Data-driven equation discovery reveals nonlinear reinforcement learning in humans.

ABSTRACT: Computational models of reinforcement learning (RL) have significantly contributed to our understanding of human behavior and decision-making. Traditional RL models, however, often adopt a linear approach to updating reward expectations, potentially oversimplifying the nuanced relationship between human behavior and rewards. To address these challenges and explore models of RL, we utilized a method of model discovery using equation discovery algorithms. This method, currently used mainly in physics and biology, attempts to capture data by proposing a differential equation from an array of suggested linear and nonlinear functions. Using this method, we were able to identify a model of RL which we termed the Quadratic Q-Weighted model. The model suggests that reward prediction errors obey no

SUBMITTER: LaFollette KJ 

PROVIDER: S-EPMC12337339 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature