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Counterfactual Choice and Learning in a Neural Network Centered on Human Lateral Frontopolar Cortex

Decision making and learning in a real-world context require organisms to track not only the choices they make and the outcomes that follow but also other untaken, or counterfactual, choices and their outcomes. Although the neural system responsible for tracking the value of choices actually taken i...

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Autores principales: Boorman, Erie D., Behrens, Timothy E., Rushworth, Matthew F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3125157/
https://www.ncbi.nlm.nih.gov/pubmed/21738446
http://dx.doi.org/10.1371/journal.pbio.1001093
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author Boorman, Erie D.
Behrens, Timothy E.
Rushworth, Matthew F.
author_facet Boorman, Erie D.
Behrens, Timothy E.
Rushworth, Matthew F.
author_sort Boorman, Erie D.
collection PubMed
description Decision making and learning in a real-world context require organisms to track not only the choices they make and the outcomes that follow but also other untaken, or counterfactual, choices and their outcomes. Although the neural system responsible for tracking the value of choices actually taken is increasingly well understood, whether a neural system tracks counterfactual information is currently unclear. Using a three-alternative decision-making task, a Bayesian reinforcement-learning algorithm, and fMRI, we investigated the coding of counterfactual choices and prediction errors in the human brain. Rather than representing evidence favoring multiple counterfactual choices, lateral frontal polar cortex (lFPC), dorsomedial frontal cortex (DMFC), and posteromedial cortex (PMC) encode the reward-based evidence favoring the best counterfactual option at future decisions. In addition to encoding counterfactual reward expectations, the network carries a signal for learning about counterfactual options when feedback is available—a counterfactual prediction error. Unlike other brain regions that have been associated with the processing of counterfactual outcomes, counterfactual prediction errors within the identified network cannot be related to regret theory. Furthermore, individual variation in counterfactual choice-related activity and prediction error-related activity, respectively, predicts variation in the propensity to switch to profitable choices in the future and the ability to learn from hypothetical feedback. Taken together, these data provide both neural and behavioral evidence to support the existence of a previously unidentified neural system responsible for tracking both counterfactual choice options and their outcomes.
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spelling pubmed-31251572011-07-07 Counterfactual Choice and Learning in a Neural Network Centered on Human Lateral Frontopolar Cortex Boorman, Erie D. Behrens, Timothy E. Rushworth, Matthew F. PLoS Biol Research Article Decision making and learning in a real-world context require organisms to track not only the choices they make and the outcomes that follow but also other untaken, or counterfactual, choices and their outcomes. Although the neural system responsible for tracking the value of choices actually taken is increasingly well understood, whether a neural system tracks counterfactual information is currently unclear. Using a three-alternative decision-making task, a Bayesian reinforcement-learning algorithm, and fMRI, we investigated the coding of counterfactual choices and prediction errors in the human brain. Rather than representing evidence favoring multiple counterfactual choices, lateral frontal polar cortex (lFPC), dorsomedial frontal cortex (DMFC), and posteromedial cortex (PMC) encode the reward-based evidence favoring the best counterfactual option at future decisions. In addition to encoding counterfactual reward expectations, the network carries a signal for learning about counterfactual options when feedback is available—a counterfactual prediction error. Unlike other brain regions that have been associated with the processing of counterfactual outcomes, counterfactual prediction errors within the identified network cannot be related to regret theory. Furthermore, individual variation in counterfactual choice-related activity and prediction error-related activity, respectively, predicts variation in the propensity to switch to profitable choices in the future and the ability to learn from hypothetical feedback. Taken together, these data provide both neural and behavioral evidence to support the existence of a previously unidentified neural system responsible for tracking both counterfactual choice options and their outcomes. Public Library of Science 2011-06-28 /pmc/articles/PMC3125157/ /pubmed/21738446 http://dx.doi.org/10.1371/journal.pbio.1001093 Text en Boorman 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, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Boorman, Erie D.
Behrens, Timothy E.
Rushworth, Matthew F.
Counterfactual Choice and Learning in a Neural Network Centered on Human Lateral Frontopolar Cortex
title Counterfactual Choice and Learning in a Neural Network Centered on Human Lateral Frontopolar Cortex
title_full Counterfactual Choice and Learning in a Neural Network Centered on Human Lateral Frontopolar Cortex
title_fullStr Counterfactual Choice and Learning in a Neural Network Centered on Human Lateral Frontopolar Cortex
title_full_unstemmed Counterfactual Choice and Learning in a Neural Network Centered on Human Lateral Frontopolar Cortex
title_short Counterfactual Choice and Learning in a Neural Network Centered on Human Lateral Frontopolar Cortex
title_sort counterfactual choice and learning in a neural network centered on human lateral frontopolar cortex
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3125157/
https://www.ncbi.nlm.nih.gov/pubmed/21738446
http://dx.doi.org/10.1371/journal.pbio.1001093
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