28 October 2020: Computational models for compulsivity: Structure learning and control in OCD and gambling disorder

Frederike Petzschner
Institut für Biomedizinische Technik
Universität Zürich und ETH Zürich

While OCD and gambling are often conceptualized as disorders of compulsivity arising from dysfunctional beliefs, the nature of the beliefs leading to symptom manifestation remains unclear. Computational modeling can help disentangle the complex interplay between beliefs and symptoms, identify core components, and suggest targets for novel treatment approaches.

This talk covers three lines of work that center around a computational informed understanding of the origins of compulsive behavior: The first part relates individual structure learning differences to obsessive-compulsive traits and SSRIs – the first-line pharmacological treatment of OCD. The second part delves deeper into the origin of compulsive behavior. While there are a number of theoretical explanatory frameworks mostly centered around belief alterations, there is no clear consensus on what beliefs are necessary or sufficient to cause compulsive behavior.
We used a minimal model approach (based on a POMDP) to address this question and pinpoint to the belief about the success of preventive actions as being centrally important for eliciting and maintaining compulsive behavior. Finally, the third part of the talk highlights that similar beliefs about the efficacy of one’s actions may not only be associated with compulsive behavior in OCD but may also play an essential role in the compulsive aspect of pathological gambling.

View this recorded session here.