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

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 learning task with a sample of 291 participants aged 8-30, we found that adolescents 13-15 years old outperformed both younger and older participants. We developed two independent cognitive models, and used hierarchical Bayesian model fitting to assess developmental changes in underlying cognitive mechanisms. Choice parameters in both models improved monotonously. By contrast, up-date parameters peaked closest to optimal values in 13-15 year-olds. Combining both models using principal component analysis yielded new insights, revealing that three components contributed to the early to mid-adolescent performance peak. This research highlights early to mid-adolescence as a neurodevelopmental window that may be more optimal for behavioral adjustment in volatile and uncertain environments. It also shows how detailed insights can be gleaned by combining cognitive models.