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Early prediction of high depressive symptoms is crucial for selective intervention and the minimization of functional impairment. Recent cross-sectional studies indicated decision-making deficits in depression, which may be an important contributor to the disorder. Our goal was to test whether description- and experience-based decision making, two major neuroeconomic paradigms of decision-making under uncertainty, predict future depressive symptoms in young adults. One hundred young adults performed two decision-making tasks, one description-based, in which subjects chose between two gambling options given explicitly stated rewards and their probabilities, and the other experience-based, in which subjects were shown rewards but had to learn the probability of those rewards (or cue-outcome contingencies) via trial-and-error experience. We evaluated subjects' depressive symptoms with BDI-II at baseline (T1) and half a year later (T2). Comparing subjects with low versus high levels of depressive symptoms at T2 showed that the latter performed worse on the experience- but not description-based task at T1. Computational modeling of the decision-making process suggested that subjects with high levels of depressive symptoms had a more concave utility function, indicating enhanced risk aversion. Furthermore, a more concave utility function at T1 increased the odds of high depressive symptoms at T2, even after controlling depressive symptoms at T1, perceived stress at T2, and several covariates (OR = 0.251, 95% CI [0.085, 0.741]). This is the first study to demonstrate a prospective link between experience-based decision-making and depressive symptoms. Our results suggest that enhanced risk aversion in experience-based decision-making may be an important contributor to the development of depressive symptoms. [ABSTRACT FROM AUTHOR]

Both depressive and anxiety disorders have been associated with excessive risk avoidant behaviors, which are considered an important contributor to the maintenance and recurrence of these disorders. However, given the high comorbidity between the two disorders, their independent association with risk preference remains unclear. Furthermore, due to the involvement of multiple cognitive computational factors in the decision-making tasks employed so far, the precise underlying mechanisms of risk preference are unknown. In the present study, we set out to investigate the common versus unique cognitive computational mechanisms of risk preference in depression and anxiety using a reward-based decision-making task and computational modeling based on economic theories. Specifically, in model-based analysis, we decomposed risk preference into utility sensitivity (a power function) and probability weighting (the one-parameter Prelec weighting function). Multiple linear regression incorporating depression (BDI-II) and anxiety (STAI state anxiety) simultaneously indicated that only depression was associated with one such risk preference parameter, probability weighting. As the symptoms of depression increased, subjects' tendency to overweight small probabilities and underweight large probabilities decreased. Neither depression nor anxiety was associated with utility sensitivity. These associations remained even after controlling covariates or excluding anxiety-relevant items from the depression scale. To our knowledge, this is the first study to assess risk preference due to a concave utility function and nonlinear probability weighting separately for depression and anxiety using computational modeling. Our results provide a mechanistic account of risk avoidance and may improve our understanding of decision-making deficits in depression and anxiety. [ABSTRACT FROM AUTHOR]