Abstract
Background Symptom expression in a range of psychiatric conditions is linked to altered threat perception, manifesting particularly in uncertain environments. How precise computational mechanisms that support aversive learning, and uncertainty estimation, relate to the presence of specific psychiatric symptoms remains undetermined. 400 subjects completed an online game-based aversive learning task, requiring avoidance of negative outcomes, in conjunction with completing measures of common psychiatric symptoms. We used a probabilistic computational model to measure distinct processes involved in learning, in addition to inferred estimates of safety likelihood and uncertainty, and tested for associations between these variables and traditional psychiatric constructs alongside transdiagnostic dimensions. We used partial least squares regression to identify components of psychopathology grounded in both aversive learning behaviour and symptom self-report. We show that state anxiety and a transdiagnostic compulsivity-related factor are associated with enhanced learning from safety, and data-driven analysis indicated the presence of two separable components across our behavioural and questionnaire data: one linked enhanced safety learning and lower estimated uncertainty to physiological anxiety, compulsivity, and impulsivity; the other linked enhanced threat learning, and heightened uncertainty estimation, to symptoms of depression and social anxiety. Our findings implicate distinct aversive learning processes in the expression of psychiatric symptoms that transcend traditional diagnostic boundaries.