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Psychol Rev. 2017 Mar;124(2):130-153. doi: 10.1037/rev0000046. Epub 2017 Jan 16.

Cocaine addiction as a homeostatic reinforcement learning disorder.

Author information

Gatsby Computational Neuroscience Unit.
Institut des Maladies Neurodégénératives, Université de Bordeaux.
Group for Neural Theory, INSERM U960, Departément des Etudes Cognitives, Ecole Normale Supérieure, PSL Research University.


Drug addiction implicates both reward learning and homeostatic regulation mechanisms of the brain. This has stimulated 2 partially successful theoretical perspectives on addiction. Many important aspects of addiction, however, remain to be explained within a single, unified framework that integrates the 2 mechanisms. Building upon a recently developed homeostatic reinforcement learning theory, the authors focus on a key transition stage of addiction that is well modeled in animals, escalation of drug use, and propose a computational theory of cocaine addiction where cocaine reinforces behavior due to its rapid homeostatic corrective effect, whereas its chronic use induces slow and long-lasting changes in homeostatic setpoint. Simulations show that our new theory accounts for key behavioral and neurobiological features of addiction, most notably, escalation of cocaine use, drug-primed craving and relapse, individual differences underlying dose-response curves, and dopamine D2-receptor downregulation in addicts. The theory also generates unique predictions about cocaine self-administration behavior in rats that are confirmed by new experimental results. Viewing addiction as a homeostatic reinforcement learning disorder coherently explains many behavioral and neurobiological aspects of the transition to cocaine addiction, and suggests a new perspective toward understanding addiction. (PsycINFO Database Record.

[Indexed for MEDLINE]

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