Pain: A Statistical Account

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...

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Bibliographic Details
Published in:PLoS computational biology 2017-01, Vol.13 (1), p.e1005142-e1005142
Main Authors: Tabor, Abby, Thacker, Michael A, Moseley, G Lorimer, Körding, Konrad P
Format: Article
Language:English
Subjects:
Bayes Theorem
Biology and Life Sciences
Brain research
Colleges & universities
Funding
Humans
Medical research
Medicine and Health Sciences
Models, Neurological
Models, Statistical
Pain
Pain - physiopathology
Pain Perception - physiology
Perceptions
Physical Sciences
Psychological aspects
Review
Social Sciences
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Summary: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.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1005142