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Since noxious stimulation usually leads to the perception of pain, pain has traditionally been considered sensory nociception. But its variability and sensitivity to a broad array of cognitive and motivational factors have meant it is commonly viewed as inherently imprecise and intangibly subjective. However, the core function of pain is motivational-to direct both short- and long-term behavior away from harm. Here, we illustrate that a reinforcement learning model of pain offers a mechanistic understanding of how the brain supports this, illustrating the underlying computational architecture of the pain system. Importantly, it explains why pain is tuned by multiple factors and necessarily supported by a distributed network of brain regions, recasting pain as a precise and objectifiable control signal.

Original publication

DOI

10.1016/j.neuron.2019.01.055

Type

Journal article

Journal

Neuron

Publication Date

20/03/2019

Volume

101

Pages

1029 - 1041

Keywords

active inference, active sensing, avoidance learning, computome, endogenous modulation, free energy, information theory, optimal control, pain and nociception, pregenual anterior cingulate cortex, Avoidance Learning, Brain, Cognition, Conditioning, Classical, Conditioning, Operant, Humans, Learning, Motivation, Nociception, Pain, Pain Perception, Reinforcement, Psychology