Background: Fatigue is a central symptom of major depressive disorder (MDD). Research in healthy individuals has shown that heightened feelings of fatigue are associated with a reduced willingness to exert effort. However, it remains unclear how fatigue affects effort-based decision-making in individuals with MDD. In this study, we explore how cognitive effort is traded for rewards in MDD and how feelings of fatigue and depressed mood symptoms influence this decision-making process. Methods: Healthy participants (n = 26) and individuals with MDD (n = 18) took part in a forced-choice paradigm, where they decided to perform either a low-effort cognitive effort task for a small reward or a more cognitively effortful task for a larger reward. Participants also completed the Beck Depression Inventory and the Modified Fatigue Impact Scale, which were used in a factor analysis to generate combined scores for depressed mood and fatigue for each participant. These factor scores were then incorporated into hierarchical mixed-effects models of choice data to explain behavioral differences between participants with MDD. Results: Our findings reveal that individuals with MDD exhibited significantly higher preferences for low-effort/low-reward options compared to their healthy counterparts. This difference was linked to increased feelings of fatigue, which raised the perceived cost of cognitive effort. Variations in fatigue questionnaire scores showed stronger associations with effort-based choice behavior than those from questionnaires assessing depressive mood, indicating that fatigue is a key symptom with specific ties to effort in MDD. Conclusions: These findings illuminate how fatigue might lead to decreases in goal-directed behavior in MDD, thereby deepening our understanding of diminished motivation in MDD and suggesting potential pathways for more effective treatment.
Fast Approximation Algorithm for Non-Monotone DR-submodular Maximization under Size Constraint
arXiv:2511.02254v1 Announce Type: cross Abstract: This work studies the non-monotone DR-submodular Maximization over a ground set of $n$ subject to a size constraint $k$. We


