Pain: A Precision Signal for Reinforcement Learning and Control.
MetadataShow full item record
Seymour, B. (2019). Pain: A Precision Signal for Reinforcement Learning and Control.. Neuron, 101 (6), 1029-1041. https://doi.org/10.1016/j.neuron.2019.01.055
Since noxious stimulation usually leads to the perception of pain, pain has traditionally been thought of as sensory nociception, but its variability and sensitivity to a broad array of cognitive and motivational factors have led to a common view that it is inherently imprecise and intangibly subjective. However the core function of pain is motivational - to direct both short and long-term behaviour 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 provides an explanation as to why pain is tuned by multiple factors and necessarily supported by a distributed network of brain regions, and so recasts pain as a precise and objectifiable control signal.
Brain, Humans, Pain, Motivation, Cognition, Learning, Avoidance Learning, Conditioning, Classical, Conditioning, Operant, Pain Perception, Nociception, Reinforcement, Psychology
Wellcome Trust (097490/Z/11/Z)
Arthritis Research UK (21192)
Arthritis Research UK (21537)
External DOI: https://doi.org/10.1016/j.neuron.2019.01.055
This record's URL: https://www.repository.cam.ac.uk/handle/1810/289276