The informational dysregulation framework of addiction (IDFA): an information-processing model of relapse in opioid use disorder.

Abstract

Opioid use disorder is associated with high relapse risk and a persistent mismatch between intention and behavior. Contemporary neurobiological models have clarified the roles of reward learning, reinforcement, habit formation, salience attribution, stress adaptation, and executive-control dysfunction in relapse vulnerability. However, these mechanisms are not always translated into a clinically usable framework that links neurobiology with lived experience and relapse-prevention planning. The Informational Dysregulation Framework of Addiction (IDFA) was developed in response to this translational need, through structured integrative synthesis of addiction neuroscience, computational psychiatry, information theory, and clinical relapse research. IDFA conceptualizes relapse vulnerability in opioid use disorder as dysregulation in how the brain predicts, updates, and integrates information under uncertainty. The framework organizes relapse processes across three interacting domains: precision dysregulation, entropy and complexity disruption, and awareness and integration impairment. Together, these processes form a self-reinforcing loop that narrows informational bandwidth and behavioral flexibility across relapse trajectories. Alongside established reward- and habit-based accounts, IDFA provides an integrative information-processing perspective on how prediction, updating, and action selection become dysregulated during relapse vulnerability. This approach also generates clinically relevant hypotheses regarding relapse prediction, individualized case formulation, and mechanism-informed intervention planning across pharmacologic and psychosocial treatments.