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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=659440
• 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。接下来的事情可能会在短期和长期计划中进行计划，如果您缺乏对世界的工作，则不可能了解俗语。对人们认为ANK的重要性（1）事件知识是如何在对话或阅读中所代表的？没有明确说明现实事件的ns。 ）预测接下来的情况是将自己的预测与接下来的5年进行比较了解听到和阅读的内容，尤其是在使用语言来扩展，收缩或聚光灯时间的卡斯中。前面。事件知识是可以学习的，存储在内存中并可能损坏，这是在这些问题上口气途径的一部分。