Smoking Decisions: Altered Reinforcement Learning Signals Induced by Nicotine State
Smoking Decisions: Altered Reinforcement Learning Signals Induced by Nicotine State
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DOI：
10.1093/ntr/nty136
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发表时间：
2020-02-01
影响因子：
4.7
通讯作者：
Holroyd, Clay B.
中科院分区：
文献类型：
--
作者：
Baker, Travis E.;Zeighami, Yashar;Holroyd, Clay B.
简介：多巴胺信号传导的改变在强化学习和尼古丁成瘾中发挥着关键作用，但这两个过程之间的关系尚未得到很好的表征。我们结合强化学习的行为和计算测量方法，研究了年轻吸烟者的这种关系。方法：我们要求中度依赖吸烟者参与强化学习任务三次：照常吸烟、戒烟和吸烟。使用强化学习模型对参与者的试验到试验的训练选择进行建模，该模型计算与阳性和阴性预测误差相关的单独学习率。结果：我们发现，在戒烟期间从阳性预测误差信号中学习会减少，而在吸烟后会增强。相比之下，从负面预测误差信号中学习的能力在戒烟期间得到增强，而在吸烟后则减少。最后，当用新的刺激对进行测试时，参与者在照常吸烟过程中相对于避免负反馈预测刺激相对更好地选择正反馈预测刺激，这种模式在吸烟后逆转。结论：这些发现提供了吸烟状态（戒烟和吸烟）引起的强化学习改变的具体计算说明，并且可能代表治疗尼古丁成瘾的独特目标。
Introduction: Alterations in dopamine signaling play a key role in reinforcement learning and nicotine addiction, but the relationship between these two processes has not been well characterized. We investigated this relationship in young adult smokers using a combination of behavioral and computational measures of reinforcement learning.Methods: We asked moderately dependent smokers to engage in a reinforcement learning task three times: smoking as usual, smoking abstinence, and cigarette consumption. Participants' trial-to-trial training choices were modeled using a reinforcement learning model that calculates separate learning rates associated with positive and negative prediction errors.Results: We found that learning from positive prediction error signals is reduced during smoking abstinence and enhanced following cigarette consumption. By contrast, learning from negative prediction error signals was enhanced during smoking abstinence and reduced following cigarette consumption. Finally, when tested with novel pairs of stimuli, participants were relatively better at selecting the positive feedback predicting stimuli than avoiding the negative feedback predicting stimuli during the smoking as usual session, a pattern that reversed following cigarette consumption.Conclusions: These findings provide a specific computational account of altered reinforcement learning induced by smoking state (abstinence and consumption) and may represent a unique target for treatment of nicotine addiction.