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Neural surprise in somatosensory Bayesian learning

Tracking statistical regularities of the environment is important for shaping human behavior and perception. Evidence suggests that the brain learns environmental dependencies using Bayesian principles. However, much remains unknown about the employed algorithms, for somesthesis in particular. Here,...

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Detalles Bibliográficos
Autores principales: Gijsen, Sam, Grundei, Miro, Lange, Robert T., Ostwald, Dirk, Blankenburg, Felix
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7880500/
https://www.ncbi.nlm.nih.gov/pubmed/33529181
http://dx.doi.org/10.1371/journal.pcbi.1008068
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author Gijsen, Sam
Grundei, Miro
Lange, Robert T.
Ostwald, Dirk
Blankenburg, Felix
author_facet Gijsen, Sam
Grundei, Miro
Lange, Robert T.
Ostwald, Dirk
Blankenburg, Felix
author_sort Gijsen, Sam
collection PubMed
description Tracking statistical regularities of the environment is important for shaping human behavior and perception. Evidence suggests that the brain learns environmental dependencies using Bayesian principles. However, much remains unknown about the employed algorithms, for somesthesis in particular. Here, we describe the cortical dynamics of the somatosensory learning system to investigate both the form of the generative model as well as its neural surprise signatures. Specifically, we recorded EEG data from 40 participants subjected to a somatosensory roving-stimulus paradigm and performed single-trial modeling across peri-stimulus time in both sensor and source space. Our Bayesian model selection procedure indicates that evoked potentials are best described by a non-hierarchical learning model that tracks transitions between observations using leaky integration. From around 70ms post-stimulus onset, secondary somatosensory cortices are found to represent confidence-corrected surprise as a measure of model inadequacy. Indications of Bayesian surprise encoding, reflecting model updating, are found in primary somatosensory cortex from around 140ms. This dissociation is compatible with the idea that early surprise signals may control subsequent model update rates. In sum, our findings support the hypothesis that early somatosensory processing reflects Bayesian perceptual learning and contribute to an understanding of its underlying mechanisms.
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spelling pubmed-78805002021-02-19 Neural surprise in somatosensory Bayesian learning Gijsen, Sam Grundei, Miro Lange, Robert T. Ostwald, Dirk Blankenburg, Felix PLoS Comput Biol Research Article Tracking statistical regularities of the environment is important for shaping human behavior and perception. Evidence suggests that the brain learns environmental dependencies using Bayesian principles. However, much remains unknown about the employed algorithms, for somesthesis in particular. Here, we describe the cortical dynamics of the somatosensory learning system to investigate both the form of the generative model as well as its neural surprise signatures. Specifically, we recorded EEG data from 40 participants subjected to a somatosensory roving-stimulus paradigm and performed single-trial modeling across peri-stimulus time in both sensor and source space. Our Bayesian model selection procedure indicates that evoked potentials are best described by a non-hierarchical learning model that tracks transitions between observations using leaky integration. From around 70ms post-stimulus onset, secondary somatosensory cortices are found to represent confidence-corrected surprise as a measure of model inadequacy. Indications of Bayesian surprise encoding, reflecting model updating, are found in primary somatosensory cortex from around 140ms. This dissociation is compatible with the idea that early surprise signals may control subsequent model update rates. In sum, our findings support the hypothesis that early somatosensory processing reflects Bayesian perceptual learning and contribute to an understanding of its underlying mechanisms. Public Library of Science 2021-02-02 /pmc/articles/PMC7880500/ /pubmed/33529181 http://dx.doi.org/10.1371/journal.pcbi.1008068 Text en © 2021 Gijsen 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
Gijsen, Sam
Grundei, Miro
Lange, Robert T.
Ostwald, Dirk
Blankenburg, Felix
Neural surprise in somatosensory Bayesian learning
title Neural surprise in somatosensory Bayesian learning
title_full Neural surprise in somatosensory Bayesian learning
title_fullStr Neural surprise in somatosensory Bayesian learning
title_full_unstemmed Neural surprise in somatosensory Bayesian learning
title_short Neural surprise in somatosensory Bayesian learning
title_sort neural surprise in somatosensory bayesian learning
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7880500/
https://www.ncbi.nlm.nih.gov/pubmed/33529181
http://dx.doi.org/10.1371/journal.pcbi.1008068
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