Abstract
Nobody wants to experience anxiety. However, anxiety may be induced by our own implicit choices that are mis-reinforced by some imbalance in reinforcement learning. Here we focused on obsessive-compulsive disorder (OCD) as a candidate for implicitly learned anxiety. Simulations in the reinforcement learning framework showed that agents implicitly learn to become anxious when the memory trace signal for past actions decays differently for positive and negative prediction errors. In empirical data, we confirmed that OCD patients showed extremely imbalanced traces, which were normalized by serotonin enhancers. We also used fMRI to identify the neural signature of OCD and healthy participants with imbalanced traces. Beyond the spectrum of clinical phenotypes, these behavioral and neural characteristics can be generalized to variations in the healthy population.
Competing Interest Statement
The authors have declared no competing interest.
