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Distentangling the systems contributing to changes in learning during adolescence
Multiple neurocognitive systems contribute simultaneously to learning. For example, dopamine and basal ganglia (BG) systems are thought to support reinforcement learning (RL) by incrementally updating the value of choices, while the prefrontal cortex (PFC) contributes different computations, such as...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6994540/ https://www.ncbi.nlm.nih.gov/pubmed/31826837 http://dx.doi.org/10.1016/j.dcn.2019.100732 |
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author | Master, Sarah L. Eckstein, Maria K. Gotlieb, Neta Dahl, Ronald Wilbrecht, Linda Collins, Anne G.E. |
author_facet | Master, Sarah L. Eckstein, Maria K. Gotlieb, Neta Dahl, Ronald Wilbrecht, Linda Collins, Anne G.E. |
author_sort | Master, Sarah L. |
collection | PubMed |
description | Multiple neurocognitive systems contribute simultaneously to learning. For example, dopamine and basal ganglia (BG) systems are thought to support reinforcement learning (RL) by incrementally updating the value of choices, while the prefrontal cortex (PFC) contributes different computations, such as actively maintaining precise information in working memory (WM). It is commonly thought that WM and PFC show more protracted development than RL and BG systems, yet their contributions are rarely assessed in tandem. Here, we used a simple learning task to test how RL and WM contribute to changes in learning across adolescence. We tested 187 subjects ages 8 to 17 and 53 adults (25-30). Participants learned stimulus-action associations from feedback; the learning load was varied to be within or exceed WM capacity. Participants age 8-12 learned slower than participants age 13-17, and were more sensitive to load. We used computational modeling to estimate subjects’ use of WM and RL processes. Surprisingly, we found more protracted changes in RL than WM during development. RL learning rate increased with age until age 18 and WM parameters showed more subtle, gender- and puberty-dependent changes early in adolescence. These results can inform education and intervention strategies based on the developmental science of learning. |
format | Online Article Text |
id | pubmed-6994540 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-69945402020-02-04 Distentangling the systems contributing to changes in learning during adolescence Master, Sarah L. Eckstein, Maria K. Gotlieb, Neta Dahl, Ronald Wilbrecht, Linda Collins, Anne G.E. Dev Cogn Neurosci Articles from the Special Issue on Flux 2018: Mechanisms of Learning & Plasticity; Edited by Catherine Hartley, Yana Fandakova, Silvia Bunge, Eveline Crone, Ulman Lindenberger. Multiple neurocognitive systems contribute simultaneously to learning. For example, dopamine and basal ganglia (BG) systems are thought to support reinforcement learning (RL) by incrementally updating the value of choices, while the prefrontal cortex (PFC) contributes different computations, such as actively maintaining precise information in working memory (WM). It is commonly thought that WM and PFC show more protracted development than RL and BG systems, yet their contributions are rarely assessed in tandem. Here, we used a simple learning task to test how RL and WM contribute to changes in learning across adolescence. We tested 187 subjects ages 8 to 17 and 53 adults (25-30). Participants learned stimulus-action associations from feedback; the learning load was varied to be within or exceed WM capacity. Participants age 8-12 learned slower than participants age 13-17, and were more sensitive to load. We used computational modeling to estimate subjects’ use of WM and RL processes. Surprisingly, we found more protracted changes in RL than WM during development. RL learning rate increased with age until age 18 and WM parameters showed more subtle, gender- and puberty-dependent changes early in adolescence. These results can inform education and intervention strategies based on the developmental science of learning. Elsevier 2019-11-14 /pmc/articles/PMC6994540/ /pubmed/31826837 http://dx.doi.org/10.1016/j.dcn.2019.100732 Text en © 2019 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Articles from the Special Issue on Flux 2018: Mechanisms of Learning & Plasticity; Edited by Catherine Hartley, Yana Fandakova, Silvia Bunge, Eveline Crone, Ulman Lindenberger. Master, Sarah L. Eckstein, Maria K. Gotlieb, Neta Dahl, Ronald Wilbrecht, Linda Collins, Anne G.E. Distentangling the systems contributing to changes in learning during adolescence |
title | Distentangling the systems contributing to changes in learning during adolescence |
title_full | Distentangling the systems contributing to changes in learning during adolescence |
title_fullStr | Distentangling the systems contributing to changes in learning during adolescence |
title_full_unstemmed | Distentangling the systems contributing to changes in learning during adolescence |
title_short | Distentangling the systems contributing to changes in learning during adolescence |
title_sort | distentangling the systems contributing to changes in learning during adolescence |
topic | Articles from the Special Issue on Flux 2018: Mechanisms of Learning & Plasticity; Edited by Catherine Hartley, Yana Fandakova, Silvia Bunge, Eveline Crone, Ulman Lindenberger. |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6994540/ https://www.ncbi.nlm.nih.gov/pubmed/31826837 http://dx.doi.org/10.1016/j.dcn.2019.100732 |
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