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Temporal difference models describe higher-order learning in humans

机译：时间差异模型描述了人类的高阶学习

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The ability to use environmental stimuli to predict impending harm is critical for survival. Such predictions should be available as early as they are reliable. In pavlovian conditioning, chains of successively earlier predictors are studied in terms of higher-order relationships, and have inspired computational theories such as temporal difference learning(1). However, there is at present no adequate neurobiological account of how this learning occurs. Here, in a functional magnetic resonance imaging ( fMRI) study of higher-order aversive conditioning, we describe a key computational strategy that humans use to learn predictions about pain. We show that neural activity in the ventral striatum and the anterior insula displays a marked correspondence to the signals for sequential learning predicted by temporal difference models. This result reveals a flexible aversive learning process ideally suited to the changing and uncertain nature of real-world environments. Taken with existing data on reward learning(2), our results suggest a critical role for the ventral striatum in integrating complex appetitive and aversive predictions to coordinate behaviour.
机译：利用环境刺激来预测即将发生的伤害的能力对于生存至关重要。这样的预测应在可靠的情况下尽早获得。在帕夫洛夫条件下，先后预测变量的链是根据高阶关系进行研究的，并启发了诸如时差学习等计算理论（1）。但是，目前尚没有足够的神经生物学方法来说明这种学习是如何发生的。在这里，在对高阶厌恶性调节的功能磁共振成像（fMRI）研究中，我们描述了人类用来学习有关疼痛的预测的关键计算策略。我们显示腹侧纹状体和前岛中的神经活动显示出明显的对应于信号的时间差异模型预测的顺序学习信号。该结果揭示了一种灵活的厌恶学习过程，非常适合现实世界环境的变化和不确定性。根据有关奖励学习的现有数据（2），我们的结果表明腹侧纹状体在整合复杂的食性和厌恶性预测以协调行为方面起着至关重要的作用。

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