Computational Mechanisms of Addiction: Recent Evidence and Its Relevance to Addiction Medicine
Computational Mechanisms of Addiction: Recent Evidence and Its Relevance to Addiction Medicine
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DOI：
10.1007/s40429-021-00399-z
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发表时间：
2021-10-01
影响因子：
4.3
通讯作者：
Bilek, Edda
中科院分区：
文献类型：
--
作者：
Smith, Ryan;Taylor, Samuel;Bilek, Edda
在这篇文章中，我们简要回顾了最近关于物质使用障碍（sud）的计算模型研究，重点是最近5年发表的工作。虽然强化学习（RL）方法在最近的研究中最为突出，但我们也从其他角度回顾了专注于贝叶斯（主动）推理和感知处理的工作。最近关于强化学习的研究表明，目标导向（基于模型的）计划过程在sud中受损，导致冲动、习惯性的决策过程专注于短期奖励，尽管长期的负面后果。贝叶斯方法提供了一个补充的观点，表明药物引起的对先前预期的过度自信阻碍了物质使用者在面对负面结果时适当地更新他们的信念。最近的神经计算研究显示，有希望区分那些会复发和不会复发的人。计算模型在确定sud中受损的具体前瞻性决策过程方面取得了进展，但在这些方法能够在个性化层面上直接为医疗实践提供信息之前，还需要进一步的研究。
Purpose of Review In this article, we provide a brief review of recent computational modelling studies of substance use disorders (SUDs), with a focus on work published within the last 5 years. While reinforcement learning (RL) approaches are most prominent in recent studies, we also review work from other perspectives that focus on Bayesian (active) inference and perceptual processing.Recent Findings Recent work in RL shows evidence that goal-directed (model-based) planning processes are impaired in SUDs, leading to impulsive, habitual decision processes focused on short-term reward despite long-term negative consequences. Bayesian approaches offer a complementary perspective, suggesting that drug-induced overconfidence in prior expectations prevents substance users from appropriately updating their beliefs in the face of negative outcomes. Recent neurocomputational studies have shown promise in differentiating those who will and will not relapse.Summary Computational modelling has made progress in identifying specific prospective decision-making processes that are impaired in SUDs, but further research is necessary before these approaches can directly inform medical practice on an individualized level.