Researchreinforcement learningsequential samplinggaze bias
Reinforcement Learning Integrates Gaze-Constrained Sequential Sampling
|
9.3
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.
Scoring Rationale
High novelty and practical code availability drive score, with moderate scope limited to decision-modeling and lab-based eyetracking.
Practice interview problems based on real data
1,500+ SQL & Python problems across 15 industry datasets — the exact type of data you work with.
Try 250 free problems
