Transdiagnostic latent factors dissociating depression and anxiety through reinforcement learning

Background : The comorbidity of depression and anxiety has long been recognized. While mainly characterized as mood dysregulation, depression and anxiety symptoms are also manifested in learning and decision-making deficits. However, the specific cognitive mechanisms that are common to both disorders, as well as those that distinguish them, remain poorly understood. Here, we propose reinforcement learning (RL) as a unifying computational framework to disentangle the shared and distinct cognitive processes underlying depression and anxiety. Methods : We adopted a probabilistic instrumental learning task in which subjects repeatedly chose between alternative options to earn rewards (Gain) or avoid losses (Loss) in two experiment (n=190 in experiment 1, n=361 in experiment 2). Classic psychiatric questionnaires about depression and anxiety traits were collected from participants. Results : We discovered a dissociation where depression traits correlated negatively with learning rates, while anxiety traits showed the opposite pattern in two separate experiments. Experiment 2 further identified transdiagnostic latent factors of depression and anxiety traits that drove the dissociation of depression and anxiety traits on learning rates. Specifically, somatic symptoms and anhedonia were found to be the main contributors to the negative correlation between learning and depression traits. In contrast, cognitive symptoms and negative affects showed a positive correlation with learning rates. Conclusions : The dissociation between transdoagnostic factors may reflect a trade-off wherein excessive internal focus diminishes the capacity for external information processing. Together, our findings demonstrate how the transdiagnostic approach under a unified computational framework can elucidate distinct cognitive profiles of depression and anxiety.