To make sense of the world around us, we must be able to segment a continual stream of sensory inputs into discrete events. In this review, I propose that in order to comprehend events, we engage hierarchical generative models that “reverse engineer” the intentions of other agents as they produce sequential action in real time. By generating probabilistic predictions for upcoming events, generative models ensure that we are able to keep up with the rapid pace at which perceptual inputs unfold. By tracking our certainty about other agents' goals and the magnitude of prediction errors at multiple temporal scales, generative models enable us to detect event boundaries by inferring when a goal has changed. Moreover, by adapting flexibly to the broader dynamics of the environment and our own comprehension goals, generative models allow us to optimally allocate limited resources. Finally, I argue that we use generative models not only to comprehend events but also to produce events (carry out goal‐relevant sequential action) and to continually learn about new events from our surroundings. Taken together, this hierarchical generative framework provides new insights into how the human brain processes events so effortlessly while highlighting the fundamental links between event comprehension, production, and learning.

中文翻译：

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奶茶？顺序事件理解的分层生成框架

为了理解我们周围的世界，我们必须能够将持续的感官输入流分割成离散事件。在这篇评论中，我建议为了理解事件，我们采用分层生成模型，这些模型可以“逆向工程”其他代理的意图，因为它们实时产生顺序动作。通过为即将发生的事件生成概率预测，生成模型确保我们能够跟上感知输入展开的快速步伐。通过在多个时间尺度上跟踪我们对其他代理目标的确定性和预测误差的大小，生成模型使我们能够通过推断目标何时发生变化来检测事件边界。此外，通过灵活地适应更广泛的环境动态和我们自己的理解目标，生成模型允许我们优化分配有限的资源。最后，我认为我们使用生成模型不仅可以理解事件，还可以生成事件（执行与目标相关的顺序动作）并不断从我们周围的环境中了解新事件。总之，这种分层生成框架为人类大脑如何如此轻松地处理事件提供了新的见解，同时突出了事件理解、生产和学习之间的基本联系。