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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

Eating disorders are associated with one of the highest mortality rates among all mental disorders, yet there is very little research about them within the newly emerging and promising field of computational psychiatry. As such, we focus on investigating a previously unexplored, yet core aspect of eating disorders–body image dissatisfaction. We continue a freshly opened debate about model-based learning and its trade-off against model-free learning–a proxy for goal-directed and habitual behaviour. We perform a behavioural study that utilises a two-step decision-making task and a reinforcement learning model to understand the effect of body image dissatisfaction on model-based learning in a population characterised by high scores of disordered eating and negative appearance beliefs, as recruited using Prolific. We find a significantly reduced model-based contribution in the body image dissatisfaction task condition in the population of interest as compared to a healthy control. This finding suggests general deficits in deliberate control in this population, leading to habitual, compulsive-like behaviours (body checking) dominating the experience. Importantly, the results may inform treatment approaches, which could focus on enhancing the reliance on goal-directed decision making to help cope with unwanted behaviours.