Research

Pain:a statistical account


Reference:

Tabor, A., Thacker, M., Moseley, G. L. and Kording, K., 2017. Pain:a statistical account. PLoS Computational Biology, 13 (1), e1005142.

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    Official URL:

    http://dx.doi.org/10.1371/journal.pcbi.1005142

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    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.

    Details

    Item Type Articles
    CreatorsTabor, A., Thacker, M., Moseley, G. L. and Kording, K.
    DOI10.1371/journal.pcbi.1005142
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    URLURL Type
    http://dx.doi.org/10.1371/journal.pcbi.1005142Free Full-text
    DepartmentsFaculty of Humanities & Social Sciences > Health
    Research CentresCentre for Pain Research
    RefereedYes
    StatusPublished
    ID Code53479

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