Environmental Exposures and Computational Mechanisms in Psychosis Risk: Insights from a Multi-Site Cohort.

This study leverages computational psychiatry to examine weighting of perceptual priors and reinforcement learning (RL) as potential pathways connecting environmental stressors to psychosis risk. 506 participants with psychosis-like experiences (PLE) and 328 controls 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)=0.19, p<.001) and lower opportunities were associated with overweighting perceptual priors (r(290)= -0.16, p=.02) as well as issues in RL (r(307)=-0.14, p=.02) for PLE participants. Less available childhood opportunities were also related to more severe positive symptoms (r(354)=-0.14, p=.03). We found a potential pathway connecting available childhood opportunities to positive symptomatology through the mediating effect of overweighting perceptual priors (ACME=-4.91, p=.04). Environmental characteristics could shape symptom severity through computational mechanisms.