Addressing Altered Anticipation as a Transdiagnostic Target through Computational Psychiatry

Anticipation of future experiences is a crucial cognitive function that is impacted in various psychiatric conditions. Despite significant research advancements, the mechanisms underlying altered anticipation remain poorly understood and effective targeted treatments are largely lacking. This review proposes an integrated computational psychiatry approach to address these challenges. We begin by outlining how altered anticipation presents across different psychiatric conditions, including schizophrenia, major depressive disorder, anxiety disorders, substance use disorders, and eating disorders, and summarizing the insights gained from extensive research using self-report scales and task-based neuroimaging, despite notable limitations. We then explore how emerging computational modeling approaches, such as reinforcement learning and anticipatory utility theory, could overcome these limitations and offer deeper insights into underlying mechanisms and individual variations. We propose that integrating these interdisciplinary methodologies can offer comprehensive transdiagnostic insights, aiding in the discovery of new therapeutic targets and advancing precision psychiatry.