Event knowledge and language comprehension

我们的生活由一系列事件组成。在我们的一生中，我们经历了一系列由大量事件和情况组成的活动。因此，人们对日常（和非日常）事件有大量的了解，比如吃早餐、洗碗、看电影和参加婚礼。我们对事件的了解在我们理解周围发生的事情，预测接下来可能发生的事情，计划短期和长期的活动，以及驾驭社会环境方面发挥着重要作用。对常见事件的了解在我们理解语言的能力中也起着核心作用。如果你不了解世界是如何运转的，你就不可能理解人们对你说的话。由于事件知识对人们如何思考和交流的重要性，存在一些基本的科学问题，如(1)事件知识是如何学习的？(2)它在记忆中是如何表现的？(3)事件发生的顺序在记忆中是如何表现的？(4)我们如何利用事件知识来预测未来的活动？(5)当人们在听对话或阅读时，事件知识是如何被用来对未被明确陈述的事物进行推断的？我们的研究试图解决这些问题。我们已经建立了一个所谓的“神经网络模型”来学习现实世界的事件。模型学会填补事件中缺失的部分（当模型被告知“Bonnie切她的牛排”时，它推断她在用刀）。它还使用基于预测的学习。这是一种优雅的学习方法，因为模型（可能还有人）预测接下来可能发生的事情，然后通过将自己的预测与接下来实际发生的事情进行比较来学习。通过这种方式，事件知识从基于预测的学习中“浮现”出来。在接下来的5年里，我们将显著扩展我们最近的事件知识模型。我们将通过比较模型和人的表现来测试模型的预测。我们还将研究人们如何利用他们对事件的知识来理解他们所听到和读到的内容，特别是在使用语言来扩展、收缩或聚焦时间的情况下。***了解人们如何在记忆中表达事件，以及人们如何利用这些知识来理解语言，以及如何在世界各地行走，这在许多方面都是极其重要的。例如，我们需要了解像这样的记忆系统是如何工作的，这样我们才能更好地应对各种类型的痴呆症，比如阿尔茨海默氏症。我们需要了解孩子们是如何学习（或无法有效地学习）这类信息的。构建和测试事件知识如何被学习、存储在记忆中以及如何被破坏的模型，是在这些问题上取得进展的途径之一。

Our lives consist of a series of events. Over our lifetime, we experience a stream of activity that consists of a huge number of events and situations. As such, people have a tremendous amount of knowledge of everyday (and not-so-everyday) events, such as eating breakfast, washing dishes, going to a movie, and attending a wedding. Our knowledge of events plays a major role in our ability to understand what is going on around us, to predict what might happen next, to plan activities in the short and long term, and to navigate social situations. Knowledge of common events also plays a central role in our ability to understand language. It would not be possible to understand what people are saying to you if you lacked an understanding of how the world works.***Because of the importance of event knowledge for how people think and communicate, there are fundamental scientific questions such as (1) How is event knowledge learned? (2) How is it represented in memory? (3) How is the order in which events occur represented in memory? (4) How do we use event knowledge to predict future activities? (5) When people are listening to a conversation or reading, how is event knowledge used to make inferences about things that have not been explicitly stated?***Our research attempts to address these questions. We have built what is called a “neural network model” that learns about real-world events. The model learns to fill in missing components of events (when the model is told that “Bonnie cuts her steak”, it infers that she's using a knife). It also uses prediction-based learning. This is an elegant method for learning because the model (and presumably people) predicts what might happen next, and then learns by comparing its own predictions to what actually did happen next. In this way, knowledge of events “emerges” from prediction-based learning.***Over the next 5 years, we will significantly extend our recent model of event knowledge. We will test the model's predictions by comparing its performance to that of people. We also will conduct studies regarding how people use their knowledge of events to understand what they hear and read, particularly in cases in which language is used to expand, contract, or spotlight time.***Basic scientific understanding of how people represent events in memory, and how people use that knowledge to understand language, and to make their way around the world, is extremely important on a number of fronts. For example, we need to understand how memory systems like this work so that we can better confront various types of dementia, such as Alzheimer's. We need to understand how children learn (or fail to learn efficiently) this type of information. Constructing and testing models of how event knowledge is learned, stored in memory, and could be damaged, are part of the pathway to making headway on these issues.