Computational models of drug use and addiction: A review

Abstract

In this brief review, we describe current computational models of drug-use and addiction that fall into 2 broad categories: mathematically based models that rely on computational theories, and brain-based models that link computations to brain areas or circuits. Across categories, many are models of learning and decision-making, which may be compromised in addiction. Several mathematical models take predictive coding approaches, focusing on Bayesian prediction error. Other models focus on learning processes and (traditional) prediction error. Brain-based models have incorporated prefrontal cortex, basal ganglia, and the dopamine system, based on the effects of drugs on dopamine, motivation, and executive control circuits. Several models specifically describe how behavioral control may transition from habitual to goal-directed systems, consistent with computational accounts of compromised "modelbased" control. Some brain-based models have linked this to the transition of behavioral control from ventral to dorsal striatum. Overall, we propose that while computational models capture some aspects of addiction and have advanced our thinking, most have focused on the effects of drug use rather than addiction per se, most have not been tested on and/or supported by human data, and few capture multiple stages and symptoms of addiction. We conclude by suggesting a path forward for computational models of addiction.