-	          Linear,
-	          Exponential,
-	          Logistic,
-	          SoftMax,
-              LinearMatrix,
-	          Integrator,
-              BogaczEtAl,
-	          NavarroAndFuss,
-              LearningFunction,
-              Reinforcement,
-	          BackPropagation,
-              NormalDist,
-              ExponentialDist,
-              UniformDist,
-              GammaDist,
-              WaldDist,
+   :members: Function_Base,
+             ModulationParam,
+             ArgumentTherapy,
+             UserDefinedFunction,
+             Reduce,
+             LinearCombination,
+             Linear,
+             Exponential,
+             Logistic,
+             SoftMax,
+             LinearMatrix,
+             Integrator,
+             BogaczEtAl,
+             NavarroAndFuss,
+             NormalDist,
+             ExponentialDist,
+             UniformDist,
+             GammaDist,
+             WaldDist,
+             Stability,
+             Distance,
+             LearningFunction,
+             Reinforcement,
+             BackPropagation,
 ..
     :exclude-members: function
diff --git a/docs/build/html/_sources/IntegratorMechanism.txt b/docs/build/html/_sources/IntegratorMechanism.txt
index 8b0631db1f9..2d1384ebb10 100644
--- a/docs/build/html/_sources/IntegratorMechanism.txt
+++ b/docs/build/html/_sources/IntegratorMechanism.txt
@@ -6,7 +6,6 @@ IntegratorMechanism
 
    IntegratorMechanism
 
-
 .. automodule:: IntegratorMechanism
    :members:
    :exclude-members: random
\ No newline at end of file
diff --git a/docs/build/html/_sources/LearningProjection.txt b/docs/build/html/_sources/LearningProjection.txt
index 9ab5dcf4bd0..c00b49a2c5c 100644
--- a/docs/build/html/_sources/LearningProjection.txt
+++ b/docs/build/html/_sources/LearningProjection.txt
@@ -1,6 +1,6 @@
 LearningProjection
 ==================
 
-.. automodule:: LearningProjection
+.. automodule:: ModulatoryProjections.LearningProjection
    :members:
    :exclude-members: random, execute
\ No newline at end of file
diff --git a/docs/build/html/_sources/Mechanism.txt b/docs/build/html/_sources/Mechanism.txt
index d6221b13943..015ea1d20c0 100644
--- a/docs/build/html/_sources/Mechanism.txt
+++ b/docs/build/html/_sources/Mechanism.txt
@@ -2,10 +2,10 @@ Mechanism
 =========
 
 .. toctree::
-  :maxdepth: 1
+   :maxdepth: 1
 
-  ProcessingMechanism
-  AdaptiveMechanism
+   ProcessingMechanism
+   AdaptiveMechanism
 
 .. toctree::
    :maxdepth: 3
diff --git a/docs/build/html/_sources/ProcessingMechanism.txt b/docs/build/html/_sources/ProcessingMechanism.txt
index 0923557b4b4..26a9bf29510 100644
--- a/docs/build/html/_sources/ProcessingMechanism.txt
+++ b/docs/build/html/_sources/ProcessingMechanism.txt
@@ -4,11 +4,13 @@ Processing Mechanism
 .. toctree:: 
    :maxdepth: 1
 
-   TransferMechanism
    IntegratorMechanism
    DDM
+   TransferMechanism
+   RecurrentTransferMechanism
+   LCA
    ObjectiveMechanism
 
-.. automodule:: ProcessingMechanism
+.. automodule:: ProcessingMechansms.ProcessingMechanism
    :members:
    :exclude-members: ProcessingMechanism_Base, MechanismTuple, MechanismList, random
diff --git a/docs/build/html/_sources/Projection.txt b/docs/build/html/_sources/Projection.txt
index 459769c300f..35ccf7d8f8c 100644
--- a/docs/build/html/_sources/Projection.txt
+++ b/docs/build/html/_sources/Projection.txt
@@ -4,12 +4,12 @@ Projection
 .. toctree::
    :maxdepth: 1
 
-   MappingProjection
-   LearningProjection
-   ControlProjection
+   PathwayProjection
+   ModulatoryProjection
 
-.. automodule:: Projection
+.. toctree::
+   :maxdepth: 3
 
-..
+.. automodule:: Projection
    :members:
    :exclude-members: Projection_Base, _is_projection_spec, random
diff --git a/docs/build/html/_sources/State.txt b/docs/build/html/_sources/State.txt
index 7551fe2ec60..1b1c1b5fc2f 100644
--- a/docs/build/html/_sources/State.txt
+++ b/docs/build/html/_sources/State.txt
@@ -1,5 +1,5 @@
-States
-======
+State
+=====
 
 .. toctree::
    :maxdepth: 1
@@ -7,6 +7,7 @@ States
    InputState
    ParameterState
    OutputState
+   ModulatorySignal
 
 .. toctree::
    :maxdepth: 3
diff --git a/docs/build/html/_sources/TransferMechanism.txt b/docs/build/html/_sources/TransferMechanism.txt
index f6601aa1b30..05be60301d3 100644
--- a/docs/build/html/_sources/TransferMechanism.txt
+++ b/docs/build/html/_sources/TransferMechanism.txt
@@ -6,6 +6,6 @@ TransferMechanism
 
    TransferMechanism
 
-.. automodule:: TransferMechanism
+.. automodule:: ProcessingMechanisms.TransferMechanism
    :members:
    :exclude-members: random
diff --git a/docs/build/html/_sources/Utilities.txt b/docs/build/html/_sources/Utilities.txt
index 1624fdde8ba..c95f97962c5 100644
--- a/docs/build/html/_sources/Utilities.txt
+++ b/docs/build/html/_sources/Utilities.txt
@@ -2,12 +2,12 @@ Utilities
 =========
 
 .. toctree::
-	:maxdepth: 1
+   :maxdepth: 1
 
-	Functions
-	Run
-	Log
-	Preferences
+   Functions
+   Run
+   Log
+   Preferences
 
 .. automodule:: Utilities
    :members:
diff --git a/docs/build/html/_sources/index.txt b/docs/build/html/_sources/index.txt
index e362f772451..719f409a43e 100644
--- a/docs/build/html/_sources/index.txt
+++ b/docs/build/html/_sources/index.txt
@@ -282,6 +282,7 @@ Contributors
 
 with substantial assistance from:
 
+* **Abhishek Bhattacharjee**, Department of Computer Science, Rutgers University
 * **Mihai Capota**, Intel Labs, Intel Corporation
 * **Bryn Keller**, Intel Labs, Intel Corporation
 * **Garrett McGrath**, Princeton Neuroscience Institute, Princeton University
@@ -289,6 +290,8 @@ with substantial assistance from:
 * **Amitai Shenhav**, Cognitive, Linguistic, & Psychological Sciences, Brown University
 * **Michael Shvartsman**, Princeton Neuroscience Institute, Princeton University
 * **Ben Singer**, Princeton Neuroscience Institute, Princeton University
+* **Jan Vesely**, Department of Computer Science, Rutgers University
+
 
 ..
    .. toctree::
diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html
index bcc381d21bf..efe4ac90c31 100644
--- a/docs/build/html/genindex.html
+++ b/docs/build/html/genindex.html
@@ -87,7 +87,7 @@
 <li class="toctree-l1"><a class="reference internal" href="System.html">System</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Process.html">Process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Mechanism.html">Mechanism</a></li>
-<li class="toctree-l1"><a class="reference internal" href="State.html">States</a></li>
+<li class="toctree-l1"><a class="reference internal" href="State.html">State</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Projection.html">Projection</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Run.html">Run</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Component.html">Component</a></li>
@@ -197,10 +197,6 @@ <h2 id="A">A</h2>
   </dt>
 
       
-  <dt><a href="ControlSignal.html#module-AdaptiveMechanisms.ControlMechanisms.ControlSignal">AdaptiveMechanisms.ControlMechanisms.ControlSignal (module)</a>
-  </dt>
-
-      
   <dt><a href="DefaultControlMechanism.html#module-AdaptiveMechanisms.ControlMechanisms.DefaultControlMechanism">AdaptiveMechanisms.ControlMechanisms.DefaultControlMechanism (module)</a>
   </dt>
 
@@ -223,27 +219,25 @@ <h2 id="A">A</h2>
   </dt>
 
       
-  <dt><a href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.adjustment_cost">adjustment_cost (AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal attribute)</a>
+  <dt><a href="Function.html#Function.ModulationParam.ADDITIVE">ADDITIVE (Function.ModulationParam attribute)</a>
   </dt>
 
       
-  <dt><a href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.adjustment_cost_function">adjustment_cost_function (AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal attribute)</a>
+  <dt><a href="InputState.html#InputState.InputState.afferents">afferents (InputState.InputState attribute)</a>
   </dt>
 
       
-  <dt><a href="Log.html#Log.LogLevel.ALL_ASSIGNMENTS">ALL_ASSIGNMENTS (Log.LogLevel attribute)</a>
+  <dt><a href="State.html#State.State_Base.all_afferents">all_afferents (State.State_Base attribute)</a>
   </dt>
 
       
-  <dt><a href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.allocation">allocation (AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal attribute)</a>
+  <dt><a href="Log.html#Log.LogLevel.ALL_ASSIGNMENTS">ALL_ASSIGNMENTS (Log.LogLevel attribute)</a>
   </dt>
 
-      <dd><dl>
-        
-  <dt><a href="ControlProjection.html#ControlProjection.ControlProjection.allocation">(ControlProjection.ControlProjection attribute)</a>
+      
+  <dt><a href="ControlProjection.html#ModulatoryProjections.ControlProjection.ControlProjection.allocation">allocation (ModulatoryProjections.ControlProjection.ControlProjection attribute)</a>
   </dt>
 
-      </dl></dd>
       
   <dt><a href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.allocation_policy">allocation_policy (AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base attribute)</a>
   </dt>
@@ -255,10 +249,6 @@ <h2 id="A">A</h2>
 
       </dl></dd>
       
-  <dt><a href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.allocation_samples">allocation_samples (AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal attribute)</a>
-  </dt>
-
-      
   <dt><a href="Function.html#Function.ArgumentTherapy">ArgumentTherapy (class in Function)</a>
   </dt>
 
@@ -285,35 +275,47 @@ <h2 id="B">B</h2>
   </dt>
 
       
-  <dt><a href="ParameterState.html#ParameterState.ParameterState.baseValue">baseValue (ParameterState.ParameterState attribute)</a>
+  <dt><a href="State.html#State.State_Base.base_value">base_value (State.State_Base attribute)</a>
+  </dt>
+
+      
+  <dt><a href="Function.html#Function.ExponentialDist.beta">beta (Function.ExponentialDist attribute)</a>
+  </dt>
+
+      
+  <dt><a href="Function.html#Function.BogaczEtAl.bias">bias (Function.BogaczEtAl attribute)</a>
   </dt>
 
       <dd><dl>
         
-  <dt><a href="State.html#State.State_Base.baseValue">(State.State_Base attribute)</a>
+  <dt><a href="Function.html#Function.Logistic.bias">(Function.Logistic attribute)</a>
   </dt>
 
       </dl></dd>
   </dl></td>
   <td style="width: 33%" valign="top"><dl>
       
-  <dt><a href="Function.html#Function.ExponentialDist.beta">beta (Function.ExponentialDist attribute)</a>
+  <dt><a href="Function.html#Function.BogaczEtAl">BogaczEtAl (class in Function)</a>
   </dt>
 
       
-  <dt><a href="Function.html#Function.BogaczEtAl.bias">bias (Function.BogaczEtAl attribute)</a>
+  <dt><a href="Function.html#Function.Exponential.bounds">bounds (Function.Exponential attribute)</a>
   </dt>
 
       <dd><dl>
         
-  <dt><a href="Function.html#Function.Logistic.bias">(Function.Logistic attribute)</a>
+  <dt><a href="Function.html#Function.Linear.bounds">(Function.Linear attribute)</a>
   </dt>
 
-      </dl></dd>
-      
-  <dt><a href="Function.html#Function.BogaczEtAl">BogaczEtAl (class in Function)</a>
+        
+  <dt><a href="Function.html#Function.Logistic.bounds">(Function.Logistic attribute)</a>
   </dt>
 
+        
+  <dt><a href="Function.html#Function.SoftMax.bounds">(Function.SoftMax attribute)</a>
+  </dt>
+
+      </dl></dd>
   </dl></td>
 </tr></table>
 
@@ -341,20 +343,28 @@ <h2 id="C">C</h2>
   </dt>
 
       
+  <dt><a href="Utilities.html#Utilities.ContentAddressableList.component_type">component_type (Utilities.ContentAddressableList attribute)</a>
+  </dt>
+
+      
   <dt><a href="LearningMechanism.html#AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism.componentType">componentType (AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism attribute)</a>
   </dt>
 
       <dd><dl>
         
-  <dt><a href="ControlProjection.html#ControlProjection.ControlProjection.componentType">(ControlProjection.ControlProjection attribute)</a>
+  <dt><a href="Function.html#Function.Stability.MappingProjection.componentType">(Function.Stability.MappingProjection attribute)</a>
+  </dt>
+
+        
+  <dt><a href="ControlProjection.html#ModulatoryProjections.ControlProjection.ControlProjection.componentType">(ModulatoryProjections.ControlProjection.ControlProjection attribute)</a>
   </dt>
 
         
-  <dt><a href="LearningProjection.html#LearningProjection.LearningProjection.componentType">(LearningProjection.LearningProjection attribute)</a>
+  <dt><a href="GatingProjection.html#ModulatoryProjections.GatingProjection.GatingProjection.componentType">(ModulatoryProjections.GatingProjection.GatingProjection attribute)</a>
   </dt>
 
         
-  <dt><a href="MappingProjection.html#MappingProjection.MappingProjection.componentType">(MappingProjection.MappingProjection attribute)</a>
+  <dt><a href="LearningProjection.html#ModulatoryProjections.LearningProjection.LearningProjection.componentType">(ModulatoryProjections.LearningProjection.LearningProjection attribute)</a>
   </dt>
 
         
@@ -367,55 +377,55 @@ <h2 id="C">C</h2>
 
       </dl></dd>
       
-  <dt><a href="Log.html#Log.LogEntry.context">context (Log.LogEntry attribute)</a>
+  <dt><a href="Utilities.html#Utilities.ContentAddressableList">ContentAddressableList (class in Utilities)</a>
   </dt>
 
+  </dl></td>
+  <td style="width: 33%" valign="top"><dl>
       
-  <dt><a href="System.html#System.System_Base.controller">controller (System.System_Base attribute)</a>
+  <dt><a href="Log.html#Log.LogEntry.context">context (Log.LogEntry attribute)</a>
   </dt>
 
       
-  <dt><a href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base">ControlMechanism_Base (class in AdaptiveMechanisms.ControlMechanisms.ControlMechanism)</a>
+  <dt><a href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.control_projections">control_projections (AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base attribute)</a>
   </dt>
 
-  </dl></td>
-  <td style="width: 33%" valign="top"><dl>
       
-  <dt><a href="System.html#System.System_Base.controlMechanisms">controlMechanisms (System.System_Base attribute)</a>
+  <dt><a href="EVCMechanism.html#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signal_search_space">control_signal_search_space (AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism attribute)</a>
   </dt>
 
       
-  <dt><a href="ControlProjection.html#ControlProjection.ControlProjection">ControlProjection (class in ControlProjection)</a>
+  <dt><a href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.control_signals">control_signals (AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base attribute)</a>
   </dt>
 
       <dd><dl>
         
-  <dt><a href="ControlProjection.html#module-ControlProjection">(module)</a>
+  <dt><a href="EVCMechanism.html#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.control_signals">(AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism attribute)</a>
   </dt>
 
       </dl></dd>
       
-  <dt><a href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.controlProjectionCosts">controlProjectionCosts (AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base attribute)</a>
+  <dt><a href="System.html#System.System_Base.controller">controller (System.System_Base attribute)</a>
   </dt>
 
       
-  <dt><a href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.controlProjections">controlProjections (AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base attribute)</a>
+  <dt><a href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base">ControlMechanism_Base (class in AdaptiveMechanisms.ControlMechanisms.ControlMechanism)</a>
   </dt>
 
       
-  <dt><a href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal">ControlSignal (class in AdaptiveMechanisms.ControlMechanisms.ControlSignal)</a>
+  <dt><a href="System.html#System.System_Base.controlMechanisms">controlMechanisms (System.System_Base attribute)</a>
   </dt>
 
       
-  <dt><a href="EVCMechanism.html#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.ControlSignalCostOptions">ControlSignalCostOptions (class in AdaptiveMechanisms.ControlMechanisms.EVCMechanism)</a>
+  <dt><a href="ControlProjection.html#ModulatoryProjections.ControlProjection.ControlProjection">ControlProjection (class in ModulatoryProjections.ControlProjection)</a>
   </dt>
 
       
-  <dt><a href="EVCMechanism.html#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignals">controlSignals (AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism attribute)</a>
+  <dt><a href="ControlSignal.html#ModulatorySignals.ControlSignal.ControlSignal">ControlSignal (class in ModulatorySignals.ControlSignal)</a>
   </dt>
 
       
-  <dt><a href="EVCMechanism.html#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.controlSignalSearchSpace">controlSignalSearchSpace (AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism attribute)</a>
+  <dt><a href="ControlSignal.html#ModulatorySignals.ControlSignal.ControlSignalCostOptions">ControlSignalCostOptions (class in ModulatorySignals.ControlSignal)</a>
   </dt>
 
       
@@ -423,14 +433,6 @@ <h2 id="C">C</h2>
   </dt>
 
       
-  <dt><a href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.cost">cost (AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal attribute)</a>
-  </dt>
-
-      
-  <dt><a href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.cost_combination_function">cost_combination_function (AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal attribute)</a>
-  </dt>
-
-      
   <dt><a href="EVCMechanism.html#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.cost_function">cost_function (AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism attribute)</a>
   </dt>
 
@@ -445,6 +447,10 @@ <h2 id="D">D</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%" valign="top"><dl>
       
+  <dt><a href="Utilities.html#Utilities.ContentAddressableList.data">data (Utilities.ContentAddressableList attribute)</a>
+  </dt>
+
+      
   <dt><a href="DDM.html#DDM.DDM">DDM (class in DDM)</a>
   </dt>
 
@@ -455,9 +461,15 @@ <h2 id="D">D</h2>
 
       </dl></dd>
       
-  <dt><a href="DDM.html#DDM.DDM_Output">DDM_Output (class in DDM)</a>
+  <dt><a href="LCA.html#ProcessingMechanisms.LCA.LCA.decay">decay (ProcessingMechanisms.LCA.LCA attribute)</a>
+  </dt>
+
+      <dd><dl>
+        
+  <dt><a href="RecurrentTransferMechanism.html#ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism.decay">(ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism attribute)</a>
   </dt>
 
+      </dl></dd>
       
   <dt><a href="Function.html#Function.BackPropagation.default_learning_rate">default_learning_rate (Function.BackPropagation attribute)</a>
   </dt>
@@ -505,23 +517,27 @@ <h2 id="D">D</h2>
 
       </dl></dd>
       
-  <dt><a href="Function.html#Function.BogaczEtAl.drift_rate">drift_rate (Function.BogaczEtAl attribute)</a>
+  <dt><a href="Function.html#Function.ModulationParam.DISABLE">DISABLE (Function.ModulationParam attribute)</a>
   </dt>
 
-      <dd><dl>
-        
-  <dt><a href="Function.html#Function.NavarroAndFuss.drift_rate">(Function.NavarroAndFuss attribute)</a>
+      
+  <dt><a href="Function.html#Function.GammaDist.dist_shape">dist_shape (Function.GammaDist attribute)</a>
   </dt>
 
-      </dl></dd>
       
-  <dt><a href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.duration_cost">duration_cost (AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal attribute)</a>
+  <dt><a href="Function.html#Function.Distance">Distance (class in Function)</a>
   </dt>
 
       
-  <dt><a href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.duration_cost_function">duration_cost_function (AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal attribute)</a>
+  <dt><a href="Function.html#Function.BogaczEtAl.drift_rate">drift_rate (Function.BogaczEtAl attribute)</a>
+  </dt>
+
+      <dd><dl>
+        
+  <dt><a href="Function.html#Function.NavarroAndFuss.drift_rate">(Function.NavarroAndFuss attribute)</a>
   </dt>
 
+      </dl></dd>
   </dl></td>
 </tr></table>
 
@@ -529,6 +545,20 @@ <h2 id="E">E</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%" valign="top"><dl>
       
+  <dt><a href="ModulatorySignal.html#ModulatorySignals.ModulatorySignal.ModulatorySignal.efferents">efferents (ModulatorySignals.ModulatorySignal.ModulatorySignal attribute)</a>
+  </dt>
+
+      <dd><dl>
+        
+  <dt><a href="OutputState.html#OutputState.OutputState.efferents">(OutputState.OutputState attribute)</a>
+  </dt>
+
+        
+  <dt><a href="State.html#State.State_Base.efferents">(State.State_Base attribute)</a>
+  </dt>
+
+      </dl></dd>
+      
   <dt><a href="System.html#System.System_Base.enable_controller">enable_controller (System.System_Base attribute)</a>
   </dt>
 
@@ -570,6 +600,8 @@ <h2 id="E">E</h2>
   <dt><a href="EVCMechanism.html#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.EVC_max_state_values">EVC_max_state_values (AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism attribute)</a>
   </dt>
 
+  </dl></td>
+  <td style="width: 33%" valign="top"><dl>
       
   <dt><a href="EVCMechanism.html#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.EVC_policies">EVC_policies (AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism attribute)</a>
   </dt>
@@ -578,18 +610,20 @@ <h2 id="E">E</h2>
   <dt><a href="EVCMechanism.html#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.EVC_values">EVC_values (AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism attribute)</a>
   </dt>
 
-  </dl></td>
-  <td style="width: 33%" valign="top"><dl>
       
   <dt><a href="EVCMechanism.html#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism">EVCMechanism (class in AdaptiveMechanisms.ControlMechanisms.EVCMechanism)</a>
   </dt>
 
       
-  <dt><a href="Mechanism.html#Mechanism.Mechanism_Base.execute">execute() (Mechanism.Mechanism_Base method)</a>
+  <dt><a href="Function.html#Function.Stability.MappingProjection.execute">execute() (Function.Stability.MappingProjection method)</a>
   </dt>
 
       <dd><dl>
         
+  <dt><a href="Mechanism.html#Mechanism.Mechanism_Base.execute">(Mechanism.Mechanism_Base method)</a>
+  </dt>
+
+        
   <dt><a href="Process.html#Process.Process_Base.execute">(Process.Process_Base method)</a>
   </dt>
 
@@ -603,10 +637,6 @@ <h2 id="E">E</h2>
   </dt>
 
       
-  <dt><a href="System.html#System.System_Base.execution_graph_mechs">execution_graph_mechs (System.System_Base attribute)</a>
-  </dt>
-
-      
   <dt><a href="System.html#System.System_Base.execution_sets">execution_sets (System.System_Base attribute)</a>
   </dt>
 
@@ -641,15 +671,11 @@ <h2 id="F">F</h2>
   </dt>
 
       
-  <dt><a href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.function">function (AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal attribute)</a>
+  <dt><a href="EVCMechanism.html#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function">function (AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism attribute)</a>
   </dt>
 
       <dd><dl>
         
-  <dt><a href="EVCMechanism.html#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.function">(AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism attribute)</a>
-  </dt>
-
-        
   <dt><a href="LearningMechanism.html#AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism.function">(AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism attribute)</a>
   </dt>
 
@@ -670,6 +696,10 @@ <h2 id="F">F</h2>
   </dt>
 
         
+  <dt><a href="Function.html#Function.Stability.ParameterState.function">(Function.Stability.ParameterState attribute)</a>
+  </dt>
+
+        
   <dt><a href="Function.html#Function.UserDefinedFunction.function">(Function.UserDefinedFunction attribute)</a>
   </dt>
 
@@ -678,11 +708,15 @@ <h2 id="F">F</h2>
   </dt>
 
         
-  <dt><a href="LearningProjection.html#LearningProjection.LearningProjection.function">(LearningProjection.LearningProjection attribute)</a>
+  <dt><a href="Mechanism.html#Mechanism.Mechanism_Base.function">(Mechanism.Mechanism_Base attribute)</a>
   </dt>
 
         
-  <dt><a href="Mechanism.html#Mechanism.Mechanism_Base.function">(Mechanism.Mechanism_Base attribute)</a>
+  <dt><a href="LearningProjection.html#ModulatoryProjections.LearningProjection.LearningProjection.function">(ModulatoryProjections.LearningProjection.LearningProjection attribute)</a>
+  </dt>
+
+        
+  <dt><a href="ModulatorySignal.html#ModulatorySignals.ModulatorySignal.ModulatorySignal.function">(ModulatorySignals.ModulatorySignal.ModulatorySignal attribute)</a>
   </dt>
 
       </dl></dd>
@@ -691,20 +725,28 @@ <h2 id="F">F</h2>
   </dt>
 
       
-  <dt><a href="ObjectiveMechanism.html#ObjectiveMechanism.ObjectiveMechanism.function">function (ObjectiveMechanism.ObjectiveMechanism attribute)</a>
+  <dt><a href="OutputState.html#OutputState.OutputState.function">function (OutputState.OutputState attribute)</a>
   </dt>
 
       <dd><dl>
         
-  <dt><a href="OutputState.html#OutputState.OutputState.function">(OutputState.OutputState attribute)</a>
+  <dt><a href="ParameterState.html#ParameterState.ParameterState.function">(ParameterState.ParameterState attribute)</a>
   </dt>
 
         
-  <dt><a href="ParameterState.html#ParameterState.ParameterState.function">(ParameterState.ParameterState attribute)</a>
+  <dt><a href="LCA.html#ProcessingMechanisms.LCA.LCA.function">(ProcessingMechanisms.LCA.LCA attribute)</a>
+  </dt>
+
+        
+  <dt><a href="RecurrentTransferMechanism.html#ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism.function">(ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism attribute)</a>
   </dt>
 
         
-  <dt><a href="TransferMechanism.html#TransferMechanism.TransferMechanism.function">(TransferMechanism.TransferMechanism attribute)</a>
+  <dt><a href="TransferMechanism.html#ProcessingMechanisms.TransferMechanism.TransferMechanism.function">(ProcessingMechanisms.TransferMechanism.TransferMechanism attribute)</a>
+  </dt>
+
+        
+  <dt><a href="State.html#State.State_Base.function">(State.State_Base attribute)</a>
   </dt>
 
       </dl></dd>
@@ -724,11 +766,11 @@ <h2 id="F">F</h2>
   </dt>
 
         
-  <dt><a href="Function.html#Function.Exponential.function">(Function.Exponential method)</a>
+  <dt><a href="Function.html#Function.Distance.function">(Function.Distance method)</a>
   </dt>
 
         
-  <dt><a href="Function.html#Function.Integrator.function">(Function.Integrator method)</a>
+  <dt><a href="Function.html#Function.Exponential.function">(Function.Exponential method)</a>
   </dt>
 
         
@@ -764,7 +806,11 @@ <h2 id="F">F</h2>
   </dt>
 
         
-  <dt><a href="IntegratorMechanism.html#IntegratorMechanism.IntegratorMechanism.Integrator.function">(IntegratorMechanism.IntegratorMechanism.Integrator method)</a>
+  <dt><a href="Function.html#Function.Stability.function">(Function.Stability method)</a>
+  </dt>
+
+        
+  <dt><a href="IntegratorMechanism.html#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.function">(IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator method)</a>
   </dt>
 
       </dl></dd>
@@ -802,10 +848,22 @@ <h2 id="G">G</h2>
   <dt><a href="Function.html#Function.GammaDist">GammaDist (class in Function)</a>
   </dt>
 
+      
+  <dt><a href="GatingProjection.html#ModulatoryProjections.GatingProjection.GatingProjection">GatingProjection (class in ModulatoryProjections.GatingProjection)</a>
+  </dt>
+
   </dl></td>
   <td style="width: 33%" valign="top"><dl>
       
-  <dt><a href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.get_costs">get_costs() (AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal method)</a>
+  <dt><a href="GatingSignal.html#ModulatorySignals.GatingSignal.GatingSignal">GatingSignal (class in ModulatorySignals.GatingSignal)</a>
+  </dt>
+
+      
+  <dt><a href="GatingProjection.html#ModulatoryProjections.GatingProjection.GatingProjection.gatting_signal">gatting_signal (ModulatoryProjections.GatingProjection.GatingProjection attribute)</a>
+  </dt>
+
+      
+  <dt><a href="ControlSignal.html#ModulatorySignals.ControlSignal.ControlSignal.get_costs">get_costs() (ModulatorySignals.ControlSignal.ControlSignal method)</a>
   </dt>
 
       
@@ -823,7 +881,7 @@ <h2 id="H">H</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%" valign="top"><dl>
       
-  <dt><a href="MappingProjection.html#MappingProjection.MappingProjection.has_learning_projection">has_learning_projection (MappingProjection.MappingProjection attribute)</a>
+  <dt><a href="Function.html#Function.Stability.MappingProjection.has_learning_projection">has_learning_projection (Function.Stability.MappingProjection attribute)</a>
   </dt>
 
   </dl></td>
@@ -832,6 +890,10 @@ <h2 id="H">H</h2>
   <dt><a href="Function.html#Function.UniformDist.high">high (Function.UniformDist attribute)</a>
   </dt>
 
+      
+  <dt><a href="Keywords.html#Keywords.MatrixKeywords.HOLLOW_MATRIX">HOLLOW_MATRIX (Keywords.MatrixKeywords attribute)</a>
+  </dt>
+
   </dl></td>
 </tr></table>
 
@@ -847,10 +909,24 @@ <h2 id="I">I</h2>
   </dt>
 
       
-  <dt><a href="TransferMechanism.html#TransferMechanism.TransferMechanism.initial_value">initial_value (TransferMechanism.TransferMechanism attribute)</a>
+  <dt><a href="LCA.html#ProcessingMechanisms.LCA.LCA.inhibition">inhibition (ProcessingMechanisms.LCA.LCA attribute)</a>
   </dt>
 
       
+  <dt><a href="LCA.html#ProcessingMechanisms.LCA.LCA.initial_value">initial_value (ProcessingMechanisms.LCA.LCA attribute)</a>
+  </dt>
+
+      <dd><dl>
+        
+  <dt><a href="RecurrentTransferMechanism.html#ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism.initial_value">(ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism attribute)</a>
+  </dt>
+
+        
+  <dt><a href="TransferMechanism.html#ProcessingMechanisms.TransferMechanism.TransferMechanism.initial_value">(ProcessingMechanisms.TransferMechanism.TransferMechanism attribute)</a>
+  </dt>
+
+      </dl></dd>
+      
   <dt><a href="Process.html#Process.Process_Base.initial_values">initial_values (Process.Process_Base attribute)</a>
   </dt>
 
@@ -879,71 +955,55 @@ <h2 id="I">I</h2>
   </dt>
 
       
-  <dt><a href="Function.html#Function.Integrator.initializer">initializer (Function.Integrator attribute)</a>
+  <dt><a href="IntegratorMechanism.html#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.initializer">initializer (IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator attribute)</a>
   </dt>
 
-      <dd><dl>
-        
-  <dt><a href="IntegratorMechanism.html#IntegratorMechanism.IntegratorMechanism.Integrator.initializer">(IntegratorMechanism.IntegratorMechanism.Integrator attribute)</a>
-  </dt>
-
-      </dl></dd>
       
   <dt><a href="Process.html#Process.Process_Base.input">input (Process.Process_Base attribute)</a>
   </dt>
 
       
-  <dt><a href="InputState.html#InputState.InputState">InputState (class in InputState)</a>
-  </dt>
-
-      
-  <dt><a href="Mechanism.html#Mechanism.Mechanism_Base.inputState">inputState (Mechanism.Mechanism_Base attribute)</a>
-  </dt>
-
-      
-  <dt><a href="InputState.html#module-InputState">InputState (module)</a>
-  </dt>
-
-      
-  <dt><a href="Mechanism.html#Mechanism.Mechanism_Base.inputStates">inputStates (Mechanism.Mechanism_Base attribute)</a>
+  <dt><a href="Mechanism.html#Mechanism.Mechanism_Base.input_state">input_state (Mechanism.Mechanism_Base attribute)</a>
   </dt>
 
   </dl></td>
   <td style="width: 33%" valign="top"><dl>
       
-  <dt><a href="Mechanism.html#Mechanism.Mechanism_Base.inputValue">inputValue (Mechanism.Mechanism_Base attribute)</a>
+  <dt><a href="LearningMechanism.html#AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism.input_states">input_states (AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism attribute)</a>
   </dt>
 
       <dd><dl>
         
-  <dt><a href="Process.html#Process.Process_Base.inputValue">(Process.Process_Base attribute)</a>
-  </dt>
-
-        
-  <dt><a href="System.html#System.System_Base.inputValue">(System.System_Base attribute)</a>
+  <dt><a href="Mechanism.html#Mechanism.Mechanism_Base.input_states">(Mechanism.Mechanism_Base attribute)</a>
   </dt>
 
       </dl></dd>
       
-  <dt><a href="System.html#System.System_Base.inspect">inspect() (System.System_Base method)</a>
+  <dt><a href="Mechanism.html#Mechanism.Mechanism_Base.input_value">input_value (Mechanism.Mechanism_Base attribute)</a>
   </dt>
 
-      
-  <dt><a href="Function.html#Function.LinearMatrix.instantiate_matrix">instantiate_matrix() (Function.LinearMatrix method)</a>
+      <dd><dl>
+        
+  <dt><a href="Process.html#Process.Process_Base.input_value">(Process.Process_Base attribute)</a>
   </dt>
 
+      </dl></dd>
       
-  <dt><a href="Function.html#Function.Integrator.integration_type">integration_type (Function.Integrator attribute)</a>
+  <dt><a href="InputState.html#InputState.InputState">InputState (class in InputState)</a>
   </dt>
 
       <dd><dl>
         
-  <dt><a href="IntegratorMechanism.html#IntegratorMechanism.IntegratorMechanism.Integrator.integration_type">(IntegratorMechanism.IntegratorMechanism.Integrator attribute)</a>
+  <dt><a href="InputState.html#module-InputState">(module)</a>
   </dt>
 
       </dl></dd>
       
-  <dt><a href="Function.html#Function.Integrator">Integrator (class in Function)</a>
+  <dt><a href="System.html#System.System_Base.inspect">inspect() (System.System_Base method)</a>
+  </dt>
+
+      
+  <dt><a href="Function.html#Function.LinearMatrix.instantiate_matrix">instantiate_matrix() (Function.LinearMatrix method)</a>
   </dt>
 
       
@@ -957,38 +1017,40 @@ <h2 id="I">I</h2>
 
       </dl></dd>
       
-  <dt><a href="IntegratorMechanism.html#IntegratorMechanism.IntegratorMechanism.Integrator">IntegratorMechanism.Integrator (class in IntegratorMechanism)</a>
+  <dt><a href="IntegratorMechanism.html#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator">IntegratorMechanism.AdaptiveIntegrator (class in IntegratorMechanism)</a>
   </dt>
 
       
-  <dt><a href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.intensity">intensity (AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal attribute)</a>
+  <dt><a href="Function.html#Function.Linear.intercept">intercept (Function.Linear attribute)</a>
   </dt>
 
       
-  <dt><a href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.intensity_cost">intensity_cost (AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal attribute)</a>
+  <dt><a href="Keywords.html#Keywords.Keywords.INTERNAL">INTERNAL (Keywords.Keywords attribute)</a>
   </dt>
 
       
-  <dt><a href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.intensity_cost_function">intensity_cost_function (AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal attribute)</a>
+  <dt><a href="Utilities.html#Utilities.is_same_function_spec">is_same_function_spec() (in module Utilities)</a>
   </dt>
 
       
-  <dt><a href="Function.html#Function.Linear.intercept">intercept (Function.Linear attribute)</a>
+  <dt><a href="Utilities.html#Utilities.iscompatible">iscompatible() (in module Utilities)</a>
   </dt>
 
+  </dl></td>
+</tr></table>
+
+<h2 id="K">K</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%" valign="top"><dl>
       
-  <dt><a href="Keywords.html#Keywords.Keywords.INTERNAL">INTERNAL (Keywords.Keywords attribute)</a>
+  <dt><a href="Utilities.html#Utilities.ContentAddressableList.key">key (Utilities.ContentAddressableList attribute)</a>
   </dt>
 
       
-  <dt><a href="Utilities.html#Utilities.iscompatible">iscompatible() (in module Utilities)</a>
+  <dt><a href="Utilities.html#Utilities.ContentAddressableList.key_values">key_values (Utilities.ContentAddressableList attribute)</a>, <a href="Utilities.html#Utilities.ContentAddressableList.key_values">[1]</a>
   </dt>
 
   </dl></td>
-</tr></table>
-
-<h2 id="K">K</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%" valign="top"><dl>
       
   <dt><a href="Keywords.html#Keywords.Keywords">Keywords (class in Keywords)</a>
@@ -1007,23 +1069,17 @@ <h2 id="L">L</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%" valign="top"><dl>
       
-  <dt><a href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.last_allocation">last_allocation (AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal attribute)</a>
+  <dt><a href="LCA.html#ProcessingMechanisms.LCA.LCA">LCA (class in ProcessingMechanisms.LCA)</a>
   </dt>
 
       
-  <dt><a href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.last_intensity">last_intensity (AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal attribute)</a>
+  <dt><a href="LearningProjection.html#ModulatoryProjections.LearningProjection.LearningProjection.learned_projection">learned_projection (ModulatoryProjections.LearningProjection.LearningProjection attribute)</a>
   </dt>
 
       
-  <dt><a href="LearningMechanism.html#AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism.learned_projection">learned_projection (AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism attribute)</a>
-  </dt>
-
-      <dd><dl>
-        
-  <dt><a href="LearningProjection.html#LearningProjection.LearningProjection.learned_projection">(LearningProjection.LearningProjection attribute)</a>
+  <dt><a href="LearningMechanism.html#AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism.learned_projections">learned_projections (AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism attribute)</a>
   </dt>
 
-      </dl></dd>
       
   <dt><a href="Process.html#Process.Process_Base.learning">learning (Process.Process_Base attribute)</a>
   </dt>
@@ -1048,7 +1104,7 @@ <h2 id="L">L</h2>
   </dt>
 
         
-  <dt><a href="LearningProjection.html#LearningProjection.LearningProjection.learning_rate">(LearningProjection.LearningProjection attribute)</a>
+  <dt><a href="LearningProjection.html#ModulatoryProjections.LearningProjection.LearningProjection.learning_rate">(ModulatoryProjections.LearningProjection.LearningProjection attribute)</a>
   </dt>
 
         
@@ -1061,15 +1117,13 @@ <h2 id="L">L</h2>
 
       </dl></dd>
       
-  <dt><a href="LearningMechanism.html#AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism.learning_signal">learning_signal (AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism attribute)</a>
+  <dt><a href="LearningProjection.html#ModulatoryProjections.LearningProjection.LearningProjection.learning_signal">learning_signal (ModulatoryProjections.LearningProjection.LearningProjection attribute)</a>
   </dt>
 
-      <dd><dl>
-        
-  <dt><a href="LearningProjection.html#LearningProjection.LearningProjection.learning_signal">(LearningProjection.LearningProjection attribute)</a>
+      
+  <dt><a href="LearningMechanism.html#AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism.learning_signals">learning_signals (AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism attribute)</a>
   </dt>
 
-      </dl></dd>
   </dl></td>
   <td style="width: 33%" valign="top"><dl>
       
@@ -1081,15 +1135,13 @@ <h2 id="L">L</h2>
   </dt>
 
       
-  <dt><a href="LearningProjection.html#LearningProjection.LearningProjection">LearningProjection (class in LearningProjection)</a>
+  <dt><a href="LearningProjection.html#ModulatoryProjections.LearningProjection.LearningProjection">LearningProjection (class in ModulatoryProjections.LearningProjection)</a>
   </dt>
 
-      <dd><dl>
-        
-  <dt><a href="LearningProjection.html#module-LearningProjection">(module)</a>
+      
+  <dt><a href="LearningSignal.html#ModulatorySignals.LearningSignal.LearningSignal">LearningSignal (class in ModulatorySignals.LearningSignal)</a>
   </dt>
 
-      </dl></dd>
       
   <dt><a href="Function.html#Function.Linear">Linear (class in Function)</a>
   </dt>
@@ -1139,12 +1191,12 @@ <h2 id="M">M</h2>
   </dt>
 
       
-  <dt><a href="MappingProjection.html#MappingProjection.MappingProjection">MappingProjection (class in MappingProjection)</a>
+  <dt><a href="OutputState.html#OutputState.make_readonly_property">make_readonly_property() (in module OutputState)</a>
   </dt>
 
       <dd><dl>
         
-  <dt><a href="MappingProjection.html#module-MappingProjection">(module)</a>
+  <dt><a href="Utilities.html#Utilities.make_readonly_property">(in module Utilities)</a>
   </dt>
 
       </dl></dd>
@@ -1154,7 +1206,19 @@ <h2 id="M">M</h2>
 
       <dd><dl>
         
-  <dt><a href="MappingProjection.html#MappingProjection.MappingProjection.matrix">(MappingProjection.MappingProjection attribute)</a>
+  <dt><a href="Function.html#Function.Stability.matrix">(Function.Stability attribute)</a>
+  </dt>
+
+        
+  <dt><a href="Function.html#Function.Stability.MappingProjection.matrix">(Function.Stability.MappingProjection attribute)</a>
+  </dt>
+
+        
+  <dt><a href="LCA.html#ProcessingMechanisms.LCA.LCA.matrix">(ProcessingMechanisms.LCA.LCA attribute)</a>
+  </dt>
+
+        
+  <dt><a href="RecurrentTransferMechanism.html#ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism.matrix">(ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism attribute)</a>
   </dt>
 
       </dl></dd>
@@ -1173,10 +1237,6 @@ <h2 id="M">M</h2>
 
       </dl></dd>
       
-  <dt><a href="TransferMechanism.html#TransferMechanism.Transfer_Output.MEAN">MEAN (TransferMechanism.Transfer_Output attribute)</a>
-  </dt>
-
-      
   <dt><a href="Mechanism.html#module-Mechanism">Mechanism (module)</a>
   </dt>
 
@@ -1192,8 +1252,6 @@ <h2 id="M">M</h2>
   <dt><a href="Process.html#Process.Process_Base.mechanismNames">mechanismNames (Process.Process_Base attribute)</a>
   </dt>
 
-  </dl></td>
-  <td style="width: 33%" valign="top"><dl>
       
   <dt><a href="Process.html#Process.Process_Base.mechanisms">mechanisms (Process.Process_Base attribute)</a>
   </dt>
@@ -1213,19 +1271,85 @@ <h2 id="M">M</h2>
   </dt>
 
       
-  <dt><a href="Utilities.html#Utilities.ModulationOperation">ModulationOperation (class in Utilities)</a>
+  <dt><a href="Function.html#Function.Distance.metric">metric (Function.Distance attribute)</a>
   </dt>
 
+      <dd><dl>
+        
+  <dt><a href="Function.html#Function.Stability.metric">(Function.Stability attribute)</a>
+  </dt>
+
+      </dl></dd>
       
-  <dt><a href="EVCMechanism.html#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitor_for_control_weights_and_exponents">monitor_for_control_weights_and_exponents (AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism attribute)</a>
+  <dt><a href="Function.html#Function.Stability.ParameterState.mod_afferents">mod_afferents (Function.Stability.ParameterState attribute)</a>
+  </dt>
+
+      <dd><dl>
+        
+  <dt><a href="ParameterState.html#ParameterState.ParameterState.mod_afferents">(ParameterState.ParameterState attribute)</a>
   </dt>
 
+        
+  <dt><a href="State.html#State.State_Base.mod_afferents">(State.State_Base attribute)</a>
+  </dt>
+
+      </dl></dd>
+  </dl></td>
+  <td style="width: 33%" valign="top"><dl>
       
-  <dt><a href="EVCMechanism.html#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitored_output_states">monitored_output_states (AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism attribute)</a>
+  <dt><a href="Utilities.html#Utilities.Modulation">Modulation (class in Utilities)</a>
+  </dt>
+
+      
+  <dt><a href="ModulatorySignal.html#ModulatorySignals.ModulatorySignal.ModulatorySignal.modulation">modulation (ModulatorySignals.ModulatorySignal.ModulatorySignal attribute)</a>
+  </dt>
+
+      
+  <dt><a href="Function.html#Function.ModulationParam">ModulationParam (class in Function)</a>
+  </dt>
+
+      
+  <dt><a href="ControlProjection.html#module-ModulatoryProjections.ControlProjection">ModulatoryProjections.ControlProjection (module)</a>
+  </dt>
+
+      
+  <dt><a href="GatingProjection.html#module-ModulatoryProjections.GatingProjection">ModulatoryProjections.GatingProjection (module)</a>
+  </dt>
+
+      
+  <dt><a href="LearningProjection.html#module-ModulatoryProjections.LearningProjection">ModulatoryProjections.LearningProjection (module)</a>
+  </dt>
+
+      
+  <dt><a href="ModulatoryProjection.html#module-ModulatoryProjections.ModulatoryProjection">ModulatoryProjections.ModulatoryProjection (module)</a>
+  </dt>
+
+      
+  <dt><a href="ModulatorySignal.html#ModulatorySignals.ModulatorySignal.ModulatorySignal">ModulatorySignal (class in ModulatorySignals.ModulatorySignal)</a>
+  </dt>
+
+      
+  <dt><a href="ControlSignal.html#module-ModulatorySignals.ControlSignal">ModulatorySignals.ControlSignal (module)</a>
+  </dt>
+
+      
+  <dt><a href="GatingSignal.html#module-ModulatorySignals.GatingSignal">ModulatorySignals.GatingSignal (module)</a>
+  </dt>
+
+      
+  <dt><a href="LearningSignal.html#module-ModulatorySignals.LearningSignal">ModulatorySignals.LearningSignal (module)</a>
+  </dt>
+
+      
+  <dt><a href="ModulatorySignal.html#module-ModulatorySignals.ModulatorySignal">ModulatorySignals.ModulatorySignal (module)</a>
+  </dt>
+
+      
+  <dt><a href="EVCMechanism.html#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitor_for_control_weights_and_exponents">monitor_for_control_weights_and_exponents (AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism attribute)</a>
   </dt>
 
       
-  <dt><a href="ObjectiveMechanism.html#ObjectiveMechanism.ObjectiveMechanism.monitored_values">monitored_values (ObjectiveMechanism.ObjectiveMechanism attribute)</a>
+  <dt><a href="EVCMechanism.html#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitored_output_states">monitored_output_states (AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism attribute)</a>
   </dt>
 
       
@@ -1233,11 +1357,17 @@ <h2 id="M">M</h2>
   </dt>
 
       
-  <dt><a href="EVCMechanism.html#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitoring_mechanism">monitoring_mechanism (AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism attribute)</a>
+  <dt><a href="ControlMechanism.html#AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base.monitoring_mechanism">monitoring_mechanism (AdaptiveMechanisms.ControlMechanisms.ControlMechanism.ControlMechanism_Base attribute)</a>
   </dt>
 
+      <dd><dl>
+        
+  <dt><a href="EVCMechanism.html#AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism.monitoring_mechanism">(AdaptiveMechanisms.ControlMechanisms.EVCMechanism.EVCMechanism attribute)</a>
+  </dt>
+
+      </dl></dd>
       
-  <dt><a href="MappingProjection.html#MappingProjection.MappingProjection.monitoringMechanism">monitoringMechanism (MappingProjection.MappingProjection attribute)</a>
+  <dt><a href="Function.html#Function.Stability.MappingProjection.monitoringMechanism">monitoringMechanism (Function.Stability.MappingProjection attribute)</a>
   </dt>
 
       
@@ -1254,6 +1384,10 @@ <h2 id="M">M</h2>
   <dt><a href="Utilities.html#Utilities.multi_getattr">multi_getattr() (in module Utilities)</a>
   </dt>
 
+      
+  <dt><a href="Function.html#Function.ModulationParam.MULTIPLICATIVE">MULTIPLICATIVE (Function.ModulationParam attribute)</a>
+  </dt>
+
   </dl></td>
 </tr></table>
 
@@ -1261,20 +1395,20 @@ <h2 id="N">N</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%" valign="top"><dl>
       
-  <dt><a href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.name">name (AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal attribute)</a>
+  <dt><a href="LearningMechanism.html#AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism.name">name (AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism attribute)</a>
   </dt>
 
       <dd><dl>
         
-  <dt><a href="LearningMechanism.html#AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism.name">(AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism attribute)</a>
+  <dt><a href="DDM.html#DDM.DDM.name">(DDM.DDM attribute)</a>
   </dt>
 
         
-  <dt><a href="ControlProjection.html#ControlProjection.ControlProjection.name">(ControlProjection.ControlProjection attribute)</a>
+  <dt><a href="Function.html#Function.Stability.MappingProjection.name">(Function.Stability.MappingProjection attribute)</a>
   </dt>
 
         
-  <dt><a href="DDM.html#DDM.DDM.name">(DDM.DDM attribute)</a>
+  <dt><a href="Function.html#Function.Stability.ParameterState.name">(Function.Stability.ParameterState attribute)</a>
   </dt>
 
         
@@ -1286,19 +1420,23 @@ <h2 id="N">N</h2>
   </dt>
 
         
-  <dt><a href="LearningProjection.html#LearningProjection.LearningProjection.name">(LearningProjection.LearningProjection attribute)</a>
+  <dt><a href="Mechanism.html#Mechanism.Mechanism_Base.name">(Mechanism.Mechanism_Base attribute)</a>
   </dt>
 
         
-  <dt><a href="MappingProjection.html#MappingProjection.MappingProjection.name">(MappingProjection.MappingProjection attribute)</a>
+  <dt><a href="ControlProjection.html#ModulatoryProjections.ControlProjection.ControlProjection.name">(ModulatoryProjections.ControlProjection.ControlProjection attribute)</a>
   </dt>
 
         
-  <dt><a href="Mechanism.html#Mechanism.Mechanism_Base.name">(Mechanism.Mechanism_Base attribute)</a>
+  <dt><a href="GatingProjection.html#ModulatoryProjections.GatingProjection.GatingProjection.name">(ModulatoryProjections.GatingProjection.GatingProjection attribute)</a>
   </dt>
 
         
-  <dt><a href="ObjectiveMechanism.html#ObjectiveMechanism.ObjectiveMechanism.name">(ObjectiveMechanism.ObjectiveMechanism attribute)</a>
+  <dt><a href="LearningProjection.html#ModulatoryProjections.LearningProjection.LearningProjection.name">(ModulatoryProjections.LearningProjection.LearningProjection attribute)</a>
+  </dt>
+
+        
+  <dt><a href="ModulatorySignal.html#ModulatorySignals.ModulatorySignal.ModulatorySignal.name">(ModulatorySignals.ModulatorySignal.ModulatorySignal attribute)</a>
   </dt>
 
         
@@ -1314,6 +1452,18 @@ <h2 id="N">N</h2>
   </dt>
 
         
+  <dt><a href="LCA.html#ProcessingMechanisms.LCA.LCA.name">(ProcessingMechanisms.LCA.LCA attribute)</a>
+  </dt>
+
+        
+  <dt><a href="RecurrentTransferMechanism.html#ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism.name">(ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism attribute)</a>
+  </dt>
+
+        
+  <dt><a href="TransferMechanism.html#ProcessingMechanisms.TransferMechanism.TransferMechanism.name">(ProcessingMechanisms.TransferMechanism.TransferMechanism attribute)</a>
+  </dt>
+
+        
   <dt><a href="State.html#State.State_Base.name">(State.State_Base attribute)</a>
   </dt>
 
@@ -1322,13 +1472,17 @@ <h2 id="N">N</h2>
   </dt>
 
         
-  <dt><a href="TransferMechanism.html#TransferMechanism.TransferMechanism.name">(TransferMechanism.TransferMechanism attribute)</a>
+  <dt><a href="Utilities.html#Utilities.ContentAddressableList.name">(Utilities.ContentAddressableList attribute)</a>
   </dt>
 
       </dl></dd>
   </dl></td>
   <td style="width: 33%" valign="top"><dl>
       
+  <dt><a href="Utilities.html#Utilities.ContentAddressableList.names">names (Utilities.ContentAddressableList attribute)</a>, <a href="Utilities.html#Utilities.ContentAddressableList.names">[1]</a>
+  </dt>
+
+      
   <dt><a href="Function.html#Function.NavarroAndFuss">NavarroAndFuss (class in Function)</a>
   </dt>
 
@@ -1336,21 +1490,25 @@ <h2 id="N">N</h2>
   <dt><a href="Function.html#Function.BogaczEtAl.noise">noise (Function.BogaczEtAl attribute)</a>
   </dt>
 
-      <dd><dl>
+      <dd><dl>
+        
+  <dt><a href="Function.html#Function.NavarroAndFuss.noise">(Function.NavarroAndFuss attribute)</a>
+  </dt>
+
         
-  <dt><a href="Function.html#Function.Integrator.noise">(Function.Integrator attribute)</a>
+  <dt><a href="IntegratorMechanism.html#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.noise">(IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator attribute)</a>
   </dt>
 
         
-  <dt><a href="Function.html#Function.NavarroAndFuss.noise">(Function.NavarroAndFuss attribute)</a>
+  <dt><a href="LCA.html#ProcessingMechanisms.LCA.LCA.noise">(ProcessingMechanisms.LCA.LCA attribute)</a>
   </dt>
 
         
-  <dt><a href="IntegratorMechanism.html#IntegratorMechanism.IntegratorMechanism.Integrator.noise">(IntegratorMechanism.IntegratorMechanism.Integrator attribute)</a>
+  <dt><a href="RecurrentTransferMechanism.html#ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism.noise">(ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism attribute)</a>
   </dt>
 
         
-  <dt><a href="TransferMechanism.html#TransferMechanism.TransferMechanism.noise">(TransferMechanism.TransferMechanism attribute)</a>
+  <dt><a href="TransferMechanism.html#ProcessingMechanisms.TransferMechanism.TransferMechanism.noise">(ProcessingMechanisms.TransferMechanism.TransferMechanism attribute)</a>
   </dt>
 
       </dl></dd>
@@ -1359,43 +1517,45 @@ <h2 id="N">N</h2>
   </dt>
 
       
-  <dt><a href="Process.html#Process.Process_Base.numPhases">numPhases (Process.Process_Base attribute)</a>
+  <dt><a href="Function.html#Function.Distance.normalize">normalize (Function.Distance attribute)</a>
   </dt>
 
       <dd><dl>
         
-  <dt><a href="System.html#System.System_Base.numPhases">(System.System_Base attribute)</a>, <a href="System.html#System.System_Base.numPhases">[1]</a>
+  <dt><a href="Function.html#Function.Stability.normalize">(Function.Stability attribute)</a>
   </dt>
 
       </dl></dd>
-  </dl></td>
-</tr></table>
-
-<h2 id="O">O</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%" valign="top"><dl>
       
-  <dt><a href="ObjectiveMechanism.html#ObjectiveMechanism.ObjectiveMechanism">ObjectiveMechanism (class in ObjectiveMechanism)</a>
+  <dt><a href="Process.html#Process.Process_Base.numPhases">numPhases (Process.Process_Base attribute)</a>
   </dt>
 
       <dd><dl>
         
-  <dt><a href="ObjectiveMechanism.html#module-ObjectiveMechanism">(module)</a>
+  <dt><a href="System.html#System.System_Base.numPhases">(System.System_Base attribute)</a>, <a href="System.html#System.System_Base.numPhases">[1]</a>
   </dt>
 
       </dl></dd>
+  </dl></td>
+</tr></table>
+
+<h2 id="O">O</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%" valign="top"><dl>
       
   <dt><a href="Log.html#Log.LogLevel.OFF">OFF (Log.LogLevel attribute)</a>
   </dt>
 
       
-  <dt><a href="Keywords.html#Keywords.MatrixKeywords.OFF_DIAGONAL_MATRIX">OFF_DIAGONAL_MATRIX (Keywords.MatrixKeywords attribute)</a>
+  <dt><a href="Function.html#Function.LinearCombination.offset">offset (Function.LinearCombination attribute)</a>
   </dt>
 
-      
-  <dt><a href="Function.html#Function.LinearCombination.offset">offset (Function.LinearCombination attribute)</a>
+      <dd><dl>
+        
+  <dt><a href="Function.html#Function.Reduce.offset">(Function.Reduce attribute)</a>
   </dt>
 
+      </dl></dd>
       
   <dt><a href="Function.html#Function.LinearCombination.operation">operation (Function.LinearCombination attribute)</a>
   </dt>
@@ -1407,6 +1567,10 @@ <h2 id="O">O</h2>
 
       </dl></dd>
       
+  <dt><a href="Utilities.html#Utilities.optional_parameter_spec">optional_parameter_spec() (in module Utilities)</a>
+  </dt>
+
+      
   <dt><a href="Keywords.html#Keywords.Keywords.ORIGIN">ORIGIN (Keywords.Keywords attribute)</a>
   </dt>
 
@@ -1433,64 +1597,84 @@ <h2 id="O">O</h2>
   </dt>
 
       
-  <dt><a href="OutputState.html#OutputState.OutputState">OutputState (class in OutputState)</a>
+  <dt><a href="Mechanism.html#Mechanism.Mechanism_Base.output_state">output_state (Mechanism.Mechanism_Base attribute)</a>
   </dt>
 
       
-  <dt><a href="Mechanism.html#Mechanism.Mechanism_Base.outputState">outputState (Mechanism.Mechanism_Base attribute)</a>
+  <dt><a href="LearningMechanism.html#AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism.output_states">output_states (AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism attribute)</a>
   </dt>
 
-      
-  <dt><a href="OutputState.html#module-OutputState">OutputState (module)</a>
+      <dd><dl>
+        
+  <dt><a href="Mechanism.html#Mechanism.Mechanism_Base.output_states">(Mechanism.Mechanism_Base attribute)</a>
   </dt>
 
+      </dl></dd>
       
-  <dt><a href="Process.html#Process.Process_Base.outputState">outputState (Process.Process_Base attribute)</a>
+  <dt><a href="LearningMechanism.html#AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism.output_values">output_values (AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism attribute)</a>
   </dt>
 
-      
-  <dt><a href="Mechanism.html#Mechanism.Mechanism_Base.outputStates">outputStates (Mechanism.Mechanism_Base attribute)</a>
+      <dd><dl>
+        
+  <dt><a href="DDM.html#DDM.DDM.output_values">(DDM.DDM attribute)</a>
+  </dt>
+
+        
+  <dt><a href="Mechanism.html#Mechanism.Mechanism_Base.output_values">(Mechanism.Mechanism_Base attribute)</a>
+  </dt>
+
+        
+  <dt><a href="LCA.html#ProcessingMechanisms.LCA.LCA.output_values">(ProcessingMechanisms.LCA.LCA attribute)</a>
   </dt>
 
-      <dd><dl>
         
-  <dt><a href="TransferMechanism.html#TransferMechanism.TransferMechanism.outputStates">(TransferMechanism.TransferMechanism attribute)</a>
+  <dt><a href="RecurrentTransferMechanism.html#ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism.output_values">(ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism attribute)</a>
+  </dt>
+
+        
+  <dt><a href="TransferMechanism.html#ProcessingMechanisms.TransferMechanism.TransferMechanism.output_values">(ProcessingMechanisms.TransferMechanism.TransferMechanism attribute)</a>
   </dt>
 
       </dl></dd>
       
-  <dt><a href="LearningMechanism.html#AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism.outputValue">outputValue (AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism attribute)</a>
+  <dt><a href="OutputState.html#OutputState.OutputState">OutputState (class in OutputState)</a>
   </dt>
 
       <dd><dl>
         
-  <dt><a href="DDM.html#DDM.DDM.outputValue">(DDM.DDM attribute)</a>
+  <dt><a href="OutputState.html#module-OutputState">(module)</a>
   </dt>
 
-        
-  <dt><a href="Mechanism.html#Mechanism.Mechanism_Base.outputValue">(Mechanism.Mechanism_Base attribute)</a>
+      </dl></dd>
+      
+  <dt><a href="Process.html#Process.Process_Base.outputState">outputState (Process.Process_Base attribute)</a>
   </dt>
 
+      
+  <dt><a href="LCA.html#ProcessingMechanisms.LCA.LCA.outputStates">outputStates (ProcessingMechanisms.LCA.LCA attribute)</a>
+  </dt>
+
+      <dd><dl>
         
-  <dt><a href="ObjectiveMechanism.html#ObjectiveMechanism.ObjectiveMechanism.outputValue">(ObjectiveMechanism.ObjectiveMechanism attribute)</a>
+  <dt><a href="RecurrentTransferMechanism.html#ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism.outputStates">(ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism attribute)</a>
   </dt>
 
         
-  <dt><a href="TransferMechanism.html#TransferMechanism.TransferMechanism.outputValue">(TransferMechanism.TransferMechanism attribute)</a>
+  <dt><a href="TransferMechanism.html#ProcessingMechanisms.TransferMechanism.TransferMechanism.outputStates">(ProcessingMechanisms.TransferMechanism.TransferMechanism attribute)</a>
   </dt>
 
       </dl></dd>
   </dl></td>
   <td style="width: 33%" valign="top"><dl>
       
-  <dt><a href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.owner">owner (AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal attribute)</a>
+  <dt><a href="Function.html#Function.ModulationParam.OVERRIDE">OVERRIDE (Function.ModulationParam attribute)</a>
   </dt>
 
-      <dd><dl>
-        
-  <dt><a href="Function.html#Function.ArgumentTherapy.owner">(Function.ArgumentTherapy attribute)</a>
+      
+  <dt><a href="Function.html#Function.ArgumentTherapy.owner">owner (Function.ArgumentTherapy attribute)</a>
   </dt>
 
+      <dd><dl>
         
   <dt><a href="Function.html#Function.BackPropagation.owner">(Function.BackPropagation attribute)</a>
   </dt>
@@ -1500,6 +1684,10 @@ <h2 id="O">O</h2>
   </dt>
 
         
+  <dt><a href="Function.html#Function.Distance.owner">(Function.Distance attribute)</a>
+  </dt>
+
+        
   <dt><a href="Function.html#Function.Exponential.owner">(Function.Exponential attribute)</a>
   </dt>
 
@@ -1516,10 +1704,6 @@ <h2 id="O">O</h2>
   </dt>
 
         
-  <dt><a href="Function.html#Function.Integrator.owner">(Function.Integrator attribute)</a>
-  </dt>
-
-        
   <dt><a href="Function.html#Function.Linear.owner">(Function.Linear attribute)</a>
   </dt>
 
@@ -1548,7 +1732,11 @@ <h2 id="O">O</h2>
   </dt>
 
         
-  <dt><a href="Function.html#Function.SoftMax.owner">(Function.SoftMax attribute)</a>
+  <dt><a href="Function.html#Function.Stability.owner">(Function.Stability attribute)</a>
+  </dt>
+
+        
+  <dt><a href="Function.html#Function.Stability.ParameterState.owner">(Function.Stability.ParameterState attribute)</a>
   </dt>
 
         
@@ -1568,7 +1756,11 @@ <h2 id="O">O</h2>
   </dt>
 
         
-  <dt><a href="IntegratorMechanism.html#IntegratorMechanism.IntegratorMechanism.Integrator.owner">(IntegratorMechanism.IntegratorMechanism.Integrator attribute)</a>
+  <dt><a href="IntegratorMechanism.html#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.owner">(IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator attribute)</a>
+  </dt>
+
+        
+  <dt><a href="ModulatorySignal.html#ModulatorySignals.ModulatorySignal.ModulatorySignal.owner">(ModulatorySignals.ModulatorySignal.ModulatorySignal attribute)</a>
   </dt>
 
         
@@ -1591,11 +1783,7 @@ <h2 id="P">P</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%" valign="top"><dl>
       
-  <dt><a href="MappingProjection.html#MappingProjection.MappingProjection.param_modulation_operation">param_modulation_operation (MappingProjection.MappingProjection attribute)</a>
-  </dt>
-
-      
-  <dt><a href="ParameterState.html#ParameterState.ParameterState.parameterModulationOperation">parameterModulationOperation (ParameterState.ParameterState attribute)</a>
+  <dt><a href="Utilities.html#Utilities.parameter_spec">parameter_spec() (in module Utilities)</a>
   </dt>
 
       
@@ -1613,11 +1801,15 @@ <h2 id="P">P</h2>
   </dt>
 
       
-  <dt><a href="Function.html#Function.ExponentialDist.params">params (Function.ExponentialDist attribute)</a>
+  <dt><a href="Function.html#Function.Distance.params">params (Function.Distance attribute)</a>
   </dt>
 
       <dd><dl>
         
+  <dt><a href="Function.html#Function.ExponentialDist.params">(Function.ExponentialDist attribute)</a>
+  </dt>
+
+        
   <dt><a href="Function.html#Function.GammaDist.params">(Function.GammaDist attribute)</a>
   </dt>
 
@@ -1626,6 +1818,10 @@ <h2 id="P">P</h2>
   </dt>
 
         
+  <dt><a href="Function.html#Function.Stability.params">(Function.Stability attribute)</a>
+  </dt>
+
+        
   <dt><a href="Function.html#Function.UniformDist.params">(Function.UniformDist attribute)</a>
   </dt>
 
@@ -1635,10 +1831,18 @@ <h2 id="P">P</h2>
 
       </dl></dd>
       
+  <dt><a href="State.html#State.State_Base.path_afferents">path_afferents (State.State_Base attribute)</a>
+  </dt>
+
+      
   <dt><a href="Process.html#Process.Process_Base.pathway">pathway (Process.Process_Base attribute)</a>
   </dt>
 
       
+  <dt><a href="PathwayProjection.html#module-PathwayProjections.PathwayProjection">PathwayProjections.PathwayProjection (module)</a>
+  </dt>
+
+      
   <dt><a href="Function.html#Function.ArgumentTherapy.pertinacity">pertinacity (Function.ArgumentTherapy attribute)</a>
   </dt>
 
@@ -1677,19 +1881,11 @@ <h2 id="P">P</h2>
   </dt>
 
       
-  <dt><a href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.prefs">prefs (AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal attribute)</a>
+  <dt><a href="LearningMechanism.html#AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism.prefs">prefs (AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism attribute)</a>
   </dt>
 
       <dd><dl>
         
-  <dt><a href="LearningMechanism.html#AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism.prefs">(AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism attribute)</a>
-  </dt>
-
-        
-  <dt><a href="ControlProjection.html#ControlProjection.ControlProjection.prefs">(ControlProjection.ControlProjection attribute)</a>
-  </dt>
-
-        
   <dt><a href="DDM.html#DDM.DDM.prefs">(DDM.DDM attribute)</a>
   </dt>
 
@@ -1706,6 +1902,10 @@ <h2 id="P">P</h2>
   </dt>
 
         
+  <dt><a href="Function.html#Function.Distance.prefs">(Function.Distance attribute)</a>
+  </dt>
+
+        
   <dt><a href="Function.html#Function.Exponential.prefs">(Function.Exponential attribute)</a>
   </dt>
 
@@ -1722,10 +1922,6 @@ <h2 id="P">P</h2>
   </dt>
 
         
-  <dt><a href="Function.html#Function.Integrator.prefs">(Function.Integrator attribute)</a>
-  </dt>
-
-        
   <dt><a href="Function.html#Function.Linear.prefs">(Function.Linear attribute)</a>
   </dt>
 
@@ -1754,7 +1950,15 @@ <h2 id="P">P</h2>
   </dt>
 
         
-  <dt><a href="Function.html#Function.SoftMax.prefs">(Function.SoftMax attribute)</a>
+  <dt><a href="Function.html#Function.Stability.prefs">(Function.Stability attribute)</a>
+  </dt>
+
+        
+  <dt><a href="Function.html#Function.Stability.MappingProjection.prefs">(Function.Stability.MappingProjection attribute)</a>
+  </dt>
+
+        
+  <dt><a href="Function.html#Function.Stability.ParameterState.prefs">(Function.Stability.ParameterState attribute)</a>
   </dt>
 
         
@@ -1778,23 +1982,27 @@ <h2 id="P">P</h2>
   </dt>
 
         
-  <dt><a href="IntegratorMechanism.html#IntegratorMechanism.IntegratorMechanism.Integrator.prefs">(IntegratorMechanism.IntegratorMechanism.Integrator attribute)</a>
+  <dt><a href="IntegratorMechanism.html#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.prefs">(IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator attribute)</a>
   </dt>
 
         
-  <dt><a href="LearningProjection.html#LearningProjection.LearningProjection.prefs">(LearningProjection.LearningProjection attribute)</a>
+  <dt><a href="Mechanism.html#Mechanism.Mechanism_Base.prefs">(Mechanism.Mechanism_Base attribute)</a>
   </dt>
 
         
-  <dt><a href="MappingProjection.html#MappingProjection.MappingProjection.prefs">(MappingProjection.MappingProjection attribute)</a>
+  <dt><a href="ControlProjection.html#ModulatoryProjections.ControlProjection.ControlProjection.prefs">(ModulatoryProjections.ControlProjection.ControlProjection attribute)</a>
   </dt>
 
         
-  <dt><a href="Mechanism.html#Mechanism.Mechanism_Base.prefs">(Mechanism.Mechanism_Base attribute)</a>
+  <dt><a href="GatingProjection.html#ModulatoryProjections.GatingProjection.GatingProjection.prefs">(ModulatoryProjections.GatingProjection.GatingProjection attribute)</a>
+  </dt>
+
+        
+  <dt><a href="LearningProjection.html#ModulatoryProjections.LearningProjection.LearningProjection.prefs">(ModulatoryProjections.LearningProjection.LearningProjection attribute)</a>
   </dt>
 
