Background: Motivational dysregulation represents a core vulnerability factor for bipolar disorder. Whether this also comprises aberrant learning of stimulus-reinforcer contingencies is less clear.
Methods: To answer this question, we compared healthy first-degree relatives of individuals with bipolar disorder (n = 42) known to convey an increased risk of developing a bipolar spectrum disorder and healthy individuals (n = 97). Further, we investigated the effects of the behavioral activation system (BAS) on reinforcement learning across the entire sample. All participants were assessed with a probabilistic learning task that distinguishes learning from positive and negative feedback. Main outcome measures included choice frequencies and learning rate parameters generated by computational reinforcement learning algorithms.
Results: First-degree relatives choose more rewarding stimuli more consistently and showed marginally reduced learning rates from unexpected negative feedback. Further, first-degree relatives had lower BAS scores than controls, which were negatively associated with learning rates from unexpected negative feedback.
Limitations: However as probands also reported other mental disorders such as Attention-Deficit/Hyperactivity Disorder and substance abuse among their first-degree relatives, we cannot know, whether these findings are specific to the risk for bipolar disorder.
Conclusion: The behavior of first-degree relatives of individuals with bipolar disorder, who also display increased BAS sensitivity, is less influenced by unexpected negative feedback. This reduced learning from unexpected negative feedback biases subsequent choices towards stimuli with higher probabilities for a reward. In sum, our results confirm the role of aberrant reinforcement learning in the pathophysiology of bipolar disorder.
Keywords: Behavioral activation system (BAS); Bipolar disorder; Computational modeling; First-degree relatives; Reinforcement learning.
Copyright © 2019. Published by Elsevier B.V.