The Unique Advantage of Adolescents in Probabilistic Reversal: Reinforcement Learning and Bayesian Inference Provide Adequate and Complementary Models

semanticscholar(2021)

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摘要
10 During adolescence, youth venture out, explore thewiderworld, and are challenged to learn 11 how to navigate novel and uncertain environments. We investigated whether adolescents 12 are uniquely adapted to this transition, compared to younger children and adults. In a 13 stochastic, volatile reversal learning task with a sample of 291 participants aged 8-30, we 14 found that adolescents 13-15 years old outperformed both younger and older participants. 15 Wedeveloped two independent cognitivemodels, one based on Reinforcement learning (RL) 16 and the other Bayesian inference (BI), andusedhierarchical Bayesianmodel fitting to assess 17 developmental changes in underlying cognitive mechanisms. Choice parameters in both 18 models improved monotonously. By contrast, RL update parameters and BI mental-model 19 parameters peaked closest to optimal values in 13-to-15-year-olds. Combining both mod20 els using principal component analysis yielded new insights, revealing that three readily21 interpretable components contributed to the earlyto mid-adolescent performance peak. 22 This research highlights earlyto mid-adolescence as a neurodevelopmental window that 23 may be more optimal for behavioral adjustment in volatile and uncertain environments. It 24 also shows how increasingly detailed insights can be gleaned by invoking different cognitive 25
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