Blended memory for faces: preprint

[Gary Cottrell]

The following paper has been accepted for publication
in NIPS-11. It is available from my web page (given
below):

Dailey, Matthew N., Cottrell, Garrison W. and Busey, Thomas A. (1999)
Facial memory is kernel density estimation (almost).

To appear in Advances in Neural Information Processing Systems
11, MIT Press, Cambridge, MA.

We compare the ability of three exemplar models, each
using three different face stimulus representations, to
account for the probability a human subject responded
``old'' in an old/new facial memory experiment.  The
models are 1) the Generalized Context Model, 2) a
probabilistic sampling model, and 3) a novel model related
to kernel density estimation that explicitly encodes
stimulus distinctiveness.  The representations are 1)
positions of stimuli in MDS ``face space,'' 2) projections
of test faces onto the eigenfaces of the study set, and 3)
a representation based on response to a grid of Gabor
filters.  Of the 9 model/representation combinations, only
the distinctiveness model in MDS space predicts the
observed ``morph familiarity inversion'' effect, in which
subjects' false alarm rate for morphs between similar
parents is higher than their hit rate for the studied
parents of the morphs.  This evidence is consistent with
the hypothesis that human memory for faces is a kernel
density estimation task, with the caveat that distinctive
faces require larger kernels.


Gary Cottrell 619-534-6640 FAX: 619-534-7029 
Faculty Assistant Joy Gorback: 619-534-5948
Computer Science and Engineering 0114
IF USING FED EX INCLUDE THE FOLLOWING LINE:          "Only connect"
3101 Applied Physics and Math Building
University of California San Diego			-E.M. Forster
La Jolla, Ca. 92093-0114

Email: gary at cs.ucsd.edu or gcottrell at ucsd.edu
Home page: http://www-cse.ucsd.edu/~gary/