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See also:
http://www.medparse.com/gwmcv.htm .............
http://www.medparse.com/rvgodell.htm .............
http://www.medparse.com/rvneuroc.htm .............
http://www.netautopsy.org/rvflatte.htm
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Published in: Neurocomputing. 2001 Jan;42(1):.
Scarborough D, Sternberg S.
An Invitation to Cognitive Science. Second Edition.
Methods, Models, and Conceptual Issues. Volume 4.
Cambridge, MA: A Bradford Book. The MIT Press. 1998.
ISBN 0-262-65046-0, 950 pages.
This book is an overview of cognitive science,
accessible to the advanced undergraduate, that
covers all the major areas of this important field.
The book is written by multiple authors,
but is unified by an editor's introduction to each chapter,
and by a consistency of organization and style.
The book is perhaps best regarded as a companion
to an ordinary textbook.
In many cases, the author presents his/her own opinion
about an area, and defers alternate viewpoints and technical details
to the reference section.
Each chapter is intended to be read as a self-contained unit,
if necessary, in isolation from other chapters.
This means that the fundamental intellectual assumptions
of an area are summarized at the beginning of each chapter,
and each chapter achieves some level of closure at the end.
For such a long volume (over 900 pages), this is an important constraint.
Unlike Sheherezade's captor, who had to wait anxiously each night
for the conclusion of last night's episode,
the reader can set this book aside
for a few nights without losing track of the story as a whole.
Some chapters are highly informative, and attempt to provide
an overview of an entire area of investigation or methodology.
Prof. Wickens' chapter on statistical methods covers all
the major topics that one would expect in an introductory course:
a sample experiment; the role of statistics in evaluating research results;
the problem of variability; population versus samples;
hypothesis testing; decision theory; analysis of variance;
contingency tables; correlation; and multivariate analysis.
Detailed formulas and calculations obviously cannot be covered
in such a short space. However, the reader emerges from this chapter
with a good sense of what statistics is all about: why, how-to,
and how-far you can reach with statistical reasoning.
Other chapters are polemical rather than instructional.
Prof. Lewontin's chapter assumes a single point of view
on the evolution of cognition: it cannot be proved, period.
The author repeats this conviction in the form of an intellectual
spat with the editor's reviewer as the final section in the chapter.
In building his case, Prof. Lewontin lays out many of the cardinal arguments
of evolutionary theory, and explains how they might (but do not)
apply to the evolution of cognition. These arguments include:
analogy/homology arguments, such as the panda's thumb and the whale's tail;
the evolution of moth coloring during the English industrial revolution;
and linguistic studies in primates and cetaceans. Whether or not
the reader accepts the author's conclusion, the argument is impressive,
and the intellectual tour alone makes the chapter worth reading.
Another chapter that is richly laden with low-hanging
intellectual fruit is Prof. Anderson's chapter on learning
arithmetic with a neural network. Although clearly
comfortable with convoluted scientific ideas, Prof. Anderson
has a peppy writing style, and a remarkable ability to reduce
complex concepts to their essentials.
The answers to many of those nagging questions
about science and mathematics that you were embarrassed to ask in class,
are dashed off in a paragraph or two in this chapter.
For example: what is the difference beween a biological neural net
(as we currently understand it) and the mathematical formalism?
What is "associative inference" in humans and in computers?
How might one envision a mathematical proof, such as the
infinite-prime-number theorem of number theory?
How did Einstein envision special relativity?
Readers of this book who are most interested in neural nets
as computing devices will be disappointed. The book discusses
some computer algorithms, but is aimed primarily at understanding
biological processes. We learn about how humans distinguish, say,
a lower-case-c from a lower-case-e, but not how optical character
recognition software solves the same problem; how a human finds
a face in a crowd, but not how computer software addresses this problem.
The short biographies at the end of each chapter are fun to read.
Some of the authors seem to have led fairly humdrum academic lives;
but others' lives have zigzagged along interesting pathways.
One author read science fiction while attending elementary school
in a "friendly, relaxed backwater";
squandered his predoctoral years seeking intelligence among garden slugs;
and then spent two exciting postdocs studying human cognition.
This book will appeal to the intelligent but discontented
undergraduate or graduate student in fields akin to cognitive science,
including psychology, philosophy, mathematics, and biophysics.
There is a range of opinions, presented aggressively by their champions
in academia; current controversies are bared and explored;
yet behind it all, the scientific underpinnings are solid.
I highly recommend the book.
Last updated: 1/6/2006, by G. William Moore, MD, PhD.