Is decision making impaired in mental illness populations? Can behavioral economics provide insight into clinical psychology? The present project addresses these broad questions through three studies. In the first study, two meta-analyses were conducted of experiments that used the Iowa Gambling Task (IGT) to assess value based decision making in populations with mental illness. In the first meta-analysis (63 studies, combined N = 4,978), we compared IGT performance in healthy populations and populations with mental illness. In the second meta-analysis (40 studies, combined N = 1,813), we examined raw IGT performance scores as a function of type of mental illness. The first meta-analysis demonstrated that individuals with mental illness performed significantly worse than did healthy control individuals. The second meta-analysis demonstrated no performance differences based on type of mental illness. Impairment on the IGT, however, could indicate effects from several different decision processes. Accordingly, in the second study, using multiple decision tasks we explored different aspects of decision making in a single group that exhibited reliable effects in the meta-analysis, major depressive disorder. The second study answers three questions. First, how does decision making differ in clinically depressed individuals across a range of decision tasks? Second, where are the largest differences between clinically depressed and non-depressed individuals? And finally, how well can decision task performance discriminate depressed individuals from healthy controls? Depressed individuals' decision-making was significantly different across a range of decision tasks, but impaired learning and pessimism bias showed the strongest behavioral signature of depression. Decision tasks significantly predict depression, but are far outperformed by self-report measures as diagnostic tools. Overall, results suggest decision tasks are better suited to identify specific impaired processes rather than for diagnostic prediction. This study suggested depression is associated with impaired reward and punishment processing, but what are the underlying causes behind these deficits? In the third study, we performed a detailed analysis of reward and punishment learning in clinically depressed individuals, quantifying choice behavior by fitting reinforcement learning models. The results suggest that depression is characterized by hyposensitivity to reward. The reinforcement learning models show that depressed individuals engage habit-oriented model-free learning strategies in contrast to the goal-oriented model-based strategies engaged by healthy controls. Overall the three studies demonstrate how interdisciplinary research combining decision science and clinical psychology can help to better understand mental illness.