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The network architecture of value learning

Value guides behavior. With knowledge of stimulus values and action consequences, behaviors that maximize expected reward can be selected. Prior work has identified several brain structures critical for representing both stimuli and their values. Yet, it remains unclear how these structures interact...

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Detalles Bibliográficos
Autores principales: Mattar, Marcelo G., Thompson-Schill, Sharon L., Bassett, Danielle S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MIT Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6130435/
https://www.ncbi.nlm.nih.gov/pubmed/30215030
http://dx.doi.org/10.1162/netn_a_00021
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author Mattar, Marcelo G.
Thompson-Schill, Sharon L.
Bassett, Danielle S.
author_facet Mattar, Marcelo G.
Thompson-Schill, Sharon L.
Bassett, Danielle S.
author_sort Mattar, Marcelo G.
collection PubMed
description Value guides behavior. With knowledge of stimulus values and action consequences, behaviors that maximize expected reward can be selected. Prior work has identified several brain structures critical for representing both stimuli and their values. Yet, it remains unclear how these structures interact with one another and with other regions of the brain to support the dynamic acquisition of value-related knowledge. Here, we use a network neuroscience approach to examine how BOLD functional networks change as 20 healthy human subjects learn the values of novel visual stimuli over the course of four consecutive days. We show that connections between regions of the visual, frontal, and cingulate cortices become stronger as learning progresses, with some of these changes being specific to the type of feedback received during learning. These results demonstrate that functional networks dynamically track behavioral improvement in value judgments, and that interactions between network communities form predictive biomarkers of learning.
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spelling pubmed-61304352018-09-11 The network architecture of value learning Mattar, Marcelo G. Thompson-Schill, Sharon L. Bassett, Danielle S. Netw Neurosci Research Value guides behavior. With knowledge of stimulus values and action consequences, behaviors that maximize expected reward can be selected. Prior work has identified several brain structures critical for representing both stimuli and their values. Yet, it remains unclear how these structures interact with one another and with other regions of the brain to support the dynamic acquisition of value-related knowledge. Here, we use a network neuroscience approach to examine how BOLD functional networks change as 20 healthy human subjects learn the values of novel visual stimuli over the course of four consecutive days. We show that connections between regions of the visual, frontal, and cingulate cortices become stronger as learning progresses, with some of these changes being specific to the type of feedback received during learning. These results demonstrate that functional networks dynamically track behavioral improvement in value judgments, and that interactions between network communities form predictive biomarkers of learning. MIT Press 2018-06-01 /pmc/articles/PMC6130435/ /pubmed/30215030 http://dx.doi.org/10.1162/netn_a_00021 Text en © 2017 Massachusetts Institute of Technology http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Mattar, Marcelo G.
Thompson-Schill, Sharon L.
Bassett, Danielle S.
The network architecture of value learning
title The network architecture of value learning
title_full The network architecture of value learning
title_fullStr The network architecture of value learning
title_full_unstemmed The network architecture of value learning
title_short The network architecture of value learning
title_sort network architecture of value learning
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6130435/
https://www.ncbi.nlm.nih.gov/pubmed/30215030
http://dx.doi.org/10.1162/netn_a_00021
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