Animals routinely learn to associate environmental stimuli and self-generated actions with their outcomes such as rewards. One of the most popular theoretical models of such learning is the reinforcement learning (RL) framework. The simplest form of RL, model-free RL, is widely applied to explain animal behavior in numerous neuroscientific studies. More complex RL versions assume that animals build and store an explicit model of the world in memory. To apply these approaches to explain animal behavior, typical neuroscientific RL models make implicit assumptions about how real animals represent the passage of time. In this perspective, I explicitly list these assumptions and show that they have several problematic implications. I hope that the explicit discussion of these problems encourages the field to seriously examine the assumptions underlying timing and reinforcement learning. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

中文翻译：

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真实的动物在联想学习过程中如何解释时间的流逝？

动物通常会学习将环境刺激和自我产生的行为与其结果（例如奖励）联系起来。这种学习最流行的理论模型之一是强化学习（RL）框架。强化学习最简单的形式是无模型强化学习，在众多神经科学研究中广泛应用于解释动物行为。更复杂的强化学习版本假设动物在记忆中构建并存储了一个明确的世界模型。为了应用这些方法来解释动物行为，典型的神经科学强化学习模型对真实动物如何代表时间的流逝做出了隐含的假设。从这个角度来看，我明确列出了这些假设，并表明它们有几个有问题的含义。我希望对这些问题的明确讨论能够鼓励该领域认真研究时序和强化学习背后的假设。（PsycInfo 数据库记录 (c) 2022 APA，保留所有权利）。