Title: Learning, Predicting and Modulating Pain

Abstract: Pain is a dynamic experience, which varies over time. This talk explores how the brain uses Bayesian inference and confidence to learn to predict pain based on its statistical regularities, and uses these inferences to modulate pain perception. In chronic pain, these predictive and learning mechanisms may be important, allowing individuals to predict and possibly regulate their pain levels. Additionally, we show that chronic pain can amplify sensitivity to negative outcomes, influencing decision-making and reinforcing avoidance behaviors. These findings reveal that pain is not merely reactive but is dynamically shaped by the brain’s learned expectations, opening new pathways for pain management and neurotechnologies by targeting the brain’s predictive capabilities.