A computational model of OCD compulsivity as a deficit of integrating across levels of uncertainty

Obsessive compulsive disorder (OCD) is a neuropsychiatric disorder characterized by recurrent unwanted thoughts (obsessions) and repetitive stereotyped behaviors (compulsions) aimed to relieve anxiety. The strength and persistence of compulsions can severely impact patients' ability to function independently, and up to 30% of treatment interventions (including combinations of medication and therapy) may fail to relieve symptoms to a significant degree. Part of the difficulty of treating compulsions lies in the fact that the specific mechanism by which they develop remains yet unknown. Previous research has offered mixed findings on whether they link to failures in learning or to failures in goal-directed behavior, and it is unclear how they work to relieve anxiety, or why treating one compulsion can still lead to a different one arising to replace it. We used computational modeling in a predictive inference task that requires integrating information at a "local" level into the wider knowledge about the structure of the "global" world. In a sample of 20 OCD patients and 23 healthy, age-matched controls, we showed similar local learning (e.g. the ability to successfully reduce uncertainty about an underlying generative process by observing sequential samples from that process) in patients and controls, but impaired ability to integrate the local knowledge into representing the wider world structure in patients. Our model proposes a hierarchical goal structure that allows for local, short-term goals (e.g. "I will wash my hands to avoid a dangerous virus") into global, longer-term goals (e.g. "I want to stay healthy and avoid disease, accidents, crimes etc."), and shows how the intact ability to acquire information to resolve local goals ("I have washed my hands and now they're clean") but the impaired ability to integrate those into the global goal leads can produce compulsive-like behaviors.