Addiction as a computational process gone awry
Addiction as a computational process gone awry
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DOI：
10.1126/science.1102384
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发表时间：
2004-12-10
期刊：
影响因子：
56.9
通讯作者：
Redish, AD
中科院分区：
文献类型：
--
作者：
Redish, AD
人们假设成瘾药物与自然学习系统具有相同的神经生理机制。这些自然学习系统可以通过时间差异强化学习（TDRL）来建模，这需要一个被假设由多巴胺携带的奖励错误信号。TDRL通过将奖励错误信号归零来学习预测奖励。通过在TDRL模型中加入不可补偿的药物诱导多巴胺增加，构建了一个过度选择行为导致药物接受的成瘾计算模型。该模型为成瘾文献的重要方面提供了解释，并为解决其他方面提供了理论观点。
Addictive drugs have been hypothesized to access the same neurophysiological mechanisms as natural learning systems. These natural learning systems can be modeled through temporal-difference reinforcement learning (TDRL), which requires a reward-error signal that has been hypothesized to be carried by dopamine. TDRL learns to predict reward by driving that reward-error signal to zero. By adding a noncompensable drug-induced dopamine increase to a TDRL model, a computational model of addiction is constructed that over-selects actions leading to drug receipt. The model provides an explanation for important aspects of the addiction literature and provides a theoretic viewpoint with which to address other aspects.