Grant: Discovery Grants Program - Individual
University of Western Ontario
$33,000.00
Event knowledge and language comprehension
FY 2018-19
Summary
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.
Program
Discovery Grants Program - Individual
Recipient
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
RGPIN-2018-05652
Name nom
McRae, Ken
Department département
Psychology
Organization id
36
Institution établissement
University of Western Ontario
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2018
Competition year année de concours
2018
Award amount
33000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
1502
Committee name en
Biological Systems and Functions
Committee name fr
Systèmes et fonctions biologiques
Area of application code
1210
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Psychology
Area of application fr
Psychologie
Research subject code
6306
Research subject group en
Psychology
Research subject group fr
Psychologie
Research subject en
Cognitive science -- language
Research subject fr
Science cognitive -- langage
Partie
1
Nb partie
5
Application title
Event knowledge and language comprehension
Keyword
NSERC
Application summary
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.