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A Bayesian Perspective on Sensory and Cognitive Integration in Pain Perception and Placebo Analgesia

The placebo effect is a component of any response to a treatment (effective or inert), but we still ignore why it exists. We propose that placebo analgesia is a facet of pain perception, others being the modulating effects of emotions, cognition and past experience, and we suggest that a computation...

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Detalles Bibliográficos
Autores principales: Anchisi, Davide, Zanon, Marco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4321992/
https://www.ncbi.nlm.nih.gov/pubmed/25664586
http://dx.doi.org/10.1371/journal.pone.0117270
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author Anchisi, Davide
Zanon, Marco
author_facet Anchisi, Davide
Zanon, Marco
author_sort Anchisi, Davide
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description The placebo effect is a component of any response to a treatment (effective or inert), but we still ignore why it exists. We propose that placebo analgesia is a facet of pain perception, others being the modulating effects of emotions, cognition and past experience, and we suggest that a computational understanding of pain may provide a unifying explanation of these phenomena. Here we show how Bayesian decision theory can account for such features and we describe a model of pain that we tested against experimental data. Our model not only agrees with placebo analgesia, but also predicts that learning can affect pain perception in other unexpected ways, which experimental evidence supports. Finally, the model can also reflect the strategies used by pain perception, showing that modulation by disparate factors is intrinsic to the pain process.
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spelling pubmed-43219922015-02-18 A Bayesian Perspective on Sensory and Cognitive Integration in Pain Perception and Placebo Analgesia Anchisi, Davide Zanon, Marco PLoS One Research Article The placebo effect is a component of any response to a treatment (effective or inert), but we still ignore why it exists. We propose that placebo analgesia is a facet of pain perception, others being the modulating effects of emotions, cognition and past experience, and we suggest that a computational understanding of pain may provide a unifying explanation of these phenomena. Here we show how Bayesian decision theory can account for such features and we describe a model of pain that we tested against experimental data. Our model not only agrees with placebo analgesia, but also predicts that learning can affect pain perception in other unexpected ways, which experimental evidence supports. Finally, the model can also reflect the strategies used by pain perception, showing that modulation by disparate factors is intrinsic to the pain process. Public Library of Science 2015-02-09 /pmc/articles/PMC4321992/ /pubmed/25664586 http://dx.doi.org/10.1371/journal.pone.0117270 Text en © 2015 Anchisi, Zanon 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
Anchisi, Davide
Zanon, Marco
A Bayesian Perspective on Sensory and Cognitive Integration in Pain Perception and Placebo Analgesia
title A Bayesian Perspective on Sensory and Cognitive Integration in Pain Perception and Placebo Analgesia
title_full A Bayesian Perspective on Sensory and Cognitive Integration in Pain Perception and Placebo Analgesia
title_fullStr A Bayesian Perspective on Sensory and Cognitive Integration in Pain Perception and Placebo Analgesia
title_full_unstemmed A Bayesian Perspective on Sensory and Cognitive Integration in Pain Perception and Placebo Analgesia
title_short A Bayesian Perspective on Sensory and Cognitive Integration in Pain Perception and Placebo Analgesia
title_sort bayesian perspective on sensory and cognitive integration in pain perception and placebo analgesia
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4321992/
https://www.ncbi.nlm.nih.gov/pubmed/25664586
http://dx.doi.org/10.1371/journal.pone.0117270
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