Reinforcement Learning Integrates Gaze-Constrained Sequential Sampling

On March 6, 2026, Hayes and Touchard publish in PLoS Computational Biology an RL-SSM constrained by eye gaze that jointly models learned option values and relative gaze to predict choices and response times, evaluated on two eye-tracking experiments (N=133). The paper compares additive and multiplicative integration mechanisms, captures gaze-driven choice and RT biases and individual valuation differences, and provides data and code on GitHub.