         
-  <dt><a href="ObjectiveMechanism.html#ObjectiveMechanism.ObjectiveMechanism.prefs">(ObjectiveMechanism.ObjectiveMechanism attribute)</a>
+  <dt><a href="ModulatorySignal.html#ModulatorySignals.ModulatorySignal.ModulatorySignal.prefs">(ModulatorySignals.ModulatorySignal.ModulatorySignal attribute)</a>
   </dt>
 
         
@@ -1810,27 +2018,43 @@ <h2 id="P">P</h2>
   </dt>
 
         
-  <dt><a href="State.html#State.State_Base.prefs">(State.State_Base attribute)</a>
+  <dt><a href="LCA.html#ProcessingMechanisms.LCA.LCA.prefs">(ProcessingMechanisms.LCA.LCA attribute)</a>
+  </dt>
+
+        
+  <dt><a href="RecurrentTransferMechanism.html#ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism.prefs">(ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism attribute)</a>
+  </dt>
+
+        
+  <dt><a href="TransferMechanism.html#ProcessingMechanisms.TransferMechanism.TransferMechanism.prefs">(ProcessingMechanisms.TransferMechanism.TransferMechanism attribute)</a>
   </dt>
 
         
-  <dt><a href="TransferMechanism.html#TransferMechanism.TransferMechanism.prefs">(TransferMechanism.TransferMechanism attribute)</a>
+  <dt><a href="State.html#State.State_Base.prefs">(State.State_Base attribute)</a>
   </dt>
 
       </dl></dd>
   </dl></td>
   <td style="width: 33%" valign="top"><dl>
       
-  <dt><a href="Function.html#Function.Integrator.previous_value">previous_value (Function.Integrator attribute)</a>
+  <dt><a href="LCA.html#ProcessingMechanisms.LCA.LCA.previous_input">previous_input (ProcessingMechanisms.LCA.LCA attribute)</a>
   </dt>
 
       <dd><dl>
         
-  <dt><a href="IntegratorMechanism.html#IntegratorMechanism.IntegratorMechanism.Integrator.previous_value">(IntegratorMechanism.IntegratorMechanism.Integrator attribute)</a>
+  <dt><a href="RecurrentTransferMechanism.html#ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism.previous_input">(ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism attribute)</a>
+  </dt>
+
+        
+  <dt><a href="TransferMechanism.html#ProcessingMechanisms.TransferMechanism.TransferMechanism.previous_input">(ProcessingMechanisms.TransferMechanism.TransferMechanism attribute)</a>
   </dt>
 
       </dl></dd>
       
+  <dt><a href="IntegratorMechanism.html#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.previous_value">previous_value (IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator attribute)</a>
+  </dt>
+
+      
   <dt><a href="Log.html#Log.Log.print_entries">print_entries() (Log.Log method)</a>
   </dt>
 
@@ -1857,7 +2081,15 @@ <h2 id="P">P</h2>
 
       </dl></dd>
       
-  <dt><a href="ProcessingMechanism.html#module-ProcessingMechanism">ProcessingMechanism (module)</a>
+  <dt><a href="LCA.html#module-ProcessingMechanisms.LCA">ProcessingMechanisms.LCA (module)</a>
+  </dt>
+
+      
+  <dt><a href="RecurrentTransferMechanism.html#module-ProcessingMechanisms.RecurrentTransferMechanism">ProcessingMechanisms.RecurrentTransferMechanism (module)</a>
+  </dt>
+
+      
+  <dt><a href="TransferMechanism.html#module-ProcessingMechanisms.TransferMechanism">ProcessingMechanisms.TransferMechanism (module)</a>
   </dt>
 
       
@@ -1887,71 +2119,77 @@ <h2 id="R">R</h2>
   </dt>
 
       
-  <dt><a href="TransferMechanism.html#TransferMechanism.TransferMechanism.range">range (TransferMechanism.TransferMechanism attribute)</a>
-  </dt>
-
-      
-  <dt><a href="Function.html#Function.Exponential.rate">rate (Function.Exponential attribute)</a>
+  <dt><a href="LCA.html#ProcessingMechanisms.LCA.LCA.range">range (ProcessingMechanisms.LCA.LCA attribute)</a>
   </dt>
 
       <dd><dl>
         
-  <dt><a href="Function.html#Function.Integrator.rate">(Function.Integrator attribute)</a>
+  <dt><a href="RecurrentTransferMechanism.html#ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism.range">(ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism attribute)</a>
   </dt>
 
         
-  <dt><a href="IntegratorMechanism.html#IntegratorMechanism.IntegratorMechanism.Integrator.rate">(IntegratorMechanism.IntegratorMechanism.Integrator attribute)</a>
+  <dt><a href="TransferMechanism.html#ProcessingMechanisms.TransferMechanism.TransferMechanism.range">(ProcessingMechanisms.TransferMechanism.TransferMechanism attribute)</a>
   </dt>
 
       </dl></dd>
       
-  <dt><a href="ControlProjection.html#ControlProjection.ControlProjection.receiver">receiver (ControlProjection.ControlProjection attribute)</a>
+  <dt><a href="Function.html#Function.Exponential.rate">rate (Function.Exponential attribute)</a>
   </dt>
 
       <dd><dl>
         
-  <dt><a href="LearningProjection.html#LearningProjection.LearningProjection.receiver">(LearningProjection.LearningProjection attribute)</a>
-  </dt>
-
-        
-  <dt><a href="MappingProjection.html#MappingProjection.MappingProjection.receiver">(MappingProjection.MappingProjection attribute)</a>
+  <dt><a href="IntegratorMechanism.html#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.rate">(IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator attribute)</a>
   </dt>
 
       </dl></dd>
       
-  <dt><a href="InputState.html#InputState.InputState.receivesFromProjections">receivesFromProjections (InputState.InputState attribute)</a>
+  <dt><a href="Function.html#Function.Stability.MappingProjection.receiver">receiver (Function.Stability.MappingProjection attribute)</a>
   </dt>
 
       <dd><dl>
         
-  <dt><a href="ParameterState.html#ParameterState.ParameterState.receivesFromProjections">(ParameterState.ParameterState attribute)</a>
+  <dt><a href="ControlProjection.html#ModulatoryProjections.ControlProjection.ControlProjection.receiver">(ModulatoryProjections.ControlProjection.ControlProjection attribute)</a>
+  </dt>
+
+        
+  <dt><a href="GatingProjection.html#ModulatoryProjections.GatingProjection.GatingProjection.receiver">(ModulatoryProjections.GatingProjection.GatingProjection attribute)</a>
   </dt>
 
         
-  <dt><a href="State.html#State.State_Base.receivesFromProjections">(State.State_Base attribute)</a>
+  <dt><a href="LearningProjection.html#ModulatoryProjections.LearningProjection.LearningProjection.receiver">(ModulatoryProjections.LearningProjection.LearningProjection attribute)</a>
   </dt>
 
       </dl></dd>
       
-  <dt><a href="System.html#System.System_Base.recurrentInitMechanisms">recurrentInitMechanisms (System.System_Base attribute)</a>
+  <dt><a href="LCA.html#ProcessingMechanisms.LCA.LCA.recurrent_projection">recurrent_projection (ProcessingMechanisms.LCA.LCA attribute)</a>
+  </dt>
+
+      <dd><dl>
+        
+  <dt><a href="RecurrentTransferMechanism.html#ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism.recurrent_projection">(ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism attribute)</a>
   </dt>
 
+      </dl></dd>
   </dl></td>
   <td style="width: 33%" valign="top"><dl>
       
-  <dt><a href="Function.html#Function.Reduce">Reduce (class in Function)</a>
+  <dt><a href="System.html#System.System_Base.recurrentInitMechanisms">recurrentInitMechanisms (System.System_Base attribute)</a>
   </dt>
 
       
-  <dt><a href="Function.html#Function.Reinforcement">Reinforcement (class in Function)</a>
+  <dt><a href="RecurrentTransferMechanism.html#ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism">RecurrentTransferMechanism (class in ProcessingMechanisms.RecurrentTransferMechanism)</a>
   </dt>
 
       
-  <dt><a href="Log.html#Log.Log.reset_entries">reset_entries() (Log.Log method)</a>
+  <dt><a href="Function.html#Function.Reduce">Reduce (class in Function)</a>
   </dt>
 
       
-  <dt><a href="TransferMechanism.html#TransferMechanism.Transfer_Output.RESULT">RESULT (TransferMechanism.Transfer_Output attribute)</a>
+  <dt><a href="Function.html#Function.Reinforcement">Reinforcement (class in Function)</a>
+  </dt>
+
+      
+  <dt><a href="Log.html#Log.Log.reset_entries">reset_entries() (Log.Log method)</a>
   </dt>
 
       
@@ -1965,10 +2203,6 @@ <h2 id="R">R</h2>
 
       </dl></dd>
       
-  <dt><a href="ObjectiveMechanism.html#ObjectiveMechanism.ObjectiveMechanism.role">role (ObjectiveMechanism.ObjectiveMechanism attribute)</a>
-  </dt>
-
-      
   <dt><a href="Run.html#module-Run">Run (module)</a>
   </dt>
 
@@ -2018,68 +2252,74 @@ <h2 id="S">S</h2>
   </dt>
 
         
+  <dt><a href="Function.html#Function.Reduce.scale">(Function.Reduce attribute)</a>
+  </dt>
+
+        
   <dt><a href="Function.html#Function.WaldDist.scale">(Function.WaldDist attribute)</a>
   </dt>
 
       </dl></dd>
       
-  <dt><a href="ControlProjection.html#ControlProjection.ControlProjection.sender">sender (ControlProjection.ControlProjection attribute)</a>
+  <dt><a href="Function.html#Function.Stability.MappingProjection.sender">sender (Function.Stability.MappingProjection attribute)</a>
   </dt>
 
       <dd><dl>
         
-  <dt><a href="LearningProjection.html#LearningProjection.LearningProjection.sender">(LearningProjection.LearningProjection attribute)</a>
+  <dt><a href="ControlProjection.html#ModulatoryProjections.ControlProjection.ControlProjection.sender">(ModulatoryProjections.ControlProjection.ControlProjection attribute)</a>
+  </dt>
+
+        
+  <dt><a href="GatingProjection.html#ModulatoryProjections.GatingProjection.GatingProjection.sender">(ModulatoryProjections.GatingProjection.GatingProjection attribute)</a>
   </dt>
 
         
-  <dt><a href="MappingProjection.html#MappingProjection.MappingProjection.sender">(MappingProjection.MappingProjection attribute)</a>
+  <dt><a href="LearningProjection.html#ModulatoryProjections.LearningProjection.LearningProjection.sender">(ModulatoryProjections.LearningProjection.LearningProjection attribute)</a>
   </dt>
 
       </dl></dd>
       
-  <dt><a href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.sendsToProjections">sendsToProjections (AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal attribute)</a>
+  <dt><a href="Process.html#Process.Process_Base.show">show() (Process.Process_Base method)</a>
   </dt>
 
       <dd><dl>
         
-  <dt><a href="OutputState.html#OutputState.OutputState.sendsToProjections">(OutputState.OutputState attribute)</a>
+  <dt><a href="System.html#System.System_Base.show">(System.System_Base method)</a>
   </dt>
 
-        
-  <dt><a href="State.html#State.State_Base.sendsToProjections">(State.State_Base attribute)</a>
+      </dl></dd>
+      
+  <dt><a href="System.html#System.System_Base.show_graph">show_graph() (System.System_Base method)</a>
   </dt>
 
-      </dl></dd>
       
-  <dt><a href="Function.html#Function.GammaDist.shape">shape (Function.GammaDist attribute)</a>
+  <dt><a href="Keywords.html#Keywords.Keywords.SINGLETON">SINGLETON (Keywords.Keywords attribute)</a>
   </dt>
 
       
-  <dt><a href="Process.html#Process.Process_Base.show">show() (Process.Process_Base method)</a>
+  <dt><a href="TransferMechanism.html#ProcessingMechanisms.TransferMechanism.TransferMechanism.size">size (ProcessingMechanisms.TransferMechanism.TransferMechanism attribute)</a>
   </dt>
 
-      <dd><dl>
-        
-  <dt><a href="System.html#System.System_Base.show">(System.System_Base method)</a>
+      
+  <dt><a href="Function.html#Function.Linear.slope">slope (Function.Linear attribute)</a>
   </dt>
 
-      </dl></dd>
       
-  <dt><a href="System.html#System.System_Base.show_graph">show_graph() (System.System_Base method)</a>
+  <dt><a href="Function.html#Function.SoftMax">SoftMax (class in Function)</a>
   </dt>
 
   </dl></td>
   <td style="width: 33%" valign="top"><dl>
       
-  <dt><a href="Keywords.html#Keywords.Keywords.SINGLETON">SINGLETON (Keywords.Keywords attribute)</a>
+  <dt><a href="Function.html#Function.Stability">Stability (class in Function)</a>
   </dt>
 
       
-  <dt><a href="Function.html#Function.Linear.slope">slope (Function.Linear attribute)</a>
+  <dt><a href="Function.html#Function.Stability.MappingProjection">Stability.MappingProjection (class in Function)</a>
   </dt>
 
       
-  <dt><a href="Function.html#Function.SoftMax">SoftMax (class in Function)</a>
+  <dt><a href="Function.html#Function.Stability.ParameterState">Stability.ParameterState (class in Function)</a>
   </dt>
 
       
@@ -2087,6 +2327,10 @@ <h2 id="S">S</h2>
   </dt>
 
       
+  <dt><a href="OutputState.html#OutputState.StandardOutputStates">StandardOutputStates (class in OutputState)</a>
+  </dt>
+
+      
   <dt><a href="Function.html#Function.BogaczEtAl.starting_point">starting_point (Function.BogaczEtAl attribute)</a>
   </dt>
 
@@ -2205,9 +2449,19 @@ <h2 id="T">T</h2>
   </dt>
 
       
-  <dt><a href="TransferMechanism.html#TransferMechanism.TransferMechanism.time_constant">time_constant (TransferMechanism.TransferMechanism attribute)</a>
+  <dt><a href="LCA.html#ProcessingMechanisms.LCA.LCA.time_constant">time_constant (ProcessingMechanisms.LCA.LCA attribute)</a>
+  </dt>
+
+      <dd><dl>
+        
+  <dt><a href="RecurrentTransferMechanism.html#ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism.time_constant">(ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism attribute)</a>
+  </dt>
+
+        
+  <dt><a href="TransferMechanism.html#ProcessingMechanisms.TransferMechanism.TransferMechanism.time_constant">(ProcessingMechanisms.TransferMechanism.TransferMechanism attribute)</a>
   </dt>
 
+      </dl></dd>
   </dl></td>
   <td style="width: 33%" valign="top"><dl>
       
@@ -2224,17 +2478,15 @@ <h2 id="T">T</h2>
   </dt>
 
         
-  <dt><a href="TransferMechanism.html#TransferMechanism.TransferMechanism.time_scale">(TransferMechanism.TransferMechanism attribute)</a>
+  <dt><a href="LCA.html#ProcessingMechanisms.LCA.LCA.time_scale">(ProcessingMechanisms.LCA.LCA attribute)</a>
   </dt>
 
-      </dl></dd>
-      
-  <dt><a href="Function.html#Function.Integrator.time_step_size">time_step_size (Function.Integrator attribute)</a>
+        
+  <dt><a href="RecurrentTransferMechanism.html#ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism.time_scale">(ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism attribute)</a>
   </dt>
 
-      <dd><dl>
         
-  <dt><a href="IntegratorMechanism.html#IntegratorMechanism.IntegratorMechanism.Integrator.time_step_size">(IntegratorMechanism.IntegratorMechanism.Integrator attribute)</a>
+  <dt><a href="TransferMechanism.html#ProcessingMechanisms.TransferMechanism.TransferMechanism.time_scale">(ProcessingMechanisms.TransferMechanism.TransferMechanism attribute)</a>
   </dt>
 
       </dl></dd>
@@ -2259,23 +2511,17 @@ <h2 id="T">T</h2>
 
       </dl></dd>
       
-  <dt><a href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.toggle_cost_function">toggle_cost_function() (AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal method)</a>
+  <dt><a href="ControlSignal.html#ModulatorySignals.ControlSignal.ControlSignal.toggle_cost_function">toggle_cost_function() (ModulatorySignals.ControlSignal.ControlSignal method)</a>
   </dt>
 
       
-  <dt><a href="TransferMechanism.html#TransferMechanism.Transfer_Output">Transfer_Output (class in TransferMechanism)</a>
+  <dt><a href="Function.html#Function.Stability.transfer_fct">transfer_fct (Function.Stability attribute)</a>
   </dt>
 
       
-  <dt><a href="TransferMechanism.html#TransferMechanism.TransferMechanism">TransferMechanism (class in TransferMechanism)</a>
-  </dt>
-
-      <dd><dl>
-        
-  <dt><a href="TransferMechanism.html#module-TransferMechanism">(module)</a>
+  <dt><a href="TransferMechanism.html#ProcessingMechanisms.TransferMechanism.TransferMechanism">TransferMechanism (class in ProcessingMechanisms.TransferMechanism)</a>
   </dt>
 
-      </dl></dd>
   </dl></td>
 </tr></table>
 
@@ -2287,7 +2533,7 @@ <h2 id="U">U</h2>
   </dt>
 
       
-  <dt><a href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.update">update() (AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal method)</a>
+  <dt><a href="ControlSignal.html#ModulatorySignals.ControlSignal.ControlSignal.update">update() (ModulatorySignals.ControlSignal.ControlSignal method)</a>
   </dt>
 
   </dl></td>
@@ -2311,36 +2557,40 @@ <h2 id="V">V</h2>
   </dt>
 
       
-  <dt><a href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.value">value (AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal attribute)</a>
+  <dt><a href="DDM.html#DDM.DDM.value">value (DDM.DDM attribute)</a>
   </dt>
 
       <dd><dl>
         
-  <dt><a href="ControlProjection.html#ControlProjection.ControlProjection.value">(ControlProjection.ControlProjection attribute)</a>
+  <dt><a href="Function.html#Function.Stability.ParameterState.value">(Function.Stability.ParameterState attribute)</a>
   </dt>
 
         
-  <dt><a href="DDM.html#DDM.DDM.value">(DDM.DDM attribute)</a>
+  <dt><a href="InputState.html#InputState.InputState.value">(InputState.InputState attribute)</a>
   </dt>
 
         
-  <dt><a href="InputState.html#InputState.InputState.value">(InputState.InputState attribute)</a>
+  <dt><a href="Log.html#Log.LogEntry.value">(Log.LogEntry attribute)</a>
   </dt>
 
         
-  <dt><a href="LearningProjection.html#LearningProjection.LearningProjection.value">(LearningProjection.LearningProjection attribute)</a>
+  <dt><a href="Mechanism.html#Mechanism.Mechanism_Base.value">(Mechanism.Mechanism_Base attribute)</a>
   </dt>
 
         
-  <dt><a href="Log.html#Log.LogEntry.value">(Log.LogEntry attribute)</a>
+  <dt><a href="ControlProjection.html#ModulatoryProjections.ControlProjection.ControlProjection.value">(ModulatoryProjections.ControlProjection.ControlProjection attribute)</a>
   </dt>
 
         
-  <dt><a href="Mechanism.html#Mechanism.Mechanism_Base.value">(Mechanism.Mechanism_Base attribute)</a>
+  <dt><a href="GatingProjection.html#ModulatoryProjections.GatingProjection.GatingProjection.value">(ModulatoryProjections.GatingProjection.GatingProjection attribute)</a>
   </dt>
 
         
-  <dt><a href="ObjectiveMechanism.html#ObjectiveMechanism.ObjectiveMechanism.value">(ObjectiveMechanism.ObjectiveMechanism attribute)</a>
+  <dt><a href="LearningProjection.html#ModulatoryProjections.LearningProjection.LearningProjection.value">(ModulatoryProjections.LearningProjection.LearningProjection attribute)</a>
+  </dt>
+
+        
+  <dt><a href="ModulatorySignal.html#ModulatorySignals.ModulatorySignal.ModulatorySignal.value">(ModulatorySignals.ModulatorySignal.ModulatorySignal attribute)</a>
   </dt>
 
         
@@ -2356,15 +2606,23 @@ <h2 id="V">V</h2>
   </dt>
 
         
-  <dt><a href="State.html#State.State_Base.value">(State.State_Base attribute)</a>
+  <dt><a href="LCA.html#ProcessingMechanisms.LCA.LCA.value">(ProcessingMechanisms.LCA.LCA attribute)</a>
   </dt>
 
         
-  <dt><a href="System.html#System.System_Base.value">(System.System_Base attribute)</a>
+  <dt><a href="RecurrentTransferMechanism.html#ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism.value">(ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism attribute)</a>
+  </dt>
+
+        
+  <dt><a href="TransferMechanism.html#ProcessingMechanisms.TransferMechanism.TransferMechanism.value">(ProcessingMechanisms.TransferMechanism.TransferMechanism attribute)</a>
+  </dt>
+
+        
+  <dt><a href="State.html#State.State_Base.value">(State.State_Base attribute)</a>
   </dt>
 
         
-  <dt><a href="TransferMechanism.html#TransferMechanism.TransferMechanism.value">(TransferMechanism.TransferMechanism attribute)</a>
+  <dt><a href="System.html#System.System_Base.value">(System.System_Base attribute)</a>
   </dt>
 
       </dl></dd>
@@ -2377,14 +2635,18 @@ <h2 id="V">V</h2>
   </dt>
 
       
-  <dt><a href="ControlSignal.html#AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal.variable">variable (AdaptiveMechanisms.ControlMechanisms.ControlSignal.ControlSignal attribute)</a>
+  <dt><a href="Utilities.html#Utilities.ContentAddressableList.values">values (Utilities.ContentAddressableList attribute)</a>, <a href="Utilities.html#Utilities.ContentAddressableList.values">[1]</a>
   </dt>
 
-      <dd><dl>
-        
-  <dt><a href="LearningMechanism.html#AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism.variable">(AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism attribute)</a>
+      
+  <dt><a href="Utilities.html#Utilities.ContentAddressableList.values_as_lists">values_as_lists (Utilities.ContentAddressableList attribute)</a>
   </dt>
 
+      
+  <dt><a href="LearningMechanism.html#AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism.variable">variable (AdaptiveMechanisms.LearningMechanisms.LearningMechanism.LearningMechanism attribute)</a>
+  </dt>
+
+      <dd><dl>
         
   <dt><a href="DDM.html#DDM.DDM.variable">(DDM.DDM attribute)</a>
   </dt>
@@ -2402,15 +2664,15 @@ <h2 id="V">V</h2>
   </dt>
 
         
-  <dt><a href="Function.html#Function.Exponential.variable">(Function.Exponential attribute)</a>
+  <dt><a href="Function.html#Function.Distance.variable">(Function.Distance attribute)</a>
   </dt>
 
         
-  <dt><a href="Function.html#Function.Function_Base.variable">(Function.Function_Base attribute)</a>
+  <dt><a href="Function.html#Function.Exponential.variable">(Function.Exponential attribute)</a>
   </dt>
 
         
-  <dt><a href="Function.html#Function.Integrator.variable">(Function.Integrator attribute)</a>
+  <dt><a href="Function.html#Function.Function_Base.variable">(Function.Function_Base attribute)</a>
   </dt>
 
         
@@ -2442,6 +2704,14 @@ <h2 id="V">V</h2>
   </dt>
 
         
+  <dt><a href="Function.html#Function.Stability.variable">(Function.Stability attribute)</a>
+  </dt>
+
+        
+  <dt><a href="Function.html#Function.Stability.ParameterState.variable">(Function.Stability.ParameterState attribute)</a>
+  </dt>
+
+        
   <dt><a href="Function.html#Function.UserDefinedFunction.variable">(Function.UserDefinedFunction attribute)</a>
   </dt>
 
@@ -2454,15 +2724,19 @@ <h2 id="V">V</h2>
   </dt>
 
         
-  <dt><a href="IntegratorMechanism.html#IntegratorMechanism.IntegratorMechanism.Integrator.variable">(IntegratorMechanism.IntegratorMechanism.Integrator attribute)</a>
+  <dt><a href="IntegratorMechanism.html#IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator.variable">(IntegratorMechanism.IntegratorMechanism.AdaptiveIntegrator attribute)</a>
   </dt>
 
         
-  <dt><a href="LearningProjection.html#LearningProjection.LearningProjection.variable">(LearningProjection.LearningProjection attribute)</a>
+  <dt><a href="Mechanism.html#Mechanism.Mechanism_Base.variable">(Mechanism.Mechanism_Base attribute)</a>
   </dt>
 
         
-  <dt><a href="Mechanism.html#Mechanism.Mechanism_Base.variable">(Mechanism.Mechanism_Base attribute)</a>
+  <dt><a href="LearningProjection.html#ModulatoryProjections.LearningProjection.LearningProjection.variable">(ModulatoryProjections.LearningProjection.LearningProjection attribute)</a>
+  </dt>
+
+        
+  <dt><a href="ModulatorySignal.html#ModulatorySignals.ModulatorySignal.ModulatorySignal.variable">(ModulatorySignals.ModulatorySignal.ModulatorySignal attribute)</a>
   </dt>
 
         
@@ -2474,7 +2748,15 @@ <h2 id="V">V</h2>
   </dt>
 
         
-  <dt><a href="TransferMechanism.html#TransferMechanism.TransferMechanism.variable">(TransferMechanism.TransferMechanism attribute)</a>
+  <dt><a href="LCA.html#ProcessingMechanisms.LCA.LCA.variable">(ProcessingMechanisms.LCA.LCA attribute)</a>
+  </dt>
+
+        
+  <dt><a href="RecurrentTransferMechanism.html#ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism.variable">(ProcessingMechanisms.RecurrentTransferMechanism.RecurrentTransferMechanism attribute)</a>
+  </dt>
+
+        
+  <dt><a href="TransferMechanism.html#ProcessingMechanisms.TransferMechanism.TransferMechanism.variable">(ProcessingMechanisms.TransferMechanism.TransferMechanism attribute)</a>
   </dt>
 
       </dl></dd>
@@ -2484,10 +2766,6 @@ <h2 id="V">V</h2>
   <dt><a href="Function.html#Function.Reduce.variable_default">variable_default (Function.Reduce attribute)</a>
   </dt>
 
-      
-  <dt><a href="TransferMechanism.html#TransferMechanism.Transfer_Output.VARIANCE">VARIANCE (TransferMechanism.Transfer_Output attribute)</a>
-  </dt>
-
   </dl></td>
 </tr></table>
 
@@ -2499,13 +2777,13 @@ <h2 id="W">W</h2>
   </dt>
 
       
-  <dt><a href="LearningProjection.html#LearningProjection.LearningProjection.weight_change_matrix">weight_change_matrix (LearningProjection.LearningProjection attribute)</a>
+  <dt><a href="LearningProjection.html#ModulatoryProjections.LearningProjection.LearningProjection.weight_change_matrix">weight_change_matrix (ModulatoryProjections.LearningProjection.LearningProjection attribute)</a>
   </dt>
 
   </dl></td>
   <td style="width: 33%" valign="top"><dl>
       
-  <dt><a href="LearningProjection.html#LearningProjection.LearningProjection.weight_change_params">weight_change_params (LearningProjection.LearningProjection attribute)</a>
+  <dt><a href="LearningProjection.html#ModulatoryProjections.LearningProjection.LearningProjection.weight_change_params">weight_change_params (ModulatoryProjections.LearningProjection.LearningProjection attribute)</a>
   </dt>
 
       
diff --git a/docs/build/html/globals.html b/docs/build/html/globals.html
index 62bad858c06..2c36aeaeba0 100644
--- a/docs/build/html/globals.html
+++ b/docs/build/html/globals.html
@@ -86,7 +86,7 @@
 <li class="toctree-l1"><a class="reference internal" href="System.html">System</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Process.html">Process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Mechanism.html">Mechanism</a></li>
-<li class="toctree-l1"><a class="reference internal" href="State.html">States</a></li>
+<li class="toctree-l1"><a class="reference internal" href="State.html">State</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Projection.html">Projection</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Run.html">Run</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Component.html">Component</a></li>
diff --git a/docs/build/html/index.html b/docs/build/html/index.html
index 2892464f7d4..b9e7cbf8ed8 100644
--- a/docs/build/html/index.html
+++ b/docs/build/html/index.html
@@ -87,7 +87,7 @@
 <li class="toctree-l1"><a class="reference internal" href="System.html">System</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Process.html">Process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Mechanism.html">Mechanism</a></li>
-<li class="toctree-l1"><a class="reference internal" href="State.html">States</a></li>
+<li class="toctree-l1"><a class="reference internal" href="State.html">State</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Projection.html">Projection</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Run.html">Run</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Component.html">Component</a></li>
@@ -436,6 +436,7 @@ <h1>PsyNeuLink Documentation<a class="headerlink" href="#psyneulink-documentatio
 </ul>
 <p>with substantial assistance from:</p>
 <ul class="simple">
+<li><strong>Abhishek Bhattacharjee</strong>, Department of Computer Science, Rutgers University</li>
 <li><strong>Mihai Capota</strong>, Intel Labs, Intel Corporation</li>
 <li><strong>Bryn Keller</strong>, Intel Labs, Intel Corporation</li>
 <li><strong>Garrett McGrath</strong>, Princeton Neuroscience Institute, Princeton University</li>
@@ -443,6 +444,7 @@ <h1>PsyNeuLink Documentation<a class="headerlink" href="#psyneulink-documentatio
 <li><strong>Amitai Shenhav</strong>, Cognitive, Linguistic, &amp; Psychological Sciences, Brown University</li>
 <li><strong>Michael Shvartsman</strong>, Princeton Neuroscience Institute, Princeton University</li>
 <li><strong>Ben Singer</strong>, Princeton Neuroscience Institute, Princeton University</li>
+<li><strong>Jan Vesely</strong>, Department of Computer Science, Rutgers University</li>
 </ul>
 <div class="toctree-wrapper compound">
 </div>
diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv
index f5c70f98e7b..2944bb48b7c 100644
Binary files a/docs/build/html/objects.inv and b/docs/build/html/objects.inv differ
diff --git a/docs/build/html/py-modindex.html b/docs/build/html/py-modindex.html
index 608c6c7ece8..422175930a2 100644
--- a/docs/build/html/py-modindex.html
+++ b/docs/build/html/py-modindex.html
@@ -93,7 +93,7 @@
 <li class="toctree-l1"><a class="reference internal" href="System.html">System</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Process.html">Process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Mechanism.html">Mechanism</a></li>
-<li class="toctree-l1"><a class="reference internal" href="State.html">States</a></li>
+<li class="toctree-l1"><a class="reference internal" href="State.html">State</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Projection.html">Projection</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Run.html">Run</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Component.html">Component</a></li>
@@ -180,11 +180,6 @@ <h1>Python Module Index</h1>
        <td>&#160;&#160;&#160;
        <a href="ControlMechanism.html#module-AdaptiveMechanisms.ControlMechanisms.ControlMechanism"><code class="xref">AdaptiveMechanisms.ControlMechanisms.ControlMechanism</code></a></td><td>
        <em></em></td></tr>
-     <tr class="cg-1">
-       <td></td>
-       <td>&#160;&#160;&#160;
-       <a href="ControlSignal.html#module-AdaptiveMechanisms.ControlMechanisms.ControlSignal"><code class="xref">AdaptiveMechanisms.ControlMechanisms.ControlSignal</code></a></td><td>
-       <em></em></td></tr>
      <tr class="cg-1">
        <td></td>
        <td>&#160;&#160;&#160;
@@ -208,11 +203,6 @@ <h1>Python Module Index</h1>
        <td>
        <a href="Component.html#module-Component"><code class="xref">Component</code></a></td><td>
        <em></em></td></tr>
-     <tr>
-       <td></td>
-       <td>
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      <tr class="cap" id="cap-l"><td></td><td>
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-       <td></td>
-       <td>
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-       <em></em></td></tr>
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        <td></td>
        <td>
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      <tr>
        <td></td>
        <td>
-       <a href="MappingProjection.html#module-MappingProjection"><code class="xref">MappingProjection</code></a></td><td>
+       <a href="Mechanism.html#module-Mechanism"><code class="xref">Mechanism</code></a></td><td>
        <em></em></td></tr>
      <tr>
+       <td><img src="_static/minus.png" class="toggler"
+              id="toggle-2" style="display: none" alt="-" /></td>
+       <td>
+       <code class="xref">ModulatoryProjections</code></td><td>
+       <em></em></td></tr>
+     <tr class="cg-2">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="ControlProjection.html#module-ModulatoryProjections.ControlProjection"><code class="xref">ModulatoryProjections.ControlProjection</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-2">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="GatingProjection.html#module-ModulatoryProjections.GatingProjection"><code class="xref">ModulatoryProjections.GatingProjection</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-2">
        <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="LearningProjection.html#module-ModulatoryProjections.LearningProjection"><code class="xref">ModulatoryProjections.LearningProjection</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-2">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="ModulatoryProjection.html#module-ModulatoryProjections.ModulatoryProjection"><code class="xref">ModulatoryProjections.ModulatoryProjection</code></a></td><td>
+       <em></em></td></tr>
+     <tr>
+       <td><img src="_static/minus.png" class="toggler"
+              id="toggle-3" style="display: none" alt="-" /></td>
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+       <code class="xref">ModulatorySignals</code></td><td>
+       <em></em></td></tr>
+     <tr class="cg-3">
+       <td></td>
+       <td>&#160;&#160;&#160;
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+       <em></em></td></tr>
+     <tr class="cg-3">
+       <td></td>
+       <td>&#160;&#160;&#160;
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+       <em></em></td></tr>
+     <tr class="cg-3">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="LearningSignal.html#module-ModulatorySignals.LearningSignal"><code class="xref">ModulatorySignals.LearningSignal</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-3">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="ModulatorySignal.html#module-ModulatorySignals.ModulatorySignal"><code class="xref">ModulatorySignals.ModulatorySignal</code></a></td><td>
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-     <tr>
-       <td></td>
-       <td>
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-       <em></em></td></tr>
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        <td>
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        <em></em></td></tr>
+     <tr>
+       <td><img src="_static/minus.png" class="toggler"
+              id="toggle-4" style="display: none" alt="-" /></td>
+       <td>
+       <code class="xref">PathwayProjections</code></td><td>
+       <em></em></td></tr>
+     <tr class="cg-4">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="PathwayProjection.html#module-PathwayProjections.PathwayProjection"><code class="xref">PathwayProjections.PathwayProjection</code></a></td><td>
+       <em></em></td></tr>
      <tr>
        <td></td>
        <td>
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        <a href="Process.html#module-Process"><code class="xref">Process</code></a></td><td>
        <em></em></td></tr>
      <tr>
-       <td></td>
+       <td><img src="_static/minus.png" class="toggler"
+              id="toggle-5" style="display: none" alt="-" /></td>
        <td>
-       <a href="ProcessingMechanism.html#module-ProcessingMechanism"><code class="xref">ProcessingMechanism</code></a></td><td>
+       <code class="xref">ProcessingMechanisms</code></td><td>
+       <em></em></td></tr>
+     <tr class="cg-5">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="LCA.html#module-ProcessingMechanisms.LCA"><code class="xref">ProcessingMechanisms.LCA</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-5">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="RecurrentTransferMechanism.html#module-ProcessingMechanisms.RecurrentTransferMechanism"><code class="xref">ProcessingMechanisms.RecurrentTransferMechanism</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-5">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="TransferMechanism.html#module-ProcessingMechanisms.TransferMechanism"><code class="xref">ProcessingMechanisms.TransferMechanism</code></a></td><td>
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-     <tr>
-       <td></td>
-       <td>
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Documentation"],titleterms:{"class":[2,3,4,5,8,9,10,11,12,13,15,16,17,18,19,21,22,23,25,26,29,32,33,34,35],"default":7,"enum":38,"function":[5,8,9,16,21,33],adaptiv:0,adaptivemechan:0,addition:[16,25],attribut:[1,16,25],automat:31,auxiliari:[],calcul:8,callabl:[],checking:38,comparatormechan:[],comparison:38,compon:[1,40],configur:16,construct:[],contents:38,context:31,contributor:40,control:[2,35],controlmechan:2,controlproject:3,controlsign:[4,8],controlsignalsearchspac:[],convent:40,creat:[0,1,2,3,4,5,8,9,10,11,12,13,15,16,17,18,19,21,22,23,25,26,27,29,31,32,34,35,37],creation:31,custom:25,ddm:5,default_input_valu:[],defaultcontrolmechan:6,defer:[3,17],document:40,entri:19,entry:[],evc:8,evcmechan:8,exampl:[],execut:[0,1,2,3,4,5,8,9,10,11,12,13,15,16,17,18,21,22,23,25,26,27,29,31,32,33,34,35,37],format:33,gatingproject:10,gatingsign:11,graph:35,hierarchi:40,indice:40,initial:[3,17,33,35],input:[29,33,35],inputstat:[12,16,21],installat:40,integratormechan:13,keyword:14,kvo:38,layer:16,lca:15,learn:[16,29,35],learningmechan:16,learningproject:17,learningsign:18,log:19,mappingproject:20,mechan:[0,2,5,8,16,21,29,30,33,35],method:1,modul:[11,23,34],modulatoryproject:22,modulatorysign:23,monitor:2,monitoringmechan:[],multilay:16,objectivemechan:[8,24],order:35,other:38,output:29,outputst:[16,21,25],overview:[0,1,2,3,4,5,8,9,10,11,12,13,15,16,17,18,19,21,22,23,25,26,27,29,31,32,33,34,35,37,40],paramet:[2,5,21,26],parameter:[],parameterst:[21,26],pathwai:29,pathwayproject:27,phase:35,predict:8,prefer:28,primari:25,process:[21,29,30],processingmechan:[],project:[29,31],psyneulink:40,purpos:40,receiv:31,recurrenttransfermechan:32,refer:[2,3,4,5,8,9,10,11,12,13,15,16,17,18,19,21,22,23,25,26,29,32,33,34,35],role:21,run:33,runtim:21,scope:33,sender:31,sequenc:33,singl:16,specif:[25,31],specifi:[2,4,11,18,26],standard:25,state:[21,34],structur:[0,1,3,4,5,8,9,10,11,12,13,15,16,17,18,19,21,22,23,25,26,27,29,31,32,34,35,37],system:[21,35],tabl:40,target:33,things:[],time:33,timescal:36,transfermechan:37,type:38,using:[],utiliti:38,valu:[2,33],value:38,weightederrormechan:[],what:40}})
\ No newline at end of file
diff --git a/docs/source/Component.rst b/docs/source/Component.rst
index 87f51846029..71bed3c3bdd 100644
--- a/docs/source/Component.rst
+++ b/docs/source/Component.rst
@@ -1,5 +1,5 @@
 Component
-========
+=========
 
