Dissociable roles of reward prediction error in the contrasting mood dynamics of depression and anxiety

Mood fluctuations, central to human experience, are profoundly influenced by reward prediction errors (RPE). Although depression and anxiety traditionally exhibit contrasting mood fluctuations, their interrelated nature has made it challenging to pinpoint their specific roles in RPE-induced mood variations. In this study, we employed a computational model of momentary mood within a gambling task, involving 2043 participants across five experiments. Participants also completed a battery of questionnaires designed to allow us to dissociate anxiety- and depression-specific traits through bifactor modeling. Results showed that depression was associated with dampened mood fluctuations due to mood hyposensitivity to RPE. Importantly, this pattern was also found in patients with affective disorders. In contrast, anxiety correlated with heightened mood fluctuations stemming from mood hypersensitivity to RPE in non-clinical participants. Moreover, the shared depression/anxiety component was linked to lower affective baseline and greater risk aversion. Collectively, our results uncover computational dissociation of depression vs. anxiety using RPE-based mood modeling and present multi-dimensional computational signatures for these symptoms, with clinical relevance for management of mood disorders.