CALL FOR BOOK REVIEWERS
Below is the Abstract of a Precis of SUBSYMBOLIC NATURAL LANGUAGE
PROCESSING by Risto Mikkulainen (Precis retrievable from
ftp://princeton.edu/pub/harnad/Psycoloquy
filename: psyc.94.5.46.language-network.1.miikkulainen)
This book has been selected for multiple review in PSYCOLOQUY. If you
wish to submit a formal book review (see Instructions following Precis)
please write to psyc(a)pucc.bitnet indicating what expertise you would
bring to bear on reviewing the book if you were selected to review it.
(If you have never reviewed for PSYCOLOQUY or Behavioral & Brain
Sciences before, it would be helpful if you could also append a copy of
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you will be sent a copy of the book directly by the publisher (please
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without invitation, but all reviews will be refereed. The author will
reply to all accepted reviews.
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psycoloquy.94.5.46.language-network.1.miikkulainen Monday 8 Aug 1994
ISSN 1055-0143 (34 paragraphs, 1 fig, 1 note, 16 references, 609 lines)
PSYCOLOQUY is sponsored by the American Psychological Association (APA)
Copyright 1994 Risto Miikkulainen
Precis of:
SUBSYMBOLIC NATURAL LANGUAGE PROCESSING:
AN INTEGRATED MODEL OF SCRIPTS, LEXICON, AND MEMORY
Cambridge, MA: MIT Press, 1993
15 chapters, 403 Pages
Risto Miikkulainen
Department of Computer Sciences
The University of Texas at Austin
Austin, TX 78712
risto(a)cs.utexas.edu
ABSTRACT: Distributed neural networks have been very successful in
modeling isolated cognitive phenomena, but complex high-level
behavior has been amenable only to symbolic artificial intelligence
techniques. Aiming to bridge this gap, this book describes
DISCERN, a complete natural language processing system implemented
entirely at the subsymbolic level. In DISCERN, distributed neural
network models of parsing, generating, reasoning, lexical
processing and episodic memory are integrated into a single system
that learns to read, paraphrase, and answer questions about
stereotypical narratives. Using DISCERN as an example, a general
approach to building high-level cognitive models from distributed
neural networks is introduced, and the special properties of such
networks are shown to provide insight into human performance. In
this approach, connectionist networks are not only plausible models
of isolated cognitive phenomena, but also sufficient constituents
for generating complex, high-level behavior.
KEYWORDS: computational modeling, connectionism, distributed neural
networks, episodic memory, lexicon, natural language processing,
scripts.
XII. TABLE OF CONTENTS
PART I Overview
1 Introduction
2 Background
3 Overview of DISCERN
PART II Processing Mechanisms
4 Backpropagation Networks
5 Developing Representations in FGREP Modules
6 Building from FGREP Modules
PART III Memory Mechanisms
7 Self-Organizing Feature Maps
8 Episodic Memory Organization: Hierarchical Feature Maps
9 Episodic Memory Storage and Retrieval: Trace Feature Maps
10 Lexicon
PART IV Evaluation
11 Behavior of the Complete Model
12 Discussion
13 Comparison to Related Work
14 Extensions and Future Work
15 Conclusions
APPENDICES
A Story Data
B Implementation Details
C Instructions for Obtaining the DISCERN Software
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Below is the abstract of a forthcoming target article by:
A.G. Feldman & M.F. Levin
on:
POSITIONAL FRAMES OF REFERENCE IN MOTOR CONTROL
This article has been accepted for publication in Behavioral and Brain
Sciences (BBS), an international, interdisciplinary journal providing
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____________________________________________________________________
POSITIONAL FRAMES OF REFERENCE IN MOTOR CONTROL: ORIGIN AND USE
Anatol G. Feldman (1,2,4) & Mindy F. Levin (2,3,4)
Institute of Biomedical Engineering, University of Montreal (1)
Research Centre, Rehabilitation Institute of Montreal, H3S 2J4 (2)
School of Rehabilitation, University of Montreal (3)
Centre for Research in Neurological Sciences, University of Montreal (4)
EMAIL:Feldman@ere.umontreal.ca
KEYWORDS: motor control, frames of reference, motoneurons, control
variables, proprioception, kinaesthesis, equilibrium points,
multi-muscle systems, pointing, synergy, redundancy problem.
ABSTRACT: A hypothesis about sensorimotor integration (the lambda
model) is described and applied to movement control and
kinesthesia. The nervous system organizes positional frames of
reference for the sensorimotor apparatus and produces active
movements by shifting frames in terms of spatial coordinates.
Kinematic and electromyographic patterns are not programmed but
emerge from the dynamic interaction of the system's components,
including external forces, within the designated frame of
reference. Motoneuronal threshold properties and proprioceptive
inputs to motoneurons may be important components in the
physiological mechanism which produces positional frames of
reference. The hypothesis that intentional movements are produced
by shifting the frame of reference is extended to multi-muscle and
multi-degrees of freedom systems by providing a solution for the
redundancy problem the allows the control of a joint alone or in
combination with other joints to produce any desired limb
configuration and movement trajectory. For each motor behavior, the
nervous system uses a strategy which minimizes the number of
changeable control variables and keep sthe parameters of these
changes invariant. This is illustrated by examples of simulated
kinematic and electromyographic signals from single- and
multi-joint arm movements produced by patterns of control
variables. Empirical support is provided and additional tests are
suggested. The model is contrasted with others based on the ideas
of programming of motoneuronal activity, muscle forces, stiffness
or movement kinematics.
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