Modeling changes in probabilistic reinforcement learning during adolescence.

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 you...

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Autores principales:	Liyu Xia, Sarah L Master, Maria K Eckstein, Beth Baribault, Ronald E Dahl, Linda Wilbrecht, Anne Gabrielle Eva Collins
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/c05eadd4cbf7451cb7b15e8aeda4f678
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spelling	oai:doaj.org-article:c05eadd4cbf7451cb7b15e8aeda4f6782021-12-02T19:57:35ZModeling changes in probabilistic reinforcement learning during adolescence.1553-734X1553-735810.1371/journal.pcbi.1008524https://doaj.org/article/c05eadd4cbf7451cb7b15e8aeda4f6782021-07-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1008524https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358In 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.Liyu XiaSarah L MasterMaria K EcksteinBeth BaribaultRonald E DahlLinda WilbrechtAnne Gabrielle Eva CollinsPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 7, p e1008524 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Liyu Xia Sarah L Master Maria K Eckstein Beth Baribault Ronald E Dahl Linda Wilbrecht Anne Gabrielle Eva Collins Modeling changes in probabilistic reinforcement learning during adolescence.
description	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.
format	article
author	Liyu Xia Sarah L Master Maria K Eckstein Beth Baribault Ronald E Dahl Linda Wilbrecht Anne Gabrielle Eva Collins
author_facet	Liyu Xia Sarah L Master Maria K Eckstein Beth Baribault Ronald E Dahl Linda Wilbrecht Anne Gabrielle Eva Collins
author_sort	Liyu Xia
title	Modeling changes in probabilistic reinforcement learning during adolescence.
title_short	Modeling changes in probabilistic reinforcement learning during adolescence.
title_full	Modeling changes in probabilistic reinforcement learning during adolescence.
title_fullStr	Modeling changes in probabilistic reinforcement learning during adolescence.
title_full_unstemmed	Modeling changes in probabilistic reinforcement learning during adolescence.
title_sort	modeling changes in probabilistic reinforcement learning during adolescence.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/c05eadd4cbf7451cb7b15e8aeda4f678
work_keys_str_mv	AT liyuxia modelingchangesinprobabilisticreinforcementlearningduringadolescence AT sarahlmaster modelingchangesinprobabilisticreinforcementlearningduringadolescence AT mariakeckstein modelingchangesinprobabilisticreinforcementlearningduringadolescence AT bethbaribault modelingchangesinprobabilisticreinforcementlearningduringadolescence AT ronaldedahl modelingchangesinprobabilisticreinforcementlearningduringadolescence AT lindawilbrecht modelingchangesinprobabilisticreinforcementlearningduringadolescence AT annegabrielleevacollins modelingchangesinprobabilisticreinforcementlearningduringadolescence
_version_	1718375801677676544

Modeling changes in probabilistic reinforcement learning during adolescence.

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