Computational and Biological Learning Unit, Department of Engineering, University of Cambridge, Cambridge, United Kingdom; Applied Computational Psychiatry Lab, Max Planck Centre for Computational Psychiatry and Ageing Research, Queen Square Institute of Neurology and Mental Health Neuroscience Department, Division of Psychiatry, University College London, London, United Kingdom
Nicholas Gregory
Computational and Biological Learning Unit, Department of Engineering, University of Cambridge, Cambridge, United Kingdom
Mia Whitefield
Computational and Biological Learning Unit, Department of Engineering, University of Cambridge, Cambridge, United Kingdom
Maeghal Jain
Computational and Biological Learning Unit, Department of Engineering, University of Cambridge, Cambridge, United Kingdom
Georgia Turner
Computational and Biological Learning Unit, Department of Engineering, University of Cambridge, Cambridge, United Kingdom; MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
Wellcome Centre for Integrative Neuroimaging, John Radcliffe Hospital, Headington, Oxford, United Kingdom; Center for Information and Neural Networks (CiNet), Osaka, Japan
The placebo and nocebo effects highlight the importance of expectations in modulating pain perception, but in everyday life we don’t need an external source of information to form expectations about pain. The brain can learn to predict pain in a more fundamental way, simply by experiencing fluctuating, non-random streams of noxious inputs, and extracting their temporal regularities. This process is called statistical learning. Here, we address a key open question: does statistical learning modulate pain perception? We asked 27 participants to both rate and predict pain intensity levels in sequences of fluctuating heat pain. Using a computational approach, we show that probabilistic expectations and confidence were used to weigh pain perception and prediction. As such, this study goes beyond well-established conditioning paradigms associating non-pain cues with pain outcomes, and shows that statistical learning itself shapes pain experience. This finding opens a new path of research into the brain mechanisms of pain regulation, with relevance to chronic pain where it may be dysfunctional.
