Arbitration between Action Strategies in Obsessive-Compulsive Disorder.

Area of Science:

• Neuroscience
• Cognitive Psychology
• Computational Psychiatry

Background:

• Complex decision-making involves balancing automatic, habit-like (model-free) and flexible, evaluative (model-based) strategies.
• Obsessive-compulsive disorder (OCD) is increasingly linked to an over-reliance on inflexible, habit-like decision-making processes.

Purpose of the Study:

• To propose that OCD is characterized by a functional deficit in the arbitration mechanism between model-free and model-based decision-making.
• To explore how this arbitration deficit reconciles existing findings on habit-learning and procedural learning in OCD.

Main Methods:

• Review of recent behavioral data and emerging functional imaging findings in OCD.
• Theoretical integration of reinforcement learning models (model-free vs. model-based) with clinical observations in OCD.

Main Results:

• Individuals with OCD show excessive reliance on habit-like (model-free) decision-making in reinforcement learning.
• Functional imaging data suggest potential neural underpinnings for impaired arbitration in OCD.

Conclusions:

• A weakened dynamic arbitration mechanism between model-free and model-based decision-making may underlie OCD's characteristic inflexibility.
• This hypothesized deficit offers a novel framework for understanding OCD and suggests potential targets for cognitive remediation interventions.