Pain: A statistical account

Tabor, Abby; Thacker, Michael A.; Moseley, G. Lorimer; Körding, Konrad P.

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.

Citation

Tabor, A., Thacker, M. A., Moseley, G. L., & Körding, K. P. (2017). Pain: A statistical account. PLoS Computational Biology, 13(1), https://doi.org/10.1371/journal.pcbi.1005142

• Journal Article Type: Review
• Online Publication Date: Jan 12, 2017
• Publication Date: Jan 12, 2017
• Deposit Date: Jun 9, 2023
• Publicly Available Date: Jun 13, 2023
• Journal: PLoS Computational Biology
• Print ISSN: 1553-734X
• Electronic ISSN: 1553-7358
• Publisher: Public Library of Science
• Peer Reviewed: Peer Reviewed
• Volume: 13
• Issue: 1
• DOI: https://doi.org/10.1371/journal.pcbi.1005142
• Keywords: Pain
• Public URL: https://uwe-repository.worktribe.com/output/10850192
• Publisher URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005142