 .. toctree::
    :maxdepth: 3
diff --git a/docs/source/ControlProjection.rst b/docs/source/ControlProjection.rst
index ea003d39a88..d5463fa3cf7 100644
--- a/docs/source/ControlProjection.rst
+++ b/docs/source/ControlProjection.rst
@@ -1,9 +1,6 @@
 ControlProjection
 =================
 
-.. toctree::
-   :maxdepth: 3
-
-.. automodule:: ControlProjection
+.. automodule:: ModulatoryProjections.ControlProjection
    :members:
    :exclude-members: random, execute
\ No newline at end of file
diff --git a/docs/source/ControlSignal.rst b/docs/source/ControlSignal.rst
index 79e30e2e202..1ee938d2e60 100644
--- a/docs/source/ControlSignal.rst
+++ b/docs/source/ControlSignal.rst
@@ -1,9 +1,6 @@
 ControlSignal
 =============
 
-.. toctree::
-   :maxdepth: 3
-
-.. automodule:: AdaptiveMechanisms.ControlMechanisms.ControlSignal
+.. automodule:: ModulatorySignals.ControlSignal
    :members:
    :exclude-members: random, execute
\ No newline at end of file
diff --git a/docs/source/Function.rst b/docs/source/Function.rst
index d6c36033f26..2da15c59a2a 100644
--- a/docs/source/Function.rst
+++ b/docs/source/Function.rst
@@ -5,26 +5,29 @@ Functions
    :maxdepth: 3
 
 .. automodule:: Function
-	:members: Function_Base,
-	          ArgumentTherapy,
-	          UserDefinedFunction,
-              Reduce,
-	          LinearCombination,
-	          Linear,
-	          Exponential,
-	          Logistic,
-	          SoftMax,
-              LinearMatrix,
-	          Integrator,
-              BogaczEtAl,
-	          NavarroAndFuss,
-              LearningFunction,
-              Reinforcement,
-	          BackPropagation,
-              NormalDist,
-              ExponentialDist,
-              UniformDist,
-              GammaDist,
-              WaldDist,
+   :members: Function_Base,
+             ModulationParam,
+             ArgumentTherapy,
+             UserDefinedFunction,
+             Reduce,
+             LinearCombination,
+             Linear,
+             Exponential,
+             Logistic,
+             SoftMax,
+             LinearMatrix,
+             Integrator,
+             BogaczEtAl,
+             NavarroAndFuss,
+             NormalDist,
+             ExponentialDist,
+             UniformDist,
+             GammaDist,
+             WaldDist,
+             Stability,
+             Distance,
+             LearningFunction,
+             Reinforcement,
+             BackPropagation,
 ..
     :exclude-members: function
diff --git a/docs/source/GatingProjection.rst b/docs/source/GatingProjection.rst
new file mode 100644
index 00000000000..d992190613f
--- /dev/null
+++ b/docs/source/GatingProjection.rst
@@ -0,0 +1,6 @@
+GatingProjection
+================
+
+.. automodule:: ModulatoryProjections.GatingProjection
+   :members:
+   :exclude-members: random, execute
\ No newline at end of file
diff --git a/docs/source/GatingSignal.rst b/docs/source/GatingSignal.rst
new file mode 100644
index 00000000000..33b4262722c
--- /dev/null
+++ b/docs/source/GatingSignal.rst
@@ -0,0 +1,6 @@
+GatingSignal
+==============
+
+.. automodule:: ModulatorySignals.GatingSignal
+   :members:
+   :exclude-members: Linear, random
\ No newline at end of file
diff --git a/docs/source/IntegratorMechanism.rst b/docs/source/IntegratorMechanism.rst
index 8b0631db1f9..2d1384ebb10 100644
--- a/docs/source/IntegratorMechanism.rst
+++ b/docs/source/IntegratorMechanism.rst
@@ -6,7 +6,6 @@ IntegratorMechanism
 
    IntegratorMechanism
 
-
 .. automodule:: IntegratorMechanism
    :members:
    :exclude-members: random
\ No newline at end of file
diff --git a/docs/source/LCA.rst b/docs/source/LCA.rst
new file mode 100644
index 00000000000..288cdd9461b
--- /dev/null
+++ b/docs/source/LCA.rst
@@ -0,0 +1,11 @@
+LCA
+===
+
+.. toctree::
+   :maxdepth: 3
+
+   LCA
+
+.. automodule:: ProcessingMechanisms.LCA
+   :members:
+   :exclude-members: random
\ No newline at end of file
diff --git a/docs/source/LearningMechanism.rst b/docs/source/LearningMechanism.rst
index a81d2756672..b5af9c0ee62 100644
--- a/docs/source/LearningMechanism.rst
+++ b/docs/source/LearningMechanism.rst
@@ -4,7 +4,7 @@ Learning Mechanism
 .. toctree::
    :maxdepth: 2
 
-   LearningProjection
+   LearningMechanism
 
 .. automodule:: AdaptiveMechanisms.LearningMechanisms.LearningMechanism
    :members:
diff --git a/docs/source/LearningProjection.rst b/docs/source/LearningProjection.rst
index 9ab5dcf4bd0..c00b49a2c5c 100644
--- a/docs/source/LearningProjection.rst
+++ b/docs/source/LearningProjection.rst
@@ -1,6 +1,6 @@
 LearningProjection
 ==================
 
-.. automodule:: LearningProjection
+.. automodule:: ModulatoryProjections.LearningProjection
    :members:
    :exclude-members: random, execute
\ No newline at end of file
diff --git a/docs/source/LearningSignal.rst b/docs/source/LearningSignal.rst
new file mode 100644
index 00000000000..9cacde5b7f9
--- /dev/null
+++ b/docs/source/LearningSignal.rst
@@ -0,0 +1,6 @@
+LearningSignal
+==============
+
+.. automodule:: ModulatorySignals.LearningSignal
+   :members:
+   :exclude-members: Linear, random
\ No newline at end of file
diff --git a/docs/source/Mechanism.rst b/docs/source/Mechanism.rst
index d6221b13943..015ea1d20c0 100644
--- a/docs/source/Mechanism.rst
+++ b/docs/source/Mechanism.rst
@@ -2,10 +2,10 @@ Mechanism
 =========
 
 .. toctree::
-  :maxdepth: 1
+   :maxdepth: 1
 
-  ProcessingMechanism
-  AdaptiveMechanism
+   ProcessingMechanism
+   AdaptiveMechanism
 
 .. toctree::
    :maxdepth: 3
diff --git a/docs/source/ModulatoryProjection.rst b/docs/source/ModulatoryProjection.rst
new file mode 100644
index 00000000000..ee7afa99c95
--- /dev/null
+++ b/docs/source/ModulatoryProjection.rst
@@ -0,0 +1,13 @@
+ModulatoryProjection
+====================
+
+.. toctree::
+   :maxdepth: 2
+
+   LearningProjection
+   ControlProjection
+   GatingProjection
+
+.. automodule:: ModulatoryProjections.ModulatoryProjection
+   :members:
+   :exclude-members: ModulatoryProjection_Base, Linear, random
\ No newline at end of file
diff --git a/docs/source/ModulatorySignal.rst b/docs/source/ModulatorySignal.rst
new file mode 100644
index 00000000000..c7a3960682b
--- /dev/null
+++ b/docs/source/ModulatorySignal.rst
@@ -0,0 +1,17 @@
+ModulatorySignal
+================
+
+.. toctree::
+   :maxdepth: 1
+
+   LearningSignal
+   ControlSignal
+   GatingSignal
+
+.. toctree::
+   :maxdepth: 3
+
+.. automodule:: ModulatorySignals.ModulatorySignal
+   :members:
+   :exclude-members: random, update
+
diff --git a/docs/source/PathwayProjection.rst b/docs/source/PathwayProjection.rst
new file mode 100644
index 00000000000..54a467d126f
--- /dev/null
+++ b/docs/source/PathwayProjection.rst
@@ -0,0 +1,11 @@
+PathwayProjection
+=================
+
+.. toctree::
+   :maxdepth: 2
+
+   MappingProjection
+
+.. automodule:: PathwayProjections.PathwayProjection
+   :members:
+   :exclude-members: PathwayProjection_Base, Linear, random
\ No newline at end of file
diff --git a/docs/source/ProcessingMechanism.rst b/docs/source/ProcessingMechanism.rst
index 0923557b4b4..26a9bf29510 100644
--- a/docs/source/ProcessingMechanism.rst
+++ b/docs/source/ProcessingMechanism.rst
@@ -4,11 +4,13 @@ Processing Mechanism
 .. toctree:: 
    :maxdepth: 1
 
-   TransferMechanism
    IntegratorMechanism
    DDM
+   TransferMechanism
+   RecurrentTransferMechanism
+   LCA
    ObjectiveMechanism
 
-.. automodule:: ProcessingMechanism
+.. automodule:: ProcessingMechansms.ProcessingMechanism
    :members:
    :exclude-members: ProcessingMechanism_Base, MechanismTuple, MechanismList, random
diff --git a/docs/source/Projection.rst b/docs/source/Projection.rst
index 459769c300f..35ccf7d8f8c 100644
--- a/docs/source/Projection.rst
+++ b/docs/source/Projection.rst
@@ -4,12 +4,12 @@ Projection
 .. toctree::
    :maxdepth: 1
 
-   MappingProjection
-   LearningProjection
-   ControlProjection
+   PathwayProjection
+   ModulatoryProjection
 
-.. automodule:: Projection
+.. toctree::
+   :maxdepth: 3
 
-..
+.. automodule:: Projection
    :members:
    :exclude-members: Projection_Base, _is_projection_spec, random
diff --git a/docs/source/RecurrentTransferMechanism.rst b/docs/source/RecurrentTransferMechanism.rst
new file mode 100644
index 00000000000..02931474f6c
--- /dev/null
+++ b/docs/source/RecurrentTransferMechanism.rst
@@ -0,0 +1,11 @@
+RecurrentTransferMechanism
+==========================
+
+.. toctree::
+   :maxdepth: 3
+
+   RecurrentTransferMechanism
+
+.. automodule:: ProcessingMechanisms.RecurrentTransferMechanism
+   :members:
+   :exclude-members: random
\ No newline at end of file
diff --git a/docs/source/State.rst b/docs/source/State.rst
index 7551fe2ec60..1b1c1b5fc2f 100644
--- a/docs/source/State.rst
+++ b/docs/source/State.rst
@@ -1,5 +1,5 @@
-States
-======
+State
+=====
 
 .. toctree::
    :maxdepth: 1
@@ -7,6 +7,7 @@ States
    InputState
    ParameterState
    OutputState
+   ModulatorySignal
 
 .. toctree::
    :maxdepth: 3
diff --git a/docs/source/TransferMechanism.rst b/docs/source/TransferMechanism.rst
index f6601aa1b30..05be60301d3 100644
--- a/docs/source/TransferMechanism.rst
+++ b/docs/source/TransferMechanism.rst
@@ -6,6 +6,6 @@ TransferMechanism
 
    TransferMechanism
 
-.. automodule:: TransferMechanism
+.. automodule:: ProcessingMechanisms.TransferMechanism
    :members:
    :exclude-members: random
diff --git a/docs/source/Utilities.rst b/docs/source/Utilities.rst
index 1624fdde8ba..c95f97962c5 100644
--- a/docs/source/Utilities.rst
+++ b/docs/source/Utilities.rst
@@ -2,12 +2,12 @@ Utilities
 =========
 
 .. toctree::
-	:maxdepth: 1
+   :maxdepth: 1
 
-	Functions
-	Run
-	Log
-	Preferences
+   Functions
+   Run
+   Log
+   Preferences
 
 .. automodule:: Utilities
    :members:
diff --git a/docs/source/index.rst b/docs/source/index.rst
index e362f772451..719f409a43e 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -282,6 +282,7 @@ Contributors
 
 with substantial assistance from:
 
+* **Abhishek Bhattacharjee**, Department of Computer Science, Rutgers University
 * **Mihai Capota**, Intel Labs, Intel Corporation
 * **Bryn Keller**, Intel Labs, Intel Corporation
 * **Garrett McGrath**, Princeton Neuroscience Institute, Princeton University
@@ -289,6 +290,8 @@ with substantial assistance from:
 * **Amitai Shenhav**, Cognitive, Linguistic, & Psychological Sciences, Brown University
 * **Michael Shvartsman**, Princeton Neuroscience Institute, Princeton University
 * **Ben Singer**, Princeton Neuroscience Institute, Princeton University
+* **Jan Vesely**, Department of Computer Science, Rutgers University
+
 
 ..
    .. toctree::
diff --git a/jenkins/reference_output.txt b/jenkins/reference_output.txt
index 7f08afc3ed3..fb76cf3f963 100644
--- a/jenkins/reference_output.txt
+++ b/jenkins/reference_output.txt
@@ -1,6 +1,7 @@
 
 RUNNING EVC System Laming Validation Test Script.py
 
+
 ---------------------------------------------------------
 
 EVC Test System
@@ -25,6 +26,7 @@ EVC Test System
 		Phase 1:
 			Reward
 			Reward Prediction Mechanism
+			EVCMechanism-1 Monitoring Mechanism
 			EVCMechanism-1
 
 	Origin mechanisms: 
@@ -33,14 +35,13 @@ EVC Test System
 
 	Terminal mechanisms: 
 		Decision (phase: 0)
-			DDM_decision_variable
-			DDM_response_time
-			DDM_probability_upperBound
-			DDM_probability_lowerBound
+			DECISION_VARIABLE
+			RESPONSE_TIME
+			PROBABILITY_UPPER_THRESHOLD
 		Reward (phase: 1)
-			transfer_result
-			transfer_mean 
-			transfer_variance
+			RESULT
+			MEAN
+			VARIANCE
 
 ---------------------------------------------------------
 
@@ -49,9 +50,9 @@ EVC Test System
 EVCMechanism-1
 
 	Monitoring the following mechanism outputStates:
-		Decision: DDM_response_time (exp: [-1.]; wt: [ 1.])
-		Decision: DDM_probability_upperBound (exp: [ 1.]; wt: [ 1.])
-		Reward: transfer_result (exp: [ 1.]; wt: [ 1.])
+		Decision: RESPONSE_TIME (exp: [-1.]; wt: [ 1.])
+		Decision: PROBABILITY_UPPER_THRESHOLD (exp: [ 1.]; wt: [ 1.])
+		Reward: RESULT (exp: [ 1.]; wt: [ 1.])
 
 	Controlling the following mechanism parameters:
 		Decision: drift_rate
@@ -103,15 +104,14 @@ RESULTS (time step 2):
 			Drift rate control signal (from EVC): 1.0
 			Threshold control signal (from EVC): 1.0000000000000002
 		Output:
-			DDM_decision_variable: 1.0
-			DDM_probability_lowerBound: 0.119
-			DDM_probability_upperBound: 0.881
-			DDM_response_time: 3.5
+			DECISION_VARIABLE: 1.0
+			RESPONSE_TIME: 3.5
+			PROBABILITY_UPPER_THRESHOLD: 0.881
 	Reward:
 		Output:
-			transfer_mean : 10.0
-			transfer_result: 10.0
-			transfer_variance: 0.0
+			RESULT: 10.0
+			MEAN: 10.0
+			VARIANCE: 0.0
 
 ############################ TRIAL 1 ############################
 
@@ -157,15 +157,14 @@ RESULTS (time step 4):
 			Drift rate control signal (from EVC): 1.0
 			Threshold control signal (from EVC): 1.0000000000000002
 		Output:
-			DDM_decision_variable: 1.0
-			DDM_probability_lowerBound: 0.184
-			DDM_probability_upperBound: 0.816
-			DDM_response_time: 3.84
+			DECISION_VARIABLE: 1.0
+			RESPONSE_TIME: 3.84
+			PROBABILITY_UPPER_THRESHOLD: 0.816
 	Reward:
 		Output:
-			transfer_mean : 15.0
-			transfer_result: 15.0
-			transfer_variance: 0.0
+			RESULT: 15.0
+			MEAN: 15.0
+			VARIANCE: 0.0
 
 @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
 
@@ -1295,10 +1294,10 @@ RUNNING Stroop Model Learning Test Script.py
 - MSE: 0.225
 
 Color Naming
-	Input: [[ 1.  1.]]
+	Input: [[1.0, 1.0]]
 	Target: [[[1 1]]]
 Wording Reading:
-	Input: [[-2. -2.]]
+	Input: [[-2.0, -2.0]]
 	Target: [[[1 1]]]
 
 Response: 
@@ -1335,10 +1334,10 @@ Word-Hidden:
 - MSE: 0.222
 
 Color Naming
-	Input: [[ 1.  1.]]
+	Input: [[1.0, 1.0]]
 	Target: [[[1 1]]]
 Wording Reading:
-	Input: [[-2. -2.]]
+	Input: [[-2.0, -2.0]]
 	Target: [[[1 1]]]
 
 Response: 
@@ -1543,13 +1542,9 @@ Branch System
 
 	Terminal mechanisms: 
 		c (phase: 0)
-			transfer_result
-			transfer_mean 
-			transfer_variance
+			RESULT
 		d (phase: 0)
-			transfer_result
-			transfer_mean 
-			transfer_variance
+			RESULT
 
 ---------------------------------------------------------
 A:  ORIGIN
@@ -1590,9 +1585,7 @@ Bypass System
 
 	Terminal mechanisms: 
 		d-1 (phase: 0)
-			transfer_result
-			transfer_mean 
-			transfer_variance
+			RESULT
 
 ---------------------------------------------------------
 A:  ORIGIN
@@ -1637,9 +1630,7 @@ Chain System
 
 	Terminal mechanisms: 
 		e (phase: 0)
-			transfer_result
-			transfer_mean 
-			transfer_variance
+			RESULT
 
 ---------------------------------------------------------
 A:  ORIGIN
@@ -1681,9 +1672,7 @@ Convergent System
 
 	Terminal mechanisms: 
 		e-1 (phase: 0)
-			transfer_result
-			transfer_mean 
-			transfer_variance
+			RESULT
 
 ---------------------------------------------------------
 A:  ORIGIN
@@ -1790,9 +1779,7 @@ Cyclic System with Extended Loop
 
 	Terminal mechanisms: 
 		d-4 (phase: 0)
-			transfer_result
-			transfer_mean 
-			transfer_variance
+			RESULT
 
 ---------------------------------------------------------
 A:  ORIGIN
diff --git a/setup.cfg b/setup.cfg
index 388185661bf..f291620ac96 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,12 +1,18 @@
-[pytest]
+[tool:pytest]
 addopts =
-#   --junitxml=junit-results.xml
-#    --cov-report=xml
-    --cov-report=html
-    --cov-report=term
+    --pyargs PsyNeuLink
+    -s
+    -vv
+   # --junitxml=junit-results.xml
+   # --cov-report=xml
+   # --cov-report=html
+   # --cov-report=term
+   # --pnl-seed=0
+
+pytest_plugins = ['pytest_profiling', 'helpers_namespace']
 
