binaryRL: Reinforcement Learning Modeling of Two-Alternative Forced Choice Decision Making in R — A Step-by-Step Tutorial

The reinforcement learning framework offers valuable insights into people's decision- making processes in Two-Alternative Forced Choice (TAFC) tasks, providing a deeper understanding of how individuals adjust their choices in response to feedback. However, tools to build, simulate, and compare reinforcement learning models tailored for such tasks remain limited. To address this gap, we developed binaryRL, an R package designed to support the implementation and evaluation of reinforcement learning (RL) models in the context of TAFC tasks, following best-practice guidelines for model comparison in cognitive science. Using real-world open data, we present a tutorial that demonstrate the comprehensive modelling pipeline—from raw experimental input to the construction, parameterization, and validation of RL models. Our package offers a flexible and accessible platform for advancing both theoretical and empirical research in reinforcement learning and cognitive modeling of feedback-based decision-making.