Reinforcement Learning Tools for Two-Alternative Forced Choice
Tasks
Description
Tools for building reinforcement learning (RL) models
specifically tailored for Two-Alternative Forced Choice (TAFC) tasks,
commonly employed in psychological research. These models build upon
the foundational principles of model-free reinforcement learning detailed in
Sutton and Barto (2018) . The package allows
for the intuitive definition of RL models using simple if-else
statements. Our approach to constructing and evaluating these
computational models is informed by the guidelines proposed in
Wilson & Collins (2019) . Example
datasets included with the package are sourced from the work of
Mason et al. (2024) .