Factual and counterfactual learning in major adolescent depressive disorder, evidence from an instrumental learning study.

BACKGROUND: The incidence of adolescent depressive disorder is globally skyrocketing in recent decades, albeit the causes and the decision deficits depression incurs has yet to be well-examined. With an instrumental learning task, the aim of the current study is to investigate the extent to which learning behavior deviates from that observed in healthy adolescent controls and track the underlying mechanistic channel for such a deviation. METHODS: We recruited a group of adolescents with major depression and age-matched healthy control subjects to carry out the learning task with either gain or loss outcome and applied a reinforcement learning model that dissociates valence (positive v. negative) of reward prediction error and selection (chosen v. unchosen). RESULTS: The results demonstrated that adolescent depressive patients performed significantly less well than the control group. Learning rates suggested that the optimistic bias that overall characterizes healthy adolescent subjects was absent for the depressive adolescent patients. Moreover, depressed adolescents exhibited an increased pessimistic bias for the counterfactual outcome. Lastly, individual difference analysis suggested that these observed biases, which significantly deviated from that observed in normal controls, were linked with the severity of depressive symoptoms as measured by HAMD scores. CONCLUSIONS: By leveraging an incentivized instrumental learning task with computational modeling within a reinforcement learning framework, the current study reveals a mechanistic decision-making deficit in adolescent depressive disorder. These findings, which have implications for the identification of behavioral markers in depression, could support the clinical evaluation, including both diagnosis and prognosis of this disorder.

Asymmetric valuation and belief updating over gain and loss in risky decision making: A behavioral and electrophysiological investigation.

Decisions under risk, either for gain or loss, are ubiquitous in our daily life. However, the extent to which the valence (gain or loss) of risky financial choices shapes outcome valuation and belief updating is a relatively overlooked research area. In the current study, we image neural activity using electroencephalography (EEG) combined with a financial decision task to investigate outcome valuation and belief updating. In the experimental task, subjects can either choose to take the risky gamble (stock) or the safe option (bond) and then report their belief over the quality of stock option in a trial-by-trial manner. Although the actual probabilities of the risky option are symmetric over gain and loss, we found an asymmetric effect of belief updating and risk preference, viz. the subjects tend to both report a higher probability for the stock to win and be more risk taking for potential gains compared to symmetric losses. The EEG data following feedback of stock payoff represents a parallel pattern which is resonant with the behavioral results. Notably, there is generally a greater FRN difference for feedback (correct vs. incorrect) in the gain condition compared to the loss condition, and the deflection of P300 is more prominent in gain condition than loss condition irrespective of the correctness. Lastly, while the P300 could be predictive for the subsequent probability estimate in both conditions (gain and loss), the FRN is only predictive for belief updating in the gain rather than loss condition. Therefore, both the behavioral and electrophysiological findings indicate an unbalanced processing of valence in shaping decisions under risk within financial learning in an experiential framework.