Knowledge, Concepts, and Categories / Edition 1

by Koen Lamberts, David R. Shanks

Overview

The study of mental representation is a central concern incontemporary cognitive psychology. Knowledge, Concepts, and Categories is unusual in that it presents key conclusions from across the different subfields of cognitive psychology.

The study of mental representation is a central concern in contemporary cognitive psychology. Knowledge, Concepts, and Categories is unusual in that it presents key conclusions from across the different subfields of cognitive psychology. Readers will find data from many areas, including developmental psychology, formal modeling, neuropsychology, connectionism, and philosophy. The difficulty of penetrating the fundamental operations of the mind is reflected in a number of ongoing debates discussed—for example, do distinct brain systems underlie the acquisition and storage of implicit and explicit knowledge, or can the evidence be accommodated by a single-system account of knowledge representation?

The book can be divided into three distinct parts. Chapters 1 through 5 offer an introduction to the field; each presents a systematic review of a significant aspect of research on concepts and categories. Chapters 6 through 9 are concerned primarily with issues related to the taxonomy of human knowledge. Finally, Chapters 10 through 12 discuss formal models of categorization and function learning.

Contributors
Jerome R. Busemeyer, Eunhee Byun, Nick Chater, Paul De Boeck, Edward L. Delosh, Thomas Goschke, Ulrike Hahn, James Hampton, Evan Heit, Barbara Knowlton, Koen Lamberts, Mary E. Lassaline, Mark A. McDaniel, George L. Murphy, Larissa K. Samuelson, David Shanks, Linda B. Smith, Gert Storms, Bruce W.A. Whittlesea

Product Details

ISBN-13:	9780262621182
Publisher:	MIT Press
Publication date:	10/30/1997
Series:	Studies in Cognition
Edition description:	New Edition
Pages:	478
Product dimensions:	6.00(w) x 8.80(h) x 1.10(d)
Age Range:	18 Years

Table of Contents

Contributors
Series Preface
Introduction
Koen Lamberts and David Shanks
1 Knowledge and concept learning
Evan Heit
Theoretical arguments
EXperimental evidence for specific influences of knowlegde
Influences of more general knowledge
Implications for categorization models
Relations to inductive reasoning
Relations to memory
Conclusion
References
Note
2 Concepts and similarity
Ulrike Hahn & Nick Chater
Concepts and similarity: The chicken and the egg?
Concepts
Similarity
Concepts and similarity
Conclusion
References
Notes
3 Hierarchical structure in concepts and the basic
level of categorization
Gregory L. Murphy & Mary E. Lassaline
Hierarchical structure of categories
The basic level of categorization
The other levels
The basic level in nonobject domains
EXpertise
Conclusion
References
Notes
4 Conceptual combination
James Hampton
Types of conceptual combination
Intersective combination
Modifierhead combination
Conclusions
References
Notes
5 Perceiving and remembering: Category stability,
variability and development
Linda B. Smith & Larissa K. Samuelson
Part 1: Theories about concepts
Part 2: Perceiving and remembering
Part 3: Learning names for things
What about concepts?
References
6 Distributed representations and implicit knowledge:
A brief introduction
David R. Shanks
Connectionism and knowledge representation
Structured representations
Implicit and eXplicit knowledge
Conclusion
References
Note
7 Declarative and nondeclarative knowledge: Insights
fromcognitive neuroscience
Barbara Knowlton
Introduction
The relationship between knowledge and memory
Declarative and nondeclarative knowledge
Declarative knowledge and amnesia
Nondeclarative skill learning
Dissociations in skill learning
Priming
Priming and brain imaging
Conceptual priming
Priming and recognition
Sequence Learning
Categorylevel knowledge
Artificial grammar learning
Fuzzy categories
EXemplarbased models
Amnesia as a storage deficit
Consolidation
Functional imaging of hippocampus
Electrophysiological data
Conclusion
References
8 Implicit learning and unconscious knowledge: Mental
representation, computational
mechanisms, and brain structures
Thomas Goschke
Introduction: Phenomena, tasks, and questions
Dissociations and operational criteria
Question 1: Does implicit learning lead to unconscious knowledge?
Question 2: Does implicit learning require attention or is it
automatic?
Question 3: Does implicit learning lead to abstract knowledge?
Question 4: What are the computational mechanisms underlying
implicit learning?
Question 5: Does implicit learning involve specific brain
structures?
Conclusions
References
Notes
9 The representation of general and particular
knowledge
Bruce W. A. Whittlesea
Conclusions
References
Notes
10 Process models of categorization
Koen Lamberts
A framework for perceptual categorization
Principles of formal modelling
Perceptual processing in categorization
Memory access and decision making
Another model of response times in categorization
Conclusion
References
Note
11 Learning functional relations based on eXperience
with inputoutput pairs by humans
and artificial neural
networks
Jerome R. Busemeyer, Eunhee Byun, Edward L. Delosh & Mark A.
McDaniel
Decisions, predicitions, and abstract concepts
Category versus function learning paradigm
Summary of basic findings on singlecue function learning
Cognitive models of function learning
Reproducing the basic findings of functionlearning research
Conclusions
References
12 Formal models for intracategorical structure that
can be used for data analysis
Gert Storms & Paul De Broeck
Entity by property matrices
Intracategorical structure in the major theoretical views on
concepts
. . .
Conclusions
References
Notes
IndeX