new paper: modeling hippocampal and neocortical contributions to recognition memory

[Ken Norman]

Dear Connectionists,

The following technical report is now available for downloading as:
ftp://grey.colorado.edu/pub/oreilly/papers/NormanOReilly01_recmem.pdf
webpage: http://psych.colorado.edu/~oreilly/pubs-abstr.html#01_recmem

  Modeling Hippocampal and Neocortical Contributions to Recognition
	  Memory: A Complementary Learning Systems Approach

	      Kenneth A. Norman and Randall C. O'Reilly

		       Department of Psychology
			University of Colorado
			  Boulder, CO 80309

		      ICS Technical Report 01-02

Abstract:

We present a computational neural network model of recognition memory
based on the biological structures of the hippocampus and medial
temporal lobe cortex (MTLC), which perform complementary learning
functions. The hippocampal component of the model contributes to
recognition by recalling specific studied details.  MTLC can not
support recall, but it is possible to extract a scalar familiarity
signal from MTLC that tracks how well the test item matches studied
items. We present simulations that establish key qualitative
differences in the operating characteristics of the hippocampal recall
and MTLC familiarity signals, and we identify several manipulations
(e.g., target-lure similarity, interference) that differentially
affect the two signals. We also use the model to address the
stochastic relationship between recall and familiarity (i.e., are they
independent), and the effects of partial vs. complete hippocampal
lesions on recognition.