Abstract
Recent advances in computational psychiatry have provided unique insights into the neural and cognitive underpinnings of psychotic symptoms. In particular, a host of new data has demonstrated the utility of computational frameworks for understanding how hallucinations might arise from alterations in typical perceptual processing. Of particular promise are models based in Bayesian inference that link hallucinatory perceptual experiences to latent states that may drive them. In this piece, we move beyond these findings to ask: how and why do these latent states arise, and how might we take advantage of heterogeneity in that process to develop precision approaches to the treatment of hallucinations? We leverage specific models of Bayesian inference to discuss components that might lead to the development of hallucinations. Using the unifying power of our model, we attempt to place disparate findings in the study of psychotic symptoms within a common framework. Finally, we suggest directions for future elaboration of these models in the service of a more refined psychiatric nosology based on predictable, testable, and ultimately treatable information processing derangements.