Cognitive components underpinning the development of model-based learning

Reinforcement learning theory distinguishes “model-free” learning, which fosters reflexive repetition of previously rewarded actions, from “model-based” learning, which recruits a mental model of the environment to flexibly select goal-directed actions. Whereas model-free learning is evident across...

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Autores principales: Potter, Tracey C.S., Bryce, Nessa V., Hartley, Catherine A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5410189/
https://www.ncbi.nlm.nih.gov/pubmed/27825732
http://dx.doi.org/10.1016/j.dcn.2016.10.005
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author Potter, Tracey C.S.
Bryce, Nessa V.
Hartley, Catherine A.
author_facet Potter, Tracey C.S.
Bryce, Nessa V.
Hartley, Catherine A.
author_sort Potter, Tracey C.S.
collection PubMed
description Reinforcement learning theory distinguishes “model-free” learning, which fosters reflexive repetition of previously rewarded actions, from “model-based” learning, which recruits a mental model of the environment to flexibly select goal-directed actions. Whereas model-free learning is evident across development, recruitment of model-based learning appears to increase with age. However, the cognitive processes underlying the development of model-based learning remain poorly characterized. Here, we examined whether age-related differences in cognitive processes underlying the construction and flexible recruitment of mental models predict developmental increases in model-based choice. In a cohort of participants aged 9–25, we examined whether the abilities to infer sequential regularities in the environment (“statistical learning”), maintain information in an active state (“working memory”) and integrate distant concepts to solve problems (“fluid reasoning”) predicted age-related improvements in model-based choice. We found that age-related improvements in statistical learning performance did not mediate the relationship between age and model-based choice. Ceiling performance on our working memory assay prevented examination of its contribution to model-based learning. However, age-related improvements in fluid reasoning statistically mediated the developmental increase in the recruitment of a model-based strategy. These findings suggest that gradual development of fluid reasoning may be a critical component process underlying the emergence of model-based learning.
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spelling pubmed-54101892018-06-01 Cognitive components underpinning the development of model-based learning Potter, Tracey C.S. Bryce, Nessa V. Hartley, Catherine A. Dev Cogn Neurosci Article Reinforcement learning theory distinguishes “model-free” learning, which fosters reflexive repetition of previously rewarded actions, from “model-based” learning, which recruits a mental model of the environment to flexibly select goal-directed actions. Whereas model-free learning is evident across development, recruitment of model-based learning appears to increase with age. However, the cognitive processes underlying the development of model-based learning remain poorly characterized. Here, we examined whether age-related differences in cognitive processes underlying the construction and flexible recruitment of mental models predict developmental increases in model-based choice. In a cohort of participants aged 9–25, we examined whether the abilities to infer sequential regularities in the environment (“statistical learning”), maintain information in an active state (“working memory”) and integrate distant concepts to solve problems (“fluid reasoning”) predicted age-related improvements in model-based choice. We found that age-related improvements in statistical learning performance did not mediate the relationship between age and model-based choice. Ceiling performance on our working memory assay prevented examination of its contribution to model-based learning. However, age-related improvements in fluid reasoning statistically mediated the developmental increase in the recruitment of a model-based strategy. These findings suggest that gradual development of fluid reasoning may be a critical component process underlying the emergence of model-based learning. Elsevier 2016-10-29 /pmc/articles/PMC5410189/ /pubmed/27825732 http://dx.doi.org/10.1016/j.dcn.2016.10.005 Text en © 2016 The Authors 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 Article
Potter, Tracey C.S.
Bryce, Nessa V.
Hartley, Catherine A.
Cognitive components underpinning the development of model-based learning
title Cognitive components underpinning the development of model-based learning
title_full Cognitive components underpinning the development of model-based learning
title_fullStr Cognitive components underpinning the development of model-based learning
title_full_unstemmed Cognitive components underpinning the development of model-based learning
title_short Cognitive components underpinning the development of model-based learning
title_sort cognitive components underpinning the development of model-based learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5410189/
https://www.ncbi.nlm.nih.gov/pubmed/27825732
http://dx.doi.org/10.1016/j.dcn.2016.10.005
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