preprints and thesis TR available

[David_Plaut@K.GP.CS.CMU.EDU]

I've placed two papers in the neuroprose archive.  Instructions for retrieving
them are at the end of the message.  (Thanks again to Jordan Pollack for
maintaining the archive.)

The first (plaut.thesis-summary.ps.Z) is a 15 page summary of my thesis,
entitled "Connectionist Neuropsychology: The Breakdown and Recovery of Behavior
in Lesioned Attractor Networks" (abstract below).

For people who want more detail, the second paper (plaut.dyslexia.ps.Z) is a
119 page TR, co-authored with Tim Shallice, that presents a systematic analysis
of work by Hinton & Shallice on modeling deep dyslexia, extending the approach
to a more comprehensive account of the syndrome.  FTP'ers should be forewarned
that the file is about 0.5 Mbytes compressed, 1.8 Mbytes uncompressed.

For true die-hards, the full thesis (325 pages) is available as CMU-CS-91-185
from
	Computer Science Documentation
	School of Computer Science
	Carnegie Mellon University
	Pittsburgh, PA 15213-3890
	reports at cs.cmu.edu

To defray printing/mailing costs, requests for the thesis TR must be
accompanied by a check or money order for US$10 (domestic) or US$15 (overseas)
payable to "Carnegie Mellon University."

Enjoy,
-Dave

			Connectionist Neuropsychology:
		    The Breakdown and Recovery of Behavior
			in Lesioned Attractor Networks

				David C. Plaut

An often-cited advantage of connectionist networks is that they degrade
gracefully under damage.  Most demonstrations of the effects of damage and
subsequent relearning in these networks have only looked at very general
measures of performance.  More recent studies suggest that damage in
connectionist networks can reproduce the specific patterns of behavior of
patients with neurological damage, supporting the claim that these networks
provide insight into the neural implementation of cognitive processes.
However, the existing demonstrations are not very general, and there is little
understanding of what underlying principles are responsible for the results.
This thesis investigates the effects of damage in connectionist networks in
order to analyze their behavior more thoroughly and assess their effectiveness
and generality in reproducing neuropsychological phenomena.
   We focus on connectionist networks that make familiar patterns of activity
into stable ``attractors.''  Unit interactions cause similar but unfamiliar
patterns to move towards the nearest familiar pattern, providing a type of
``clean-up.''  In unstructured tasks, in which inputs and outputs are
arbitrarily related, the boundaries between attractors can help ``pull apart''
very similar inputs into very different final patterns.  Errors arise when
damage causes the network to settle into a neighboring but incorrect attractor.
In this way, the pattern of errors produced by the damaged network reflects the
layout of the attractors that develop through learning.
   In a series of simulations in the domain of reading via meaning, networks
are trained to pronounce written words via a simplified representation of their
semantics.  This task is unstructured in the sense that there is no intrinsic
relationship between a word and its meaning.  Under damage, the networks
produce errors that show a distribution of visual and semantic influences quite
similar to that of brain-injured patients with ``deep dyslexia.''  Further
simulations replicate other characteristics of these patients, including
additional error types, better performance on concrete vs.\ abstract words,
preserved lexical decision, and greater confidence in visual vs.\ semantic
errors.  A range of network architectures and learning procedures produce
qualitatively similar results, demonstrating that the layout of attractors
depends more on the nature of the task than on the architectural details of the
network that enable the attractors to develop.
   Additional simulations address issues in relearning after damage: the speed
of recovery, degree of generalization, and strategies for optimizing recovery.
Relative differences in the degree of relearning and generalization for
different network lesion locations can be understood in terms of the amount of
structure in the subtasks performed by parts of the network.
   Finally, in the related domain of object recognition, a similar network is
trained to generate semantic representations of objects from high-level visual
representations.  In addition to the standard weights, the network has
correlational weights useful for implementing short-term associative memory.
Under damage, the network exhibits the complex semantic and perseverative
effects of patients with a visual naming disorder known as ``optic aphasia,''
in which previously presented objects influence the response to the current
object.  Like optic aphasics, the network produces predominantly semantic
rather than visual errors because, in contrast to reading, there is some
structure in the mapping from visual to semantic representations for objects.
   Taken together, the results of the thesis demonstrate that the breakdown and
recovery of behavior in lesioned attractor networks reproduces specific
neuropsychological phenomena by virtue of the way the structure of a task
shapes the layout of attractors.

unix> ftp 128.146.8.52
Name: anonymous
Password: neuron
ftp> cd pub/neuroprose
ftp> binary
ftp> get plaut.thesis-summary.ps.Z (or plaut.dyslexia.ps.Z)
ftp> quit
unix> zcat plaut.thesis-summary.ps.Z | lpr
------------------------------------------------------------------------------
David Plaut						       dcp+ at cs.cmu.edu
Department of Psychology					  412/268-5145
Carnegie Mellon University
Pittsburgh, PA  15213-3890