November 30, 2020

Understanding the Unique Advantage of Adolescents in Stochastic, Volatile Environments: Combining Reinforcement Learning and Bayesian Inference

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, com- pared 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 fit- ting 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. Com- bining 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 ad- justment in volatile and uncertain environments. It also shows how detailed insights can be gleaned by combining cognitive models.

 bioRxiv Subject Collection: Neuroscience

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