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Progressing from Simple to Multidimensional Models Towards a Biopsychosocial Framework of Addiction

Paul S. Regier

Current Addiction Reports June 11, 2026 Peer reviewed DOI: 10.1007/s40429-026-00752-0 via OpenAlex

Summary

Addiction models have evolved to recognize that psychosocial and contextual factors significantly influence addiction experiences. Recent findings show that changes in large-scale brain networks are critical, and emerging interventions like psychedelic-assisted therapies highlight the need for a comprehensive approach. Viewing addiction through a biopsychosocial lens allows for a better understanding of the varied pathways and outcomes in addiction, suggesting that integrative models can enhance research and intervention strategies.

Study at a glance

Design review
Key finding Framing addiction as a multilevel, interactive process within a biopsychosocial framework provides a unifying perspective that accommodates heterogeneity in pathways and outcomes.

Abstract

Purpose of Review: Neuroscientific models of addiction have evolved over the past four decades from single-circuit dopamine frameworks to dual-process, tripartite, and multidimensional accounts. Despite these advances, existing models incompletely account for the lived experience of addiction and for developmental, social, and mental-health factors that contribute to heterogeneity in risk and outcome. This review synthesizes this progression and evaluates the need for more integrative frameworks. Recent Findings: Recent work highlights addiction-related alterations in large-scale brain networks and demonstrates that psychosocial and contextual factors play a more central mechanistic role than previously acknowledged. Emerging interventions, including psychedelic-assisted therapies, further underscore the relevance of multilevel change across neurobiological, cognitive, and affective domains. Summary: Framing addiction as a multilevel, interactive process within a biopsychosocial framework provides a unifying perspective that accommodates heterogeneity in pathways and outcomes. Integrative models may better inform mechanistic research and guide the development of more precise, clinically meaningful interventions.

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