title: Pain: A Statistical Account.
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.
authors.affiliation: Centre for Pain Research, University of Bath, North East Somerset, United Kingdom.
authors.forename: Abby
authors.identifier: 0000-0002-8168-2052
authors.initials: A
authors.lastname: Tabor
authors.affiliation: Centre for Human and Aerospace Physiological Sciences/Pain Section, Neuroimaging, Institute of Psychiatry, Kings College London, London, United Kingdom.|Sansom Institute for Health Research, University of South Australia, Adelaide, South Australia, Australia.
authors.forename: Michael A
authors.initials: MA
authors.lastname: Thacker
authors.affiliation: Sansom Institute for Health Research, University of South Australia, Adelaide, South Australia, Australia.|Neuroscience Research Australia, Sydney, New South Wales, Australia.
authors.forename: G Lorimer
authors.initials: GL
authors.lastname: Moseley
authors.affiliation: Rehabilitation Institute of Chicago, Northwestern University, Chicago, Illinois, United States of America.
authors.forename: Konrad P
authors.identifier: 0000-0001-8408-4499
authors.initials: KP
authors.lastname: Körding
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D010146:Pain / Q000503:physiopathology; D058748:Pain Perception / Q000502:physiology*