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 "model-based" 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. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

中文翻译：

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药物使用和成瘾的计算模型：综述。

在这篇简短的评论中，我们描述了当前吸毒和成瘾的计算模型，这些模型分为两大类：依赖于计算理论的基于数学的模型，以及将计算与大脑区域或电路联系起来的基于大脑的模型。在各个类别中，许多都是学习和决策的模型，这些模型可能会因成瘾而受到损害。一些数学模型采用预测编码方法，重点关注贝叶斯预测误差。其他模型侧重于学习过程和（传统）预测误差。基于药物对多巴胺、动机和执行控制回路的影响，基于大脑的模型纳入了前额皮质、基底神经节和多巴胺系统。几个模型具体描述了行为控制如何从习惯性系统过渡到目标导向系统，这与受损的“基于模型”控制的计算帐户一致。一些基于大脑的模型将其与行为控制从腹侧纹状体到背侧纹状体的转变联系起来。总的来说，我们认为，虽然计算模型捕捉到了成瘾的某些方面并推进了我们的思考，但大多数都关注药物使用的影响而不是成瘾本身，大多数还没有经过人类数据的测试和/或支持，而且很少有捕捉成瘾的多个阶段和症状。最后，我们提出了成瘾计算模型的前进道路。（PsycInfo 数据库记录 (c) 2020 APA，保留所有权利）。