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What is TTpy?

TTpy allows you to explore behaviour in repeated decision-making tasks, i.e. tasks where an agent is confronted with a similar situation multiple times. Agents use knowledge gained from past instances of the situation to guide their understanding of the current situation. Agents can observe and act in each situation. Within TTpy, an agent is represented by a model. A simulation of a task can be run, where a model interacts with a task, but equally behaviour data collected from real participants (humans, rats, etc) can be used to identify which model parameters best match the participant's behaviour.

Tutorials (to be written)

• Basic TTpy use
• Creating your own model
• Creating your own task
• Adding fitting methods

Roadmap

v0.* (completed)

The framework is developed, the necessary components are identified and a structure is created for these.

v1.*

The aim for this version is consolidation; taking the framework that has been created, cleaning it up, creating automated tests of robustness and documenting it better. This will be done in parallel with the development of the GUI.

v2.*

Here the framework will be expanded to cope with more fitting methods and more task types.