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Brain dynamics for confidence-weighted learning
Learning in a changing, uncertain environment is a difficult problem. A popular solution is to predict future observations and then use surprising outcomes to update those predictions. However, humans also have a sense of confidence that characterizes the precision of their predictions. Bayesian mod...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Public Library of Science
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7292419/ https://www.ncbi.nlm.nih.gov/pubmed/32484806 http://dx.doi.org/10.1371/journal.pcbi.1007935 |
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author | Meyniel, Florent |
author_facet | Meyniel, Florent |
author_sort | Meyniel, Florent |
collection | PubMed |
description | Learning in a changing, uncertain environment is a difficult problem. A popular solution is to predict future observations and then use surprising outcomes to update those predictions. However, humans also have a sense of confidence that characterizes the precision of their predictions. Bayesian models use a confidence-weighting principle to regulate learning: for a given surprise, the update is smaller when the confidence about the prediction was higher. Prior behavioral evidence indicates that human learning adheres to this confidence-weighting principle. Here, we explored the human brain dynamics sub-tending the confidence-weighting of learning using magneto-encephalography (MEG). During our volatile probability learning task, subjects’ confidence reports conformed with Bayesian inference. MEG revealed several stimulus-evoked brain responses whose amplitude reflected surprise, and some of them were further shaped by confidence: surprise amplified the stimulus-evoked response whereas confidence dampened it. Confidence about predictions also modulated several aspects of the brain state: pupil-linked arousal and beta-range (15–30 Hz) oscillations. The brain state in turn modulated specific stimulus-evoked surprise responses following the confidence-weighting principle. Our results thus indicate that there exist, in the human brain, signals reflecting surprise that are dampened by confidence in a way that is appropriate for learning according to Bayesian inference. They also suggest a mechanism for confidence-weighted learning: confidence about predictions would modulate intrinsic properties of the brain state to amplify or dampen surprise responses evoked by discrepant observations. |
format | Online Article Text |
id | pubmed-7292419 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-72924192020-06-18 Brain dynamics for confidence-weighted learning Meyniel, Florent PLoS Comput Biol Research Article Learning in a changing, uncertain environment is a difficult problem. A popular solution is to predict future observations and then use surprising outcomes to update those predictions. However, humans also have a sense of confidence that characterizes the precision of their predictions. Bayesian models use a confidence-weighting principle to regulate learning: for a given surprise, the update is smaller when the confidence about the prediction was higher. Prior behavioral evidence indicates that human learning adheres to this confidence-weighting principle. Here, we explored the human brain dynamics sub-tending the confidence-weighting of learning using magneto-encephalography (MEG). During our volatile probability learning task, subjects’ confidence reports conformed with Bayesian inference. MEG revealed several stimulus-evoked brain responses whose amplitude reflected surprise, and some of them were further shaped by confidence: surprise amplified the stimulus-evoked response whereas confidence dampened it. Confidence about predictions also modulated several aspects of the brain state: pupil-linked arousal and beta-range (15–30 Hz) oscillations. The brain state in turn modulated specific stimulus-evoked surprise responses following the confidence-weighting principle. Our results thus indicate that there exist, in the human brain, signals reflecting surprise that are dampened by confidence in a way that is appropriate for learning according to Bayesian inference. They also suggest a mechanism for confidence-weighted learning: confidence about predictions would modulate intrinsic properties of the brain state to amplify or dampen surprise responses evoked by discrepant observations. Public Library of Science 2020-06-02 /pmc/articles/PMC7292419/ /pubmed/32484806 http://dx.doi.org/10.1371/journal.pcbi.1007935 Text en © 2020 Florent Meyniel 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 Meyniel, Florent Brain dynamics for confidence-weighted learning |
title | Brain dynamics for confidence-weighted learning |
title_full | Brain dynamics for confidence-weighted learning |
title_fullStr | Brain dynamics for confidence-weighted learning |
title_full_unstemmed | Brain dynamics for confidence-weighted learning |
title_short | Brain dynamics for confidence-weighted learning |
title_sort | brain dynamics for confidence-weighted learning |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7292419/ https://www.ncbi.nlm.nih.gov/pubmed/32484806 http://dx.doi.org/10.1371/journal.pcbi.1007935 |
work_keys_str_mv | AT meynielflorent braindynamicsforconfidenceweightedlearning |