Exaggerated Volatility Beliefs Drive Social Hallucinations in Paranoia

The social content of paranoid delusions (that others are ‘out to get me’) has led some to propose that paranoia stems from specific abnormalities in social cognition. For instance, in ‘social hallucinations’ paranoid perceivers are more likely to falsely attribute harmful intention and agency to unstructured, randomly moving visual patterns. However, recent work has revealed that paranoia is marked by domain-general changes in belief updating that affect learning and inference across all contexts. Whether domain-general changes in paranoid cognition are sufficient to explain social hallucinations—without invoking breakdowns in specifically-social cognitive processes—has not been systematically investigated. To test this possibility, across two experiments we examined probabilistic reversal learning and perceived animacy in chasing displays in individuals with high and low levels of paranoia. Participants completed one of four probabilistic reversal learning pre-training conditions (social/non-social × easy/hard) before a perceived animacy task where high confidence false alarms indicated an individual’s proneness to social hallucinations. High-paranoia participants showed increased win-switching in probabilistic reversal learning and elevated high-confidence false alarms in perceived animacy, with atypicalities in general learning behaviour predicting the severity of social hallucinations —suggesting linked mechanisms. Hierarchical Gaussian Filter modeling revealed context-dependent mechanisms: when volatility was salient, prior volatility beliefs (μ₃) linked win-switching, social hallucinations, and paranoia; when social context was salient, meta-volatility learning rates (ω₃) mediated these relationships. These findings are consistent with a picture where social hallucinations have their genesis in domain-general belief updating deficits, advancing our mechanistic understanding of how paranoid percepts and cognitions arise.