Computational psychiatry provides a powerful new approach for linking the behavioral manifestations of addiction to their precise cognitive and neurobiological substrates. However, this emerging area of research is still limited in important ways. While research has identified features of reinforcement learning and decision-making in substance users that differ from health, less emphasis has been placed on capturing addiction cycles/states dynamically, within-person. In addition, the focus on few behavioral variables at a time has precluded more detailed consideration of related processes and heterogeneous clinical profiles. We propose that a longitudinal and multidimensional examination of value-based processes, a type of dynamic ‘computational fingerprint’, will provide a more complete understanding of addiction as well as aid in developing better tailored and timed interventions.

计算精神病学提供了一种强大的新方法，可以将成瘾的行为表现与其精确的认知和神经生物学基础联系起来。然而，这个新兴的研究领域在重要方面仍然受到限制。虽然研究已经确定了物质使用者的强化学习和决策与健康不同的特征，但较少强调动态地在人体内捕捉成瘾周期/状态。此外，一次只关注少数行为变量已经排除了对相关过程和异质临床特征的更详细考虑。我们建议对基于价值的过程进行纵向和多维检查，这是一种动态“计算指纹”，