Reinforcement learning detuned in addiction: integrative and translational approaches

Suboptimal decision-making strategies have been proposed to contribute to the pathophysiology of addiction. Decision-making, however, arises from a collection of computational components that can independently influence behavior. Disruptions in these different components can lead to decision-making deficits that appear similar behaviorally, but differ at the computational, and likely the neurobiological, level. Here, we discuss recent studies that have used computational approaches to investigate the decision-making processes underlying addiction. Studies in animal models have found that value updating following positive, but not negative, outcomes is predictive of drug use, whereas value updating following negative, but not positive, outcomes is disrupted following drug self-administration. We contextualize these findings with studies on the circuit and biological mechanisms of decision-making to develop a framework for revealing the biobehavioral mechanisms of addiction. Maladaptive decision-making in substance-dependent populations reflects both pre-existing risk factors and drug-induced adaptations in specific neurobiological processes.Delineating the relevant decision-making processes is essential for identifying biological markers for addiction susceptibility and for developing novel targets for the treatment of addiction.New neuroscience techniques probing learning mechanisms demonstrated that different decision-making computations are mediated by distinct neural, circuit, and cellular systems.Computational biomarkers of decision-making functions are key for revealing the neurobiological mechanisms of addiction.