 [coverage:run]
 branch = True
 
 [coverage:report]
-fail_under = 90
\ No newline at end of file
+fail_under = 90
diff --git a/setup.py b/setup.py
index b23e9151644..1527685463f 100644
--- a/setup.py
+++ b/setup.py
@@ -64,4 +64,9 @@
     # requirements files see:
     # https://packaging.python.org/en/latest/requirements.html
     install_requires=['toposort==1.4', 'numpy', 'typecheck-decorator==1.2'], #mpi4py only if MPI_IMPLEMENTATION becomes True
-)
\ No newline at end of file
+    tests_require=[
+        'pytest',
+        'pytest-profiling',
+        'pytest-helpers-namespace',
+    ]
+)
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 00000000000..259fecb0dec
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,41 @@
+import pytest
+import random
+import time
+import numpy as np
+
+# def pytest_addoption(parser):
+#     parser.addoption(
+#         '--pnl-seed',
+#         action='store',
+#         default=int(time.time() * 256),
+#         help='the seed to use for each test'
+#     )
+
+def pytest_runtest_call(item):
+    # seed = int(item.config.getoption('--pnl-seed'))
+    seed = 0
+    random.seed(seed)
+    np.random.seed(seed)
+
+@pytest.helpers.register
+def expand_np_ndarray(arr):
+    try:
+        iter(arr)
+    except TypeError:
+        return arr.tolist()
+
+    results_list = []
+    for elem in arr:
+        try:
+            iter(elem)
+        except TypeError:
+            elem = [elem]
+
+        for nested_elem in elem:
+            nested_elem = nested_elem.tolist()
+            try:
+                iter(nested_elem)
+            except TypeError:
+                nested_elem = [nested_elem]
+            results_list.extend(nested_elem)
+    return results_list
diff --git a/tests/control/test_EVC.py b/tests/control/test_EVC.py
new file mode 100644
index 00000000000..a13932c404b
--- /dev/null
+++ b/tests/control/test_EVC.py
@@ -0,0 +1,634 @@
+import pytest
+import numpy as np
+
+from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.EVCMechanism import EVCMechanism
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import DDM, DECISION_VARIABLE, PROBABILITY_UPPER_THRESHOLD, RESPONSE_TIME
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
+from PsyNeuLink.Components.Process import process
+from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
+from PsyNeuLink.Components.System import system
+from PsyNeuLink.Components.Functions.Function import Linear, BogaczEtAl, Exponential, DRIFT_RATE, THRESHOLD
+from PsyNeuLink.Globals.Keywords import ALLOCATION_SAMPLES, IDENTITY_MATRIX, MEAN, RESULT, VARIANCE
+from PsyNeuLink.Globals.Preferences.ComponentPreferenceSet import ComponentPreferenceSet, kpVerbosePref, kpReportOutputPref
+from PsyNeuLink.Globals.Preferences.PreferenceSet import PreferenceEntry, PreferenceLevel
+
+
+def test_EVC():
+    # Mechanisms
+    Input = TransferMechanism(
+        name='Input'
+    )
+    Reward = TransferMechanism(
+        output_states=[RESULT, MEAN, VARIANCE],
+        name='Reward'
+    )
+    Decision = DDM(
+        function=BogaczEtAl(
+            drift_rate=(
+                1.0,
+                ControlProjection(
+                    function=Linear,
+                    control_signal_params={
+                        ALLOCATION_SAMPLES: np.arange(0.1, 1.01, 0.3)
+                    },
+                ),
+            ),
+            threshold=(
+                1.0,
+                ControlProjection(
+                    function=Linear,
+                    control_signal_params={
+                        ALLOCATION_SAMPLES: np.arange(0.1, 1.01, 0.3)
+                    },
+                ),
+            ),
+            noise=(0.5),
+            starting_point=(0),
+            t0=0.45
+        ),
+        output_states=[
+            DECISION_VARIABLE,
+            RESPONSE_TIME,
+            PROBABILITY_UPPER_THRESHOLD
+        ],
+        name='Decision',
+    )
+
+    # Processes:
+    TaskExecutionProcess = process(
+        default_input_value=[0],
+        pathway=[(Input), IDENTITY_MATRIX, (Decision)],
+        name='TaskExecutionProcess',
+    )
+
+    RewardProcess = process(
+        default_input_value=[0],
+        pathway=[(Reward)],
+        name='RewardProcess',
+    )
+
+    # System:
+    mySystem = system(
+        processes=[TaskExecutionProcess, RewardProcess],
+        controller=EVCMechanism,
+        enable_controller=True,
+        monitor_for_control=[
+            Reward,
+            Decision.PROBABILITY_UPPER_THRESHOLD,
+            (Decision.RESPONSE_TIME, -1, 1)
+        ],
+        name='EVC Test System',
+    )
+
+    # Stimuli
+    stim_list_dict = {
+        Input: [0.5, 0.123],
+        Reward: [20, 20]
+    }
+
+    mySystem.run(
+        inputs=stim_list_dict,
+    )
+
+    RewardPrediction = mySystem.executionList[3]
+    InputPrediction = mySystem.executionList[4]
+
+    # rearranging mySystem.results into a format that we can compare with pytest
+    results_array = []
+    for elem in mySystem.results:
+        elem_array = []
+        for inner_elem in elem:
+            elem_array.append(float(inner_elem))
+        results_array.append(elem_array)
+
+    # mySystem.results expected output properly formatted
+    expected_results_array = [
+        [10., 10.0, 0.0, -0.1, 0.48999867, 0.50499983],
+        [10., 10.0, 0.0, -0.4, 1.08965888, 0.51998934],
+        [10., 10.0, 0.0, 0.7, 2.40680493, 0.53494295],
+        [10., 10.0, 0.0, -1., 4.43671978, 0.549834],
+        [10., 10.0, 0.0, 0.1, 0.48997868, 0.51998934],
+        [10., 10.0, 0.0, -0.4, 1.08459402, 0.57932425],
+        [10., 10.0, 0.0, 0.7, 2.36033556, 0.63645254],
+        [10., 10.0, 0.0, 1., 4.24948962, 0.68997448],
+        [10., 10.0, 0.0, 0.1, 0.48993479, 0.53494295],
+        [10., 10.0, 0.0, 0.4, 1.07378304, 0.63645254],
+        [10., 10.0, 0.0, 0.7, 2.26686573, 0.72710822],
+        [10., 10.0, 0.0, 1., 3.90353015, 0.80218389],
+        [10., 10.0, 0.0, 0.1, 0.4898672, 0.549834],
+        [10., 10.0, 0.0, -0.4, 1.05791834, 0.68997448],
+        [10., 10.0, 0.0, 0.7, 2.14222978, 0.80218389],
+        [10., 10.0, 0.0, 1., 3.49637662, 0.88079708],
+        [10., 10.0, 0.0, 1., 3.49637662, 0.88079708],
+        [15., 15.0, 0.0, 0.1, 0.48999926, 0.50372993],
+        [15., 15.0, 0.0, -0.4, 1.08981011, 0.51491557],
+        [15., 15.0, 0.0, 0.7, 2.40822035, 0.52608629],
+        [15., 15.0, 0.0, 1., 4.44259627, 0.53723096],
+        [15., 15.0, 0.0, 0.1, 0.48998813, 0.51491557],
+        [15., 15.0, 0.0, 0.4, 1.0869779, 0.55939819],
+        [15., 15.0, 0.0, -0.7, 2.38198336, 0.60294711],
+        [15., 15.0, 0.0, 1., 4.33535807, 0.64492386],
+        [15., 15.0, 0.0, 0.1, 0.48996368, 0.52608629],
+        [15., 15.0, 0.0, 0.4, 1.08085171, 0.60294711],
+        [15., 15.0, 0.0, 0.7, 2.32712843, 0.67504223],
+        [15., 15.0, 0.0, 1., 4.1221271, 0.7396981],
+        [15., 15.0, 0.0, 0.1, 0.48992596, 0.53723096],
+        [15., 15.0, 0.0, -0.4, 1.07165729, 0.64492386],
+        [15., 15.0, 0.0, 0.7, 2.24934228, 0.7396981],
+        [15., 15.0, 0.0, 1., 3.84279648, 0.81637827],
+        [15., 15.0, 0.0, 1., 3.84279648, 0.81637827]
+    ]
+
+    expected_output = [
+        # Decision Output | Second Trial
+        (Decision.output_states[0].value, np.array(1.0)),
+
+        # Input Prediction Output | Second Trial
+        (InputPrediction.output_states[0].value, np.array(0.1865)),
+
+        # RewardPrediction Output | Second Trial
+        (RewardPrediction.output_states[0].value, np.array(15.0)),
+
+        # --- Decision Mechanism ---
+        #    Output State Values
+        #       decision variable
+        (Decision.output_states[DECISION_VARIABLE].value, np.array([1.0])),
+        #       response time
+        (Decision.output_states[RESPONSE_TIME].value, np.array([3.84279648])),
+        #       upper bound
+        (Decision.output_states[PROBABILITY_UPPER_THRESHOLD].value, np.array([0.81637827])),
+        #       lower bound
+        # (round(float(Decision.output_states['DDM_probability_lowerBound'].value),3), 0.184),
+
+        # --- Reward Mechanism ---
+        #    Output State Values
+        #       transfer mean
+        (Reward.output_states[RESULT].value, np.array([15.])),
+        #       transfer_result
+        (Reward.output_states[MEAN].value, np.array(15.0)),
+        #       transfer variance
+        (Reward.output_states[VARIANCE].value, np.array(0.0)),
+
+        # System Results Array
+        #   (all intermediate output values of system)
+        (results_array, expected_results_array)
+    ]
+
+    for i in range(len(expected_output)):
+        val, expected = expected_output[i]
+        np.testing.assert_allclose(val, expected, atol=1e-08, err_msg='Failed on expected_output[{0}]'.format(i))
+
+
+def test_EVC_gratton():
+
+    def test_search_function(controller=None, **kwargs):
+        result = np.array(controller.allocationPolicy).reshape(len(controller.allocationPolicy), -1)
+        return result
+
+    def test_outcome_function(**kwargs):
+        result = np.array([0])
+        return result
+
+    # Preferences:
+    mechanism_prefs = ComponentPreferenceSet(
+        prefs={
+            kpVerbosePref: PreferenceEntry(False, PreferenceLevel.INSTANCE),
+            kpReportOutputPref: PreferenceEntry(True, PreferenceLevel.INSTANCE)
+        }
+    )
+
+    process_prefs = ComponentPreferenceSet(
+        reportOutput_pref=PreferenceEntry(False, PreferenceLevel.INSTANCE),
+        verbose_pref=PreferenceEntry(True, PreferenceLevel.INSTANCE)
+    )
+
+    # Control Parameters
+    signalSearchRange = np.arange(1.0, 2.0, 0.2)
+
+    # Stimulus Mechanisms
+    Target_Stim = TransferMechanism(name='Target Stimulus', function=Linear(slope=0.3324))
+    Flanker_Stim = TransferMechanism(name='Flanker Stimulus', function=Linear(slope=0.3545221843))
+
+    # Processing Mechanisms (Control)
+    Target_Rep = TransferMechanism(
+        name='Target Representation',
+        function=Linear(
+            slope=(
+                1.0,
+                ControlProjection(
+                    function=Linear,
+                    control_signal_params={ALLOCATION_SAMPLES: signalSearchRange}
+                )
+            )
+        ),
+        prefs=mechanism_prefs
+    )
+    Flanker_Rep = TransferMechanism(
+        name='Flanker Representation',
+        function=Linear(
+            slope=(
+                1.0,
+                ControlProjection(
+                    function=Linear,
+                    control_signal_params={ALLOCATION_SAMPLES: signalSearchRange}
+                )
+            )
+        ),
+        prefs=mechanism_prefs
+    )
+
+    # Processing Mechanism (Automatic)
+    Automatic_Component = TransferMechanism(
+        name='Automatic Component',
+        function=Linear(slope=(1.0)),
+        prefs=mechanism_prefs
+    )
+
+    # Decision Mechanisms
+    Decision = DDM(
+        function=BogaczEtAl(
+            drift_rate=(1.0),
+            threshold=(0.2645),
+            noise=(0.5),
+            starting_point=(0),
+            t0=0.15
+        ),
+        prefs=mechanism_prefs,
+        name='Decision',
+        output_states=[
+            DECISION_VARIABLE,
+            RESPONSE_TIME,
+            PROBABILITY_UPPER_THRESHOLD
+        ],
+    )
+
+    # Outcome Mechanisms:
+    Reward = TransferMechanism(name='Reward')
+
+    # Processes:
+    TargetControlProcess = process(
+        default_input_value=[0],
+        pathway=[Target_Stim, Target_Rep, Decision],
+        prefs=process_prefs,
+        name='Target Control Process'
+    )
+
+    FlankerControlProcess = process(
+        default_input_value=[0],
+        pathway=[Flanker_Stim, Flanker_Rep, Decision],
+        prefs=process_prefs,
+        name='Flanker Control Process'
+    )
+
+    TargetAutomaticProcess = process(
+        default_input_value=[0],
+        pathway=[Target_Stim, Automatic_Component, Decision],
+        prefs=process_prefs,
+        name='Target Automatic Process'
+    )
+
+    FlankerAutomaticProcess = process(
+        default_input_value=[0],
+        pathway=[Flanker_Stim, Automatic_Component, Decision],
+        prefs=process_prefs,
+        name='Flanker1 Automatic Process'
+    )
+
+    RewardProcess = process(
+        default_input_value=[0],
+        pathway=[Reward],
+        prefs=process_prefs,
+        name='RewardProcess'
+    )
+
+    # System:
+    mySystem = system(
+        processes=[
+            TargetControlProcess,
+            FlankerControlProcess,
+            TargetAutomaticProcess,
+            FlankerAutomaticProcess,
+            RewardProcess
+        ],
+        controller=EVCMechanism,
+        enable_controller=True,
+        monitor_for_control=[
+            Reward,
+            Decision.PROBABILITY_UPPER_THRESHOLD
+        ],
+        # monitor_for_control=[Reward, DDM_PROBABILITY_UPPER_THRESHOLD, (DDM_RESPONSE_TIME, -1, 1)],
+        name='EVC Gratton System'
+    )
+
+    # Show characteristics of system:
+    mySystem.show()
+    mySystem.controller.show()
+
+    # configure EVC components
+    mySystem.controller.control_signals[0].intensity_cost_function = Exponential(rate=0.8046).function
+    mySystem.controller.control_signals[1].intensity_cost_function = Exponential(rate=0.8046).function
+
+    for mech in mySystem.controller.predictionMechanisms.mechanisms:
+        if mech.name == 'Flanker Stimulus Prediction Mechanism' or mech.name == 'Target Stimulus Prediction Mechanism':
+            # when you find a key mechanism (transfer mechanism) with the correct name, print its name
+            print(mech.name)
+            mech.function_object.rate = 1.0
+
+        if 'Reward' in mech.name:
+            print(mech.name)
+            mech.function_object.rate = 1.0
+            # mySystem.controller.prediction_mechanisms[mech].parameterStates['rate'].base_value = 1.0
+
+    print('new rate of integration mechanisms before system execution:')
+    # for mech in mySystem.controller.prediction_mechanisms.keys():
+    for mech in mySystem.controller.predictionMechanisms.mechanisms:
+        print(mech.name)
+        print(mech.function_object.rate)
+        print('----')
+
+    # generate stimulus environment
+
+    nTrials = 3
+    targetFeatures = [1, 1, 1]
+    flankerFeatures = [1, -1, 1]  # for full simulation: flankerFeatures = [-1,1]
+    reward = [100, 100, 100]
+
+    targetInputList = targetFeatures
+    flankerInputList = flankerFeatures
+    rewardList = reward
+
+    # targetInputList = np.random.choice(targetFeatures, nTrials).tolist()
+    # flankerInputList = np.random.choice(flankerFeatures, nTrials).tolist()
+    # rewardList = (np.ones(nTrials) * reward).tolist() #np.random.choice(reward, nTrials).tolist()
+
+    stim_list_dict = {Target_Stim: targetInputList,
+                      Flanker_Stim: flankerInputList,
+                      Reward: rewardList}
+
+    mySystem.controller.reportOutputPref = True
+
+    expected_results_array = [
+        0.2645,  0.32257753,  0.94819408, 100.,
+        0.2645,  0.31663196,  0.95508757, 100.,
+        0.2645,  0.31093566,  0.96110142, 100.,
+        0.2645,  0.30548947,  0.96633839, 100.,
+        0.2645,  0.30029103,  0.97089165, 100.,
+        0.2645,  0.3169957,  0.95468427, 100.,
+        0.2645,  0.31128378,  0.9607499, 100.,
+        0.2645,  0.30582202,  0.96603252, 100.,
+        0.2645,  0.30060824,  0.9706259, 100.,
+        0.2645,  0.29563774,  0.97461444, 100.,
+        0.2645,  0.31163288,  0.96039533, 100.,
+        0.2645,  0.30615555,  0.96572397, 100.,
+        0.2645,  0.30092641,  0.97035779, 100.,
+        0.2645,  0.2959409,  0.97438178, 100.,
+        0.2645,  0.29119255,  0.97787196, 100.,
+        0.2645,  0.30649004,  0.96541272, 100.,
+        0.2645,  0.30124552,  0.97008732, 100.,
+        0.2645,  0.29624499,  0.97414704, 100.,
+        0.2645,  0.29148205,  0.97766847, 100.,
+        0.2645,  0.28694892,  0.98071974, 100.,
+        0.2645,  0.30156558,  0.96981445, 100.,
+        0.2645,  0.29654999,  0.97391021, 100.,
+        0.2645,  0.29177245,  0.97746315, 100.,
+        0.2645,  0.28722523,  0.98054192, 100.,
+        0.2645,  0.28289958,  0.98320731, 100.,
+        0.2645,  0.28289958,  0.98320731, 100.,
+        0.2645,  0.42963678,  0.47661181, 100.,
+        0.2645,  0.42846471,  0.43938586, 100.,
+        -0.2645,  0.42628176,  0.40282965, 100.,
+        0.2645,  0.42314468,  0.36732207, 100.,
+        -0.2645,  0.41913221,  0.333198, 100.,
+        0.2645,  0.42978939,  0.51176048, 100.,
+        0.2645,  0.42959394,  0.47427693, 100.,
+        -0.2645,  0.4283576,  0.43708106, 100.,
+        0.2645,  0.4261132,  0.40057958, 100.,
+        -0.2645,  0.422919,  0.36514906, 100.,
+        0.2645,  0.42902209,  0.54679323, 100.,
+        0.2645,  0.42980788,  0.50942101, 100.,
+        -0.2645,  0.42954704,  0.47194318, 100.,
+        -0.2645,  0.42824656,  0.43477897, 100.,
+        0.2645,  0.42594094,  0.3983337, 100.,
+        -0.2645,  0.42735293,  0.58136855, 100.,
+        -0.2645,  0.42910149,  0.54447221, 100.,
+        0.2645,  0.42982229,  0.50708112, 100.,
+        -0.2645,  0.42949608,  0.46961065, 100.,
+        -0.2645,  0.42813159,  0.43247968, 100.,
+        -0.2645,  0.42482049,  0.61516258, 100.,
+        0.2645,  0.42749136,  0.57908829, 100.,
+        0.2645,  0.42917687,  0.54214925, 100.,
+        -0.2645,  0.42983261,  0.50474093, 100.,
+        -0.2645,  0.42944107,  0.46727945, 100.,
+        -0.2645,  0.42944107,  0.46727945, 100.,
+        0.2645,  0.32257753,  0.94819408, 100.,
+        0.2645,  0.31663196,  0.95508757, 100.,
+        0.2645,  0.31093566,  0.96110142, 100.,
+        0.2645,  0.30548947,  0.96633839, 100.,
+        0.2645,  0.30029103,  0.97089165, 100.,
+        0.2645,  0.3169957,  0.95468427, 100.,
+        0.2645,  0.31128378,  0.9607499, 100.,
+        0.2645,  0.30582202,  0.96603252, 100.,
+        0.2645,  0.30060824,  0.9706259, 100.,
+        0.2645,  0.29563774,  0.97461444, 100.,
+        0.2645,  0.31163288,  0.96039533, 100.,
+        0.2645,  0.30615555,  0.96572397, 100.,
+        0.2645,  0.30092641,  0.97035779, 100.,
+        0.2645,  0.2959409,  0.97438178, 100.,
+        0.2645,  0.29119255,  0.97787196, 100.,
+        0.2645,  0.30649004,  0.96541272, 100.,
+        0.2645,  0.30124552,  0.97008732, 100.,
+        0.2645,  0.29624499,  0.97414704, 100.,
+        0.2645,  0.29148205,  0.97766847, 100.,
+        0.2645,  0.28694892,  0.98071974, 100.,
+        0.2645,  0.30156558,  0.96981445, 100.,
+        0.2645,  0.29654999,  0.97391021, 100.,
+        0.2645,  0.29177245,  0.97746315, 100.,
+        0.2645,  0.28722523,  0.98054192, 100.,
+        0.2645,  0.28289958,  0.98320731, 100.,
+        0.2645,  0.28289958,  0.98320731, 100.,
+    ]
+
+    mySystem.run(
+        num_executions=nTrials,
+        inputs=stim_list_dict,
+    )
+
+    np.testing.assert_allclose(
+        pytest.helpers.expand_np_ndarray(mySystem.results),
+        expected_results_array,
+        atol=1e-08,
+        verbose=True,
+    )
+
+
+def test_laming_validation_specify_control_signals():
+    # Mechanisms:
+    Input = TransferMechanism(
+        name='Input'
+    )
+    Reward = TransferMechanism(
+        name='Reward',
+        output_states=[RESULT, MEAN, VARIANCE]
+    )
+    Decision = DDM(
+        function=BogaczEtAl(
+            drift_rate=1.0,
+            threshold=1.0,
+            noise=0.5,
+            starting_point=0,
+            t0=0.45
+        ),
+        output_states=[
+            DECISION_VARIABLE,
+            RESPONSE_TIME,
+            PROBABILITY_UPPER_THRESHOLD
+        ],
+        name='Decision'
+    )
+
+    # Processes:
+    TaskExecutionProcess = process(
+        default_input_value=[0],
+        pathway=[Input, IDENTITY_MATRIX, Decision],
+        name='TaskExecutionProcess'
+    )
+
+    RewardProcess = process(
+        default_input_value=[0],
+        pathway=[Reward],
+        name='RewardProcess'
+    )
+
+    # System:
+    mySystem = system(
+        processes=[TaskExecutionProcess, RewardProcess],
+        controller=EVCMechanism,
+        enable_controller=True,
+        monitor_for_control=[
+            Reward,
+            Decision.PROBABILITY_UPPER_THRESHOLD,
+            (Decision.RESPONSE_TIME, -1, 1)
+        ],
+        control_signals=[
+            (DRIFT_RATE, Decision),
+            (THRESHOLD, Decision)
+        ],
+        name='EVC Test System'
+    )
+    # Stimulus
+    stim_list_dict = {
+        Input: [0.5, 0.123],
+        Reward: [20, 20]
+    }
+
+    # Run system:
+    mySystem.run(
+        inputs=stim_list_dict
+    )
+
+    RewardPrediction = mySystem.executionList[3]
+    InputPrediction = mySystem.executionList[4]
+
+    # rearranging mySystem.results into a format that we can compare with pytest
+    results_array = []
+    for elem in mySystem.results:
+        elem_array = []
+        for inner_elem in elem:
+            elem_array.append(float(inner_elem))
+        results_array.append(elem_array)
+
+    # mySystem.results expected output properly formatted
+    expected_results_array = [
+        [10., 10.0, 0.0, -0.1, 0.48999867, 0.50499983],
+        [10., 10.0, 0.0, -0.4, 1.08965888, 0.51998934],
+        [10., 10.0, 0.0, 0.7, 2.40680493, 0.53494295],
+        [10., 10.0, 0.0, -1., 4.43671978, 0.549834],
+        [10., 10.0, 0.0, 0.1, 0.48997868, 0.51998934],
+        [10., 10.0, 0.0, -0.4, 1.08459402, 0.57932425],
+        [10., 10.0, 0.0, 0.7, 2.36033556, 0.63645254],
+        [10., 10.0, 0.0, 1., 4.24948962, 0.68997448],
+        [10., 10.0, 0.0, 0.1, 0.48993479, 0.53494295],
+        [10., 10.0, 0.0, 0.4, 1.07378304, 0.63645254],
+        [10., 10.0, 0.0, 0.7, 2.26686573, 0.72710822],
+        [10., 10.0, 0.0, 1., 3.90353015, 0.80218389],
+        [10., 10.0, 0.0, 0.1, 0.4898672, 0.549834],
+        [10., 10.0, 0.0, -0.4, 1.05791834, 0.68997448],
+        [10., 10.0, 0.0, 0.7, 2.14222978, 0.80218389],
+        [10., 10.0, 0.0, 1., 3.49637662, 0.88079708],
+        [10., 10.0, 0.0, 1., 3.49637662, 0.88079708],
+        [15., 15.0, 0.0, 0.1, 0.48999926, 0.50372993],
+        [15., 15.0, 0.0, -0.4, 1.08981011, 0.51491557],
+        [15., 15.0, 0.0, 0.7, 2.40822035, 0.52608629],
+        [15., 15.0, 0.0, 1., 4.44259627, 0.53723096],
+        [15., 15.0, 0.0, 0.1, 0.48998813, 0.51491557],
+        [15., 15.0, 0.0, 0.4, 1.0869779, 0.55939819],
+        [15., 15.0, 0.0, -0.7, 2.38198336, 0.60294711],
+        [15., 15.0, 0.0, 1., 4.33535807, 0.64492386],
+        [15., 15.0, 0.0, 0.1, 0.48996368, 0.52608629],
+        [15., 15.0, 0.0, 0.4, 1.08085171, 0.60294711],
+        [15., 15.0, 0.0, 0.7, 2.32712843, 0.67504223],
+        [15., 15.0, 0.0, 1., 4.1221271, 0.7396981],
+        [15., 15.0, 0.0, 0.1, 0.48992596, 0.53723096],
+        [15., 15.0, 0.0, -0.4, 1.07165729, 0.64492386],
+        [15., 15.0, 0.0, 0.7, 2.24934228, 0.7396981],
+        [15., 15.0, 0.0, 1., 3.84279648, 0.81637827],
+        [15., 15.0, 0.0, 1., 3.84279648, 0.81637827]
+    ]
+
+    expected_output = [
+        # Decision Output | Second Trial
+        (Decision.output_states[0].value, np.array(1.0)),
+
+        # Input Prediction Output | Second Trial
+        (InputPrediction.output_states[0].value, np.array(0.1865)),
+
+        # RewardPrediction Output | Second Trial
+        (RewardPrediction.output_states[0].value, np.array(15.0)),
+
+        # --- Decision Mechanism ---
+
+        #   ControlSignal Values
+        #       drift rate
+        # ALT: float(Decision._parameter_states[DRIFT_RATE].value
+        # (mySystem.controller.control_signals[0].value, np.array(1.0)),
+        # #       threshold
+        #
+        # # ALT: float(Decision._parameter_states[THRESHOLD].value
+        # (mySystem.controller.control_signals[1].value, np.array(1.0)),
+
+        #    Output State Values
+        #       decision variable
+        (Decision.output_states[DECISION_VARIABLE].value, np.array([1.0])),
+        #       response time
+        (Decision.output_states[RESPONSE_TIME].value, np.array([3.84279648])),
+        #       upper bound
+        (Decision.output_states[PROBABILITY_UPPER_THRESHOLD].value, np.array([0.81637827])),
+        #       lower bound
+        # (round(float(Decision.output_states['DDM_probability_lowerBound'].value),3), 0.184),
+
+        # --- Reward Mechanism ---
+        #    Output State Values
+        #       transfer mean
+        (Reward.output_states[RESULT].value, np.array([15.])),
+        #       transfer_result
+        (Reward.output_states[MEAN].value, np.array(15.0)),
+        #       transfer variance
+        (Reward.output_states[VARIANCE].value, np.array(0.0)),
+
+        # System Results Array
+        #   (all intermediate output values of system)
+        (results_array, expected_results_array)
+    ]
+
+    for i in range(len(expected_output)):
+        val, expected = expected_output[i]
+        np.testing.assert_allclose(val, expected, atol=1e-08, err_msg='Failed on expected_output[{0}]'.format(i))
+
+    np.testing.assert_almost_equal(
+        Decision._parameter_states[DRIFT_RATE].value,
+        Decision._parameter_states[DRIFT_RATE].mod_afferents[0].value * Decision._parameter_states[DRIFT_RATE].function_object.value
+    )
+    np.testing.assert_almost_equal(
+        Decision._parameter_states[THRESHOLD].value,
+        Decision._parameter_states[THRESHOLD].mod_afferents[0].value * Decision._parameter_states[THRESHOLD].function_object.value
+    )
diff --git a/tests/learning/test_multilayer.py b/tests/learning/test_multilayer.py
new file mode 100644
index 00000000000..d856382c631
--- /dev/null
+++ b/tests/learning/test_multilayer.py
@@ -0,0 +1,181 @@
+import numpy as np
+
+from PsyNeuLink.Components.Functions.Function import Logistic
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
+from PsyNeuLink.Components.Process import process
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ObjectiveMechanisms.ComparatorMechanism import MSE
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
+from PsyNeuLink.Components.System import system
+from PsyNeuLink.Globals.Keywords import LEARNING, SOFT_CLAMP
+from PsyNeuLink.Globals.Preferences.ComponentPreferenceSet import REPORT_OUTPUT_PREF, VERBOSE_PREF
+
+
+def test_multilayer():
+    Input_Layer = TransferMechanism(
+        name='Input Layer',
+        function=Logistic,
+        default_input_value=np.zeros((2,)),
+    )
+
+    Hidden_Layer_1 = TransferMechanism(
+        name='Hidden Layer_1',
+        function=Logistic(),
+        default_input_value=np.zeros((5,)),
+    )
+
+    Hidden_Layer_2 = TransferMechanism(
+        name='Hidden Layer_2',
+        function=Logistic(),
+        default_input_value=[0, 0, 0, 0],
+    )
+
+    Output_Layer = TransferMechanism(
+        name='Output Layer',
+        function=Logistic,
+        default_input_value=[0, 0, 0],
+    )
+
+    Input_Weights_matrix = (np.arange(2 * 5).reshape((2, 5)) + 1) / (2 * 5)
+    Middle_Weights_matrix = (np.arange(5 * 4).reshape((5, 4)) + 1) / (5 * 4)
+    Output_Weights_matrix = (np.arange(4 * 3).reshape((4, 3)) + 1) / (4 * 3)
+
+    # TEST PROCESS.LEARNING WITH:
+    # CREATION OF FREE STANDING PROJECTIONS THAT HAVE NO LEARNING (Input_Weights, Middle_Weights and Output_Weights)
+    # INLINE CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
+    # NO EXPLICIT CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
+
+    # This projection will be used by the process below by referencing it in the process' pathway;
+    #    note: sender and receiver args don't need to be specified
+    Input_Weights = MappingProjection(
+        name='Input Weights',
+        matrix=Input_Weights_matrix,
+    )
+
+    # This projection will be used by the process below by assigning its sender and receiver args
+    #    to mechanismss in the pathway
+    Middle_Weights = MappingProjection(
+        name='Middle Weights',
+        sender=Hidden_Layer_1,
+        receiver=Hidden_Layer_2,
+        matrix=Middle_Weights_matrix,
+    )
+
+    # Commented lines in this projection illustrate variety of ways in which matrix and learning signals can be specified
+    Output_Weights = MappingProjection(
+        name='Output Weights',
+        sender=Hidden_Layer_2,
+        receiver=Output_Layer,
+        matrix=Output_Weights_matrix,
+    )
+
+    p = process(
+        default_input_value=[0, 0],
+        pathway=[
+            Input_Layer,
+            # The following reference to Input_Weights is needed to use it in the pathway
+            #    since it's sender and receiver args are not specified in its declaration above
+            Input_Weights,
+            Hidden_Layer_1,
+            # No projection specification is needed here since the sender arg for Middle_Weights
+            #    is Hidden_Layer_1 and its receiver arg is Hidden_Layer_2
+            # Middle_Weights,
+            Hidden_Layer_2,
+            # Output_Weights does not need to be listed for the same reason as Middle_Weights
+            # If Middle_Weights and/or Output_Weights is not declared above, then the process
+            #    will assign a default for missing projection
+            # Output_Weights,
+            Output_Layer
+        ],
+        clamp_input=SOFT_CLAMP,
+        learning=LEARNING,
+        learning_rate=1.0,
+        target=[0, 0, 1],
+        prefs={
+            VERBOSE_PREF: False,
+            REPORT_OUTPUT_PREF: False
+        },
+    )
+
+    stim_list = {Input_Layer: [[-1, 30]]}
+    target_list = {Output_Layer: [[0, 0, 1]]}
+
+    def show_target():
+        i = s.input
+        t = s.targetInputStates[0].value
+        print('\nOLD WEIGHTS: \n')
+        print('- Input Weights: \n', Input_Weights.matrix)
+        print('- Middle Weights: \n', Middle_Weights.matrix)
+        print('- Output Weights: \n', Output_Weights.matrix)
+        print('\nSTIMULI:\n\n- Input: {}\n- Target: {}\n'.format(i, t))
+        print('ACTIVITY FROM OLD WEIGHTS: \n')
+        print('- Middle 1: \n', Hidden_Layer_1.value)
+        print('- Middle 2: \n', Hidden_Layer_2.value)
+        print('- Output:\n', Output_Layer.value)
+
+    s = system(
+        processes=[p],
+        targets=[0, 0, 1],
+        learning_rate=1.0,
+    )
+
+    s.reportOutputPref = True
+
+    results = s.run(
+        num_executions=10,
+        inputs=stim_list,
+        targets=target_list,
+        call_after_trial=show_target,
+    )
+
+    objective_output_layer = s.mechanisms[4]
+
+    results_list = []
+    for elem in s.results:
+        for nested_elem in elem:
+            nested_elem = nested_elem.tolist()
+            try:
+                iter(nested_elem)
+            except TypeError:
+                nested_elem = [nested_elem]
+            results_list.extend(nested_elem)
+
+    expected_output = [
+        (Output_Layer.output_states.values, [np.array([0.22686074, 0.25270212, 0.91542149])]),
+        (objective_output_layer.output_states[MSE].value, np.array(0.04082589331852094)),
+        (Input_Weights.matrix, np.array([
+            [ 0.09900247, 0.19839653, 0.29785764, 0.39739191, 0.49700232],
+            [ 0.59629092, 0.69403786, 0.79203411, 0.89030237, 0.98885379],
+        ])),
+        (Middle_Weights.matrix, np.array([
+            [ 0.09490249, 0.10488719, 0.12074013, 0.1428774 ],
+            [ 0.29677354, 0.30507726, 0.31949676, 0.3404652 ],
+            [ 0.49857336, 0.50526254, 0.51830509, 0.53815062],
+            [ 0.70029406, 0.70544225, 0.71717037, 0.73594383],
+            [ 0.90192903, 0.90561554, 0.91609668, 0.93385292],
+        ])),
+        (Output_Weights.matrix, np.array([
+            [-0.74447522, -0.71016859, 0.31575293],
+            [-0.50885177, -0.47444784, 0.56676582],
+            [-0.27333719, -0.23912033, 0.8178167 ],
+            [-0.03767547, -0.00389039, 1.06888608],
+        ])),
+        (results, [
+            [np.array([0.8344837 , 0.87072018, 0.89997433])],
+            [np.array([0.77970193, 0.83263138, 0.90159627])],
+            [np.array([0.70218502, 0.7773823 , 0.90307765])],
+            [np.array([0.60279149, 0.69958079, 0.90453143])],
+            [np.array([0.4967927 , 0.60030321, 0.90610082])],
+            [np.array([0.4056202 , 0.49472391, 0.90786617])],
+            [np.array([0.33763025, 0.40397637, 0.90977675])],
+            [np.array([0.28892812, 0.33633532, 0.9117193 ])],
+            [np.array([0.25348771, 0.28791896, 0.9136125 ])],
+            [np.array([0.22686074, 0.25270212, 0.91542149])]
+        ]),
+    ]
+
+    for i in range(len(expected_output)):
+        val, expected = expected_output[i]
+        # setting absolute tolerance to be in accordance with reference_output precision
+        # if you do not specify, assert_allcose will use a relative tolerance of 1e-07,
+        # which WILL FAIL unless you gather higher precision values to use as reference
+        np.testing.assert_allclose(val, expected, atol=1e-08, err_msg='Failed on expected_output[{0}]'.format(i))
diff --git a/tests/learning/test_reinforcement.py b/tests/learning/test_reinforcement.py
new file mode 100644
index 00000000000..a87ae9eae6b
--- /dev/null
+++ b/tests/learning/test_reinforcement.py
@@ -0,0 +1,123 @@
+import numpy as np
+import pytest
+
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
+from PsyNeuLink.Components.Process import process
+from PsyNeuLink.Components.Projections.ModulatoryProjections.LearningProjection import LearningProjection
+from PsyNeuLink.Components.Functions.Function import PROB
+from PsyNeuLink.Components.Functions.Function import SoftMax, Reinforcement
+from PsyNeuLink.Components.System import system
+from PsyNeuLink.Globals.TimeScale import CentralClock
+
+
+def test_reinforcement():
+    input_layer = TransferMechanism(
+        default_input_value=[0, 0, 0],
+        name='Input Layer',
+    )
+
+    action_selection = TransferMechanism(
+        default_input_value=[0, 0, 0],
+        function=SoftMax(
+            output=PROB,
+            gain=1.0,
+        ),
+        name='Action Selection',
+    )
+
+    p = process(
+        default_input_value=[0, 0, 0],
+        pathway=[input_layer, action_selection],
+        learning=LearningProjection(learning_function=Reinforcement(learning_rate=0.05)),
+        target=0,
+    )
+
+    # print ('reward prediction weights: \n', action_selection.input_states[0].path_afferents[0].matrix)
+    # print ('targetMechanism weights: \n', action_selection.output_states.sendsToProjections[0].matrix)
+
+    reward_values = [10, 10, 10]
+
+    # Must initialize reward (won't be used, but needed for declaration of lambda function)
+    action_selection.output_states.value = [0, 0, 1]
+    # Get reward value for selected action)
+    reward = lambda: [reward_values[int(np.nonzero(action_selection.output_states.value)[0])]]
+
+    def print_header():
+        print("\n\n**** TRIAL: ", CentralClock.trial)
+
+    def show_weights():
+        print('Reward prediction weights: \n', action_selection.input_states[0].path_afferents[0].matrix)
+        print('\nAction selected:  {}; predicted reward: {}'.format(
+            np.nonzero(action_selection.output_states.value)[0][0],
+            action_selection.output_states.value[np.nonzero(action_selection.output_states.value)[0][0]],
+        ))
+
+    input_list = {input_layer: [[1, 1, 1]]}
+
+    s = system(
+        processes=[p],
+        # learning_rate=0.05,
+        targets=[0],
+    )
+
+    results = s.run(
+        num_executions=10,
+        inputs=input_list,
+        targets=reward,
+        call_before_trial=print_header,
+        call_after_trial=show_weights,
+    )
+
+    results_list = []
+    for elem in s.results:
+        for nested_elem in elem:
+            nested_elem = nested_elem.tolist()
+            try:
+                iter(nested_elem)
+            except TypeError:
+                nested_elem = [nested_elem]
+            results_list.extend(nested_elem)
+
+    mech_objective_action = s.mechanisms[2]
+    mech_learning_input_to_action = s.mechanisms[3]
+
+    reward_prediction_weights = action_selection.input_states[0].path_afferents[0]
+
+    expected_output = [
+        (input_layer.output_states.values, [np.array([1., 1., 1.])]),
+        (action_selection.output_states.values, [np.array([0., 3.38417298, 0.])]),
+        (pytest.helpers.expand_np_ndarray(mech_objective_action.output_states.values), pytest.helpers.expand_np_ndarray([np.array([6.61582702]), np.array(43.7691671006736)])),
+        (pytest.helpers.expand_np_ndarray(mech_learning_input_to_action.output_states.values), pytest.helpers.expand_np_ndarray([np.array(
+                [0.        , 0.33079135, 0.        ]
+            ),
+            np.array([
+                [0.        , 0.        , 0.        ],
+                [0.        , 0.33079135, 0.        ],
+                [0.        , 0.        , 0.        ],
+            ])
+        ])),
+        (reward_prediction_weights.matrix, np.array([
+            [ 1.,          0.,          0.,        ],
+            [ 0.,          3.71496434,  0.,        ],
+            [ 0.,          0.,          2.283625,  ],
+        ])),
+        (results, [
+            [np.array([0., 1., 0.])],
+            [np.array([0., 1.45, 0.])],
+            [np.array([0., 1.8775, 0.])],
+            [np.array([0., 2.283625, 0.])],
+            [np.array([0., 0., 1.])],
+            [np.array([0., 0., 1.45])],
+            [np.array([0., 2.66944375, 0.])],
+            [np.array([0., 0., 1.8775])],
+            [np.array([0., 3.03597156, 0.])],
+            [np.array([0., 3.38417298, 0.])]
+        ]),
+    ]
+
+    for i in range(len(expected_output)):
+        val, expected = expected_output[i]
+        # setting absolute tolerance to be in accordance with reference_output precision
