Preprint: Effects of Word Abstractness in a Connectionist Model of Deep Dyslexia

[David Plaut]

The following paper is available in the neuroprose archive as
plaut.cogsci91.ps.Z.  It will appear in this year's Cognitive Science
Conference proceedings.  A much longer paper presenting a wide range of related
work is in preparation and will be announced shortly.

    Effects of Word Abstractness in a Connectionist Model of Deep Dyslexia

	David C. Plaut			Tim Shallice              
	School of Computer Science	Department of Psychology  
	Carnegie Mellon University	University College, London
	dcp at cs.cmu.edu			ucjtsts at ucl.ac.uk         

Deep dyslexics are patients with neurological damage who exhibit a variety of
symptoms in oral reading, including semantic, visual and morphological effects
in their errors, a part-of-speech effect, and better performance on concrete
than abstract words.  Extending work by Hinton & Shallice (1991), we develop a
recurrent connectionist network that pronounces both concrete and abstract
words via their semantics, defined so that abstract words have fewer semantic
features.  The behavior of this network under a variety of ``lesions''
reproduces the main effects of abstractness on deep dyslexic reading: better
correct performance for concrete words, a tendency for error responses to be
more concrete than stimuli, and a higher proportion of visual errors in
response to abstract words.  Surprisingly, severe damage within the semantic
system yields better performance on *abstract* words, reminiscent of CAV, the
single, enigmatic patient with ``concrete word dyslexia.''

To retrieve this from the neuroprose archive type the following:
unix> ftp 128.146.8.62
Name: anonymous
Password: neuron
ftp> binary
ftp> cd pub/neuroprose
ftp> get plaut.cogsci91.ps.Z
ftp> quit
unix> zcat plaut.cogsci91.ps.Z | lpr

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David Plaut				dcp+ at cs.cmu.edu
School of Computer Science		412/268-8102
Carnegie Mellon University
Pittsburgh, PA  15213-3890