14.1823, Review: Neurolinguistics: Arbib, ed. (2002)

Tue Jul 1 15:26:34 UTC 2003

LINGUIST List:  Vol-14-1823. Tue Jul 1 2003. ISSN: 1068-4875.

Subject: 14.1823, Review: Neurolinguistics: Arbib, ed. (2002)

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1)
Date:  Mon, 30 Jun 2003 11:34:49 +0000
From:  terry at linguistlist.org
Subject: The Handbook of Brain Theory and Neural Networks.

-------------------------------- Message 1 -------------------------------

Date:  Mon, 30 Jun 2003 11:34:49 +0000
From:  terry at linguistlist.org
Subject: The Handbook of Brain Theory and Neural Networks.

The Handbook of Brain Theory and Neural Networks, 2nd ed.
Arbib, Michael A., ed. (2002) The Handbook of Brain Theory and Neural
Networks, 2nd ed., MIT Press.

Announced at http://linguistlist.org/issues/14/14-864.html

Bilge Say, Middle East Technical University, Turkey

DESCRIPTION OF THE BOOK

This comprehensive reference volume is a collection of around 300
articles by various experts on mostly the fundamentals of
neuroscientific brain theory, neural modeling for neuroscience as well
as for higher level cognitive processes and selected applications of
artificial neural networks. These articles are complemented by
articles on the mathematics of the architectures and dynamics of
artificial neural networks.  The Handbook consists of three
parts. Part I and Part II, written by Arbib himself, form an
introductory passage into the bulk of 285 articles in Part III. Part I
is a brief and condensed introduction to the elements of brain theory
and neural networks. The biological neuron and its artificial models
are introduced followed by a general introduction to the history and
styles of neural modeling and a concluding section on artificial
neural networks as dynamic and adaptive systems.  Part II states 22
roadmaps organized under 8 major roadmaps, each roadmap consisting of
a subset of articles in Part III selected within that interest area.
For each roadmap, Arbib has written an introductory guided tour,
giving some background on the theme of the roadmap and previewing how
each article contributes to that interest area. Additionally, there is
a meta-map introducing the 8 major roadmaps: Grounding Models of
Neurons and Networks; Brain, Behaviour, and Cognition; Psychology,
Linguistics, and Artificial Intelligence; Biological Neurons and
Networks; Dynamics and Learning in Artificial Networks; Sensory
Systems; Motor Systems; and Applications, Implementations, and
Analysis.  All articles in Part III (each of 3-6 pages of length,
double column) have an Introduction section where the subject is
introduced in an accessible manner and basic research pointers are
given, and a Discussion section at the end, where a critical
evaluation of the research in the subject area of the article is
presented. In between, major contributions to research in the area of
the article are reviewed, sometimes concentrating on the author(s)'
own work, sometimes presented in a more comparative framework. Each
article's main text is followed by three intra-Handbook pieces of
information: Roadmap, the roadmaps this article is related with;
Background, the articles in the Handbook that might serve as
background reading for the article and Related Readings, related
articles in the Handbook. These are further followed by a list of
references, where major references are marked by a black diamond.  The
sheer size of the Handbook makes it impossible to review each article
so I have decided to review only the 16 articles in the Linguistics
roadmap. Of course, there will be other articles of interest for each
reader; the roadmaps in Part II make it easier to form one's own
roadmap.  Language Processing (Shillcock) is a review of connectionist
language processing models for both spoken and written
language. Written with a cognitive science perspective, especially the
strengths of such models in modeling change, namely, language
acquisition, language breakdown and language evolution are
emphasized. Emergence of complex linguistic behaviour from basic
building blocks and gradedness of linguistic phenomena are
demonstrated by example models. In Optimality Theory in Linguistics,
Zuraw reviews the basic mechanisms of Optimality Theory and extensions
such as stochastic constraint rankings.  Advantages and disadvantages
are presented alongside a brief review of how Optimality Theory has
been inspired by neural networks. In Constituency and Recursion in
Language, Christiansen and Chater reevaluate constituency and
recursion in language from a connectionist perspective. They review
various ways that these two concepts can be modeled in connectionist
models and the implications of such models for language processing.
Neurolinguistics (Gordon) introduces neurolinguistics as the study of
all aspects of brain and language: basic methods and models, and how
those models are extended by data obtained from normal humans as well
as those with language deficits. In Reading, Holden and Van Orden
review psycholinguistic experimental research and connectionist
modeling on reading. They concentrate on the dichotomy as to whether
word recognition is analytic or synthetic (or holistic) and show how
nonlinear dynamic view of language processing can help resolve this
issue. In Imaging the Grammatical Brain, Grodzinsky gives an account
of how studies on lesioning data and functional neuroimaging can help
shape hypotheses on the basic units of analysis in language and their
neural plausibility.  In Speech Processing: Psycholinguistics, Chater
and Christiansen show how connectionist models can bridge the gap
between experimental studies of psycholinguistics within brain theory
and traditional linguistic accounts of language processing on issues
such as modularity, concentrating on speech segmentation and aural
word recognition. In Speech Production, Byrd and Saltzman summarize
current research on speech production and show how a dynamic
perspective brought about by connectionist models can blur the
distinction between phonology and phonetics. Fowler, in Motor Theories
of Perception, evaluate the hypothesis of motor systems being used in
perception using motor theory of speech perception as a principle
example. The studies presented in Language Evolution: The Mirror
System Hypothesis (Arbib) can be taken as evidence for a motor theory
of speech perception as well as for a theory of language evolution:
mirror neurons found in the premotor cortex of the monkey (akin to
language area in human brain) respond both when the monkey performs an
action or when it perceives someone else doing the same action.  In
Language Evolution and Change, Christiansen and Dale review the role
of connectionist modeling as tests for hypotheses about language
evolution, language variation and change. Language Acquisition (Mac
Whinney) is an exploration of how connectionist models have challenged
(and have been challenged by) Chomskyan assumptions about a language
acquisition device. Almor, in Past Tense Learning, reveals the past
tense debate, various theoretical issues raised as a result of
connectionist modeling of linguistic processes and structures for the
acquisition of past tense inflectional forms.  In Speech Recognition
Technology, Beaufays et al. review some neural network models that are
used in large vocabulary, continous speech recognition. Convolutional
Networks for Images, Speech and Time Series (LeCun and Bengio) is a
presentation of a specific, biologically inspired neural network
architecture successfully used in applications such as handwritten
character or speech recognition. Likewise, Bourlard and Bengio in
Hidden Markov Models, introduce a specific architecture, namely hidden
Markov models, as a special case of stochastic finite state automata
and outline the benefits of their hybrid usage with artificial neural
networks.  It is stated in the Handbook that the second edition is an
extensive update of the first: of the 266 articles in the first
edition, only 9 have remained unchanged; all others have been updated
or completely rewritten. About one third of the articles in this
edition are on new topics; the emphasis of the shift in coverage has
been from applications of artificial neural networks to cognitive
modeling, including language and especially modeling for computational
neuroscience.  It is announced by the publisher that an online version
of the Handbook will become available in August on CogNet
(http://cognet.mit.edu/), MIT Press' electronic resources and
community tools site for researchers in cognitive and brain sciences.

