Clinical and experimental observations show individual differences in the development of addiction. Increasing evidence supports the hypothesis that dopamine receptor availability in the nucleus accumbens (NAc) predisposes drug reinforcement. Here, modeling striatal-midbrain dopaminergic circuit, we propose a reinforcement learning model for addiction based on the actor-critic model of striatum. Modeling dopamine receptors in the NAc as modulators of learning rate for appetitive—but not aversive—stimuli in the critic—but not the actor—we define vulnerability to addiction as a relatively lower learning rate for the appetitive stimuli, compared to aversive stimuli, in the critic. We hypothesize that an imbalance in this learning parameter used by appetitive and aversive learning systems can result in addiction. We elucidate that the interaction Mohammad Mahdi Keramati and Amir Dezfouli contributed equally to this work. Neural Computation 22, 2334–2368 (2010) C ○ 2010 Massachusetts Institute of TechnologyA Computational Model for Vulnerability to Addiction 233
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