Does active inference provide a comprehensive theory of placebo analgesia?

Placebo interventions generate mismatches between expected pain and sensory signals from which pain states are inferred. Since we lack direct access to bodily states, we can only infer whether nociceptive activity indicates tissue damage or results from noise in sensory channels. Predictive processing models propose to make optimal inferences using prior knowledge given noisy sensory data. However, these models do not provide a satisfactory explanation of how pain relief expectations are translated into physiological manifestations of placebo responses. Furthermore, they do not account for individual differences in the ability to endogenously regulate nociceptive activity in predicting placebo analgesia. The brain not only passively integrates prior pain expectations with nociceptive activity to infer pain states (perceptual inference), but also initiates various types of 'actions' to ensure sensory data are consistent with prior pain expectations (active inference). We argue that depending on whether the brain interprets conflicting sensory data (prediction errors) as 'signal to learn from' or 'noise to be attenuated', the brain initiates opposing types of action to facilitate learning from sensory data or, conversely, enhance the biasing influence of prior pain expectations on pain perception. Furthermore, we discuss the role of stress, anxiety, and unpredictability of pain in influencing the weighting of prior pain expectations and sensory data and how they relate to the individual ability to regulate nociceptive activity (endogenous pain modulation). Finally, we provide suggestions for future studies to test the implications of the active inference model of placebo analgesia.