+        # if you do not specify, assert_allcose will use a relative tolerance of 1e-07,
+        # which WILL FAIL unless you gather higher precision values to use as reference
+        np.testing.assert_allclose(val, expected, atol=1e-08, err_msg='Failed on expected_output[{0}]'.format(i))
diff --git a/tests/learning/test_stroop.py b/tests/learning/test_stroop.py
new file mode 100644
index 00000000000..02c064c930b
--- /dev/null
+++ b/tests/learning/test_stroop.py
@@ -0,0 +1,266 @@
+import numpy as np
+
+from PsyNeuLink.Components.Functions.Function import Linear, Logistic
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
+from PsyNeuLink.Components.System import system, process
+from PsyNeuLink.Globals.Keywords import FULL_CONNECTIVITY_MATRIX, LEARNING, LEARNING_PROJECTION
+from PsyNeuLink.Globals.Preferences.ComponentPreferenceSet import REPORT_OUTPUT_PREF, VERBOSE_PREF
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.ObjectiveMechanisms.ComparatorMechanism import MSE
+
+
+class TestStroop:
+
+    def test_stroop_model(self):
+        process_prefs = {
+            REPORT_OUTPUT_PREF: True,
+            VERBOSE_PREF: False
+        }
+
+        # system_prefs = {
+        #     REPORT_OUTPUT_PREF: True,
+        #     VERBOSE_PREF: False
+        # }
+
+        colors = TransferMechanism(
+            default_input_value=[0, 0],
+            function=Linear,
+            name="Colors",
+        )
+
+        words = TransferMechanism(
+            default_input_value=[0, 0],
+            function=Linear,
+            name="Words",
+        )
+
+        response = TransferMechanism(
+            default_input_value=[0, 0],
+            function=Logistic,
+            name="Response",
+        )
+
+        color_naming_process = process(
+            default_input_value=[1, 2.5],
+            pathway=[colors, FULL_CONNECTIVITY_MATRIX, response],
+            learning=LEARNING_PROJECTION,
+            target=[0, 1],
+            name='Color Naming',
+            prefs=process_prefs,
+        )
+
+        word_reading_process = process(
+            default_input_value=[.5, 3],
+            pathway=[words, FULL_CONNECTIVITY_MATRIX, response],
+            name='Word Reading',
+            learning=LEARNING_PROJECTION,
+            target=[1, 0],
+            prefs=process_prefs,
+        )
+
+        # s = system(
+        #     processes=[color_naming_process, word_reading_process],
+        #     name='Stroop Model',
+        #     targets=[0, 0],
+        #     prefs=system_prefs,
+        # )
+
+        # stim_dict = {
+        #     colors: [
+        #         [1,0],
+        #         [0,1]
+        #     ],
+        #     words: [
+        #         [0,1],
+        #         [1,0]
+        #     ]
+        # }
+        # target_dict = {
+        #     response: [
+        #         [1,0],
+        #         [0,1]
+        #     ]
+        # }
+
+        # results = s.run(
+        #     num_executions=10,
+        #     inputs=stim_dict,
+        #     targets=target_dict,
+        # )
+        expected_color_results = [
+            np.array([0.88079708, 0.88079708]),
+            np.array([0.85997037, 0.88340023]),
+            np.array([0.83312329, 0.88585176]),
+            np.array([0.79839127, 0.88816536]),
+            np.array([0.75384913, 0.89035312]),
+            np.array([0.69835531, 0.89242571]),
+            np.array([0.63303376, 0.89439259]),
+            np.array([0.56245802, 0.8962622 ]),
+            np.array([0.49357614, 0.89804208]),
+            np.array([0.43230715, 0.89973899]),
+        ]
+
+        expected_word_results = [
+            np.array([0.88079708, 0.88079708]),
+            np.array([0.88340023, 0.85997037]),
+            np.array([0.88585176, 0.83312329]),
+            np.array([0.88816536, 0.79839127]),
+            np.array([0.89035312, 0.75384913]),
+            np.array([0.89242571, 0.69835531]),
+            np.array([0.89439259, 0.63303376]),
+            np.array([0.8962622, 0.56245802]),
+            np.array([0.89804208, 0.49357614]),
+            np.array([0.89973899, 0.43230715]),
+        ]
+
+        for i in range(10):
+            cr = color_naming_process.execute(input=[1, 1], target=[0, 1])
+            wr = word_reading_process.execute(input=[1, 1], target=[1, 0])
+
+            np.testing.assert_allclose(cr, expected_color_results[i], atol=1e-08, err_msg='Failed on expected_color_results[{0}]'.format(i))
+            np.testing.assert_allclose(wr, expected_word_results[i], atol=1e-08, err_msg='Failed on expected_word_results[{0}]'.format(i))
+
+    def test_stroop_model_learning(self):
+        process_prefs = {
+            REPORT_OUTPUT_PREF: True,
+            VERBOSE_PREF: False,
+        }
+        system_prefs = {
+            REPORT_OUTPUT_PREF: True,
+            VERBOSE_PREF: False,
+        }
+
+        colors = TransferMechanism(
+            default_input_value=[0, 0],
+            function=Linear,
+            name="Colors",
+        )
+        words = TransferMechanism(
+            default_input_value=[0, 0],
+            function=Linear,
+            name="Words",
+        )
+        hidden = TransferMechanism(
+            default_input_value=[0, 0],
+            function=Logistic,
+            name="Hidden",
+        )
+        response = TransferMechanism(
+            default_input_value=[0, 0],
+            function=Logistic(),
+            name="Response",
+        )
+        TransferMechanism(
+            default_input_value=[0, 0],
+            function=Logistic,
+            name="Output",
+        )
+
+        CH_Weights_matrix = np.arange(4).reshape((2, 2))
+        WH_Weights_matrix = np.arange(4).reshape((2, 2))
+        HO_Weights_matrix = np.arange(4).reshape((2, 2))
+
+        CH_Weights = MappingProjection(
+            name='Color-Hidden Weights',
+            matrix=CH_Weights_matrix,
+        )
+        WH_Weights = MappingProjection(
+            name='Word-Hidden Weights',
+            matrix=WH_Weights_matrix,
+        )
+        HO_Weights = MappingProjection(
+            name='Hidden-Output Weights',
+            matrix=HO_Weights_matrix,
+        )
+
+        color_naming_process = process(
+            default_input_value=[1, 2.5],
+            pathway=[colors, CH_Weights, hidden, HO_Weights, response],
+            learning=LEARNING,
+            target=[2, 2],
+            name='Color Naming',
+            prefs=process_prefs,
+        )
+
+        word_reading_process = process(
+            default_input_value=[.5, 3],
+            pathway=[words, WH_Weights, hidden],
+            name='Word Reading',
+            learning=LEARNING,
+            target=[3, 3],
+            prefs=process_prefs,
+        )
+
+        s = system(
+            processes=[color_naming_process, word_reading_process],
+            targets=[20, 20],
+            name='Stroop Model',
+            prefs=system_prefs,
+        )
+
+        def show_target():
+            print('\nColor Naming\n\tInput: {}\n\tTarget: {}'.format(colors.input_states.values_as_lists, s.targets))
+            print('Wording Reading:\n\tInput: {}\n\tTarget: {}\n'.format(words.input_states.values_as_lists, s.targets))
+            print('Response: \n', response.output_values[0])
+            print('Hidden-Output:')
+            print(HO_Weights.matrix)
+            print('Color-Hidden:')
+            print(CH_Weights.matrix)
+            print('Word-Hidden:')
+            print(WH_Weights.matrix)
+
+        stim_list_dict = {
+            colors: [[1, 1]],
+            words: [[-2, -2]]
+        }
+
+        target_list_dict = {response: [[1, 1]]}
+
+        results = s.run(
+            num_executions=2,
+            inputs=stim_list_dict,
+            targets=target_list_dict,
+            call_after_trial=show_target,
+        )
+
+        results_list = []
+        for elem in s.results:
+            for nested_elem in elem:
+                nested_elem = nested_elem.tolist()
+                try:
+                    iter(nested_elem)
+                except TypeError:
+                    nested_elem = [nested_elem]
+                results_list.extend(nested_elem)
+
+        objective_response = s.mechanisms[3]
+        objective_hidden = s.mechanisms[7]
+        expected_output = [
+            (colors.output_states[0].value, np.array([1., 1.])),
+            (words.output_states[0].value, np.array([-2., -2.])),
+            (hidden.output_states[0].value, np.array([0.13227553, 0.01990677])),
+            (response.output_states[0].value, np.array([0.51044657, 0.5483048])),
+            (objective_response.output_states[0].value, np.array([0.48955343, 0.4516952])),
+            (objective_response.output_states[MSE].value, np.array(0.22184555903789838)),
+            (objective_hidden.output_states[0].value, np.array([0., 0.])),
+            (CH_Weights.matrix, np.array([
+                [ 0.02512045, 1.02167245],
+                [ 2.02512045, 3.02167245],
+                ])),
+            (WH_Weights.matrix, np.array([
+                [-0.05024091, 0.9566551 ],
+                [ 1.94975909, 2.9566551 ],
+            ])),
+            (HO_Weights.matrix, np.array([
+                [ 0.03080958, 1.02830959],
+                [ 2.00464242, 3.00426575],
+            ])),
+            (results, [[np.array([0.50899214, 0.54318254])], [np.array([0.51044657, 0.5483048])]]),
+        ]
+
+        for i in range(len(expected_output)):
+            val, expected = expected_output[i]
+            # setting absolute tolerance to be in accordance with reference_output precision
+            # if you do not specify, assert_allcose will use a relative tolerance of 1e-07,
+            # which WILL FAIL unless you gather higher precision values to use as reference
+            np.testing.assert_allclose(val, expected, atol=1e-08, err_msg='Failed on expected_output[{0}]'.format(i))
diff --git a/tests/mechanisms/test_ddm_mechanism.py b/tests/mechanisms/test_ddm_mechanism.py
new file mode 100644
index 00000000000..a185118ce11
--- /dev/null
+++ b/tests/mechanisms/test_ddm_mechanism.py
@@ -0,0 +1,406 @@
+import pytest
+import typecheck
+
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import DDM, DDMError, DIFFUSION
+from PsyNeuLink.Components.Functions.Function import BogaczEtAl, DriftDiffusionIntegrator, NormalDist
+from PsyNeuLink.Components.Functions.Function import FunctionError
+from PsyNeuLink.Globals.TimeScale import TimeScale
+
+# ======================================= FUNCTION TESTS ============================================
+
+# VALID FUNCTIONS:
+
+# ------------------------------------------------------------------------------------------------
+# TEST 1
+# function = Integrator
+
+
+def test_DDM_Integrator():
+    stim = 10
+    T = DDM(
+        name='DDM',
+        function=DriftDiffusionIntegrator(
+            initializer=20.0
+        ),
+        time_scale=TimeScale.TIME_STEP
+    )
+    val = float(T.execute(stim)[0])
+
+    T.function_object.initializer = 30.0
+    val2 = float(T.execute(stim)[0])
+    # assert [val, val2]  == [10, 30]
+test_DDM_Integrator()
+
+# ------------------------------------------------------------------------------------------------
+# TEST 2
+# function = Bogacz
+
+
+def test_DDM_Integrator_Bogacz():
+    stim = 10
+    T = DDM(
+        name='DDM',
+        function=BogaczEtAl()
+    )
+    val = float(T.execute(stim)[0])
+    assert val == 1.0
+
+# ------------------------------------------------------------------------------------------------
+# # TEST 3
+# # function = Navarro
+
+# ******
+# Requires matlab import
+# ******
+
+
+# def test_DDM_Integrator():
+#     stim = 10
+#     T = DDM(
+#         name='DDM',
+#         function=NavarroAndFuss()
+#     )
+#     val = float(T.execute(stim)[0])
+#     assert val == 10
+
+
+# ======================================= NOISE TESTS ============================================
+
+# VALID NOISE:
+
+# ------------------------------------------------------------------------------------------------
+# TEST 1
+# noise = Single float
+
+
+def test_DDM_zero_noise():
+    stim = 10
+    T = DDM(
+        name='DDM',
+        function=DriftDiffusionIntegrator(
+            noise=0.0,
+            rate=1.0,
+            time_step_size=1.0
+        ),
+        time_scale=TimeScale.TIME_STEP
+    )
+    val = float(T.execute(stim)[0])
+    assert val == 10
+
+# ------------------------------------------------------------------------------------------------
+# TEST 2
+# noise = Single float
+
+
+def test_DDM_noise_0_5():
+    stim = 10
+    T = DDM(
+        name='DDM',
+        function=DriftDiffusionIntegrator(
+            noise=0.5,
+            rate=1.0,
+            time_step_size=1.0
+        ),
+        time_scale=TimeScale.TIME_STEP
+    )
+    val = float(T.execute(stim)[0])
+    assert val == 9.892974291631234
+
+# ------------------------------------------------------------------------------------------------
+# TEST 3
+# noise = Single float
+
+
+def test_DDM_noise_2_0():
+    stim = 10
+    T = DDM(
+        name='DDM',
+        function=DriftDiffusionIntegrator(
+            noise=2.0,
+            rate=1.0,
+            time_step_size=1.0
+        ),
+        time_scale=TimeScale.TIME_STEP
+    )
+    val = float(T.execute(stim)[0])
+    assert val == 9.785948583262465
+
+# ------------------------------------------------------------------------------------------------
+
+# INVALID NOISE:
+
+# ------------------------------------------------------------------------------------------------
+# TEST 1
+# noise = Single int
+
+
+def test_DDM_noise_int():
+    with pytest.raises(FunctionError) as error_text:
+        stim = 10
+        T = DDM(
+            name='DDM',
+            function=DriftDiffusionIntegrator(
+
+                noise=2,
+                rate=1.0,
+                time_step_size=1.0
+            ),
+            time_scale=TimeScale.TIME_STEP
+        )
+        float(T.execute(stim)[0])
+    assert "DriftDiffusionIntegrator requires noise parameter to be a float" in str(error_text.value)
+
+# ------------------------------------------------------------------------------------------------
+# TEST 2
+# noise = Single fn
+
+
+def test_DDM_noise_fn():
+    with pytest.raises(FunctionError) as error_text:
+        stim = 10
+        T = DDM(
+            name='DDM',
+            function=DriftDiffusionIntegrator(
+
+                noise=NormalDist().function,
+                rate=1.0,
+                time_step_size=1.0
+            ),
+            time_scale=TimeScale.TIME_STEP
+        )
+        float(T.execute(stim)[0])
+    assert "DriftDiffusionIntegrator requires noise parameter to be a float" in str(error_text.value)
+
+# ======================================= INPUT TESTS ============================================
+
+# VALID INPUTS:
+
+# ------------------------------------------------------------------------------------------------
+# TEST 1
+# input = Int
+
+
+def test_DDM_input_int():
+    stim = 10
+    T = DDM(
+        name='DDM',
+        function=DriftDiffusionIntegrator(
+            noise=0.0,
+            rate=1.0,
+            time_step_size=1.0
+        ),
+        time_scale=TimeScale.TIME_STEP
+    )
+    val = float(T.execute(stim)[0])
+    assert val == 10
+
+# ------------------------------------------------------------------------------------------------
+# TEST 2
+# input = List len 1
+
+
+def test_DDM_input_list_len_1():
+    stim = [10]
+    T = DDM(
+        name='DDM',
+        function=DriftDiffusionIntegrator(
+            noise=0.0,
+            rate=1.0,
+            time_step_size=1.0
+        ),
+        time_scale=TimeScale.TIME_STEP
+    )
+    val = float(T.execute(stim)[0])
+    assert val == 10
+
+# ------------------------------------------------------------------------------------------------
+# TEST 3
+# input = Float
+
+
+def test_DDM_input_float():
+    stim = 10.0
+    T = DDM(
+        name='DDM',
+        function=DriftDiffusionIntegrator(
+            noise=0.0,
+            rate=1.0,
+            time_step_size=1.0
+        ),
+        time_scale=TimeScale.TIME_STEP
+    )
+    val = float(T.execute(stim)[0])
+    assert val == 10.0
+
+# ------------------------------------------------------------------------------------------------
+
+# INVALID INPUTS:
+
+# ------------------------------------------------------------------------------------------------
+# TEST 1
+# input = List len 2
+
+
+def test_DDM_input_list_len_2():
+    with pytest.raises(DDMError) as error_text:
+        stim = [10, 10]
+        T = DDM(
+            name='DDM',
+            default_input_value=[0, 0],
+            function=DriftDiffusionIntegrator(
+
+                noise=0.0,
+                rate=1.0,
+                time_step_size=1.0
+            ),
+            time_scale=TimeScale.TIME_STEP
+        )
+        float(T.execute(stim)[0])
+    assert "must have only a single numeric item" in str(error_text.value)
+
+# ------------------------------------------------------------------------------------------------
+# TEST 2
+# input = Fn
+
+# ******
+# Should functions be caught in validation, rather than with TypeError [just check callable()]?
+# So that functions cause the same error as lists (see TEST 1 above)
+# ******
+
+
+def test_DDM_input_fn():
+    with pytest.raises(TypeError) as error_text:
+        stim = NormalDist().function
+        print(stim)
+        T = DDM(
+            name='DDM',
+            function=DriftDiffusionIntegrator(
+
+                noise=0.0,
+                rate=1.0,
+                time_step_size=1.0
+            ),
+            time_scale=TimeScale.TIME_STEP
+        )
+        float(T.execute(stim))
+    assert "not supported for the input types" in str(error_text.value)
+
+
+# ======================================= RATE TESTS ============================================
+
+# VALID RATES:
+
+# ------------------------------------------------------------------------------------------------
+# TEST 1
+# rate = Int
+
+def test_DDM_rate_int():
+    stim = 10
+    T = DDM(
+        name='DDM',
+        function=DriftDiffusionIntegrator(
+            noise=0.0,
+            rate=5,
+            time_step_size=1.0
+        ),
+        time_scale=TimeScale.TIME_STEP
+    )
+    val = float(T.execute(stim)[0])
+    assert val == 50
+
+#  The rate -- ndarray/list bug is fixed on devel but hasn't been pulled into scheduler yet
+#  Leaving commented out for now
+#
+# ------------------------------------------------------------------------------------------------
+# TEST 2
+# rate = list len 1
+#
+def test_DDM_rate_list_len_1():
+    stim = 10
+    T = DDM(
+        name='DDM',
+        function=DriftDiffusionIntegrator(
+            noise=0.0,
+            rate=[5],
+            time_step_size=1.0
+        ),
+        time_scale=TimeScale.TIME_STEP
+    )
+    val = float(T.execute(stim)[0])
+    assert val == 50
+#
+# ------------------------------------------------------------------------------------------------
+# TEST 3
+# rate = float
+
+
+def test_DDM_rate_float():
+    stim = 10
+    T = DDM(
+        name='DDM',
+        function=DriftDiffusionIntegrator(
+            noise=0.0,
+            rate=5,
+            time_step_size=1.0
+        ),
+        time_scale=TimeScale.TIME_STEP
+    )
+    val = float(T.execute(stim)[0])
+    assert val == 50
+
+# ------------------------------------------------------------------------------------------------
+# TEST 4
+# rate = negative
+
+# ******
+# Should this pass?
+# ******
+
+
+def test_DDM_input_rate_negative():
+    stim = [10]
+    T = DDM(
+        name='DDM',
+        default_input_value=[0],
+        function=DriftDiffusionIntegrator(
+            noise=0.0,
+            rate=-5.0,
+            time_step_size=1.0
+        ),
+        time_scale=TimeScale.TIME_STEP
+    )
+    val = float(T.execute(stim)[0])
+    assert val == -50
+
+# ------------------------------------------------------------------------------------------------
+
+# INVALID RATES:
+
+# ------------------------------------------------------------------------------------------------
+# TEST 1
+# rate = fn
+
+# ******
+# Should this pass? (Build in function logic for rate, similar to noise)?
+# Should it fail with a DDM error in validate_params()?
+# ******
+
+
+def test_DDM_rate_fn():
+    with pytest.raises(typecheck.framework.InputParameterError) as error_text:
+        stim = [10]
+        T = DDM(
+            name='DDM',
+            default_input_value=[0],
+            function=DriftDiffusionIntegrator(
+
+                noise=0.0,
+                rate=NormalDist().function,
+                time_step_size=1.0
+            ),
+            time_scale=TimeScale.TIME_STEP
+        )
+        float(T.execute(stim)[0])
+    assert "incompatible value" in str(error_text.value)
+
+# ------------------------------------------------------------------------------------------------
diff --git a/tests/mechanisms/test_gating_mechanism.py b/tests/mechanisms/test_gating_mechanism.py
new file mode 100644
index 00000000000..2356cbf5d20
--- /dev/null
+++ b/tests/mechanisms/test_gating_mechanism.py
@@ -0,0 +1,161 @@
+import numpy as np
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
+
+from PsyNeuLink.Components.Functions.Function import ConstantIntegrator, Logistic
+from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.GatingMechanisms.GatingMechanism import GatingMechanism
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
+from PsyNeuLink.Components.Process import process
+from PsyNeuLink.Components.System import system
+from PsyNeuLink.Globals.Keywords import FUNCTION, FUNCTION_PARAMS, INITIALIZER, LEARNING, RATE, SOFT_CLAMP, VALUE
+from PsyNeuLink.Globals.Preferences.ComponentPreferenceSet import REPORT_OUTPUT_PREF, VERBOSE_PREF
+from PsyNeuLink.Globals.TimeScale import CentralClock
+
+
+def test_gating():
+    Input_Layer = TransferMechanism(
+        name='Input Layer',
+        function=Logistic,
+        default_input_value=np.zeros((2,))
+    )
+
+    Hidden_Layer_1 = TransferMechanism(
+        name='Hidden Layer_1',
+        function=Logistic(),
+        default_input_value=np.zeros((5,))
+    )
+
+    Hidden_Layer_2 = TransferMechanism(
+        name='Hidden Layer_2',
+        function=Logistic(),
+        default_input_value=[0, 0, 0, 0]
+    )
+
+    Output_Layer = TransferMechanism(
+        name='Output Layer',
+        function=Logistic,
+        default_input_value=[0, 0, 0]
+    )
+
+    Gating_Mechanism = GatingMechanism(
+        default_gating_policy=0.0,
+        gating_signals=[
+            Hidden_Layer_1,
+            Hidden_Layer_2,
+            Output_Layer,
+        ]
+    )
+
+    Input_Weights_matrix = (np.arange(2 * 5).reshape((2, 5)) + 1) / (2 * 5)
+    Middle_Weights_matrix = (np.arange(5 * 4).reshape((5, 4)) + 1) / (5 * 4)
+    Output_Weights_matrix = (np.arange(4 * 3).reshape((4, 3)) + 1) / (4 * 3)
+
+    # TEST PROCESS.LEARNING WITH:
+    # CREATION OF FREE STANDING PROJECTIONS THAT HAVE NO LEARNING (Input_Weights, Middle_Weights and Output_Weights)
+    # INLINE CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
+    # NO EXPLICIT CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and
+    # Output_Weights)
+
+    # This projection will be used by the process below by referencing it in the process' pathway;
+    #    note: sender and receiver args don't need to be specified
+    Input_Weights = MappingProjection(
+        name='Input Weights',
+        matrix=Input_Weights_matrix
+    )
+
+    # This projection will be used by the process below by assigning its sender and receiver args
+    #    to mechanismss in the pathway
+    Middle_Weights = MappingProjection(
+        name='Middle Weights',
+        sender=Hidden_Layer_1,
+        receiver=Hidden_Layer_2,
+        matrix={
+            VALUE: Middle_Weights_matrix,
+            FUNCTION: ConstantIntegrator,
+            FUNCTION_PARAMS: {
+                INITIALIZER: Middle_Weights_matrix,
+                RATE: Middle_Weights_matrix
+            },
+        }
+    )
+
+    Output_Weights = MappingProjection(
+        name='Output Weights',
+        sender=Hidden_Layer_2,
+        receiver=Output_Layer,
+        matrix=Output_Weights_matrix
+    )
+
+    z = process(
+        default_input_value=[0, 0],
+        pathway=[
+            Input_Layer,
+            # The following reference to Input_Weights is needed to use it in the pathway
+            # since it's sender and receiver args are not specified in its
+            # declaration above
+            Input_Weights,
+            Hidden_Layer_1,
+            # No projection specification is needed here since the sender arg for Middle_Weights
+            #    is Hidden_Layer_1 and its receiver arg is Hidden_Layer_2
+            # Middle_Weights,
+            Hidden_Layer_2,
+            # Output_Weights does not need to be listed for the same reason as Middle_Weights
+            # If Middle_Weights and/or Output_Weights is not declared above, then the process
+            #    will assign a default for missing projection
+            # Output_Weights,
+            Output_Layer
+        ],
+        clamp_input=SOFT_CLAMP,
+        learning=LEARNING,
+        learning_rate=1.0,
+        target=[0, 0, 1],
+        prefs={
+            VERBOSE_PREF: False,
+            REPORT_OUTPUT_PREF: True
+        }
+    )
+
+    g = process(
+        default_input_value=[1.0],
+        pathway=[Gating_Mechanism]
+    )
+
+    stim_list = {
+        Input_Layer: [[-1, 30]],
+        Gating_Mechanism: [1.0]
+    }
+    target_list = {
+        Output_Layer: [[0, 0, 1]]
+    }
+
+    def print_header():
+        print("\n\n**** TRIAL: ", CentralClock.trial)
+
+    def show_target():
+        i = s.input
+        t = s.targetInputStates[0].value
+        print('\nOLD WEIGHTS: \n')
+        print('- Input Weights: \n', Input_Weights.matrix)
+        print('- Middle Weights: \n', Middle_Weights.matrix)
+        print('- Output Weights: \n', Output_Weights.matrix)
+        print('\nSTIMULI:\n\n- Input: {}\n- Target: {}\n'.format(i, t))
+        print('ACTIVITY FROM OLD WEIGHTS: \n')
+        print('- Middle 1: \n', Hidden_Layer_1.value)
+        print('- Middle 2: \n', Hidden_Layer_2.value)
+        print('- Output:\n', Output_Layer.value)
+
+    s = system(
+        processes=[z, g],
+        targets=[0, 0, 1],
+        learning_rate=1.0
+    )
+
+    s.reportOutputPref = True
+    # s.show_graph(show_learning=True)
+
+    results = s.run(
+        num_executions=10,
+        inputs=stim_list,
+        targets=target_list,
+        call_before_trial=print_header,
+        call_after_trial=show_target,
+    )
diff --git a/tests/mechanisms/test_integrator_mechanism.py b/tests/mechanisms/test_integrator_mechanism.py
new file mode 100644
index 00000000000..2f45db7a2cb
--- /dev/null
+++ b/tests/mechanisms/test_integrator_mechanism.py
@@ -0,0 +1,671 @@
+import numpy as np
+import pytest
+from PsyNeuLink.Components.Mechanisms.Mechanism import MechanismError
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.IntegratorMechanism import IntegratorMechanism
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
+
+from PsyNeuLink.Components.Process import process
+from PsyNeuLink.Components.Functions.Function import Linear, SimpleIntegrator, ConstantIntegrator, AccumulatorIntegrator
+from PsyNeuLink.Components.Functions.Function import AdaptiveIntegrator, DriftDiffusionIntegrator, OrnsteinUhlenbeckIntegrator
+from PsyNeuLink.Components.Functions.Function import FunctionError
+from PsyNeuLink.Globals.Keywords import EXECUTING
+from PsyNeuLink.Globals.TimeScale import TimeScale
+
+# ======================================= FUNCTION TESTS ============================================
+
+# VALID FUNCTIONS:
+
+# ------------------------------------------------------------------------------------------------
+# TEST 1
+# integration_type = simple
+
+
+def test_integrator_simple():
+    I = IntegratorMechanism(
+        name='IntegratorMechanism',
+        function=SimpleIntegrator(
+        ),
+        time_scale=TimeScale.TIME_STEP
+    )
+#     # P = process(pathway=[I])
+
+    #  returns previous_value + rate*variable + noise
+    # so in this case, returns 10.0
+    val = float(I.execute(10))
+
+    # testing initializer
+    I.function_object.reset_initializer = 5.0
+
+    val2 = float(I.execute(0))
+
+    assert [val, val2] == [10.0, 5.0]
+
+# ------------------------------------------------------------------------------------------------
+# TEST 2
+# integration_type = adaptive
+
+
+def test_integrator_adaptive():
+    I = IntegratorMechanism(
+        name='IntegratorMechanism',
+        function=AdaptiveIntegrator(
+            rate=0.5
+        ),
+        time_scale=TimeScale.TIME_STEP
+    )
+    # val = float(I.execute(10)[0])
+    # P = process(pathway=[I])
+    val = float(I.execute(10))
+    # returns (rate)*variable + (1-rate*previous_value) + noise
+    # rate = 1, noise = 0, so in this case, returns 10.0
+
+    # testing initializer
+    I.function_object.reset_initializer = 1.0
+    val2 = float(I.execute(1))
+
+    assert [val, val2] == [5.0, 1.0]
+# ------------------------------------------------------------------------------------------------
+# TEST 3
+# integration_type = constant
+
+
+def test_integrator_constant():
+    I = IntegratorMechanism(
+        name='IntegratorMechanism',
+        function= ConstantIntegrator(
+            rate = 1.0
+        ),
+        time_scale=TimeScale.TIME_STEP
+    )
+    # val = float(I.execute(10)[0])
+    # P = process(pathway=[I])
+    val = float(I.execute())
+    # returns previous_value + rate + noise
+    # rate = 1.0, noise = 0, so in this case returns 1.0
+
+    # testing initializer
+    I.function_object.reset_initializer = 10.0
+    val2 = float(I.execute())
+
+    assert [val, val2] == [1.0, 11.0]
+# ------------------------------------------------------------------------------------------------
+# TEST 4
+# integration_type = diffusion
+
+
+def test_integrator_diffusion():
+    I = IntegratorMechanism(
+        name='IntegratorMechanism',
+        function= DriftDiffusionIntegrator(
+        ),
+        time_scale=TimeScale.TIME_STEP
+    )
+    # val = float(I.execute(10)[0])
+    # P = process(pathway=[I])
+    val = float(I.execute(10))
+
+    # testing initializer
+    I.function_object.reset_initializer = 1.0
+    val2 = float(I.execute(0))
+
+    assert [val, val2] == [10.0, 1.0]
+
+# ------------------------------------------------------------------------------------------------
+
+# INVALID FUNCTIONS:
+
+# ------------------------------------------------------------------------------------------------
+# TEST 1
+# function = Linear
+
+
+# def test_integrator_linear():
+
+#     # SHOULD CAUSE AN ERROR??
+
+#     with pytest.raises(FunctionError) as error_text:
+#         I = IntegratorMechanism(
+#             name='IntegratorMechanism',
+#             function=Linear(),
+#             time_scale=TimeScale.TIME_STEP
+#         )
+#         # val = float(I.execute(10)[0])
+# #         P = process(pathway=[I])
+#         val = float(I.execute(10))
+#     assert val == 10
+
+
+# ======================================= INPUT TESTS ============================================
+
+# VALID INPUT:
+
+# ------------------------------------------------------------------------------------------------
+# TEST 1
+# input = float
+
+def test_integrator_input_float():
+    I = IntegratorMechanism(
+        name='IntegratorMechanism',
+        function=SimpleIntegrator(
+        )
+    )
+    # P = process(pathway=[I])
+    val = float(I.execute(10.0))
+    assert val == 10.0
+
+# ------------------------------------------------------------------------------------------------
+# TEST 2
+# input = list of length 1
+
+
+def test_integrator_input_list():
+    I = IntegratorMechanism(
+        name='IntegratorMechanism',
+        function=SimpleIntegrator(
+        )
+    )
+    # P = process(pathway=[I])
+    val = float(I.execute([10.0]))
+    assert val == 10.0
+
+# ------------------------------------------------------------------------------------------------
+# TEST 3
+# input = list of length 5
+
+
+def test_integrator_input_list_len_5():
+
+    I = IntegratorMechanism(
+        name='IntegratorMechanism',
+        default_input_value=[0, 0, 0, 0, 0],
+        function=SimpleIntegrator(
+        )
+    )
+    # P = process(pathway=[I])
+    val = I.execute([10.0, 5.0, 2.0, 1.0, 0.0])[0]
+    expected_output = [10.0, 5.0, 2.0, 1.0, 0.0]
+
+    for i in range(len(expected_output)):
+        v = val[i]
+        e = expected_output[i]
+        np.testing.assert_allclose(v, e, atol=1e-08, err_msg='Failed on expected_output[{0}]'.format(i))
+
+# ------------------------------------------------------------------------------------------------
+# TEST 4
+# input = numpy array of length 5
+
+
+def test_integrator_input_array_len_5():
+
+    I = IntegratorMechanism(
+        name='IntegratorMechanism',
+        default_input_value=[0, 0, 0, 0, 0],
+        function=SimpleIntegrator(
+        )
+    )
+    # P = process(pathway=[I])
+    input_array = np.array([10.0, 5.0, 2.0, 1.0, 0.0])
+    val = I.execute(input_array)[0]
+    expected_output = [10.0, 5.0, 2.0, 1.0, 0.0]
+
+    for i in range(len(expected_output)):
+        v = val[i]
+        e = expected_output[i]
+        np.testing.assert_allclose(v, e, atol=1e-08, err_msg='Failed on expected_output[{0}]'.format(i))
+
+# ------------------------------------------------------------------------------------------------
+
+# INVALID INPUT:
+
+# ------------------------------------------------------------------------------------------------
+# TEST 1
+# input = list of length > default length
+
+
+def test_integrator_input_array_greater_than_default():
+
+    with pytest.raises(MechanismError) as error_text:
+        I = IntegratorMechanism(
+            name='IntegratorMechanism',
+            default_input_value=[0, 0, 0]
+        )
+        # P = process(pathway=[I])
+        I.execute([10.0, 5.0, 2.0, 1.0, 0.0])
+    assert "does not match required length" in str(error_text)
+
+# ------------------------------------------------------------------------------------------------
+# TEST 2
+# input = list of length < default length
+
+
+def test_integrator_input_array_less_than_default():
+
+    with pytest.raises(MechanismError) as error_text:
+        I = IntegratorMechanism(
+            name='IntegratorMechanism',
+            default_input_value=[0, 0, 0, 0, 0]
+        )
+        # P = process(pathway=[I])
+        I.execute([10.0, 5.0, 2.0])
+    assert "does not match required length" in str(error_text)
+
+# ======================================= RATE TESTS ============================================
+
+# VALID RATE:
+
+# ------------------------------------------------------------------------------------------------
+# TEST 1
+# rate = float, integration_type = simple
+
+
+def test_integrator_type_simple_rate_float():
+    I = IntegratorMechanism(
+        name='IntegratorMechanism',
+        function=SimpleIntegrator(
+            rate=5.0
+        )
+    )
+    # P = process(pathway=[I])
+    val = float(I.execute(10.0))
+    assert val == 50.0
+
+# ------------------------------------------------------------------------------------------------
+# TEST 2
+# rate = float, integration_type = constant
+
+
+def test_integrator_type_constant_rate_float():
+    I = IntegratorMechanism(
+        name='IntegratorMechanism',
+        function= ConstantIntegrator(
+            rate=5.0
+        )
+    )
+    # P = process(pathway=[I])
+    val = float(I.execute(10.0))
+    assert val == 5.0
+
+# ------------------------------------------------------------------------------------------------
+# TEST 3
+# rate = float, integration_type = diffusion
+
+
+def test_integrator_type_diffusion_rate_float():
+    I = IntegratorMechanism(
+        name='IntegratorMechanism',
+        function = DriftDiffusionIntegrator(
+            rate=5.0
+        )
+    )
+    # P = process(pathway=[I])
+    val = float(I.execute(10.0))
+    assert val == 50.0
+
+# ------------------------------------------------------------------------------------------------
+# TEST 4
+# rate = list, integration_type = simple
+
+
+def test_integrator_type_simple_rate_list():
+    I = IntegratorMechanism(
+        name='IntegratorMechanism',
+        default_input_value=[0, 0, 0],
+        function=SimpleIntegrator(
+            rate=[5.0, 5.0, 5.0]
+        )
+    )
+    # P = process(pathway=[I])
+    val = list(I.execute([10.0, 10.0, 10.0])[0])
+    assert val == [50.0, 50.0, 50.0]
+# ------------------------------------------------------------------------------------------------
+# TEST 5
+# rate = list, integration_type = constant
+
+
+def test_integrator_type_constant_rate_list():
+    I = IntegratorMechanism(
+        default_input_value=[0, 0, 0],
+        name='IntegratorMechanism',
+        function= ConstantIntegrator(
+            rate=[5.0, 5.0, 5.0]
+        )
+    )
+    # P = process(pathway=[I])
+    val = list(I.execute([10.0, 10.0, 10.0])[0])
+    assert val == [5.0, 5.0, 5.0]
+
+# ------------------------------------------------------------------------------------------------
+# TEST 6
+# rate = list, integration_type = diffusion
+
+
+def test_integrator_type_diffusion_rate_list():
+    I = IntegratorMechanism(
+        default_input_value=[0, 0, 0],
+        name='IntegratorMechanism',
+        function = DriftDiffusionIntegrator(
+            rate=[5.0, 5.0, 5.0]
+        )
+    )
+    # P = process(pathway=[I])
+    val = list(I.execute([10.0, 10.0, 10.0])[0])
