Psychedelics and schizophrenia: Distinct alterations to Bayesian inference

Introduction

Rajpal and colleagues situate this study within two converging lines of interest: the recent scientific and clinical resurgence in research on classical serotonergic psychedelics, and the longstanding idea that some psychoactive drugs can produce “psychotomimetic” effects that resemble aspects of schizophrenia. Previous empirical work shows that both psychedelics (notably LSD) and dissociatives (notably ketamine) alter large-scale neural dynamics, including increases in measures of signal diversity and changes in inter-regional information transfer, and EEG work has reported similar increases in signal diversity in schizophrenia. However, a parsimonious account that explains both the commonalities and the divergences between drug-induced states and schizophrenia at a neurophysiological level remains lacking. This paper aims to compare resting-state neuroimaging data from healthy subjects under LSD and ketamine with EEG data from patients with schizophrenia, and to interpret the empirical patterns through a computational model grounded in the predictive processing (PP) framework. Specifically, the investigators test whether observed changes in Lempel–Ziv complexity (LZ, a measure of signal diversity) and transfer entropy (TE, a directed measure of information transfer) can be reproduced by perturbations to a single Bayesian state-space model, instantiated as changes in the precision of priors versus sensory inputs. The overarching goal is to refine psychotomimetic models of psychosis and to clarify how different disruptions to Bayesian inference map onto distinct neural signatures in resting-state activity.