Abstract Pain is a complex, multi-level phenomenon integrating sensory, motivational, and cognitive processes. Computational approaches bridge theoretical frameworks with neural and behavioural data, providing descriptive, mechanistic, and normative explanations. We review key computational approaches, including reinforcement learning, control theory, Bayesian inference, and active inference, illustrating their role in understanding pain prediction, avoidance, and modulation. Forward and reverse engineering techniques synergistically refine our models and generate testable hypotheses. This framework not only advances fundamental neuroscience but also informs clinical applications, offering potential for computational phenotyping, personalised therapies, and adaptive neuro-engineering interventions for pain management.
10.1097/j.pain.0000000000003705
Journal article
Ovid Technologies (Wolters Kluwer Health)
2025-11-01T00:00:00+00:00
166
S75 - S78