Utility

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 (1998) <ISBN:0262039249>. 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>.

YuKi 

binaryRL

Reinforcement Learning Tools for Two-Alternative Forced Choice
Tasks

Utility function

<dl><dt>params</dt>
<dd>[vector] algorithm packages accept only one argument</dd></dl>

Arguments

Model: Utility — Utility

<dl>

<dt>params</dt>
<dd>[vector] algorithm packages accept only one argument</dd>

</dl>

Utility: Model: Utility

Description

Usage

Value

Arguments