Pain: A Statistical Account

Journal article


Tabor, A, Thacker, MA, Moseley, GL and Körding, KP (2017). Pain: A Statistical Account. PLoS Computational Biology. 13 (1), pp. e1005142-e1005142.
AuthorsTabor, A, Thacker, MA, Moseley, GL and Körding, KP
Abstract

Perception is seen as a process that utilises partial and noisy information to construct a coherent understanding of the world. Here we argue that the experience of pain is no different; it is based on incomplete, multimodal information, which is used to estimate potential bodily threat. We outline a Bayesian inference model, incorporating the key components of cue combination, causal inference, and temporal integration, which highlights the statistical problems in everyday perception. It is from this platform that we are able to review the pain literature, providing evidence from experimental, acute, and persistent phenomena to demonstrate the advantages of adopting a statistical account in pain. Our probabilistic conceptualisation suggests a principles-based view of pain, explaining a broad range of experimental and clinical findings and making testable predictions

Year2017
JournalPLoS Computational Biology
Journal citation13 (1), pp. e1005142-e1005142
ISSN1553-7358
Digital Object Identifier (DOI)doi:10.1371/journal.pcbi.1005142
Publication dates
Print12 Jan 2017
Publication process dates
Deposited25 Jun 2019
Accepted12 Jan 2017
Publisher's version
License
CC BY 4.0
EditorsBlohm, Gunnar
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https://openresearch.lsbu.ac.uk/item/870x6

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