Theoretical models of bipolar disorders (BD) posit core deficits in reward system function. However, specifying which among the multiple reward system's neurobehavioral processes are abnormal in BD is necessary to develop appropriately targeted interventions. Research on probabilistic-reinforcement learning deficits in BD is limited, particularly during adolescence, a period of significant neurodevelopmental changes in the reward system. The present study investigated probabilistic-reinforcement learning, using a probabilistic selection task (PST), and its correlates, using self-reported reward/threat sensitivities and cognitive tasks, in 104 adolescents with and without BD. Compared with healthy peers, adolescents with BD were less likely to persist with their choices based on prior positive feedback (i.e., lower win-stay rates) in the PST's acquisition phase. Across groups, a greater win-stay rate appeared to be a more efficient learning strategy - associated with fewer acquisition trials and better testing phase performance. Win-stay rates were also related to verbal learning indices, but not self-reported reward/threat sensitivities. Finally, lower win-stay rates had significant incremental validity in predicting a BD diagnosis, after accounting for effects of current symptoms, reward sensitivities, verbal learning, and IQ. The present findings support multiple dysfunctional processes of the reward system in adolescent BD that require additional examinations.
- bipolar disorder
- probabilistic reinforcement learning