From drugs to deprivation: a Bayesian framework for understanding models of psychosis

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Abstract
Various experimental manipulations, usually involving drug administration, have been used to produce symptoms of psychosis in healthy volunteers. Different drugs produce both common and distinct symptoms. A challenge is to understand how apparently different manipulations can produce overlapping symptoms. We suggest that current Bayesian formulations of information processing in the brain provide a framework that maps onto neural circuitry and gives us a context within which we can relate the symptoms of psychosis to their underlying causes. This helps us to understand the similarities and differences across the common models of psychosis. The Bayesian approach emphasises processing of information in terms of both prior expectancies and current inputs. A mismatch between these leads us to update inferences about the world and to generate new predictions for the future. According to this model, what we experience shapes what we learn, and what we learn modifies how we experience things. This simple idea gives us a powerful and flexible way of understanding the symptoms of psychosis where perception, learning and inference are deranged. We examine the predictions of the cognitive model in light of what we understand about the neuropharmacology of psychotomimetic drugs and thereby attempt to account for the common and the distinctive effects of NMDA receptor antagonists, serotonergic hallucinogens, cannabinoids and dopamine agonists. By acknowledging the importance of perception and perceptual aberration in mediating the positive symptoms of psychosis, the model also provides a useful setting in which to consider an under-researched model of psychosis—sensory deprivation.