Pain: A Precision Signal for Reinforcement Learning and Control
© 2019 Elsevier Inc. 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. One of the puzzles of pain is its variability, subjectivity, and distributed brain processing. Seymour presents a model of the pain system arguing pain is a precision learning and control signal, constructed in the brain based on principles of optimal control.