Bayesian Learning Models of Pain: A Call to Action

Abby Tabor; Christopher Burr

doi:10.1016/j.cobeha.2018.10.006

Bayesian Learning Models of Pain: A Call to Action

Abby Tabor, Christopher Burr

University of Bristol

Research output: Contribution to journal › Review article › peer-review

29 Citations (SciVal)

464 Downloads (Pure)

Abstract

Learning is fundamentally about action, enabling the successful navigation of a changing and uncertain environment. The experience of pain is central to this process, indicating the need for a change in action so as to mitigate potential threat to bodily integrity. This review considers the application of Bayesian models of learning in pain that inherently accommodate uncertainty and action, which, we shall propose are essential in understanding learning in both acute and persistent cases of pain.

Original language	English
Pages (from-to)	54-61
Number of pages	8
Journal	Current Opinion in Behavioral Sciences
Volume	26
Early online date	25 Oct 2018
DOIs	https://doi.org/10.1016/j.cobeha.2018.10.006
Publication status	Published - 1 Apr 2019

ASJC Scopus subject areas

Cognitive Neuroscience
Psychiatry and Mental health
Behavioral Neuroscience

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.cobeha.2018.10.006

BayesianLearningModelsPain_FINALrevisionAccepted author manuscript, 765 KBLicence: CC BY-NC-ND

Cite this

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abstract = "Learning is fundamentally about action, enabling the successful navigation of a changing and uncertain environment. The experience of pain is central to this process, indicating the need for a change in action so as to mitigate potential threat to bodily integrity. This review considers the application of Bayesian models of learning in pain that inherently accommodate uncertainty and action, which, we shall propose are essential in understanding learning in both acute and persistent cases of pain.",

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AB - Learning is fundamentally about action, enabling the successful navigation of a changing and uncertain environment. The experience of pain is central to this process, indicating the need for a change in action so as to mitigate potential threat to bodily integrity. This review considers the application of Bayesian models of learning in pain that inherently accommodate uncertainty and action, which, we shall propose are essential in understanding learning in both acute and persistent cases of pain.

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Bayesian Learning Models of Pain: A Call to Action

Abstract

ASJC Scopus subject areas

UN SDGs

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Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA)

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Bayesian Learning Models of Pain: A Call to Action

Abstract

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Projects

Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA)

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