+    assert val == [50.0, 50.0, 50.0]
+
+# ------------------------------------------------------------------------------------------------
+# TEST 7
+# rate = list, integration_type = diffusion
+
+
+def test_integrator_type_adaptive_rate_list():
+    I = IntegratorMechanism(
+        default_input_value=[0, 0, 0],
+        name='IntegratorMechanism',
+        function=AdaptiveIntegrator(
+            rate=[0.5, 0.5, 0.5]
+        )
+    )
+    # P = process(pathway=[I])
+    val = list(I.execute([10.0, 10.0, 10.0])[0])
+    assert val == [5.0, 5.0, 5.0]
+
+# ------------------------------------------------------------------------------------------------
+# TEST 8
+# rate = float, integration_type = adaptive
+
+
+def test_integrator_type_adaptive_rate_float_input_list():
+    I = IntegratorMechanism(
+        default_input_value=[0, 0, 0],
+        name='IntegratorMechanism',
+        function=AdaptiveIntegrator(
+            rate=0.5
+        )
+    )
+    # P = process(pathway=[I])
+    val = list(I.execute([10.0, 10.0, 10.0])[0])
+    assert val == [5.0, 5.0, 5.0]
+
+# ------------------------------------------------------------------------------------------------
+# TEST 9
+# rate = float, integration_type = adaptive
+
+
+def test_integrator_type_adaptive_rate_float():
+    I = IntegratorMechanism(
+        name='IntegratorMechanism',
+        function=AdaptiveIntegrator(
+            rate=0.5
+        )
+    )
+    # P = process(pathway=[I])
+    val = list(I.execute(10.0))
+    assert val == [5.0]
+
+# ------------------------------------------------------------------------------------------------
+
+# INVALID INPUT:
+
+# ------------------------------------------------------------------------------------------------
+# TEST 1
+# rate = list of length > default length
+# integration_type = SIMPLE
+
+
+def test_integrator_type_simple_rate_list_input_float():
+    with pytest.raises(FunctionError) as error_text:
+        I = IntegratorMechanism(
+            name='IntegratorMechanism',
+            function=SimpleIntegrator(
+
+                rate=[5.0, 5.0, 5.0]
+            )
+        )
+        # P = process(pathway=[I])
+        float(I.execute(10.0))
+    assert (
+        "array specified for the rate parameter" in str(error_text)
+        and "must match the length" in str(error_text)
+        and "of the default input" in str(error_text)
+    )
+# ------------------------------------------------------------------------------------------------
+# TEST 2
+# rate = list of length > default length
+# integration_type = CONSTANT
+
+
+def test_integrator_type_constant_rate_list_input_float():
+    with pytest.raises(FunctionError) as error_text:
+        I = IntegratorMechanism(
+            name='IntegratorMechanism',
+            function=ConstantIntegrator(
+                rate=[5.0, 5.0, 5.0]
+            )
+        )
+        # P = process(pathway=[I])
+        float(I.execute(10.0))
+    assert (
+        "array specified for the rate parameter" in str(error_text)
+        and "must match the length" in str(error_text)
+        and "of the default input" in str(error_text)
+    )
+
+# ------------------------------------------------------------------------------------------------
+# TEST 3
+# rate = list of length > default length
+# integration_type = DIFFUSION
+
+
+def test_integrator_type_diffusion_rate_list_input_float():
+    with pytest.raises(FunctionError) as error_text:
+        I = IntegratorMechanism(
+            name='IntegratorMechanism',
+            function=DriftDiffusionIntegrator(
+                rate=[5.0, 5.0, 5.0]
+            )
+        )
+        # P = process(pathway=[I])
+        float(I.execute(10.0))
+    assert (
+        "array specified for the rate parameter" in str(error_text)
+        and "must match the length" in str(error_text)
+        and "of the default input" in str(error_text)
+    )
+
+
+## NEW INTEGRATOR FUNCTIONS ------------------------------------
+
+
+# ------------------------------------------------------------------------------------------------
+# TEST 1
+
+def test_simple_integrator():
+    I = IntegratorMechanism(
+            function = SimpleIntegrator(
+                initializer = 10.0,
+                rate = 5.0,
+                offset = 10,
+            )
+        )
+    # P = process(pathway=[I])
+    val = I.execute(1)
+    assert val == 25
+
+
+# ------------------------------------------------------------------------------------------------
+
+
+# ------------------------------------------------------------------------------------------------
+# TEST 2
+
+def test_constant_integrator():
+    I = IntegratorMechanism(
+            function = ConstantIntegrator(
+                initializer = 10.0,
+                rate = 5.0,
+                offset = 10
+            )
+        )
+    # P = process(pathway=[I])
+    # constant integrator does not use input value (self.variable)
+
+    # step 1:
+    val = I.execute(20000)
+    # value = 10 + 5
+    # adjusted_value = 15 + 10
+    # previous_value = 25
+    # RETURN 25
+
+    # step 2:
+    val2 = I.execute(70000)
+    # value = 25 + 5
+    # adjusted_value = 30 + 10
+    # previous_value = 30
+    # RETURN 40
+    assert (val, val2) == (25, 40)
+
+
+# ------------------------------------------------------------------------------------------------
+
+# ------------------------------------------------------------------------------------------------
+# TEST 3
+
+def test_adaptive_integrator():
+    I = IntegratorMechanism(
+            function = AdaptiveIntegrator(
+                initializer = 10.0,
+                rate = 0.5,
+                offset = 10,
+            )
+        )
+    # P = process(pathway=[I])
+    # 10*0.5 + 1*0.5 + 10
+    val = I.execute(1)
+    assert val == 15.5
+
+
+# ------------------------------------------------------------------------------------------------
+
+
+# ------------------------------------------------------------------------------------------------
+# TEST 4
+
+def test_drift_diffusion_integrator():
+    I = IntegratorMechanism(
+            function = DriftDiffusionIntegrator(
+                initializer = 10.0,
+                rate = 10,
+                time_step_size = 0.5,
+                offset=10,
+            )
+        )
+    # P = process(pathway=[I])
+    # 10 + 10*0.5 + 0 + 10 = 25
+    val = I.execute(1)
+    assert val == 25
+
+
+# ------------------------------------------------------------------------------------------------
+
+# ------------------------------------------------------------------------------------------------
+# TEST 5
+
+def test_ornstein_uhlenbeck_integrator():
+    I = IntegratorMechanism(
+            function = OrnsteinUhlenbeckIntegrator(
+                initializer = 10.0,
+                rate = 10,
+                time_step_size=0.5,
+                decay = 0.1,
+                offset=10,
+            )
+        )
+    # P = process(pathway=[I])
+    # value = previous_value + decay * rate * new_value * time_step_size + np.sqrt(
+            #time_step_size * noise) * np.random.normal()
+    # step 1:
+    val = I.execute(1)
+    # value = 10 + 0.1*10*1*0.5 + 0
+    # adjusted_value = 10.5 + 10
+    # previous_value = 20.5
+    # RETURN 20.5
+
+    # step 2:
+    val2 = I.execute(1)
+    # value = 20.5 + 0.1*10*1*0.5 + 0
+    # adjusted_value = 21 + 10
+    # previous_value = 31
+    # RETURN 31
+
+    # step 3:
+    val3 = I.execute(1)
+    # value = 31 + 0.1*10*1*0.5 + 0
+    # adjusted_value = 31.5 + 10
+    # previous_value = 41.5
+    # RETURN 41.5
+
+    assert (val, val2, val3) == (20.5, 31, 41.5)
+# ------------------------------------------------------------------------------------------------
+# Test 6
+# ------------------------------------------------------------------------------------------------
+def test_integrator_no_function():
+    I = IntegratorMechanism(time_scale=TimeScale.TIME_STEP)
+    # P = process(pathway=[I])
+    val = float(I.execute(10))
+    assert val == 5
+
+
+# # ------------------------------------------------------------------------------------------------
+# # Test 7
+# # ------------------------------------------------------------------------------------------------
+# #
+# def test_accumulator_integrator():
+#     I = IntegratorMechanism(
+#             function = AccumulatorIntegrator(
+#                 initializer = 10.0,
+#                 rate = 5.0,
+#                 increment= 1.0
+#             )
+#         )
+# #     P = process(pathway=[I])
+
+#     # value = previous_value * rate + noise + increment
+#     # step 1:
+#     val = I.execute()
+#     # value = 10.0 * 5.0 + 0 + 1.0
+#     # RETURN 51
+
+#     # step 2:
+#     val2 = I.execute(2000)
+#     # value = 51*5 + 0 + 1.0
+#     # RETURN 256
+#     assert (val, val2) == (51, 256)
+
+def test_mechanisms_without_system_or_process():
+    I = IntegratorMechanism(
+            name='IntegratorMechanism',
+            function=SimpleIntegrator(
+            ),
+            time_scale=TimeScale.TIME_STEP
+        )
+    T = TransferMechanism(function=Linear(slope=2.0, intercept=5.0))
+    M = MappingProjection(sender=I, receiver=T)
+
+    res1 = float(I.execute(10, context=EXECUTING))
+    res2 = float(T.execute(context=EXECUTING))
+    assert (res1, res2) == (10,25)
+
+def test_mechanisms_without_system_or_process_no_input():
+    I = IntegratorMechanism(
+            name='IntegratorMechanism',
+            default_input_value= 10,
+            function=SimpleIntegrator(
+            ),
+            time_scale=TimeScale.TIME_STEP
+        )
+    T = TransferMechanism(function=Linear(slope=2.0, intercept=5.0))
+    M = MappingProjection(sender=I, receiver=T)
+
+    res1 = float(I.execute(context=EXECUTING))
+    res2 = float(T.execute(context=EXECUTING))
+    assert (res1, res2) == (10, 25)
diff --git a/tests/mechanisms/test_transfer_mechanism.py b/tests/mechanisms/test_transfer_mechanism.py
new file mode 100644
index 00000000000..422732ccc5f
--- /dev/null
+++ b/tests/mechanisms/test_transfer_mechanism.py
@@ -0,0 +1,635 @@
+import numpy as np
+import pytest
+
+from PsyNeuLink.Components.Component import ComponentError
+from PsyNeuLink.Components.Mechanisms.Mechanism import MechanismError
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferError
+from PsyNeuLink.Components.Functions.Function import Exponential, ConstantIntegrator, Linear, Logistic, Reduce, Reinforcement, SoftMax
+from PsyNeuLink.Components.Functions.Function import ExponentialDist, GammaDist, NormalDist, UniformDist, WaldDist
+from PsyNeuLink.Globals.TimeScale import TimeScale
+from PsyNeuLink.Globals.Utilities import UtilitiesError
+
+# ======================================= INPUT TESTS ============================================
+
+# VALID INPUTS
+
+# ------------------------------------------------------------------------------------------------
+# TEST 1
+# variable = list of ints
+
+
+def test_transfer_mech_inputs_list_of_ints():
+
+    T = TransferMechanism(
+        name='T',
+        default_input_value=[0, 0, 0, 0],
+        time_scale=TimeScale.TIME_STEP
+    )
+    val = T.execute([10, 10, 10, 10]).tolist()
+    assert val == [[10.0, 10.0, 10.0, 10.0]]
+
+# ------------------------------------------------------------------------------------------------
+# TEST 2
+# variable = list of floats
+
+
+def test_transfer_mech_inputs_list_of_floats():
+
+    T = TransferMechanism(
+        name='T',
+        default_input_value=[0, 0, 0, 0],
+        time_scale=TimeScale.TIME_STEP
+    )
+    val = T.execute([10.0, 10.0, 10.0, 10.0]).tolist()
+    assert val == [[10.0, 10.0, 10.0, 10.0]]
+
+# ------------------------------------------------------------------------------------------------
+# TEST 3
+# variable = list of fns
+
+# ******
+# Should transfer mech specifically support taking the output of a PNL function as input & reformat it to make sense?
+# AND/OR Should transfer mech allow functions to be passed in as input and know how to execute them
+# ******
+
+
+def test_transfer_mech_inputs_list_of_fns():
+
+    T = TransferMechanism(
+        name='T',
+        default_input_value=[0, 0, 0, 0],
+        time_scale=TimeScale.TIME_STEP
+    )
+    val = T.execute([Linear().execute(), NormalDist().execute(), Exponential().execute(), ExponentialDist().execute()]).tolist()
+    assert val == [[np.array([0.]), 0.4001572083672233, np.array([1.]), 0.7872011523172707]]
+
+# ------------------------------------------------------------------------------------------------
+
+# INVALID INPUTS
+
+# ------------------------------------------------------------------------------------------------
+# TEST 1
+# variable = list of strings
+
+
+def test_transfer_mech_inputs_list_of_strings():
+    with pytest.raises(UtilitiesError) as error_text:
+        T = TransferMechanism(
+            name='T',
+            default_input_value=[0, 0, 0, 0],
+            time_scale=TimeScale.TIME_STEP
+        )
+        T.execute(["one", "two", "three", "four"]).tolist()
+    assert "has non-numeric entries" in str(error_text.value)
+
+# ------------------------------------------------------------------------------------------------
+# TEST 2
+# variable = list of greater length than default input
+
+
+def test_transfer_mech_inputs_mismatched_with_default_longer():
+    with pytest.raises(MechanismError) as error_text:
+        T = TransferMechanism(
+            name='T',
+            default_input_value=[0, 0, 0, 0],
+            time_scale=TimeScale.TIME_STEP
+        )
+        T.execute([1, 2, 3, 4, 5]).tolist()
+    assert "does not match required length" in str(error_text.value)
+
+# ------------------------------------------------------------------------------------------------
+
+# TEST 3
+# variable = list of shorter length than default input
+
+
+def test_transfer_mech_inputs_mismatched_with_default_shorter():
+    with pytest.raises(MechanismError) as error_text:
+        T = TransferMechanism(
+            name='T',
+            default_input_value=[0, 0, 0, 0, 0, 0],
+            time_scale=TimeScale.TIME_STEP
+        )
+        T.execute([1, 2, 3, 4, 5]).tolist()
+    assert "does not match required length" in str(error_text.value)
+
+# ======================================= NOISE TESTS ============================================
+
+# VALID NOISE:
+
+# ------------------------------------------------------------------------------------------------
+# TEST 1
+# variable = List
+# noise = Single float
+
+
+def test_transfer_mech_array_var_float_noise():
+
+    T = TransferMechanism(
+        name='T',
+        default_input_value=[0, 0, 0, 0],
+        function=Linear(),
+        noise=5.0,
+        time_constant=1.0,
+        time_scale=TimeScale.TIME_STEP
+    )
+    val = T.execute([0, 0, 0, 0]).tolist()
+    assert val == [[5.0, 5.0, 5.0, 5.0]]
+
+# ------------------------------------------------------------------------------------------------
+# TEST 2
+# variable = List
+# noise = Single function
+
+
+def test_transfer_mech_array_var_normal_len_1_noise():
+
+    T = TransferMechanism(
+        name='T',
+        default_input_value=[0, 0, 0, 0],
+        function=Linear(),
+        noise=NormalDist().function,
+        time_constant=1.0,
+        time_scale=TimeScale.TIME_STEP
+    )
+    val = T.execute([0, 0, 0, 0]).tolist()
+    assert val == [[2.240893199201458, 2.240893199201458, 2.240893199201458, 2.240893199201458]]
+
+# ------------------------------------------------------------------------------------------------
+# TEST 3
+# variable = List
+# noise = List of functions
+
+
+def test_transfer_mech_array_var_normal_array_noise():
+
+    T = TransferMechanism(
+        name='T',
+        default_input_value=[0, 0, 0, 0],
+        function=Linear(),
+        noise=[NormalDist().function, NormalDist().function, NormalDist().function, NormalDist().function],
+        time_constant=1.0,
+        time_scale=TimeScale.TIME_STEP
+    )
+    val = T.execute([0, 0, 0, 0]).tolist()
+    assert val == [[0.7610377251469934, 0.12167501649282841, 0.44386323274542566, 0.33367432737426683]]
+
+# ------------------------------------------------------------------------------------------------
+# TEST 4
+# variable = List
+# noise = List of floats
+
+
+def test_transfer_mech_array_var_normal_array_noise2():
+
+    T = TransferMechanism(
+        name='T',
+        default_input_value=[0, 0, 0, 0],
+        function=Linear(),
+        noise=[5.0, 5.0, 5.0, 5.0],
+        time_constant=1.0,
+        time_scale=TimeScale.TIME_STEP
+    )
+    val = T.execute([0, 0, 0, 0]).tolist()
+    assert val == [[5.0, 5.0, 5.0, 5.0]]
+
+# ------------------------------------------------------------------------------------------------
+# TEST 5
+# Noise distribution: Normal
+
+
+def test_transfer_mech_normal_noise():
+
+    T = TransferMechanism(
+        name='T',
+        default_input_value=[0, 0, 0, 0],
+        function=Linear(),
+        noise=NormalDist().function,
+        time_constant=1.0,
+        time_scale=TimeScale.TIME_STEP
+    )
+    val = T.execute([0, 0, 0, 0]).tolist()
+    assert val == [[2.240893199201458, 2.240893199201458, 2.240893199201458, 2.240893199201458]]
+
+# ------------------------------------------------------------------------------------------------
+# TEST 6
+# Noise distribution: Exponential
+
+
+def test_transfer_mech_exponential_noise():
+
+    T = TransferMechanism(
+        name='T',
+        default_input_value=[0, 0, 0, 0],
+        function=Linear(),
+        noise=ExponentialDist().function,
+        time_constant=1.0,
+        time_scale=TimeScale.TIME_STEP
+    )
+    val = T.execute([0, 0, 0, 0]).tolist()
+    assert val == [[0.7872011523172707, 0.7872011523172707, 0.7872011523172707, 0.7872011523172707]]
+
+# ------------------------------------------------------------------------------------------------
+# TEST 7
+# Noise distribution: Uniform
+
+
+def test_transfer_mech_Uniform_noise():
+
+    T = TransferMechanism(
+        name='T',
+        default_input_value=[0, 0, 0, 0],
+        function=Linear(),
+        noise=UniformDist().function,
+        time_constant=1.0,
+        time_scale=TimeScale.TIME_STEP
+    )
+    val = T.execute([0, 0, 0, 0]).tolist()
+    assert val == [[0.5448831829968969, 0.5448831829968969, 0.5448831829968969, 0.5448831829968969]]
+
+# ------------------------------------------------------------------------------------------------
+# TEST 8
+# Noise distribution: Gamma
+
+
+def test_transfer_mech_Gamma_noise():
+
+    T = TransferMechanism(
+        name='T',
+        default_input_value=[0, 0, 0, 0],
+        function=Linear(),
+        noise=GammaDist().function,
+        time_constant=1.0,
+        time_scale=TimeScale.TIME_STEP
+    )
+    val = T.execute([0, 0, 0, 0]).tolist()
+    assert val == [[0.7872011523172707, 0.7872011523172707, 0.7872011523172707, 0.7872011523172707]]
+
+# ------------------------------------------------------------------------------------------------
+# TEST 9
+# Noise distribution: Wald
+
+
+def test_transfer_mech_Wald_noise():
+
+    T = TransferMechanism(
+        name='T',
+        default_input_value=[0, 0, 0, 0],
+        function=Linear(),
+        noise=WaldDist().function,
+        time_constant=1.0,
+        time_scale=TimeScale.TIME_STEP
+    )
+    val = T.execute([0, 0, 0, 0]).tolist()
+    assert val == [[2.5652458745028266, 2.5652458745028266, 2.5652458745028266, 2.5652458745028266]]
+
+# ------------------------------------------------------------------------------------------------
+
+# INVALID NOISE:
+
+# ------------------------------------------------------------------------------------------------
+# TEST 1
+# variable = List
+# noise = Int
+
+
+def test_transfer_mech_integer_noise():
+    with pytest.raises(MechanismError) as error_text:
+        T = TransferMechanism(
+            name='T',
+            default_input_value=[0, 0],
+            function=Linear(),
+            noise=5,
+            time_constant=0.1,
+            time_scale=TimeScale.TIME_STEP
+        )
+        T.execute([0, 0])
+    assert 'noise parameter' in str(error_text.value)
+
+# ------------------------------------------------------------------------------------------------
+# TEST 2
+# variable = List
+# noise = List of incorrect shape (>var)
+
+
+def test_transfer_mech_mismatched_shape_noise():
+    with pytest.raises(MechanismError) as error_text:
+        T = TransferMechanism(
+            name='T',
+            default_input_value=[0, 0],
+            function=Linear(),
+            noise=[5.0, 5.0, 5.0],
+            time_constant=0.1,
+            time_scale=TimeScale.TIME_STEP
+        )
+        T.execute()
+    assert 'noise parameter' in str(error_text.value)
+
+# ------------------------------------------------------------------------------------------------
+# TEST 3
+# variable = List
+# noise = List of incorrect shape (<var)
+
+
+def test_transfer_mech_mismatched_shape_noise_2():
+    with pytest.raises(MechanismError) as error_text:
+
+        T = TransferMechanism(
+            name='T',
+            default_input_value=[0, 0, 0],
+            function=Linear(),
+            noise=[5.0, 5.0],
+            time_constant=0.1,
+            time_scale=TimeScale.TIME_STEP
+        )
+        T.execute()
+    assert 'noise parameter' in str(error_text.value)
+
+# ------------------------------------------------------------------------------------------------
+
+
+# ====================================== FUNCTION TESTS ==========================================
+
+# VALID FUNCTIONS:
+
+# ------------------------------------------------------------------------------------------------
+# TEST 1
+# function = logistic
+
+def test_transfer_mech_logistic_fun():
+
+    T = TransferMechanism(
+        name='T',
+        default_input_value=[0, 0, 0, 0],
+        function=Logistic(),
+        time_constant=1.0,
+        time_scale=TimeScale.TIME_STEP
+    )
+    val = T.execute([0, 0, 0, 0]).tolist()
+    assert val == [[0.5, 0.5, 0.5, 0.5]]
+
+# ------------------------------------------------------------------------------------------------
+# TEST 2
+# function = exponential
+
+
+def test_transfer_mech_exponential_fun():
+
+    T = TransferMechanism(
+        name='T',
+        default_input_value=[0, 0, 0, 0],
+        function=Exponential(),
+        time_constant=1.0,
+        time_scale=TimeScale.TIME_STEP
+    )
+    val = T.execute([0, 0, 0, 0]).tolist()
+    assert val == [[1.0, 1.0, 1.0, 1.0]]
+
+# ------------------------------------------------------------------------------------------------
+# TEST 3
+# function = soft max
+
+
+def test_transfer_mech_softmax_fun():
+
+    T = TransferMechanism(
+        name='T',
+        default_input_value=[0, 0, 0, 0],
+        function=SoftMax(),
+        time_constant=1.0,
+        time_scale=TimeScale.TIME_STEP
+    )
+    val = T.execute([0, 0, 0, 0]).tolist()
+    assert val == [[1.0, 1.0, 1.0, 1.0]]
+
+# ------------------------------------------------------------------------------------------------
+# TEST 4
+# function = linear matrix
+
+
+# def test_transfer_mech_linearmatrix_fun():
+
+#     T = TransferMechanism(
+#         name='T',
+#         default_input_value=[0, 0, 0, 0],
+#         function=LinearMatrix(),
+#         time_constant=1.0,
+#         time_scale=TimeScale.TIME_STEP
+#     )
+#     val = T.execute([0, 0, 0, 0]).tolist()
+#     assert val == [[1.0, 1.0, 1.0, 1.0]]
+
+# ------------------------------------------------------------------------------------------------
+
+# INVALID FUNCTIONS:
+
+# ------------------------------------------------------------------------------------------------
+# TEST 1
+# function = Distribution Function
+
+
+def test_transfer_mech_normal_fun():
+    with pytest.raises(TransferError) as error_text:
+        T = TransferMechanism(
+            name='T',
+            default_input_value=[0, 0, 0, 0],
+            function=NormalDist(),
+            time_constant=1.0,
+            time_scale=TimeScale.TIME_STEP
+        )
+        T.execute([0, 0, 0, 0]).tolist()
+    assert "must be a TRANSFER FUNCTION TYPE" in str(error_text.value)
+
+
+# ------------------------------------------------------------------------------------------------
+# TEST 2
+# function = Learning Function
+
+def test_transfer_mech_reinforcement_fun():
+    with pytest.raises(TransferError) as error_text:
+        T = TransferMechanism(
+            name='T',
+            default_input_value=[0, 0, 0, 0],
+            function=Reinforcement(),
+            time_constant=1.0,
+            time_scale=TimeScale.TIME_STEP
+        )
+        T.execute([0, 0, 0, 0]).tolist()
+    assert "must be a TRANSFER FUNCTION TYPE" in str(error_text.value)
+
+# ------------------------------------------------------------------------------------------------
+# TEST 3
+# function = Integrator Function
+
+
+def test_transfer_mech_integrator_fun():
+    with pytest.raises(TransferError) as error_text:
+        T = TransferMechanism(
+            name='T',
+            default_input_value=[0, 0, 0, 0],
+            function=ConstantIntegrator(),
+            time_constant=1.0,
+            time_scale=TimeScale.TIME_STEP
+        )
+        T.execute([0, 0, 0, 0]).tolist()
+    assert "must be a TRANSFER FUNCTION TYPE" in str(error_text.value)
+
+# ------------------------------------------------------------------------------------------------
+# TEST 4
+# function = Combination Function
+
+
+def test_transfer_mech_reduce_fun():
+    with pytest.raises(TransferError) as error_text:
+        T = TransferMechanism(
+            name='T',
+            default_input_value=[0, 0, 0, 0],
+            function=Reduce(),
+            time_constant=1.0,
+            time_scale=TimeScale.TIME_STEP
+        )
+        T.execute([0, 0, 0, 0]).tolist()
+    assert "must be a TRANSFER FUNCTION TYPE" in str(error_text.value)
+
+
+# ======================================= TIME_CONSTANT TESTS ============================================
+
+# VALID TIME_CONSTANT PARAMS:
+
+# ------------------------------------------------------------------------------------------------
+# TEST 1
+# time_constant = 0.8
+
+def test_transfer_mech_time_constant_0_8():
+    T = TransferMechanism(
+        name='T',
+        default_input_value=[0, 0, 0, 0],
+        function=Linear(),
+        time_constant=0.8,
+        time_scale=TimeScale.TIME_STEP
+    )
+    val = T.execute([1, 1, 1, 1]).tolist()
+    assert val == [[0.8, 0.8, 0.8, 0.8]]
+
+# ------------------------------------------------------------------------------------------------
+# TEST 2
+# time_constant = 1.0
+
+
+def test_transfer_mech_time_constant_1_0():
+    T = TransferMechanism(
+        name='T',
+        default_input_value=[0, 0, 0, 0],
+        function=Linear(),
+        time_constant=1.0,
+        time_scale=TimeScale.TIME_STEP
+    )
+    val = T.execute([1, 1, 1, 1]).tolist()
+    assert val == [[1.0, 1.0, 1.0, 1.0]]
+
+# ------------------------------------------------------------------------------------------------
+# TEST 1
+# time_constant = 0.0
+
+
+def test_transfer_mech_time_constant_0_0():
+    T = TransferMechanism(
+        name='T',
+        default_input_value=[0, 0, 0, 0],
+        function=Linear(),
+        time_constant=0.0,
+        time_scale=TimeScale.TIME_STEP
+    )
+    val = T.execute([1, 1, 1, 1]).tolist()
+    assert val == [[0.0, 0.0, 0.0, 0.0]]
+
+# ------------------------------------------------------------------------------------------------
+
+# INVALID TIME_CONSTANT PARAMS:
+
+# ------------------------------------------------------------------------------------------------
+# TEST 1
+# time_constant = [0.8,0.8,0.8,0.8]
+
+# *******
+# Should PNL support a time_constant list with a separate value for each input?
+# *******
+
+
+def test_transfer_mech_time_constant_0_8_list():
+    with pytest.raises(ComponentError) as error_text:
+        T = TransferMechanism(
+            name='T',
+            default_input_value=[0, 0, 0, 0],
+            function=Linear(),
+            time_constant=[0.8, 0.8, 0.8, 0.8],
+            time_scale=TimeScale.TIME_STEP
+        )
+        T.execute([1, 1, 1, 1]).tolist()
+    assert (
+        "Value of time_constant param" in str(error_text.value)
+        and "must be a float" in str(error_text.value)
+    )
+
+# ------------------------------------------------------------------------------------------------
+# TEST 2
+# time_constant = 2
+
+
+def test_transfer_mech_time_constant_2():
+    with pytest.raises(TransferError) as error_text:
+        T = TransferMechanism(
+            name='T',
+            default_input_value=[0, 0, 0, 0],
+            function=Linear(),
+            time_constant=2,
+            time_scale=TimeScale.TIME_STEP
+        )
+        T.execute([1, 1, 1, 1]).tolist()
+    assert (
+        "time_constant parameter" in str(error_text.value)
+        and "must be a float between 0 and 1" in str(error_text.value)
+    )
+
+# ------------------------------------------------------------------------------------------------
+# TEST 3
+# time_constant = 1
+
+
+def test_transfer_mech_time_constant_1():
+    with pytest.raises(TransferError) as error_text:
+        T = TransferMechanism(
+            name='T',
+            default_input_value=[0, 0, 0, 0],
+            function=Linear(),
+            time_constant=1,
+            time_scale=TimeScale.TIME_STEP
+        )
+        T.execute([1, 1, 1, 1]).tolist()
+    assert (
+        "time_constant parameter" in str(error_text.value)
+        and "must be a float between 0 and 1" in str(error_text.value)
+    )
+
+# ------------------------------------------------------------------------------------------------
+# TEST 4
+# time_constant = 1
+
+
+def test_transfer_mech_time_constant_0():
+    with pytest.raises(TransferError) as error_text:
+        T = TransferMechanism(
+            name='T',
+            default_input_value=[0, 0, 0, 0],
+            function=Linear(),
+            time_constant=0,
+            time_scale=TimeScale.TIME_STEP
+        )
+        T.execute([1, 1, 1, 1]).tolist()
+    assert (
+        "time_constant parameter" in str(error_text.value)
+        and "must be a float between 0 and 1" in str(error_text.value)
+    )
+
+# ------------------------------------------------------------------------------------------------
diff --git a/tests/no-learning/test_DDM.py b/tests/no-learning/test_DDM.py
new file mode 100644
index 00000000000..08b3f2057b9
--- /dev/null
+++ b/tests/no-learning/test_DDM.py
@@ -0,0 +1,63 @@
+import numpy as np
+
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import DDM
+from PsyNeuLink.Components.Process import process
+from PsyNeuLink.Components.Functions.Function import BogaczEtAl
+from PsyNeuLink.Globals.Keywords import IDENTITY_MATRIX, FULL_CONNECTIVITY_MATRIX
+
+
+# does not run a system, can be used to ensure that running processes alone still works
+def test_DDM():
+    myMechanism = DDM(
+        function=BogaczEtAl(
+            drift_rate=(1.0),
+            threshold=(10.0),
+            starting_point=0.0,
+        ),
+        name='My_DDM',
+    )
+
+    myMechanism_2 = DDM(
+        function=BogaczEtAl(
+            drift_rate=2.0,
+            threshold=20.0),
+        name='My_DDM_2'
+    )
+
+    myMechanism_3 = DDM(
+        function=BogaczEtAl(
+            drift_rate=3.0,
+            threshold=30.0
+        ),
+        name='My_DDM_3',
+    )
+
+    z = process(
+        default_input_value=[[30], [10]],
+        pathway=[
+            myMechanism,
+            (IDENTITY_MATRIX),
+            myMechanism_2,
+            (FULL_CONNECTIVITY_MATRIX),
+            myMechanism_3
+        ],
+    )
+
+    result = z.execute([[30], [10]])
+
+    expected_output = [
+        (myMechanism.input_states[0].value, np.array([40.])),
+        (myMechanism.output_states[0].value, np.array([10.])),
+        (myMechanism_2.input_states[0].value, np.array([10.])),
+        (myMechanism_2.output_states[0].value, np.array([20.])),
+        (myMechanism_3.input_states[0].value, np.array([20.])),
+        (myMechanism_3.output_states[0].value, np.array([30.])),
+        (result, np.array([30.])),
+    ]
+
+    for i in range(len(expected_output)):
+        val, expected = expected_output[i]
+        # setting absolute tolerance to be in accordance with reference_output precision
+        # if you do not specify, assert_allcose will use a relative tolerance of 1e-07,
+        # which WILL FAIL unless you gather higher precision values to use as reference
+        np.testing.assert_allclose(val, expected, atol=1e-08, err_msg='Failed on expected_output[{0}]'.format(i))
diff --git a/tests/no-learning/test_no-learning.py b/tests/no-learning/test_no-learning.py
new file mode 100644
index 00000000000..7b2b0538860
--- /dev/null
+++ b/tests/no-learning/test_no-learning.py
@@ -0,0 +1,95 @@
+import logging
+import numpy as np
+
+from PsyNeuLink.Components.Functions.Function import Logistic
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
+from PsyNeuLink.Components.Process import process
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
+from PsyNeuLink.Components.System import system
+from PsyNeuLink.Globals.Preferences.ComponentPreferenceSet import REPORT_OUTPUT_PREF, VERBOSE_PREF
+from PsyNeuLink.Globals.Keywords import SOFT_CLAMP
+
+logger = logging.getLogger(__name__)
+
+
+class TestNoLearning:
+
+    def test_multilayer(self):
+        Input_Layer = TransferMechanism(
+            name='Input Layer',
+            function=Logistic,
+            default_input_value=np.zeros((2,)),
+        )
+
+        Hidden_Layer_1 = TransferMechanism(
+            name='Hidden Layer_1',
+            function=Logistic(),
+            default_input_value=np.zeros((5,)),
+        )
+
+        Hidden_Layer_2 = TransferMechanism(
+            name='Hidden Layer_2',
+            function=Logistic(),
+            default_input_value=[0, 0, 0, 0],
+        )
+
+        Output_Layer = TransferMechanism(
+            name='Output Layer',
+            function=Logistic,
+            default_input_value=[0, 0, 0],
+        )
+
+        Input_Weights_matrix = (np.arange(2 * 5).reshape((2, 5)) + 1) / (2 * 5)
+
+        # TEST PROCESS.LEARNING WITH:
+        # CREATION OF FREE STANDING PROJECTIONS THAT HAVE NO LEARNING (Input_Weights, Middle_Weights and Output_Weights)
+        # INLINE CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
+        # NO EXPLICIT CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
+
+        # This projection will be used by the process below by referencing it in the process' pathway;
+        #    note: sender and receiver args don't need to be specified
+        Input_Weights = MappingProjection(
+            name='Input Weights',
+            matrix=Input_Weights_matrix,
+        )
+
+        p = process(
+            default_input_value=[0, 0],
+            pathway=[
+                Input_Layer,
+                # The following reference to Input_Weights is needed to use it in the pathway
+                #    since it's sender and receiver args are not specified in its declaration above
+                Input_Weights,
+                Hidden_Layer_1,
+                # No projection specification is needed here since the sender arg for Middle_Weights
+                #    is Hidden_Layer_1 and its receiver arg is Hidden_Layer_2
+                # Middle_Weights,
+                Hidden_Layer_2,
+                # Output_Weights does not need to be listed for the same reason as Middle_Weights
+                # If Middle_Weights and/or Output_Weights is not declared above, then the process
+                #    will assign a default for missing projection
+                # Output_Weights,
+                Output_Layer
+            ],
+            clamp_input=SOFT_CLAMP,
+            target=[0, 0, 1],
+            prefs={
+                VERBOSE_PREF: False,
+                REPORT_OUTPUT_PREF: True
+            }
+        )
+
+        s = system(processes=[p])
+
+        s.reportOutputPref = True
+
+        stim_list = {Input_Layer: [[-1, 30]]}
+
+        s.run(
+            num_executions=10,
+            inputs=stim_list,
+        )
+
+        expected_Output_Layer_output = [np.array([0.97988347, 0.97988347, 0.97988347])]
+
+        np.testing.assert_allclose(expected_Output_Layer_output, Output_Layer.output_values)
diff --git a/tests/scheduling/test_system_newsched.py b/tests/scheduling/test_system_newsched.py
new file mode 100644
index 00000000000..fa69a754e41
--- /dev/null
+++ b/tests/scheduling/test_system_newsched.py
@@ -0,0 +1,956 @@
+import numpy
+
+from PsyNeuLink.Components.System import system
+from PsyNeuLink.Components.Process import process
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.IntegratorMechanism import IntegratorMechanism
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import DDM
+from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.EVCMechanism import EVCMechanism
+from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
+from PsyNeuLink.Components.Functions.Function import Linear, Logistic, BogaczEtAl, SimpleIntegrator
+from PsyNeuLink.scheduling.Scheduler import Scheduler
+from PsyNeuLink.scheduling.condition import AfterNCalls, All, Any, AtNCalls, AtPass, EveryNCalls, JustRan
+from PsyNeuLink.Globals.Keywords import SIMPLE
+from PsyNeuLink.Globals.TimeScale import TimeScale
+
+
+class TestInit:
+
+    def test_create_scheduler_from_system_StroopDemo(self):
+        Color_Input = TransferMechanism(name='Color Input', function=Linear(slope=0.2995))
+        Word_Input = TransferMechanism(name='Word Input', function=Linear(slope=0.2995))
