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Abstract:
We compare the ability of three exemplar-based memory 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) SimSample, a probabilistic sampling model, and 3)
DBM, 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 filter jets. Of
the 9 model/representation combinations, only the distinctiveness
model in MDS space predicts the observed ``morph familiarity
inversion'' effect, in which the subjects' false alarm rate for
morphs between similar faces is higher than their hit rate for many
of the studied faces. 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 than do typical faces.
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