Pain: A Precision Signal for Reinforcement Learning and Control.
View / Open Files
Authors
Seymour, Ben
Publication Date
2019-03-20Journal Title
Neuron
ISSN
0896-6273
Publisher
Elsevier BV
Volume
101
Issue
6
Pages
1029-1041
Language
eng
Type
Article
This Version
AM
Physical Medium
Print
Metadata
Show full item recordCitation
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
Abstract
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.
Keywords
Brain, Humans, Pain, Motivation, Cognition, Learning, Avoidance Learning, Conditioning, Classical, Conditioning, Operant, Pain Perception, Nociception, Reinforcement, Psychology
Sponsorship
Wellcome Trust (097490/Z/11/Z)
Arthritis Research UK (21192)
Arthritis Research UK (21537)
Identifiers
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
Rights
Licence:
http://www.rioxx.net/licenses/all-rights-reserved
Statistics
Total file downloads (since January 2020). For more information on metrics see the
IRUS guide.