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Modeling changes in probabilistic reinforcement learning during adolescence

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  • Liyu Xia
  • Sarah L Master
  • Maria K Eckstein
  • Beth Baribault
  • Ronald E Dahl
  • Linda Wilbrecht
  • Anne Gabrielle Eva Collins

Abstract

In the real world, many relationships between events are uncertain and probabilistic. Uncertainty is also likely to be a more common feature of daily experience for youth because they have less experience to draw from than adults. Some studies suggest probabilistic learning may be inefficient in youths compared to adults, while others suggest it may be more efficient in youths in mid adolescence. Here we used a probabilistic reinforcement learning task to test how youth age 8-17 (N = 187) and adults age 18-30 (N = 110) learn about stable probabilistic contingencies. Performance increased with age through early-twenties, then stabilized. Using hierarchical Bayesian methods to fit computational reinforcement learning models, we show that all participants’ performance was better explained by models in which negative outcomes had minimal to no impact on learning. The performance increase over age was driven by 1) an increase in learning rate (i.e. decrease in integration time scale); 2) a decrease in noisy/exploratory choices. In mid-adolescence age 13-15, salivary testosterone and learning rate were positively related. We discuss our findings in the context of other studies and hypotheses about adolescent brain development.Author summary: Adolescence is a time of great uncertainty. It is also a critical time for brain development, learning, and decision making in social and educational domains. There are currently contradictory findings about learning in adolescence. We sought to better isolate how learning from stable probabilistic contingencies changes during adolescence with a task that previously showed interesting results in adolescents. We collected a relatively large sample size (297 participants) across a wide age range (8–30), to trace the adolescent developmental trajectory of learning under stable but uncertain conditions. We found that age in our sample was positively associated with higher learning rates and lower choice exploration. Within narrow age bins, we found that higher saliva testosterone levels were associated with higher learning rates in participants age 13–15 years. These findings can help us better isolate the trajectory of maturation of core learning and decision making processes during adolescence.

Suggested Citation

  • Liyu Xia & Sarah L Master & Maria K Eckstein & Beth Baribault & Ronald E Dahl & Linda Wilbrecht & Anne Gabrielle Eva Collins, 2021. "Modeling changes in probabilistic reinforcement learning during adolescence," PLOS Computational Biology, Public Library of Science, vol. 17(7), pages 1-22, July.
  • Handle: RePEc:plo:pcbi00:1008524
    DOI: 10.1371/journal.pcbi.1008524
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    Cited by:

    1. Ruth Pauli & Inti A. Brazil & Gregor Kohls & Miriam C. Klein-Flügge & Jack C. Rogers & Dimitris Dikeos & Roberta Dochnal & Graeme Fairchild & Aranzazu Fernández-Rivas & Beate Herpertz-Dahlmann & Amaia, 2023. "Action initiation and punishment learning differ from childhood to adolescence while reward learning remains stable," Nature Communications, Nature, vol. 14(1), pages 1-15, December.

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