binaryRL-package {binaryRL}R Documentation

binaryRL: 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) <ISBN:9780262039246>. 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) doi:10.7554/eLife.49547. Example datasets included with the package are sourced from the work of Mason et al. (2024) doi:10.3758/s13423-023-02415-x.

Example Data

Steps

Models

Functions

Processes

Summary

Author(s)

Maintainer: YuKi hmz1969a@gmail.com (ORCID)

See Also

Useful links:


[Package binaryRL version 0.9.0 Index]