Reward-induced endogenous pain inhibition scales with action-outcome certainty in humans

Endogenous pain modulation is thought to encompass a crucial evolutionary purpose in guiding decision-making away from harm. This is well exemplified by the idea that mechanisms of learning through error-correction and endogenous pain modulation are inherently intertwined. However, adaptive behavior requires more than learning through error-correction. Biological environments are volatile, which can cause decision-makers to be uncertain about what actions lead to rewards or punishments. Evidence on how uncertainty in action-outcome distributions impacts endogenous pain modulation is lacking. In the present study, we extend and adapt a well-established paradigm for the study of endogenous pain modulation with the implementation of a reversal learning outcome schedule. Thirty healthy human volunteers took part in this probabilistic gambling task, where they had to gamble for the obtainment of pain relief and the avoidance of pain punishments. Using a computational approach, we show that decision-making in a situation of acute pain is best explained by models that account for uncertainty. Such uncertainty is associated with the observed pain modulation in the task, indicating that endogenous pain modulation may be sensitive to volatility and the perception of uncertainty. Specifically, pain inhibition from winning pain relief increases as a function of certainty in what actions lead to pain relief. Our findings emphasize the importance of considering mechanisms of uncertainty processing in reinforcement learning from painful outcomes and endogenous pain modulation. Those mechanisms could be relevant to understanding behavioral changes in chronic pain, where altered reinforcement learning has already been established.