Study gives better knowledge of how OCD develops and may lead to improved treatment options

A behavioural model was utilised by researchers to better understand the origins of obsessive-compulsive disorder. They demonstrated that when learning parameters for reinforcement and punishment are significantly out of balance, the cycle of obsession and compulsion can be exacerbated. This study might lead to better mental health treatments.

Obsessive-compulsive disorder (OCD) might be viewed as a result of uneven learning between reward and punishment, according to researchers from Nara Institute of Science and Technology (NAIST), Advanced Telecommunications Research Institute International, and Tamagawa University.
They demonstrated that asymmetries in brain computations that relate present results to previous actions can lead to disordered behaviour using actual testing of their theoretical model.

This can occur when the memory trace signal for previous activities decays differentially for good and poor results. In this situation, “good” indicates that the outcome was better than predicted, while “bad” indicates that it was worse than expected. This research contributes to our understanding of how OCD develops.

OCD as mental condition

OCD is a mental condition characterised by intrusive and repetitive thoughts, known as obsessions, and specific repetitive activities, known as compulsions. Patients with OCD frequently feel helpless to modify their behaviour, even when they are aware that their obsessions or compulsions are unreasonable. In severe circumstances, these may leave the individual unable to lead a regular life.

Compulsive behaviours, such as frequent hand washing or constantly checking if doors are secured before leaving the house, are attempts to ease anxiety produced by obsessions. However, the mechanism by which the cycle of obsessions and compulsions is intensified was previously unknown.

NAIST researchers have now employed reinforcement learning theory to describe the chaotic cycle associated with OCD. In this approach, a better-than-expected event becomes more likely (positive prediction error), while a worse-than-expected outcome is suppressed (negative prediction error). Delays and positive/negative prediction mistakes must also be considered when using reinforcement learning.