+
+        # Processing Mechanisms (Control)
+        Color_Hidden = TransferMechanism(
+            name='Colors Hidden',
+            function=Logistic(gain=(1.0, ControlProjection)),
+        )
+        Word_Hidden = TransferMechanism(
+            name='Words Hidden',
+            function=Logistic(gain=(1.0, ControlProjection)),
+        )
+        Output = TransferMechanism(
+            name='Output',
+            function=Logistic(gain=(1.0, ControlProjection)),
+        )
+
+        # Decision Mechanisms
+        Decision = DDM(
+            function=BogaczEtAl(
+                drift_rate=(1.0),
+                threshold=(0.1654),
+                noise=(0.5),
+                starting_point=(0),
+                t0=0.25,
+            ),
+            name='Decision',
+        )
+        # Outcome Mechanisms:
+        Reward = TransferMechanism(name='Reward')
+
+        # Processes:
+        ColorNamingProcess = process(
+            default_input_value=[0],
+            pathway=[Color_Input, Color_Hidden, Output, Decision],
+            name='Color Naming Process',
+        )
+
+        WordReadingProcess = process(
+            default_input_value=[0],
+            pathway=[Word_Input, Word_Hidden, Output, Decision],
+            name='Word Reading Process',
+        )
+
+        RewardProcess = process(
+            default_input_value=[0],
+            pathway=[Reward],
+            name='RewardProcess',
+        )
+
+        # System:
+        mySystem = system(
+            processes=[ColorNamingProcess, WordReadingProcess, RewardProcess],
+            controller=EVCMechanism,
+            enable_controller=True,
+            # monitor_for_control=[Reward, (PROBABILITY_UPPER_THRESHOLD, 1, -1)],
+            name='EVC Gratton System',
+        )
+
+        sched = Scheduler(system=mySystem)
+
+        integrator_ColorInputPrediction = mySystem.executionList[7]
+        integrator_RewardPrediction = mySystem.executionList[8]
+        integrator_WordInputPrediction = mySystem.executionList[9]
+        objective_EVC_mech = mySystem.executionList[10]
+
+        expected_consideration_queue = [
+            {Color_Input, Word_Input, Reward, integrator_ColorInputPrediction, integrator_WordInputPrediction, integrator_RewardPrediction},
+            {Color_Hidden, Word_Hidden},
+            {Output},
+            {Decision},
+            {objective_EVC_mech},
+        ]
+
+        assert sched.consideration_queue == expected_consideration_queue
+
+
+class TestLinear:
+
+    def test_one_run_twice(self):
+        A = IntegratorMechanism(
+            name='A',
+            default_input_value=[0],
+            function=SimpleIntegrator(
+                rate=.5,
+            )
+        )
+
+        p = process(
+            default_input_value=[0],
+            pathway=[A],
+            name='p'
+        )
+
+        s = system(
+            processes=[p],
+            name='s'
+        )
+
+        term_conds = {TimeScale.TRIAL: AfterNCalls(A, 2)}
+        stim_list = {A: [[1]]}
+
+        s.run(
+            inputs=stim_list,
+            termination_processing=term_conds
+        )
+
+        terminal_mech = A
+        expected_output = [
+            numpy.array([1.]),
+        ]
+
+        for i in range(len(expected_output)):
+            numpy.testing.assert_allclose(expected_output[i], terminal_mech.output_values[i])
+
+    def test_two_AAB(self):
+        A = IntegratorMechanism(
+            name='A',
+            default_input_value=[0],
+            function=SimpleIntegrator(
+                rate=.5
+            )
+        )
+
+        B = TransferMechanism(
+            name='B',
+            default_input_value=[0],
+            function=Linear(slope=2.0),
+        )
+
+        p = process(
+            default_input_value=[0],
+            pathway=[A, B],
+            name='p'
+        )
+
+        s = system(
+            processes=[p],
+            name='s'
+        )
+
+        term_conds = {TimeScale.TRIAL: AfterNCalls(B, 1)}
+        stim_list = {A: [[1]]}
+
+        sched = Scheduler(system=s)
+        sched.add_condition(B, EveryNCalls(A, 2))
+        s.scheduler_processing = sched
+
+        s.run(
+            inputs=stim_list,
+            termination_processing=term_conds
+        )
+
+        terminal_mech = B
+        expected_output = [
+            numpy.array([2.]),
+        ]
+
+        for i in range(len(expected_output)):
+            numpy.testing.assert_allclose(expected_output[i], terminal_mech.output_values[i])
+
+    def test_two_ABB(self):
+        A = TransferMechanism(
+            name='A',
+            default_input_value=[0],
+            function=Linear(slope=2.0),
+        )
+
+        B = IntegratorMechanism(
+            name='B',
+            default_input_value=[0],
+            function=SimpleIntegrator(
+                rate=.5
+            )
+        )
+
+        p = process(
+            default_input_value=[0],
+            pathway=[A, B],
+            name='p'
+        )
+
+        s = system(
+            processes=[p],
+            name='s'
+        )
+
+        term_conds = {TimeScale.TRIAL: AfterNCalls(B, 2)}
+        stim_list = {A: [[1]]}
+
+        sched = Scheduler(system=s)
+        sched.add_condition(A, Any(AtPass(0), AfterNCalls(B, 2)))
+        sched.add_condition(B, Any(JustRan(A), JustRan(B)))
+        s.scheduler_processing = sched
+
+        s.run(
+            inputs=stim_list,
+            termination_processing=term_conds
+        )
+
+        terminal_mech = B
+        expected_output = [
+            numpy.array([2.]),
+        ]
+
+        for i in range(len(expected_output)):
+            numpy.testing.assert_allclose(expected_output[i], terminal_mech.output_values[i])
+
+
+class TestBranching:
+
+    def test_three_ABAC(self):
+        A = IntegratorMechanism(
+            name='A',
+            default_input_value=[0],
+            function=SimpleIntegrator(
+                rate=.5
+            )
+        )
+
+        B = TransferMechanism(
+            name='B',
+            default_input_value=[0],
+            function=Linear(slope=2.0),
+        )
+        C = TransferMechanism(
+            name='C',
+            default_input_value=[0],
+            function=Linear(slope=2.0),
+        )
+
+        p = process(
+            default_input_value=[0],
+            pathway=[A, B],
+            name='p'
+        )
+
+        q = process(
+            default_input_value=[0],
+            pathway=[A, C],
+            name='q'
+        )
+
+        s = system(
+            processes=[p, q],
+            name='s'
+        )
+
+        term_conds = {TimeScale.TRIAL: AfterNCalls(C, 1)}
+        stim_list = {A: [[1]]}
+
+        sched = Scheduler(system=s)
+        sched.add_condition(B, Any(AtNCalls(A, 1), EveryNCalls(A, 2)))
+        sched.add_condition(C, EveryNCalls(A, 2))
+        s.scheduler_processing = sched
+
+        s.run(
+            inputs=stim_list,
+            termination_processing=term_conds
+        )
+
+        terminal_mechs = [B, C]
+        expected_output = [
+            [
+                numpy.array([1.]),
+            ],
+            [
+                numpy.array([2.]),
+            ],
+        ]
+
+        for m in range(len(terminal_mechs)):
+            for i in range(len(expected_output[m])):
+                numpy.testing.assert_allclose(expected_output[m][i], terminal_mechs[m].output_values[i])
+
+    def test_three_ABACx2(self):
+        A = IntegratorMechanism(
+            name='A',
+            default_input_value=[0],
+            function=SimpleIntegrator(
+                rate=.5
+            )
+        )
+
+        B = TransferMechanism(
+            name='B',
+            default_input_value=[0],
+            function=Linear(slope=2.0),
+        )
+        C = TransferMechanism(
+            name='C',
+            default_input_value=[0],
+            function=Linear(slope=2.0),
+        )
+
+        p = process(
+            default_input_value=[0],
+            pathway=[A, B],
+            name='p'
+        )
+
+        q = process(
+            default_input_value=[0],
+            pathway=[A, C],
+            name='q'
+        )
+
+        s = system(
+            processes=[p, q],
+            name='s'
+        )
+
+        term_conds = {TimeScale.TRIAL: AfterNCalls(C, 2)}
+        stim_list = {A: [[1]]}
+
+        sched = Scheduler(system=s)
+        sched.add_condition(B, Any(AtNCalls(A, 1), EveryNCalls(A, 2)))
+        sched.add_condition(C, EveryNCalls(A, 2))
+        s.scheduler_processing = sched
+
+        s.run(
+            inputs=stim_list,
+            termination_processing=term_conds
+        )
+
+        terminal_mechs = [B, C]
+        expected_output = [
+            [
+                numpy.array([3.]),
+            ],
+            [
+                numpy.array([4.]),
+            ],
+        ]
+
+        for m in range(len(terminal_mechs)):
+            for i in range(len(expected_output[m])):
+                numpy.testing.assert_allclose(expected_output[m][i], terminal_mechs[m].output_values[i])
+
+    def test_three_2_ABC(self):
+        A = IntegratorMechanism(
+            name='A',
+            default_input_value=[0],
+            function=SimpleIntegrator(
+                rate=.5
+            )
+        )
+
+        B = IntegratorMechanism(
+            name='B',
+            default_input_value=[0],
+            function=SimpleIntegrator(
+                rate=1
+            )
+        )
+
+        C = TransferMechanism(
+            name='C',
+            default_input_value=[0],
+            function=Linear(slope=2.0),
+        )
+
+        p = process(
+            default_input_value=[0],
+            pathway=[A, C],
+            name='p'
+        )
+
+        q = process(
+            default_input_value=[0],
+            pathway=[B, C],
+            name='q'
+        )
+
+        s = system(
+            processes=[p, q],
+            name='s'
+        )
+
+        term_conds = {TimeScale.TRIAL: AfterNCalls(C, 1)}
+        stim_list = {A: [[1]], B: [[2]]}
+
+        sched = Scheduler(system=s)
+        sched.add_condition(C, All(EveryNCalls(A, 1), EveryNCalls(B, 1)))
+        s.scheduler_processing = sched
+
+        s.run(
+            inputs=stim_list,
+            termination_processing=term_conds
+        )
+
+        terminal_mechs = [C]
+        expected_output = [
+            [
+                numpy.array([5.]),
+            ],
+        ]
+
+        for m in range(len(terminal_mechs)):
+            for i in range(len(expected_output[m])):
+                numpy.testing.assert_allclose(expected_output[m][i], terminal_mechs[m].output_values[i])
+
+    def test_three_2_ABCx2(self):
+        A = IntegratorMechanism(
+            name='A',
+            default_input_value=[0],
+            function=SimpleIntegrator(
+                rate=.5
+            )
+        )
+
+        B = IntegratorMechanism(
+            name='B',
+            default_input_value=[0],
+            function=SimpleIntegrator(
+                rate=1
+            )
+        )
+
+        C = TransferMechanism(
+            name='C',
+            default_input_value=[0],
+            function=Linear(slope=2.0),
+        )
+
+        p = process(
+            default_input_value=[0],
+            pathway=[A, C],
+            name='p'
+        )
+
+        q = process(
+            default_input_value=[0],
+            pathway=[B, C],
+            name='q'
+        )
+
+        s = system(
+            processes=[p, q],
+            name='s'
+        )
+
+        term_conds = {TimeScale.TRIAL: AfterNCalls(C, 2)}
+        stim_list = {A: [[1]], B: [[2]]}
+
+        sched = Scheduler(system=s)
+        sched.add_condition(C, All(EveryNCalls(A, 1), EveryNCalls(B, 1)))
+        s.scheduler_processing = sched
+
+        s.run(
+            inputs=stim_list,
+            termination_processing=term_conds
+        )
+
+        terminal_mechs = [C]
+        expected_output = [
+            [
+                numpy.array([10.]),
+            ],
+        ]
+
+        for m in range(len(terminal_mechs)):
+            for i in range(len(expected_output[m])):
+                numpy.testing.assert_allclose(expected_output[m][i], terminal_mechs[m].output_values[i])
+
+    def test_three_integrators(self):
+        A = IntegratorMechanism(
+            name='A',
+            default_input_value=[0],
+            function=SimpleIntegrator(
+                rate=1
+            )
+        )
+
+        B = IntegratorMechanism(
+            name='B',
+            default_input_value=[0],
+            function=SimpleIntegrator(
+                rate=1
+            )
+        )
+
+        C = IntegratorMechanism(
+            name='C',
+            default_input_value=[0],
+            function=SimpleIntegrator(
+                rate=1
+            )
+        )
+
+        p = process(
+            default_input_value=[0],
+            pathway=[A, C],
+            name='p'
+        )
+
+        q = process(
+            default_input_value=[0],
+            pathway=[B, C],
+            name='q'
+        )
+
+        s = system(
+            processes=[p, q],
+            name='s'
+        )
+
+        term_conds = {TimeScale.TRIAL: AfterNCalls(C, 2)}
+        stim_list = {A: [[1]], B: [[1]]}
+
+        sched = Scheduler(system=s)
+        sched.add_condition(B, EveryNCalls(A, 2))
+        sched.add_condition(C, Any(EveryNCalls(A, 1), EveryNCalls(B, 1)))
+        s.scheduler_processing = sched
+
+        s.run(
+            inputs=stim_list,
+            termination_processing=term_conds
+        )
+
+        mechs = [A, B, C]
+        expected_output = [
+            [
+                numpy.array([2.]),
+            ],
+            [
+                numpy.array([1.]),
+            ],
+            [
+                numpy.array([4.]),
+            ],
+        ]
+
+        for m in range(len(mechs)):
+            for i in range(len(expected_output[m])):
+                numpy.testing.assert_allclose(expected_output[m][i], mechs[m].output_values[i])
+
+    def test_four_ABBCD(self):
+        A = TransferMechanism(
+            name='A',
+            default_input_value=[0],
+            function=Linear(slope=2.0),
+        )
+
+        B = IntegratorMechanism(
+            name='B',
+            default_input_value=[0],
+            function=SimpleIntegrator(
+                rate=.5
+            )
+        )
+
+        C = IntegratorMechanism(
+            name='C',
+            default_input_value=[0],
+            function=SimpleIntegrator(
+                rate=.5
+            )
+        )
+
+        D = TransferMechanism(
+            name='D',
+            default_input_value=[0],
+            function=Linear(slope=1.0),
+        )
+
+        p = process(
+            default_input_value=[0],
+            pathway=[A, B, D],
+            name='p'
+        )
+
+        q = process(
+            default_input_value=[0],
+            pathway=[A, C, D],
+            name='q'
+        )
+
+        s = system(
+            processes=[p, q],
+            name='s'
+        )
+
+        term_conds = {TimeScale.TRIAL: AfterNCalls(D, 1)}
+        stim_list = {A: [[1]]}
+
+        sched = Scheduler(system=s)
+        sched.add_condition(B, EveryNCalls(A, 1))
+        sched.add_condition(C, EveryNCalls(A, 2))
+        sched.add_condition(D, Any(EveryNCalls(B, 3), EveryNCalls(C, 3)))
+        s.scheduler_processing = sched
+
+        s.run(
+            inputs=stim_list,
+            termination_processing=term_conds
+        )
+
+        terminal_mechs = [D]
+        expected_output = [
+            [
+                numpy.array([4.]),
+            ],
+        ]
+
+        for m in range(len(terminal_mechs)):
+            for i in range(len(expected_output[m])):
+                numpy.testing.assert_allclose(expected_output[m][i], terminal_mechs[m].output_values[i])
+
+    def test_four_integrators_mixed(self):
+        A = IntegratorMechanism(
+            name='A',
+            default_input_value=[0],
+            function=SimpleIntegrator(
+                rate=1
+            )
+        )
+
+        B = IntegratorMechanism(
+            name='B',
+            default_input_value=[0],
+            function=SimpleIntegrator(
+                rate=1
+            )
+        )
+
+        C = IntegratorMechanism(
+            name='C',
+            default_input_value=[0],
+            function=SimpleIntegrator(
+                rate=1
+            )
+        )
+
+        D = IntegratorMechanism(
+            name='D',
+            default_input_value=[0],
+            function=SimpleIntegrator(
+                rate=1
+            )
+        )
+
+        p = process(
+            default_input_value=[0],
+            pathway=[A, C],
+            name='p'
+        )
+
+        p1 = process(
+            default_input_value=[0],
+            pathway=[A, D],
+            name='p1'
+        )
+
+        q = process(
+            default_input_value=[0],
+            pathway=[B, C],
+            name='q'
+        )
+
+        q1 = process(
+            default_input_value=[0],
+            pathway=[B, D],
+            name='q1'
+        )
+
+        s = system(
+            processes=[p, p1, q, q1],
+            name='s'
+        )
+
+        term_conds = {TimeScale.TRIAL: All(AfterNCalls(C, 1), AfterNCalls(D, 1))}
+        stim_list = {A: [[1]], B: [[1]]}
+
+        sched = Scheduler(system=s)
+        sched.add_condition(B, EveryNCalls(A, 2))
+        sched.add_condition(C, EveryNCalls(A, 1))
+        sched.add_condition(D, EveryNCalls(B, 1))
+        s.scheduler_processing = sched
+
+        s.run(
+            inputs=stim_list,
+            termination_processing=term_conds
+        )
+
+        mechs = [A, B, C, D]
+        expected_output = [
+            [
+                numpy.array([2.]),
+            ],
+            [
+                numpy.array([1.]),
+            ],
+            [
+                numpy.array([4.]),
+            ],
+            [
+                numpy.array([3.]),
+            ],
+        ]
+
+        for m in range(len(mechs)):
+            for i in range(len(expected_output[m])):
+                numpy.testing.assert_allclose(expected_output[m][i], mechs[m].output_values[i])
+
+    def test_five_ABABCDE(self):
+        A = TransferMechanism(
+            name='A',
+            default_input_value=[0],
+            function=Linear(slope=2.0),
+        )
+
+        B = TransferMechanism(
+            name='B',
+            default_input_value=[0],
+            function=Linear(slope=2.0),
+        )
+
+        C = IntegratorMechanism(
+            name='C',
+            default_input_value=[0],
+            function=SimpleIntegrator(
+                rate=.5
+            )
+        )
+
+        D = TransferMechanism(
+            name='D',
+            default_input_value=[0],
+            function=Linear(slope=1.0),
+        )
+
+        E = TransferMechanism(
+            name='E',
+            default_input_value=[0],
+            function=Linear(slope=2.0),
+        )
+
+        p = process(
+            default_input_value=[0],
+            pathway=[A, C, D],
+            name='p'
+        )
+
+        q = process(
+            default_input_value=[0],
+            pathway=[B, C, E],
+            name='q'
+        )
+
+        s = system(
+            processes=[p, q],
+            name='s'
+        )
+
+        term_conds = {TimeScale.TRIAL: AfterNCalls(E, 1)}
+        stim_list = {A: [[1]], B: [[2]]}
+
+        sched = Scheduler(system=s)
+        sched.add_condition(C, Any(EveryNCalls(A, 1), EveryNCalls(B, 1)))
+        sched.add_condition(D, EveryNCalls(C, 1))
+        sched.add_condition(E, EveryNCalls(C, 1))
+        s.scheduler_processing = sched
+
+        s.run(
+            inputs=stim_list,
+            termination_processing=term_conds
+        )
+
+        terminal_mechs = [D, E]
+        expected_output = [
+            [
+                numpy.array([3.]),
+            ],
+            [
+                numpy.array([6.]),
+            ],
+        ]
+
+        for m in range(len(terminal_mechs)):
+            for i in range(len(expected_output[m])):
+                numpy.testing.assert_allclose(expected_output[m][i], terminal_mechs[m].output_values[i])
+
+    #
+    #   A  B
+    #   |\/|
+    #   C  D
+    #   |\/|
+    #   E  F
+    #
+    def test_six_integrators_threelayer_mixed(self):
+        A = IntegratorMechanism(
+            name='A',
+            default_input_value=[0],
+            function=SimpleIntegrator(
+                rate=1
+            )
+        )
+
+        B = IntegratorMechanism(
+            name='B',
+            default_input_value=[0],
+            function=SimpleIntegrator(
+                rate=1
+            )
+        )
+
+        C = IntegratorMechanism(
+            name='C',
+            default_input_value=[0],
+            function=SimpleIntegrator(
+                rate=1
+            )
+        )
+
+        D = IntegratorMechanism(
+            name='D',
+            default_input_value=[0],
+            function=SimpleIntegrator(
+                rate=1
+            )
+        )
+
+        E = IntegratorMechanism(
+            name='E',
+            default_input_value=[0],
+            function=SimpleIntegrator(
+                rate=1
+            )
+        )
+
+        F = IntegratorMechanism(
+            name='F',
+            default_input_value=[0],
+            function=SimpleIntegrator(
+                rate=1
+            )
+        )
+
+        p = [
+            process(
+                default_input_value=[0],
+                pathway=[A, C, E],
+                name='p'
+            ),
+            process(
+                default_input_value=[0],
+                pathway=[A, C, F],
+                name='p1'
+            ),
+            process(
+                default_input_value=[0],
+                pathway=[A, D, E],
+                name='p2'
+            ),
+            process(
+                default_input_value=[0],
+                pathway=[A, D, F],
+                name='p3'
+            ),
+            process(
+                default_input_value=[0],
+                pathway=[B, C, E],
+                name='q'
+            ),
+            process(
+                default_input_value=[0],
+                pathway=[B, C, F],
+                name='q1'
+            ),
+            process(
+                default_input_value=[0],
+                pathway=[B, D, E],
+                name='q2'
+            ),
+            process(
+                default_input_value=[0],
+                pathway=[B, D, F],
+                name='q3'
+            )
+        ]
+
+        s = system(
+            processes=p,
+            name='s'
+        )
+
+        term_conds = {TimeScale.TRIAL: All(AfterNCalls(E, 1), AfterNCalls(F, 1))}
+        stim_list = {A: [[1]], B: [[1]]}
+
+        sched = Scheduler(system=s)
+        sched.add_condition(B, EveryNCalls(A, 2))
+        sched.add_condition(C, EveryNCalls(A, 1))
+        sched.add_condition(D, EveryNCalls(B, 1))
+        sched.add_condition(E, EveryNCalls(C, 1))
+        sched.add_condition(F, EveryNCalls(D, 2))
+        s.scheduler_processing = sched
+
+        s.run(
+            inputs=stim_list,
+            termination_processing=term_conds
+        )
+
+        # Intermediate time steps
+        #
+        #     0   1   2   3
+        #
+        # A   1   2   3   4
+        # B       1       2
+        # C   1   4   8   14
+        # D       3       9
+        # E   1   8   19  42
+        # F               23
+        #
+        expected_output = {
+            A: [
+                numpy.array([4.]),
+            ],
+            B: [
+                numpy.array([2.]),
+            ],
+            C: [
+                numpy.array([14.]),
+            ],
+            D: [
+                numpy.array([9.]),
+            ],
+            E: [
+                numpy.array([42.]),
+            ],
+            F: [
+                numpy.array([23.]),
+            ],
+        }
+
+        for m in expected_output:
+            for i in range(len(expected_output[m])):
+                numpy.testing.assert_allclose(expected_output[m][i], m.output_values[i])
diff --git a/tests/system/test_system.py b/tests/system/test_system.py
new file mode 100644
index 00000000000..f7a4afd5edd
--- /dev/null
+++ b/tests/system/test_system.py
@@ -0,0 +1,357 @@
+import numpy as np
+
+from PsyNeuLink.Components.System import system
+from PsyNeuLink.Components.Process import process
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.TransferMechanism import TransferMechanism
+from PsyNeuLink.Components.Mechanisms.ProcessingMechanisms.DDM import DDM
+from PsyNeuLink.Components.Mechanisms.AdaptiveMechanisms.ControlMechanisms.EVCMechanism import EVCMechanism
+from PsyNeuLink.Components.Projections.ModulatoryProjections.ControlProjection import ControlProjection
+from PsyNeuLink.Components.Projections.PathwayProjections.MappingProjection import MappingProjection
+from PsyNeuLink.Components.Functions.Function import Linear, Logistic, BogaczEtAl
+from PsyNeuLink.Components.Functions.Function import THRESHOLD
+from PsyNeuLink.Globals.Keywords import ALLOCATION_SAMPLES, LEARNING_PROJECTION, NOISE, PARAMETER_STATE_PARAMS, RANDOM_CONNECTIVITY_MATRIX
+from PsyNeuLink.Globals.Keywords import CYCLE, INITIALIZE_CYCLE, INTERNAL, ORIGIN, TERMINAL
+
+
+def test_danglingControlledMech():
+    #
+    #   first section is from Stroop Demo
+    #
+    Color_Input = TransferMechanism(name='Color Input', function=Linear(slope=0.2995))
+    Word_Input = TransferMechanism(name='Word Input', function=Linear(slope=0.2995))
+
+    # Processing Mechanisms (Control)
+    Color_Hidden = TransferMechanism(
+        name='Colors Hidden',
+        function=Logistic(gain=(1.0, ControlProjection)),
+    )
+    Word_Hidden = TransferMechanism(
+        name='Words Hidden',
+        function=Logistic(gain=(1.0, ControlProjection)),
+    )
+    Output = TransferMechanism(
+        name='Output',
+        function=Logistic(gain=(1.0, ControlProjection)),
+    )
+
+    # Decision Mechanisms
+    Decision = DDM(
+        function=BogaczEtAl(
+            drift_rate=(1.0),
+            threshold=(0.1654),
+            noise=(0.5),
+            starting_point=(0),
+            t0=0.25,
+        ),
+        name='Decision',
+    )
+    # Outcome Mechanisms:
+    Reward = TransferMechanism(name='Reward')
+
+    # Processes:
+    ColorNamingProcess = process(
+        default_input_value=[0],
+        pathway=[Color_Input, Color_Hidden, Output, Decision],
+        name='Color Naming Process',
+    )
+
+    WordReadingProcess = process(
+        default_input_value=[0],
+        pathway=[Word_Input, Word_Hidden, Output, Decision],
+        name='Word Reading Process',
+    )
+
+    RewardProcess = process(
+        default_input_value=[0],
+        pathway=[Reward],
+        name='RewardProcess',
+    )
+
+    # add another DDM but do not add to system
+    second_DDM = DDM(
+        function=BogaczEtAl(
+            drift_rate=(
+                1.0,
+                ControlProjection(
+                    function=Linear,
+                    control_signal_params={
+                        ALLOCATION_SAMPLES: np.arange(0.1, 1.01, 0.3)
+                    },
+                ),
+            ),
+            threshold=(
+                1.0,
+                ControlProjection(
+                    function=Linear,
+                    control_signal_params={
+                        ALLOCATION_SAMPLES: np.arange(0.1, 1.01, 0.3)
+                    },
+                ),
+            ),
+            noise=(0.5),
+            starting_point=(0),
+            t0=0.45
+        ),
+        name='second_DDM',
+    )
+
+    # System:
+    mySystem = system(
+        processes=[ColorNamingProcess, WordReadingProcess, RewardProcess],
+        controller=EVCMechanism,
+        enable_controller=True,
+        # monitor_for_control=[Reward, (DDM_PROBABILITY_UPPER_THRESHOLD, 1, -1)],
+        name='EVC Gratton System',
+    )
+
+    # no assert, should only complete without error
+
+
+class TestDocumentationExamples:
+
+    def test_mechs_in_pathway(seed0):
+        mechanism_1 = TransferMechanism()
+        mechanism_2 = DDM()
+        some_params = {PARAMETER_STATE_PARAMS: {THRESHOLD: 2, NOISE: 0.1}}
+        my_process = process(pathway=[mechanism_1, TransferMechanism, (mechanism_2, some_params)])
+        result = my_process.execute()
+        # changed result from 2 to 1
+        assert(result == np.array([1]))
+
+    def test_default_projection(seed0):
+        mechanism_1 = TransferMechanism()
+        mechanism_2 = TransferMechanism()
+        mechanism_3 = DDM()
+        my_process = process(pathway=[mechanism_1, mechanism_2, mechanism_3])
+        result = my_process.execute()
+
+        assert(result == np.array([1.]))
+
+    def test_inline_projection_using_existing_projection(seed0):
+        mechanism_1 = TransferMechanism()
+        mechanism_2 = TransferMechanism()
+        mechanism_3 = DDM()
+        projection_A = MappingProjection()
+        my_process = process(pathway=[mechanism_1, projection_A, mechanism_2, mechanism_3])
+        result = my_process.execute()
+
+        assert(result == np.array([1.]))
+
+    def test_inline_projection_using_keyword(seed0):
+        mechanism_1 = TransferMechanism()
+        mechanism_2 = TransferMechanism()
+        mechanism_3 = DDM()
+        my_process = process(pathway=[mechanism_1, RANDOM_CONNECTIVITY_MATRIX, mechanism_2, mechanism_3])
+        result = my_process.execute()
+
+        assert(result == np.array([1.]))
+
+    def test_standalone_projection(seed0):
+        mechanism_1 = TransferMechanism()
+        mechanism_2 = TransferMechanism()
+        mechanism_3 = DDM()
+        projection_A = MappingProjection(sender=mechanism_1, receiver=mechanism_2)
+        my_process = process(pathway=[mechanism_1, mechanism_2, mechanism_3])
+        result = my_process.execute()
+
+        assert(result == np.array([1.]))
+
+    def test_process_learning(seed0):
+        mechanism_1 = TransferMechanism(function=Logistic)
+        mechanism_2 = TransferMechanism(function=Logistic)
+        mechanism_3 = TransferMechanism(function=Logistic)
+        my_process = process(
+            pathway=[mechanism_1, mechanism_2, mechanism_3],
+            learning=LEARNING_PROJECTION,
+            target=[0],
+        )
+        result = my_process.execute()
+
+        np.testing.assert_allclose(result, np.array([0.65077768]))
+
+
+class TestGraphAndInput:
+
+    def test_branch(self):
+        a = TransferMechanism(name='a', default_input_value=[0, 0])
+        b = TransferMechanism(name='b')
+        c = TransferMechanism(name='c')
+        d = TransferMechanism(name='d')
+
+        p1 = process(pathway=[a, b, c], name='p1')
+        p2 = process(pathway=[a, b, d], name='p2')
+
+        s = system(
+            processes=[p1, p2],
+            name='Branch System',
+            initial_values={a: [1, 1]},
+        )
+
+        inputs = {a: [2, 2]}
+        s.run(inputs)
+
+        assert [a] == s.originMechanisms.mechanisms
+        assert set([c, d]) == set(s.terminalMechanisms.mechanisms)
+
+        assert a.systems[s] == ORIGIN
+        assert b.systems[s] == INTERNAL
+        assert c.systems[s] == TERMINAL
+        assert d.systems[s] == TERMINAL
+
+    def test_bypass(self):
+        a = TransferMechanism(name='a', default_input_value=[0, 0])
+        b = TransferMechanism(name='b', default_input_value=[0, 0])
+        c = TransferMechanism(name='c')
+        d = TransferMechanism(name='d')
+
+        p1 = process(pathway=[a, b, c, d], name='p1')
+        p2 = process(pathway=[a, b, d], name='p2')
+
+        s = system(
+            processes=[p1, p2],
+            name='Bypass System',
+            initial_values={a: [1, 1]},
+        )
+
+        inputs = {a: [[2, 2], [0, 0]]}
+        s.run(inputs=inputs)
+
+        assert [a] == s.originMechanisms.mechanisms
+        assert [d] == s.terminalMechanisms.mechanisms
+
+        assert a.systems[s] == ORIGIN
+        assert b.systems[s] == INTERNAL
+        assert c.systems[s] == INTERNAL
+        assert d.systems[s] == TERMINAL
+
+    def test_chain(self):
+        a = TransferMechanism(name='a', default_input_value=[0, 0, 0])
+        b = TransferMechanism(name='b')
+        c = TransferMechanism(name='c')
+        d = TransferMechanism(name='d')
+        e = TransferMechanism(name='e')
+
+        p1 = process(pathway=[a, b, c], name='p1')
+        p2 = process(pathway=[c, d, e], name='p2')
+
+        s = system(
+            processes=[p1, p2],
+            name='Chain System',
+            initial_values={a: [1, 1, 1]},
+        )
+
+        inputs = {a: [[2, 2, 2], [0, 0, 0]]}
+        s.run(inputs=inputs)
+
+        assert [a] == s.originMechanisms.mechanisms
+        assert [e] == s.terminalMechanisms.mechanisms
+
+        assert a.systems[s] == ORIGIN
+        assert b.systems[s] == INTERNAL
+        assert c.systems[s] == INTERNAL
+        assert d.systems[s] == INTERNAL
+        assert e.systems[s] == TERMINAL
+
+    def test_convergent(self):
+        a = TransferMechanism(name='a', default_input_value=[0, 0])
+        b = TransferMechanism(name='b')
+        c = TransferMechanism(name='c')
+        c = TransferMechanism(name='c', default_input_value=[0])
+        d = TransferMechanism(name='d')
+        e = TransferMechanism(name='e')
+
+        p1 = process(pathway=[a, b, e], name='p1')
+        p2 = process(pathway=[c, d, e], name='p2')
+
+        s = system(
+            processes=[p1, p2],
+            name='Convergent System',
+            initial_values={a: [1, 1]},
+        )
+
+        inputs = {a: [[2, 2]], c: [[0]]}
+        s.run(inputs=inputs)
+
+        assert set([a, c]) == set(s.originMechanisms.mechanisms)
+        assert [e] == s.terminalMechanisms.mechanisms
+
+        assert a.systems[s] == ORIGIN
+        assert b.systems[s] == INTERNAL
+        assert c.systems[s] == ORIGIN
+        assert d.systems[s] == INTERNAL
+        assert e.systems[s] == TERMINAL
+
+    def cyclic_one_process(self):
+        a = TransferMechanism(name='a', default_input_value=[0, 0])
+        b = TransferMechanism(name='b', default_input_value=[0, 0])
+
+        p1 = process(pathway=[a, b, a], name='p1')
+
+        s = system(
+            processes=[p1],
+            name='Cyclic System with one Process',
+            initial_values={a: [1, 1]},
+        )
+
+        inputs = {a: [1, 1]}
+        s.run(inputs=inputs)
+
+        assert [a] == s.originMechanisms.mechanisms
+        assert [] == s.terminalMechanisms.mechanisms
+
+        assert a.systems[s] == ORIGIN
+        assert b.systems[s] == INITIALIZE_CYCLE
+
+    def cyclic_two_processes(self):
+        a = TransferMechanism(name='a', default_input_value=[0, 0])
+        b = TransferMechanism(name='b', default_input_value=[0, 0])
+        c = TransferMechanism(name='c', default_input_value=[0, 0])
+
+        p1 = process(pathway=[a, b, a], name='p1')
+        p2 = process(pathway=[a, c, a], name='p2')
+
+        s = system(
+            processes=[p1, p2],
+            name='Cyclic System with two Processes',
+            initial_values={a: [1, 1]},
+        )
+
+        inputs = {a: [1, 1]}
+        s.run(inputs=inputs)
+
+        assert [a] == s.originMechanisms.mechanisms
+        assert [] == s.terminalMechanisms.mechanisms
+
+        assert a.systems[s] == ORIGIN
+        assert b.systems[s] == INITIALIZE_CYCLE
+        assert c.systems[s] == INITIALIZE_CYCLE
+
+    def cyclic_extended_loop(self):
+        a = TransferMechanism(name='a', default_input_value=[0, 0])
+        b = TransferMechanism(name='b')
+        c = TransferMechanism(name='c')
+        d = TransferMechanism(name='d')
+        e = TransferMechanism(name='e', default_input_value=[0])
+        f = TransferMechanism(name='f')
+
+        p1 = process(pathway=[a, b, c, d], name='p1')
+        p2 = process(pathway=[e, c, f, b, d], name='p2')
+
+        s = system(
+            processes=[p1, p2],
+            name='Cyclic System with Extended Loop',
+            initial_values={a: [1, 1]},
+        )
+
+        inputs = {a: [2, 2], e: [0]}
+        s.run(inputs=inputs)
+
+        assert set([a, c]) == set(s.originMechanisms.mechanisms)
+        assert [d] == s.terminalMechanisms.mechanisms
+
+        assert a.systems[s] == ORIGIN
+        assert b.systems[s] == CYCLE
+        assert c.systems[s] == INTERNAL
+        assert d.systems[s] == TERMINAL
+        assert e.systems[s] == ORIGIN
+        assert f.systems[s] == INITIALIZE_CYCLE
diff --git a/tests/test_pytest.py b/tests/test_pytest.py
deleted file mode 100644
index 811e7820def..00000000000
--- a/tests/test_pytest.py
+++ /dev/null
@@ -1,11 +0,0 @@
-import pytest
-
-def test_pytest_fct():
-    assert 1==0
-
-# def test_update():
-#     result = _compute_EVC(params)
-#     assert isinsatance(result, tuple)
-
-def test_EVC_script():
-    __import__("Scripts.- EVC System Test Script")
diff --git a/vcs.xml b/vcs.xml
deleted file mode 100644
index ac13eb79234..00000000000
--- a/vcs.xml
+++ /dev/null
@@ -1,13 +0,0 @@
-<application>
-  <component name="GitRememberedInputs">
-    <option name="visitedUrls">
-      <list>
-        <UrlAndUserName>
-          <option name="url" value="https://github.com/PrincetonUniversity/EVCM.git" />
-          <option name="userName" value="" />
-        </UrlAndUserName>
-      </list>
-    </option>
-    <option name="cloneParentDir" value="$USER_HOME$/PycharmProjects/PsyNeuLink" />
-  </component>
-</application>
\ No newline at end of file