Bayesian Learning Models of Pain: A Call to Action. (April 2019)

• Record Type: Journal Article
• Authors: Tabor, Abby; Burr, Christopher
• Is Part Of: Current opinion in behavioral sciences. Volume 26(2019)
• Page Start: 54
• Page End: 61
• Publication Date: 2019-04
• Subjects: Psychology -- Periodicals; 150.5
• DOI: 10.1016/j.cobeha.2018.10.006
• Languages: English
• ISSNs: 2352-1546

Abstract

Highlights: The experience of pain sits awkwardly in traditional stimulus-response paradigms. Accommodating uncertainty and action is imperative to learning models of pain. Bayesian models provide a normative, probabilistic account of learning in pain. Learning in pain is conceptualised as a process of ongoing precision-weighting of information relevant to the consequences of action. 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.