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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 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.
