Environmental Exposures and Computational Mechanisms in Psychosis Risk: Insights From a Multisite Cohort

In this study, we leverage computational psychiatry to examine weighting of perceptual priors and reinforcement learning (RL) as potential pathways connecting environmental stressors to psychosis risk. Five hundred and six participants with psychosis-like experiences (PLEs) and 328 control subjects performed behavioral tasks measuring these mechanisms in addition to symptom assessments. Participants’ childhood-area social fragmentation and childhood opportunities were also extracted. The association between environmental, computational, and clinical measures was assessed, as were mediation pathways. Higher social fragmentation was associated with deficits in RL, r (410) = .19, p < .001, and lower opportunities were associated with overweighting perceptual priors, r (290) = −.16, p = .02, and issues in RL, r (307) = −.14, p = .02, for PLE participants. Fewer available childhood opportunities were also related to greater positive symptom severity, r (354) = −.14, p = .03. We found a potential pathway connecting available childhood opportunities to positive symptoms through the mediating effect of overweighting perceptual priors (average causal mediation effect = −4.91, p = .04). Environmental characteristics could shape symptom severity through computational mechanisms.