Computational mechanisms of learning and forgetting differentiate affective and substance use disorders.

Depression and anxiety are common, highly co-morbid conditions associated with maladaptive learning and decision-making processes. While the computational mechanisms underlying these deficits have received growing attention, the transdiagnostic vs. diagnosis-specific nature of these mechanisms remains insufficiently characterized. In this discovery-focused study, we aim to better characterize these mechanisms and generate novel hypotheses. To do so, we employed a commonly used, domain-general decision-making task, combined with computational models of learning, to assess differences in patterns of choice and reaction times in individuals with affective disorders (iADs; i.e., depression with or without co-morbid anxiety; N = 168 and 74, respectively). To establish diagnostic specificity, we further incorporated data from individuals with substance use disorders (iSUDs; N = 147) and healthy comparisons (HCs; N = 54). Computational modeling afforded separate measures of learning and forgetting rates, among other parameters. Bayesian analyses indicated that forgetting rates (reflecting recency bias) were elevated in both iADs and iSUDs compared to HCs (posterior probabilities [pp] = 0.99 and 1, respectively). In contrast, iADs showed faster learning rates for negative outcomes than iSUDs (pp = 0.98), but they did not differ from HCs. Reaction times in iSUDs also showed less sensitivity to uncertainty than both iADs and HCs using model-based metrics. Finally, exploratory dimensional analyses suggested possible links between learning rates for negative outcomes and early adversity. These findings demonstrate two model-based metrics that differentiate iADs from iSUDs (learning from negative outcomes and sensitivity to uncertainty) as well as a third metric (forgetting rate) that appears transdiagnostic, differentiating both disorders from HCs. This pattern of results points to distinct cognitive mechanisms that could inform disease models for each disorder and paves the way for future work investigating their potential clinical utility.