Pain: A Statistical Account
Open accessAbstract
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.
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Current perspectives on the perceptual mechanism behind expectation effects suggest that an agent maintains multiple potential hypotheses about the cause of a sensory input, and selects the one which best represents the incoming sensory information at any given time.
“Current perspectives on the perceptual mechanism behind expectation effects10,35–38 suggest that an agent maintains multiple potential hypotheses about the cause of a sensory input, and selects the one which best represents the incoming sensory information at any given time.”
From Boundary effects of expectation in human pain perception (2019) · cites this paper as [37]
In contemporary theories of perception, the influence of expectation on perceived pain is also modulated by the certainty (the inverse variability) of the pain stimulus intensity.
“Finally, in contemporary theories of perception, the influence of expectation on perceived pain is also modulated by the certainty (the inverse variability) of the pain stimulus intensity10,35,37,55,56.”
From Boundary effects of expectation in human pain perception (2019) · cites this paper as [37]
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Boyun Ağrısı Tedavisinde Sanal Gerçeklik Uygulamaları
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- Authors
- Tabor, A., Thacker, M. A., Moseley, G. L., & Körding, K. P.
- Affiliations
- University of Bath (GB)
- University of South Australia (AU)
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2017-01-12
- Volume
- 13
- Issue
- 1
- Pages
- e1005142
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC5230746
- OpenAlex
- W2573967569
- Cited by
- 111
- References
- 74
- Citations by year
- 2026: 10 · 2025: 15 · 2024: 11 · 2023: 13 · 2022: 22 · 2021: 9 · 2020: 6 · 2019: 15 · 2018: 7 · 2017: 3
- FWCI
- 1.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Open (London South Bank University) (acceptedVersion)
- Research Open (London South Bank University) (acceptedVersion, cc-by)
- The University of Bath Online Publications Store (The University of Bath) (submittedVersion, cc-by)
- Pure (University of Bath) (submittedVersion, cc-by)
- UWE Research Repository (UWE Bristol) (submittedVersion, cc-by)
- UniSA Research Outputs Repository (University of South Australia) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Research Portal (King's College London) (submittedVersion, cc-by)
- UNSWorks (University of New South Wales, Sydney, Australia) (submittedVersion, cc-by)
- Funders
- National Institute of Neurological Disorders and Stroke (R01NS063399)
- Topics
- Health Sciences › Medicine › Physiology › Pain Mechanisms and Treatments
- Health Sciences › Medicine › Anesthesiology and Pain Medicine › Pain Management and Treatment
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Keywords
- pain perception, Bayesian inference, cue combination, causal inference, temporal integration, multimodal data, acute pain, chronic pain
- MeSH terms
- Bayes Theorem, Humans, Models, Neurological, Pain, Models, Statistical, Pain Perception
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