Neural signature of fictive learning signals in a sequential investment task
Neural signature of fictive learning signals in a sequential investment task
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DOI：
10.1073/pnas.0608842104
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发表时间：
2007-05-29
影响因子：
11.1
通讯作者：
Montague, P. Read
中科院分区：
文献类型：
--
作者：
Lohrenz, Terry;McCabe, Kevin;Montague, P. Read
强化学习模型现在为动物和人类的广泛奖励学习实验提供了原则性指导。这些模型中的一个关键学习（错误）信号是经验性的，并报告预期和经验奖励之间的持续时间差异。然而，这些相同的抽象学习模型也适应了另一类学习信号的存在，这种学习信号采取虚构错误的形式，对经验回报和如果决策不同则“可能已经经历”的回报之间的持续差异进行编码。这些观察结果表明，对于所有现实世界的学习任务，人们应该期待经验和虚构的学习信号的存在。受这种可能性的启发，我们使用了一个连续的投资游戏和功能磁共振成像来探测大脑对整个游戏过程中产生的经验和虚构学习信号的持续反应。使用一个大的队列的主题（n = 54），我们报告说，虚构的学习信号强烈预测受试者的投资行为的变化，并与功能性磁共振成像信号测量已知参与评估和选择的多巴胺结构。
Reinforcement learning models now provide principled guides for a wide range of reward learning experiments in animals and humans. One key learning (error) signal in these models is experiential and reports ongoing temporal differences between expected and experienced reward. However, these same abstract learning models also accommodate the existence of another class of learning signal that takes the form of a fictive error encoding ongoing differences between experienced returns and returns that "could-have-been-experienced" if decisions had been different. These observations suggest the hypothesis that, for all real-world learning tasks, one should expect the presence of both experiential and fictive learning signals. Motivated by this possibility, we used a sequential investment game and fMRI to probe ongoing brain responses to both experiential and fictive learning signals generated throughout the game. Using a large cohort of subjects (n = 54), we report that fictive learning signals strongly predict changes in subjects' investment behavior and correlate with fMRI signals measured in dopaminoceptive structures known to be involved in valuation and choice.