Knowledge of common events is central to many aspects of cognition. Intuitively, it seems as though events are linear chains of the activities of which they are comprised. In line with this intuition, a number of theories of the temporal structure of event knowledge have posited mental representations (data structures) consisting of linear chains of activities. Competing theories focus on the hierarchical nature of event knowledge, with representations comprising ordered scenes, and chains of activities within those scenes. We present evidence that the temporal structure of events typically is not well‐defined, but it is much richer and more variable both within and across events than has usually been assumed. We also present evidence that prediction‐based neural network models can learn these rich and variable event structures and produce behaviors that reflect human performance. We conclude that knowledge of the temporal structure of events in the human mind emerges as a consequence of prediction‐based learning.

中文翻译：

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基于预测的事件知识学习与处理

常见事件的知识是认知的许多方面的核心。直觉上，事件似乎是组成它们的活动的线性链。与这种直觉一致，许多关于事件知识时间结构的理论都假设了由线性活动链组成的心理表征（数据结构）。竞争理论侧重于事件知识的层次性质，表示包括有序场景和这些场景中的活动链。我们提供的证据表明，事件的时间结构通常没有明确定义，但它在事件内部和事件之间比通常假设的要丰富得多，变化也更大。我们还提供了证据，表明基于预测的神经网络模型可以学习这些丰富多变的事件结构，并产生反映人类表现的行为。我们得出结论，人类思维中事件的时间结构知识是基于预测的学习的结果。