Paul Schrater

The paper develops a computational model to explain action selection in cluttered environments by integrating goal pursuit and obstacle avoidance through a weighted combination of control policies.

This study uses a virtual reality task and Inverse Rational Control to identify sensory and motor differences in ASD compared to neurotypical individuals.

This study examines how freely-moving macaques encode reward prediction dynamics in their prefrontal cortex during foraging decisions.

The paper investigates how generative models aid human generalization across tasks by analyzing sequence learning with multiple tasks from a common generative family.

This paper introduces a method to extract rational thoughts from agent behavior by learning internal models and reward functions using Inverse Rational Control.

The paper investigates the computation underlying confidence judgment in dynamic action decisions, showing that action selection, reaction time, and confidence arise from a common mechanism of competing prepared actions.