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Confidence of probabilistic predictions modulates the cortical response to pain
Pain typically evolves over time, and the brain needs to learn this temporal evolution to predict how pain is likely to change in the future and orient behavior. This process is termed temporal statistical learning (TSL). Recently, it has been shown that TSL for pain sequences can be achieved using...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9942789/ https://www.ncbi.nlm.nih.gov/pubmed/36669115 http://dx.doi.org/10.1073/pnas.2212252120 |
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author | Mulders, Dounia Seymour, Ben Mouraux, André Mancini, Flavia |
author_facet | Mulders, Dounia Seymour, Ben Mouraux, André Mancini, Flavia |
author_sort | Mulders, Dounia |
collection | PubMed |
description | Pain typically evolves over time, and the brain needs to learn this temporal evolution to predict how pain is likely to change in the future and orient behavior. This process is termed temporal statistical learning (TSL). Recently, it has been shown that TSL for pain sequences can be achieved using optimal Bayesian inference, which is encoded in somatosensory processing regions. Here, we investigate whether the confidence of these probabilistic predictions modulates the EEG response to noxious stimuli, using a TSL task. Confidence measures the uncertainty about the probabilistic prediction, irrespective of its actual outcome. Bayesian models dictate that the confidence about probabilistic predictions should be integrated with incoming inputs and weight learning, such that it modulates the early components of the EEG responses to noxious stimuli, and this should be captured by a negative correlation: when confidence is higher, the early neural responses are smaller as the brain relies more on expectations/predictions and less on sensory inputs (and vice versa). We show that participants were able to predict the sequence transition probabilities using Bayesian inference, with some forgetting. Then, we find that the confidence of these probabilistic predictions was negatively associated with the amplitude of the N2 and P2 components of the vertex potential: the more confident were participants about their predictions, the smaller the vertex potential. These results confirm key predictions of a Bayesian learning model and clarify the functional significance of the early EEG responses to nociceptive stimuli, as being implicated in confidence-weighted statistical learning. |
format | Online Article Text |
id | pubmed-9942789 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-99427892023-02-22 Confidence of probabilistic predictions modulates the cortical response to pain Mulders, Dounia Seymour, Ben Mouraux, André Mancini, Flavia Proc Natl Acad Sci U S A Biological Sciences Pain typically evolves over time, and the brain needs to learn this temporal evolution to predict how pain is likely to change in the future and orient behavior. This process is termed temporal statistical learning (TSL). Recently, it has been shown that TSL for pain sequences can be achieved using optimal Bayesian inference, which is encoded in somatosensory processing regions. Here, we investigate whether the confidence of these probabilistic predictions modulates the EEG response to noxious stimuli, using a TSL task. Confidence measures the uncertainty about the probabilistic prediction, irrespective of its actual outcome. Bayesian models dictate that the confidence about probabilistic predictions should be integrated with incoming inputs and weight learning, such that it modulates the early components of the EEG responses to noxious stimuli, and this should be captured by a negative correlation: when confidence is higher, the early neural responses are smaller as the brain relies more on expectations/predictions and less on sensory inputs (and vice versa). We show that participants were able to predict the sequence transition probabilities using Bayesian inference, with some forgetting. Then, we find that the confidence of these probabilistic predictions was negatively associated with the amplitude of the N2 and P2 components of the vertex potential: the more confident were participants about their predictions, the smaller the vertex potential. These results confirm key predictions of a Bayesian learning model and clarify the functional significance of the early EEG responses to nociceptive stimuli, as being implicated in confidence-weighted statistical learning. National Academy of Sciences 2023-01-20 2023-01-24 /pmc/articles/PMC9942789/ /pubmed/36669115 http://dx.doi.org/10.1073/pnas.2212252120 Text en Copyright © 2023 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by/4.0/This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY) (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Biological Sciences Mulders, Dounia Seymour, Ben Mouraux, André Mancini, Flavia Confidence of probabilistic predictions modulates the cortical response to pain |
title | Confidence of probabilistic predictions modulates the cortical response to pain |
title_full | Confidence of probabilistic predictions modulates the cortical response to pain |
title_fullStr | Confidence of probabilistic predictions modulates the cortical response to pain |
title_full_unstemmed | Confidence of probabilistic predictions modulates the cortical response to pain |
title_short | Confidence of probabilistic predictions modulates the cortical response to pain |
title_sort | confidence of probabilistic predictions modulates the cortical response to pain |
topic | Biological Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9942789/ https://www.ncbi.nlm.nih.gov/pubmed/36669115 http://dx.doi.org/10.1073/pnas.2212252120 |
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