Computations and neural dynamics of audiovisual causal and perceptual inference in schizophrenia

Hallucinations and perceptual abnormalities in psychosis are thought to arise from imbalanced integration of prior information and sensory inputs during perceptual inference. In this study, we combined psychophysics, Bayesian modelling and electroencephalography (EEG) to investigate potential changes in perceptual and causal inference in medicated individuals with schizophrenia when exposed to audiovisual sequences with varying numbers of flashes and beeps from either common or independent sources. Our findings reveal that individuals with schizophrenia, like their healthy controls, balance sensory integration and segregation in line with Bayesian causal inference rather than resorting to simpler heuristics. Both groups showed comparable weighting of prior information regarding the signals causal structure, with the schizophrenia group slightly overweighting prior information about the number of flashes or beeps. At the neural level, both groups computed Bayesian causal inference through dynamic encoding of perceptual estimates that segregate and flexibly combine audiovisual inputs. In conclusion, our results demonstrate that the computational and neural mechanisms of multisensory perceptual and causal inference remain remarkably intact in medicated individuals with schizophrenia during flash-beep scenarios.