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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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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. |
format | Online Article Text |
id | pubmed-6126878 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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