Compensatory hallucinogenesis across three neuropsychiatric disorders: a Bayesian account.

Emerging evidence suggests that hallucinations may arise because of an over-reliance on prior knowledge during perception. While best established in psychosis-spectrum illness, data also support the presence of this abnormality in other hallucination-prone neuropsychiatric illnesses that vary in their association with disruption of sensory circuits. In this piece, we ask whether an over-weighting of expectations may be conceived of as a compensatory response to degraded incoming sensory information. We make the case that visual hallucinogenesis across a wide array of neuropsychiatric disorders can be captured within a common Bayesian computational framework, as a compensatory response to sensory signal disruptions at different levels of the visual processing hierarchy. We focus on three specific disorders (Charles Bonnet syndrome, dementia with Lewy Bodies and psychosis) with prominent visual hallucinations and highlight the fact that these disorders describe a spectrum of visual impairment where the overtness and localization of the visual processing disruption is reflected in the characteristics of the emergent visual hallucinations. We examine how discrete sensory disruptions in Charles Bonnet syndrome translate to hallucinations via known circuits, and then how different disruptions in dementia with Lewy Bodies and Schizophrenia may lead to hallucinations with distinct phenomenology, comorbidities and circuit involvement. Finally, we appeal to emerging computational theories to unite these observations under a common conceptual umbrella. Taken together, this work presents a means of understanding how sensory disruptions could interact with other aspects of cognitive and neural architecture to produce hallucinations across neuropsychiatric disease. It is our hope that this framework will help in efforts to identify pathophysiologically distinct patient subgroups and new pharmacological and circuit-based interventions.