Reinforcement and punishment learning helpful in explaining OCD: Study

Scientists have shown that uneven reinforcement and punishment learning can be used to explain obsessive-compulsive disorder (OCD). They demonstrated that disparities in brain calculations that relate present outcomes to prior actions can result in disordered behavior based on empirical tests of their theoretical model.

Specifically, this can happen when the memory trace signal for past actions decays differently for good and bad outcomes. In this case, "good" means the result was better than expected, and "bad" means that it was worse than expected. This work helps to explain how OCD develops.

Now, a team led by researchers at NAIST has used reinforcement learning theory to model the disordered cycle associated with OCD. Using simulations, NAIST scientists found that agents implicitly learn obsessive-compulsive behavior when the trace decay factor for memory traces of past actions related to negative prediction errors is much smaller than that related to positive prediction errors . To test this prediction the researchers had 45 patients with OC and 168 healthy control subjects play a computer-based game with monetary rewards and penalties. Patients with OCD showed much smaller  compared with , as predicted computational characteristics of OCD. In addition, this imbalanced setting of trace decay factors was normalized by serotonin enhancers, which are first-line medications f treatment of OCD.

Ref:

Yuki Sakai*, Yutaka Sakai*, Yoshinari Abe, Jin Narumoto & Saori C. Tanaka,Memory trace imbalance in reinforcement and punishment systems can reinforce implicit choices leading to obsessive-compulsive behavior,Cell Reports,10.1016/j.celrep.2022.111275