preprint available in neuroprose archive

[David Plaut]

******************* PLEASE DO NOT FORWARD TO OTHER BBOARDS *******************

	      Relearning after Damage in Connectionist Networks:
		    Implications for Patient Rehabilitation

				David C. Plaut
			   Department of Psychology
			  Carnegie Mellon University

       To appear in the Proceedings of the 14th Annual Conference of the
	   Cognitive Science Society, Bloomington, IN, August, 1992.

				   Abstract

Connectionist modeling is applied to issues in cognitive rehabilitation,
concerning the degree and speed of recovery through retraining, the extent of
generalization to untreated items, and how treated items are selected to
maximize this generalization.  A network previously used to model impairments
in mapping orthography to semantics is retrained after damage.  The degree of
relearning and generalization varies considerably for different lesion
locations, and has interesting implications for understanding the nature and
variability of recovery in patients.  In a second simulation, retraining on
words whose semantics are atypical of their category yields more generalization
than retraining on more prototypical words, suggesting a surprising strategy
for selecting items in patient therapy to maximize recovery.

To retrieve:

unix> ftp 128.146.8.62			# archive.cis.ohio-state.edu
Name: anonymous
Password: <identification>
ftp> cd pub/neuroprose
ftp> binary
ftp> get plaut.relearning.ps.Z
ftp> quit
unix> zcat plaut.relearning.ps.Z | lpr

Thanks again to Jordan Pollack for maintaining the archive....

David Plaut						        plaut+ at cmu.edu
Department of Psychology					  412/268-5145
Carnegie Mellon University
Pittsburgh, PA  15213-3890