The placebo and nocebo effects highlight the importance of expectations in modulating pain perception, but in every day 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 two key open questions: (1) does statistical learning modulate pain perception? and (2) is it different in people with chronic musculoskeletal pain? In a first experiment, 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 weight pain perception and prediction. Given that statistical learning involves supramodal processes, we developed an online, stock market game to assess the ability to explicitly predict volatile and stochastic time series, probing the most fundamental components of statistical learning. The game was played by 56 chronic back pain and 55 healthy participants. We show that back pain participants learn the statistics of the sequence more slowly than controls. In conclusion, this study shows that statistical learning shapes pain experience and can be disrupted in common chronic pain conditions, opening a new path of research into the brain mechanisms of pain regulation

The authors have declared no competing interest.

The study was funded by a MRC Career Development Award to FM (MR/T010614/1) and a UKRI Advanced846 Pain Discovery Platform grant to both F.M. and B.S. (MR/W027593/1). B.S. was also funded by Wellcome (214251/Z/18/Z), Versus Arthritis (21537), and IITP (MSIT 2019-0-01371). This work has been performed using resources provided by the Cambridge Tier-2 system operated by the University of Cambridge Research Computing Service (www.hpc.cam.ac.uk) funded by EPSRC Tier-2 capital grant (EP/T022159/1). HPC access was additionally funded by an EPSRC research infrastructure grant to F.M..

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Department of Engineering, University Cambridge Ethics Committee gave ethical approval for this work.

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