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Dorsal anterior cingulate-brainstem ensemble as a reinforcement meta-learner

Optimal decision-making is based on integrating information from several dimensions of decisional space (e.g., reward expectation, cost estimation, effort exertion). Despite considerable empirical and theoretical efforts, the computational and neural bases of such multidimensional integration have r...

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Autores principales: Silvetti, Massimo, Vassena, Eliana, Abrahamse, Elger, Verguts, Tom
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6126878/
https://www.ncbi.nlm.nih.gov/pubmed/30142152
http://dx.doi.org/10.1371/journal.pcbi.1006370
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author Silvetti, Massimo
Vassena, Eliana
Abrahamse, Elger
Verguts, Tom
author_facet Silvetti, Massimo
Vassena, Eliana
Abrahamse, Elger
Verguts, Tom
author_sort Silvetti, Massimo
collection PubMed
description Optimal decision-making is based on integrating information from several dimensions of decisional space (e.g., reward expectation, cost estimation, effort exertion). Despite considerable empirical and theoretical efforts, the computational and neural bases of such multidimensional integration have remained largely elusive. Here we propose that the current theoretical stalemate may be broken by considering the computational properties of a cortical-subcortical circuit involving the dorsal anterior cingulate cortex (dACC) and the brainstem neuromodulatory nuclei: ventral tegmental area (VTA) and locus coeruleus (LC). From this perspective, the dACC optimizes decisions about stimuli and actions, and using the same computational machinery, it also modulates cortical functions (meta-learning), via neuromodulatory control (VTA and LC). We implemented this theory in a novel neuro-computational model–the Reinforcement Meta Learner (RML). We outline how the RML captures critical empirical findings from an unprecedented range of theoretical domains, and parsimoniously integrates various previous proposals on dACC functioning.
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spelling pubmed-61268782018-09-17 Dorsal anterior cingulate-brainstem ensemble as a reinforcement meta-learner Silvetti, Massimo Vassena, Eliana Abrahamse, Elger Verguts, Tom PLoS Comput Biol Research Article Optimal decision-making is based on integrating information from several dimensions of decisional space (e.g., reward expectation, cost estimation, effort exertion). Despite considerable empirical and theoretical efforts, the computational and neural bases of such multidimensional integration have remained largely elusive. Here we propose that the current theoretical stalemate may be broken by considering the computational properties of a cortical-subcortical circuit involving the dorsal anterior cingulate cortex (dACC) and the brainstem neuromodulatory nuclei: ventral tegmental area (VTA) and locus coeruleus (LC). From this perspective, the dACC optimizes decisions about stimuli and actions, and using the same computational machinery, it also modulates cortical functions (meta-learning), via neuromodulatory control (VTA and LC). We implemented this theory in a novel neuro-computational model–the Reinforcement Meta Learner (RML). We outline how the RML captures critical empirical findings from an unprecedented range of theoretical domains, and parsimoniously integrates various previous proposals on dACC functioning. Public Library of Science 2018-08-24 /pmc/articles/PMC6126878/ /pubmed/30142152 http://dx.doi.org/10.1371/journal.pcbi.1006370 Text en © 2018 Silvetti et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Silvetti, Massimo
Vassena, Eliana
Abrahamse, Elger
Verguts, Tom
Dorsal anterior cingulate-brainstem ensemble as a reinforcement meta-learner
title Dorsal anterior cingulate-brainstem ensemble as a reinforcement meta-learner
title_full Dorsal anterior cingulate-brainstem ensemble as a reinforcement meta-learner
title_fullStr Dorsal anterior cingulate-brainstem ensemble as a reinforcement meta-learner
title_full_unstemmed Dorsal anterior cingulate-brainstem ensemble as a reinforcement meta-learner
title_short Dorsal anterior cingulate-brainstem ensemble as a reinforcement meta-learner
title_sort dorsal anterior cingulate-brainstem ensemble as a reinforcement meta-learner
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6126878/
https://www.ncbi.nlm.nih.gov/pubmed/30142152
http://dx.doi.org/10.1371/journal.pcbi.1006370
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