Emmanuel Daucé

The paper investigates how the brain encodes epistemic value during goal-directed causal learning, showing that expected information gain is represented in sensorimotor regions and challenges traditional views of reward-based valuation.

This study compares deep reinforcement learning models with monkey behavior in a reaching task, showing convergence toward optimal motor paths and revealing sub-optimal trade-offs in movement duration and energy cost.

The paper develops a dual visual processing model that improves saccade selection efficiency by combining foveal accuracy with peripheral visual field analysis.