Pisupati Sashank, Langdon Angela, Konova Anna B, Niv Yael
Limbic Limited, London UK.
Princeton Neuroscience Institute & Department of Psychology, Princeton University, Princeton NJ, USA.
Addict Neurosci. 2024 Mar;10. doi: 10.1016/j.addicn.2024.100143. Epub 2024 Jan 15.
Computational models of addiction often rely on a model-free reinforcement learning (RL) formulation, owing to the close associations between model-free RL, habitual behavior and the dopaminergic system. However, such formulations typically do not capture key recurrent features of addiction phenomena such as craving and relapse. Moreover, they cannot account for goal-directed aspects of addiction that necessitate contrasting, model-based formulations. Here we synthesize a growing body of evidence and propose that a latent-cause framework can help unify our understanding of several recurrent phenomena in addiction, by viewing them as the inferred return of previous, persistent "latent causes". We demonstrate that applying this framework to Pavlovian and instrumental settings can help account for defining features of craving and relapse such as outcome-specificity, generalization, and cyclical dynamics. Finally, we argue that this framework can bridge model-free and model-based formulations, and account for individual variability in phenomenology by accommodating the memories, beliefs, and goals of those living with addiction, motivating a centering of the individual, subjective experience of addiction and recovery.
成瘾的计算模型通常依赖于无模型强化学习（RL）公式，这是由于无模型RL、习惯性行为和多巴胺能系统之间存在密切关联。然而，此类公式通常无法捕捉成瘾现象的关键反复出现的特征，如渴望和复发。此外，它们无法解释成瘾的目标导向方面，而这需要基于模型的对比公式。在此，我们综合了越来越多的证据，并提出一个潜在原因框架可以帮助统一我们对成瘾中几种反复出现现象的理解，即将它们视为先前持续的“潜在原因”的推断回报。我们证明，将此框架应用于巴甫洛夫式和工具性情境中，可以帮助解释渴望和复发的定义特征，如结果特异性、泛化和周期性动态。最后，我们认为这个框架可以弥合无模型和基于模型的公式之间的差距，并通过考虑成瘾者的记忆、信念和目标来解释现象学中的个体差异，从而促使成瘾和康复的个体主观体验成为核心。
