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Charles R. Gallistel - The organization of learning

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How do animals represent space, time, number and rate? From insects to humans, Charles Gallistel explores the sophisticated computations performed in these ubiquitous yet neglected domains of animal learning. He proposes new and imaginative hypotheses about brain and mental processes and provides original insights about animal behavior using a computational-representational framework that is an exciting alternative to traditional associative theories of learning.Gallistel argues compellingly that experimental psychologists should begin to view the phenomena of learning within a framework that utilizes as the proper unit of analysis the computation and storage of a quantity, rather than the formation of an association that has been the basis of traditional learning theory. His approach reveals the formal structure of the environmental relationships that animals master to time and orient their behavior. It clarifies what representations different animals can and cannot compute and the nature of the computations by which animals derive these representations.The author backs up this thesis with studies that encompass a vast range of animal learning: animal navigation (the use of dead reckoning and cognitive maps); the mechanisms of timekeeping in the nervous system; the registration and utilization of time of occurrence (circadian phase) in learned behavior; the learning and use of temporal intervals and of numerosity; the computation of rates of occurrence; modern findings and theories of classical conditioning.Gallistel surveys the experimental literature in zoology, biology, neuroscience, and psychology that bears on those aspects of their environment that animals represent and the computations they perform in constructing and utilizing those representations. He reveals the fundamental role these representations play in learning and memory, and the implications of these findings in the search for the cellular basis of memory.Charles R. Gallistel is Professor of Psychology at the University of Pennsylvania. The Organization of Learning is included in the series Learning, Development, and Conceptual Change, edited by Lila Gleitman, Susan Carey, Elissa Newport, and Elizabeth Spelke. A Bradford Book

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title The Organization of Learning Learning Development and Conceptual - photo 1

title:The Organization of Learning Learning, Development, and Conceptual Change
author:Gallistel, C. R.
publisher:MIT Press
isbn10 | asin:026257098X
print isbn13:9780262570985
ebook isbn13:9780585175867
language:English
subjectLearning in animals, Cognition in animals.
publication date:1993
lcc:QL785.G22 1993eb
ddc:153.1/5
subject:Learning in animals, Cognition in animals.
Page iii
The Organization of Learning
C.R. Gallistel
A Bradford Book
The MIT Press
Cambridge, Massachusetts
London, England
Page iv
First MIT Press paperback edition, 1993
1990 Massachusetts Institute of Technology
All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher.
This book was set in Palatino by the MIT Press. It was printed and bound in the United States of America.
Library of Congress Cataloging-in-Publication Data
Gallistel, C.R., 1941
The organization of learning.
(Learning, development, and conceptual change)
"A Bradford book."
Includes index.
1. Learning in animals. 2. Cognition in animals.
I. Title. II. Series.
QL785.G22 1989 53.1'5 88-26656
ISBN 0-262-07113-4 (HB), 0-262-57098-X (PB)
Page v
To A.F. and E.R.
Page vii
Contents
Series Foreword
viii
Acknowledgments
ix
Chapter 1
A Computational-Representational Approach
1
Chapter 2
Representations
15
Chapter 3
Navigation
35
Chapter 4
Dead Reckoning
57
Chapter 5
The Cognitive Map
103
Chapter 6
The Geometric Module in the Rat
173
Chapter 7
Biological and Computational Foundations for the Representation of Time
221
Chapter 8
Time of Occurrence
243
Chapter 9
Temporal Intervals
287
Chapter 10
Number
317
Chapter 11
Rate
351

Page viii
Chapter 12
Classical Conditioning: Modern Results and Theory
385
Chapter 13
Classical Conditioning: A Representational Model
421
Chapter 14
Vector Spaces in the Nervous System
475
Chapter 15
The Unity of Remembered Experience
523
Chapter 16
The Search for the Engram
561
Chapter 17
Synthesis
581
References
597
Index
623

Page ix
Series Foreword
This series in learning, development, and conceptual change will include state-of-the-art reference works, seminal book-length monographs, and texts on the development of concepts and mental structures. It will span learning in all domains of knowledge, from syntax to geometry to the social world, and will be concerned with all phases of development, from infancy through adulthood.
The series intends to engage such fundamental questions as
The nature and limits of learning and maturation: The influence of the environment, of initial structures, and of maturational changes in the nervous system on human development; learnability theory; the problem of induction; domain-specific constraints on development.
The nature of conceptual change: conceptual organization and conceptual change in child development, in the acquisition of expertise, and in the history of science.
Picture 2
LILA GLEITMAN
SUSAN CAREY
ELISSA NEWPORT
ELIZABETH SPELKE
Page xi
Acknowledgments
I wrote most of the initial draft of this book while on sabbatical leave as a Fellow at the Center for Advanced Study in the Behavioral Sciences. I am grateful for financial support from the Alfred P. Sloan Foundation while at the Center. I am also grateful to the staff there, particularly to Margaret O'Mara and Bruce Harley whose help in obtaining source material was invaluable. The first draft of the representational theory of classical conditioning was done during the month that I was at the Center for Interdisciplinary Research (ZiF) at the University of Bielefeld as a member of the workgroup on "Perception and Action." I am grateful for the support there and for the atmosphere, which was ideal for work that required prolonged periods of intense concentration. I am grateful also to Jrgen Engels, who refreshed my knowledge of matrix algebra. Many colleagues have read and commented critically on drafts of various parts of the manuscript and/or discussed the material with me at length. Heartfelt gratitude for comments and discussion goes to Ken Cheng, Russ Church, Tom Collett, Ruth Colwill, Nelson Donegan, Rochel Gelman, Paul Glimcher, Jim Gould, Dottie Jameson, George Mandler, Jack Nachmias, Frank Norman, David Olton, Harold Pashler, Ed Pugh, Paul Rozin, Roger Shepard, Peter Shizgal, Liz Spelke, Saul Sternberg, Meg Waraczynski, and Jeff Wine. If I have inadvertently left anyone off this list, I beg forgiveness. I have profited greatly from discussion with my colleagues at the University of Pennsylvania over the years, and I am grateful to them all for the contributions they have made to my education. I acknowledge support for my research from NSF Grant BNS-8619759, which helped defray some of the costs of preparing the manuscript for publication. I am grateful to Ken Cheng and Alan Spector for calling to my attention a number of typographical errors and inaccuracies in the text and figures, which have been corrected in this paperback edition.
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