UC Berkeley

As we transition from child to adult, we navigate the world differently. In this world, many of the relationships between events are unclear or uncertain because they are probabilistic in nature. We wanted to know how learning about probabilistic relationships changes with development and to interrogate the underlying processes. We investigated these questions in a probabilistic reinforcement learning task (The Butterfly Task) with 302 participants aged 8-30. We found performance in this task increased with age through early-twenties, then stabilized. Using hierarchical Bayesian methods to fit computational reinforcement learning models, we showed that this performance increase was driven by 1) an increase in learning rate (i.e. decrease in integration time horizon); 2) a decrease in exploratory choices. By contrast, forgetting rates did not change with age. We discuss our findings in the context of other studies and hypotheses about adolescent brain development.