Background: Previous research has found that compulsions in obsessive-compulsive disorder (OCD) are associated with an imbalance between goal-directed and habitual responses. However, the cognitive mechanisms underlying how goal-directed and habitual behaviors are learned, and how these learning deficits affect the response process, remain unclear. The present study aimed to investigate these cognitive mechanisms and examine how they were involved in the mechanism of compulsions.
Methods: A total of 49 patients with OCD and 38 healthy controls (HCs) were recruited to perform the revised "slip of action test". A reinforcement learning model was constructed, and model parameters including learning rates, reinforcement sensitivity, and perseveration were estimated using a hierarchical Bayesian approach. Comparisons of these parameters were made between the OCD group and HCs, and the associations with performance during the outcome devalued stage and clinical presentations were assessed.
Results: In the outcome devalued stage, patients with OCD exhibited greatet responsiveness to the devalued outcome, indicating their impairment in flexible and goal-directed behavioral control. Computational modeling further revealed that, during the instrumental learning stage, patients with OCD showed reduced learning rates, decreased perseveration, and heightened reinforcement sensitivity as compared with HCs. The learning rate and perseveration during instrumental learning were significantly correlated with the performance in the outcome devalued stage and compulsive scores in OCD.
Conclusions: The results indicate that patients with OCD exhibit deficits in updating the associative strength based on prediction errors and are more likely to doubt established correct associations during goal-directed and habitual learning. These deficits may contribute to the inflexible goal-directed behavioral control and are involved in the mechanism of compulsion in OCD.
Keywords: Goal-directed behavior; Habitual behavior; Obsessive-compulsive disorder; Reinforcement learning modeling.
© 2024 The Authors.