CRITICAL EVALUATION

The inspiration to this comprehensive Handbook is stated by its editor
as the two great questions: ''How does the brain work?'' and ''How can
we build intelligent machines?''. The collection of articles answer
the second question more indirectly, mostly in the form of selected
applications and mechanisms of neural network modeling. To me, the
main attraction the Handbook brings is the shareability and mutual
understandability of resources and knowledge among three, sometimes
disparate approaches that take neural function and form as their
starting points: computational neuroscience-''systematic use of
mathematical analysis and computer simulation to provide better models
of the structure and function of living brains''-, connectionist
computational modeling for cognition-studies modeling human cognitive
processes in terms of artificial neural networks, usually using more
abstract and simple models than computational neuroscience- and neural
computing-''design of machines (software and hardware) with circuitry
inspired by but which need not faithfully emulate the neural networks
of brains''.
The articles are concise, well-edited and comprehensible given the
right background. For the interested linguist though, some more
technical background presented in a more gentle and accessible manner
than the Handbook might be necessary to understand some of the
articles in full. Thus, complementary resources such as Lytton (2002)
and McLeod et al. (1998) might be advisable. The roadmaps and the
various ways of cross-referencing used make the Handbook suitable for
exploratory as well as reference reading. The only thing I found
missing was a glossary, or at least some highlighting of definitional
references in the Subject Index, given the vast terminology covered by
the Handbook.

REFERENCES

Lytton, W. W. (2002). From Computer to Brain: Foundations of
Computational Neuroscience. Springer.

McLeod,P., K. Plunkett and E. T. Rolls (1998).  Introduction to
Connectionist Modelling of Cognitive Processes. Oxford University
Press.

ABOUT THE REVIEWER

Bilge Say is a faculty member at the Cognitive Science Program of
Middle East Technical University, Turkey. Her research interests lie
mainly in Computational modeling for cognitive science and
computational linguistics.

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