binaryRL-package: binaryRL: Reinforcement Learning Tools for Two-Alternative...

In binaryRL: Reinforcement Learning Tools for Two-Alternative Forced Choice Tasks

binaryRL: Reinforcement Learning Tools for Two-Alternative Forced Choice Tasks

Description

Tools for building Rescorla-Wagner Models for Two-Alternative Forced Choice tasks, commonly employed in psychological research. Most concepts and ideas within this R package are referenced from Sutton and Barto (2018) <ISBN:9780262039246>. The package allows for the intuitive definition of RL models using simple if-else statements and three basic models built into this R package are referenced from Niv et al. (2012)\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1523/JNEUROSCI.5498-10.2012")}. Our approach to constructing and evaluating these computational models is informed by the guidelines proposed in Wilson & Collins (2019) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.7554/eLife.49547")}. Example datasets included with the package are sourced from the work of Mason et al. (2024) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3758/s13423-023-02415-x")}.

Example Data

• Mason_2024_G1: Group 1 of Mason et al. (2024)

• Mason_2024_G2: Group 2 of Mason et al. (2024)

Steps

• run_m: Step 1: Building reinforcement learning model

• rcv_d: Step 2: Generating fake data for parameter and model recovery

• fit_p: Step 3: Optimizing parameters to fit real data

• rpl_e: Step 4: Replaying the experiment with optimal parameters

Models

• TD: TD Model

• RSTD: RSTD Model

• Utility: Utility Model

Functions

• func_gamma: Utility Function

• func_eta: Learning Rate

• func_epsilon: Epsilon Related

• func_pi: Upper-Confidence-Bound

• func_tau: Soft-Max

• func_logl: Loss Function

Processes

• optimize_para: optimizing free parameters

• simulate_list: simulating fake datasets

• recovery_data: parameter and model recovery

Summary

• summary.binaryRL: summary(binaryRL.res)

Author(s)

Maintainer: YuKi [e-mail] (ORCID)

See Also

Useful links:

• https://yuki-961004.github.io/binaryRL/

• Report bugs at https://github.com/yuki-961004/binaryRL/issues

binaryRL documentation built on Aug. 21, 2025, 6:01 p.m.