Event knowledge and language comprehension

事件知识和语言理解

• 批准号: RGPIN-2018-05652
• 负责人: McRae, Ken
• 金额: $ 2.4万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=713764
• 关键词: Event; knowledge; language; comprehension

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.

我们的生活包括我们经历的活动。接下来，可能会在短期和长期计划中计划活动，而不可能了解如果您对世界的工作方式不了解。 由于thople thople thople Tomunate t知识的知识是如何在记忆中发生的？说？ 我们的研究试图这些论文学会填补丢失的事件组成部分（当模型被告知“ Bonnie削减牛排”时，它会发现她正在使用刀子）。通过撰写是对接下来实际发生的事情的预测。 在临近，我们将大大扩展我们的最新知识。哪种语言用于扩展，收缩或聚焦时间。 基本的科学理解，人们在世界各地以记忆方式代表，例如，我们需要了解像这样的记忆系统如何工作，以便我们面对各种类型的痴呆症（或失败的toarn输入）这种类型的信息。构建和测试如何学习事件知识，存储在内存中并可能损坏，这是在这些方面取得进展的一部分。