Reinforcement Learning and Bayesian Inference Provide Complementary Models for the Unique Advantage of Adolescents in Stochastic Reversal

Abstract

During adolescence, youth venture out, explore the wider world, and are challenged to learn how to navigate novel and uncertain environments. We investigated whether adolescents are uniquely adapted to this transition, compared to younger children and adults. In a stochastic, volatile reversal-learning task with a sample of 291 participants aged 8-30, we found that adolescents outperformed both younger and older participants. We developed two independent cognitive models, based on Reinforcement learning (RL) and Bayesian inference (BI). The RL parameter for learning from negative outcomes and the BI parameters specifying participants mental models peaked closest to optimal in adolescents, suggesting a central role in adolescent cognitive processing. By contrast, persistence and noise parameters improved monotonously with age. We distilled the insights of RL and BI using principal component analysis and found that three shared components interacted to form the adolescent performance peak: adult-like behavioral quality, child-like time scales, and developmentally-unique processing of positive feedback. This research highlights adolescence as a neurodevelopmental window that may be specifically adapted for volatile and uncertain environments. It also shows how detailed insights can be gleaned by using cognitive models in new ways.