Department of Psychology
Developmental tuning of action selection
Computational reinforcement learning models provide a framework for understanding how individuals can evaluate which actions are beneficial and which are best avoided. To date, these models have primarily been leveraged to understand learning and decision-making in adults. In this talk, I will present studies characterizing developmental changes, from childhood to adulthood, in the cognitive representations and computations engaged to evaluate and select actions. I will discuss how these changes may optimize behavior for an individual’s developmental stage and unique life experiences.
The talk will begin at 12:00pm. A pizza lunch will be served at 11:45am.