Memory trace imbalance in reinforcement and punishment systems can reinforce implicit choices leading to obsessive-compulsive behavior

We may view most of our daily activities as rational action selections; however, we sometimes reinforce maladaptive behaviors despite having explicit environmental knowledge. In this study, we model obsessive-compulsive disorder (OCD) symptoms as implicitly learned maladaptive behaviors. Simulations in the reinforcement learning framework show that agents implicitly learn to respond to intrusive thoughts when the memory trace signal for past actions decays differently for positive and negative prediction errors. Moreover, this model extends our understanding of therapeutic effects of behavioral therapy in OCD. Using empirical data, we confirm that patients with OCD show extremely imbalanced traces, which are normalized by serotonin enhancers. We find that healthy participants also vary in their obsessive-compulsive tendencies, consistent with the degree of imbalanced traces. These behavioral characteristics can be generalized to variations in the healthy population beyond the spectrum of clinical phenotypes. [Display omitted] •We present a computational model of OCD•Imbalanced learning between reinforcement and punishment induces OC behaviors•Patients with OCD show imbalanced learning parameters consistent with our model•Serotonin enhancers normalize imbalanced learning parameters in OCD Sakai et al. show that imbalanced learning between reinforcement and punishment can induce obsessive-compulsive disorder (OCD). A computational model of OCD reveals extremely imbalanced learning parameters in patients with OCD. Moreover, effects of behavioral therapy and psychotropic medication are reflected in the model and behavioral results.