Authors
Abby Tabor
University of South Australia
Christopher Burr
The Alan Turing Institute
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.
Keywords pain  bayesian learning  active inference  predictive processing  generative models
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