Humans can automatically detect and learn to exploit repeated aspects (regularities) of the environment. Timing research suggests that such learning is not only used to anticipate what will happen, but also when it will happen. However, in timing experiments, the intervals to be timed are presented in isolation from other stimuli and explicitly cued, contrasting with naturalistic environments in which intervals are embedded in a constant stream of events and individuals are hardly aware of them. It is unclear whether laboratory findings from timing research translate to a more ecologically valid, implicit environment. Here we show in a game-like experiment, specifically designed to measure naturalistic behavior, that participants implicitly use regular intervals to anticipate future events, even when these intervals are constantly interrupted by irregular yet behaviorally relevant events. This finding extends previous research by showing that individuals not only detect such regularities but can also use this knowledge to decide when to act in a complex environment. Furthermore, this finding demonstrates that this type of learning can occur independently from the ordinal sequence of motor actions, which contrasts this work with earlier motor learning studies. Taken together, our results demonstrate that regularities in the time between events are implicitly monitored and used to predict and act on what happens when, thereby showing that laboratory findings from timing research can generalize to naturalistic environments. Additionally, with the development of our game-like experiment, we demonstrate an approach to test cognitive theories in less controlled, ecologically more valid environments.

人类可以自动检测并学习利用环境的重复方面（规律性）。时序研究表明，这种学习不仅用于预测将要发生的事情，还用于预测何时发生。然而，在计时实验中，要计时的间隔是与其他刺激隔离开来的，并有明确的提示，这与自然环境形成鲜明对比，在自然环境中，间隔嵌入在持续不断的事件流中，个人几乎不会意识到它们。目前尚不清楚时间研究的实验室发现是否转化为更具生态有效性的隐性环境。在这里，我们在一个类似游戏的实验中展示，该实验专门设计用于测量自然行为，参与者隐含地使用规则间隔来预测未来事件，即使这些间隔不断被不规则但与行为相关的事件打断。这一发现扩展了之前的研究，表明个人不仅可以检测到此类规律性，还可以利用这些知识来决定何时在复杂环境中采取行动。此外，这一发现表明，这种类型的学习可以独立于运动动作的顺序发生，这将这项工作与早期的运动学习研究形成对比。综上所述，我们的结果表明事件之间的时间规律性被隐式监控并用于预测和采取行动，从而表明时间研究的实验室发现可以推广到自然环境。此外，随着我​​们游戏式实验的发展，