Psychopy Implementation of the Complex Span Task

Description

Working Memory is a critical component of human cognition and can be defined as the ability to store and process information simultaneously.

A complex span task is the commonly used tool by researchers and practitioners to assess working memory capacity; it does so by interleaving to-be-recalled material (e.g., digits, letters, visual patterns) with processing tasks (e.g., verbal, spatiovisual). One example would be to have participants read a series of sentences and deciding whether they were correct while remembering the last word of each sentence for serial recall at the end of each trial (Daneman & Carpenter, 1980).

However, research and clinical settings currently face a few issues regarding the use of complex span tasks:

• Whether they are measuring what they should be (i.e. working memory capacity); many studies operationalize the interleaved processing tasks as a binary yes/no response - this encourages random guessing and fails to maximize cognitive load.
• Multiple complex span tasks are often used in one single session, which results in increasing fatigue for participants.

Aim

The goal is to build a short, single task that measures working memory capacity. We aimed to move away from the use of large-number-trials, multiple span tasks to reduce proactive interference throughout successive tasks, as this can significantly compromise the accurate assessment of working memory capacity.

How to use

Requirements

Softwares needed:

• PsychoPy (https://www.psychopy.org/download.html)
• R 3.6.1 (https://cran.r-project.org/bin/windows/base/)
• R Studio (https://rstudio.com/products/rstudio/download/)

Packages needed in R:

• tidyverse
• easystats

Run the task

1. Download and open the experiment in Psychopy: source("https://github.com/neuropsychology/ComplexSpan/blob/master/psychopy/task.psyexp")
2. Click on the green button to begin the task
3. Enter participant number and session number
4. Run task

• Instruct participants not to press "Enter" at any point during the task unless required, as the experiment would quit if so

5. Task outputs 3 files: a csv file, a text document, and a psydat file

Task Components

This is a Psychopy implementation of a composite complex span task adapted from Gonthier et al. (2015). It comprises of 2 components, the simple span and the complex span:

Simple Span: Presentation of consonants to be remembered

• Consonants to remember
• Set size increases after 2 correct answers in a set size
• Simple span determined as set size attained after 3 incorrect answers

Complex Span: Components include the following:

• Processing: Practice block containing open-ended arithmetic operations to respond to

  • All correct answers are one-digit
  • Feedback provided with each processing trial
  • Processing block to repeat if cumulative score attained < 65%

• Complex Span Practice: Practice block containing open-ended arithmetic operations and to-be-recalled consonants

  • Consonant presented after each correctly-answered arithmetic operation (if incorrectly answered, a new arithmetic operation is presented)
  • There is a time limit (mean RT + 2.5 standard deviations of the processing practice trials) on the processing trials where the next arithmetic operation is presented if no answer is given and the trial is recorded as incorrect
  • Set size of 2 only
  • Feedback provided with each processing trial and each recall trial
  • Block ends after 3 consecutive correct recall

• Complex Span Task: Test block containing open-ended arithmetic operations and to-be-recalled consonants

  • Consonant presented after each correctly-answered arithmetic operation (if incorrectly answered, a new arithmetic operation is presented)
  • There is a time limit (mean RT + 2.5 standard deviations of the processing practice trials) on the processing trials where the next arithmetic operation is presented if no answer is given and the trial is recorded as incorrect
  • No feedback provided
  • Start with set size of 2; set size increases by 1 with each correct recall
  • Block ends after 2 consecutive wrong recall

Preprocess the Data

Open R studio and start by running the following:

# Download the function used to preprocess the data
source("https://github.com/neuropsychology/ComplexSpan/blob/master/statistics/preprocessing.R")
source("https://github.com/neuropsychology/ComplexSpan/blob/master/statistics/processing.R")

Then, set your working directory to the source file location (Session > Set Working Directory > To Source File Location). Create a folder in your working directory called "data" and create the path for your output csv file, for example:

path_to_csv <- "./data/pilot_1.csv"

This will allow you to use the functions created to preprocess the datafile. You can then simply run:

preprocess_WM(path_to_csv)
# This creates a single dataframe for the participant comprising of the simple span and complex span task components.

process_WM(path_to_csv)
# This creates 3 dataframes:
# The participant's span scores and mean reaction time of the processing trials;
# The participant's proportion of correct recall, mean processing reaction time, and proportion of correct processing across set sizes;
# The participant's mean processing reaction time and proportion of correct processing across different distractors 

To create figures, create a folder called "figures" in your working directory and download the make_figures.R file; running the code will save the figures in this folder.

• source("https://github.com/neuropsychology/ComplexSpan/blob/master/statistics/make_figures.R")

Example: Preprocess data for several participants

# ---- Example Script ---- #
# > getwd()
# [1] "/Users/lauzenjuen/Documents/GitHub/ComplexSpan/statistics"

# Load packages
library(tidyverse)
library(easystats)

# Load functions
source("preprocessing.R")
source("processing.R")

# Combine Data Frames
data <- data.frame()
data_subject <- data.frame()
data_within <- data.frame()
data_distractors <- data.frame()

for(participant in list.files("./data/")){
  data <- rbind(data, preprocess_WM(paste0("./data/", participant)))
  
  processed <- process_WM(paste0("./data/", participant))
  data_subject <- rbind(data_subject, processed$between)
  data_within <- rbind(data_within, processed$within)
  data_distractors <- rbind(data_distractors, processed$distractors)
}

data_subject  # produces a table of scores across participants

Scoring Schemes

The analysis script computes scores according to different scoring schemes as per Conway et al. (2005)

1. Partial-Credit Unit Scoring (PCU): Mean proportion of elements within an item that were recalled correctly
2. All-or-Nothing Unit scoring (ANU): Proportion of items for which all elements were recalled correctly
3. Partial-Credit Load Scoring (PCL): Sum of correctly recalled elements from all items, regardless of whether the items are perfectly recalled or not (also without respect to serial order within items)
4. All-or-Nothing Load Scoring (ANL): Sum of correctly recalled elements from only the items in which all elements are recalled in correct serial order

Credits

This task can be referenced as follows:

• Lau, Z. J., Pham, T. T., Makowski, D., & S H Chen, A. (2019). A Psychopy Implementation of the Complex Span for Working Memory Assessment. Retrieved from http://doi.org/10.5281/zenodo.3529329

@misc{complexspan2019,
  title={A Psychopy Implementation of the Complex Span for Working Memory Assessment},
  author={Lau, Zen J and Pham, Thanh T and Makowski, Dominique and S H Chen, Annabel},
  month = {nov},
  year = {2019},
  doi = {10.5281/zenodo.3529329},
  version ={0.0.1},
  publisher={Zenodo},
  url = {http://doi.org/10.5281/zenodo.3529329}
}

Figures

These graphs were produced by make_figures.R

Resources

Here are some other resources that may be helpful!

• Follow PsychoPy updates here
• Well-documented memory tasks
  • Verbal working memory
  • Py-Span-Task
  • Digit Span
  • N Back