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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

03/2019

Volume

101

Pages

1029 - 1041

Addresses

Center for Information and Neural Networks, National Institute of Information and Communications Technology, 1-4 Yamadaoka, Suita, Osaka 565-0871, Japan; Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK. Electronic address: bjs49@cam.ac.uk.

Keywords

Brain, Humans, Pain, Motivation, Cognition, Learning, Avoidance Learning, Conditioning, Classical, Conditioning, Operant, Pain Perception, Nociception, Reinforcement, Psychology