Altered Perception of Environmental Volatility During Social Learning in Emerging Psychosis.

Area of Science:

• Computational psychiatry
• Neuroscience of psychosis
• Bayesian inference in mental health

Background:

• Paranoid delusions are common in early psychosis, yet their development is poorly understood.
• Theories suggest unstable world models from precise prediction errors may foster delusions.
• Understanding computational mechanisms is key to addressing paranoia in psychosis.

Purpose of the Study:

• To investigate computational mechanisms underlying paranoia using a Bayesian approach.
• To test if an unstable world model, characterized by altered volatility perception, is present in early psychosis.
• To compare Bayesian belief updating models in first-episode psychosis (FEP), clinical high risk for psychosis (CHR-P), and healthy controls (HC).

Main Methods:

• Behavioral modeling of 18 FEP, 19 CHR-P, and 19 HC participants during an advice-taking task.
• Utilizing the Hierarchical Gaussian Filter (HGF) and a mean-reverting HGF to assess perception of environmental volatility.
• Comparing model fits to identify differences in belief updating strategies across groups.

Main Results:

• A significant group-by-volatility interaction indicated reduced adaptability to environmental changes in CHR-P and FEP groups.
• Model comparison favored the standard HGF in HC, but a mean-reverting HGF in CHR-P and FEP, suggesting heightened volatility perception.
• Perceiving increased volatility correlated with positive symptoms and paranoid delusion frequency.

Conclusions:

• First-episode psychosis is associated with a distinct computational mechanism of perceiving the environment as increasingly volatile.
• This finding aligns with Bayesian accounts of psychosis and offers a potential explanation for paranoia.
• The approach may help elucidate heterogeneity in CHR-P and identify psychosis transition vulnerability.