Jeung-Hyun Lee*, Eunhwi Lee*, Jooyeon Jaime Im, John P. O’Doherty, & Woo-Young Ahn. (2025). psyArxiv
DOI: preprint
Abstract
While mental disorders are complex and characterized by heterogeneous symptoms, a unified framework that fully and mechanistically captures these complexities remains elusive. Reinforcement learning offers a promising way to understand mental health by modeling the decision-making processes that underlie psychiatric conditions. By breaking decision-making down into key components—such as state representation, valuation, action selection, and outcome evaluation—reinforcement learning provides a structured approach to studying how disruptions in these processes contribute to disorders like depression, anxiety, and addiction. This review explores how reinforcement learning can help clarify the cognitive and neural mechanisms involved in mental disorders and offers insights into their interactions with other psychological and physiological systems. We also discuss the potential of the framework to improve clinical practice through more personalized treatments and highlight the challenges that remain in applying this approach to mental health research. Future directions will focus on expanding and using the reinforcement learning models to naturalistic paradigms and incorporation with advanced technologies like artificial intelligence.
