Symptom perception, placebo effects, and the Bayesian brain
The standard and ideal biomedical model of symptom perception treats the brain largely as a passive stimulus-driven organ. It embraces the notion that the brain absorbs sensory signals from the body and converts them, directly, into conscious experience. Accordingly, biomedicine operates under the assumption that symptoms are the direct consequences of physiological dysfunction and improvement is the direct consequence of the restoration of bodily function. Despite its success, the biomedical model has failed to provide an adequate account of 2 well- demonstrated phenomena in medicine: (1) the experience of symptoms without pathophysiological disruption, and (2) the experience of relief after the administration of placebo treatments. This topical review advances the idea that “predictive processing,” a Bayesian approach to the perception that is rapidly taking hold in neuroscience, significantly helps to accommodate these 2 phenomena. It expands on recent high-quality empirical work on predictive processing1,7,19,24 and outlines, more broadly, how Bayesian models offer an altogether different picture of how the brain perceives symptoms and relief.