Pain: a statistical account

Abby Tabor, Michael Thacker, G Lorimer Moseley, Konrad Kording

Research output: Contribution to journalArticle

26 Citations (Scopus)
145 Downloads (Pure)

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.
Original languageEnglish
Article numbere1005142
JournalPlos Computational Biology
Volume13
Issue number1
DOIs
Publication statusPublished - 12 Jan 2017

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    Tabor, A., Thacker, M., Moseley, G. L., & Kording, K. (2017). Pain: a statistical account. Plos Computational Biology, 13(1), [e1005142]. https://doi.org/10.1371/journal.pcbi.1005142