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

Abstract

Introduction: 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.

Materials and methods: 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.

Discussion: 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.

Conclusion: 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.

Fig. 1

A schematic representation of the effects of a shift in balance between bottom-up and top-down processing. Bottom-up signal is represented by blue arrows and top-down signal (‘priors’) by red arrows. Discrepancies between these signals are presented by differences in the thickness of the arrows and by the cartoon scales. Under normal circumstances (left panel), the match between bottom-up signal and prior knowledge means that there is no requirement to change prior beliefs and perception is normal. If, however, there is persistent bottom-up firing (prediction error), prior beliefs will continually fail to match the incoming signal and will need to be changed in order to accommodate the signal and minimise the persistent prediction error. This, we suggest, is a basis for changed beliefs characteristic of delusions (middle panel). If, on the other hand (right panel), strong priors exist in the absence of strong reliable bottom-up signal, these priors may be sufficient to create a percept, a basis, we suggest for hallucinations

Fig. 2

A summary of the suggested effects of key manipulations used to induce psychosis on bottom-up/top-down balance