Paranoid delusions or unfounded beliefs that others intend to deliberately cause harm are a frequent and burdensome symptom in early psychosis, but their emergence and consolidation still remains opaque. Recent theories suggest that aberrant prediction errors lead to a brittle model of the world providing a breeding ground for delusions. Here, we employ a Bayesian approach to test for a more unstable model of the world and investigate the computational mechanisms underlying emerging paranoia.We modelled behaviour of 18 first-episode psychosis patients (FEP), 19 individuals at clinical high-risk for psychosis (CHR-P), and 19 healthy controls (HC) during an advice-taking task, designed to probe learning about others’ changing intentions. We formulated competing hypotheses comparing the standard Hierarchical Gaussian Filter (HGF), a Bayesian belief updating scheme, with a mean-reverting HGF to model an altered perception of volatility.There was a significant group-by-volatility interaction on advice-taking suggesting that CHR-P and FEP displayed reduced adaptability to environmental volatility. Model comparison favored the standard HGF in HC, but the mean-reverting HGF in CHR-P and FEP in line with perceiving increased volatility, although model attributions in CHR-P were heterogeneous. We observed correlations between shifts in perceived volatility and positive symptoms generally as well as with frequency of paranoid delusions specifically.Our results suggest that FEP are characterised by a different computational mechanism – perceiving the environment as increasingly volatile – in line with Bayesian accounts of psychosis. This approach may prove useful to investigate heterogeneity in CHR-P and identify vulnerability for transition to psychosis.
