RT Journal Article
SR Electronic
T1 The Unique Advantage of Adolescents in Probabilistic Reversal: Reinforcement Learning and Bayesian Inference Provide Adequate and Complementary Models
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.07.04.187971
DO 10.1101/2020.07.04.187971
A1 Maria K. Eckstein
A1 Sarah L. Master
A1 Ronald E. Dahl
A1 Linda Wilbrecht
A1 Anne G.E. Collins
YR 2021
UL http://biorxiv.org/content/early/2021/03/07/2020.07.04.187971.abstract
AB 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 13-15 years old outperformed both younger and older participants. We developed two independent cognitive models, one based on Reinforcement learning (RL) and the other Bayesian inference (BI), and used hierarchical Bayesian model fitting to assess developmental changes in underlying cognitive mechanisms. Choice parameters in both models improved monotonously. By contrast, RL update parameters and BI mental-model parameters peaked closest to optimal values in 13-to-15-year-olds. Combining both models using principal component analysis yielded new insights, revealing that three readily-interpretable 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 increasingly detailed insights can be gleaned by invoking different cognitive models.Competing Interest StatementThe authors have declared no competing interest.