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 relatd to kernel density estimation that explicity 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 hgher than their hit rate for many of the studied faces. This evidence is consistent with the hypotesis that human memory for faces is a faces is a kernel density estimatin task, with the caveat that distinctive faces require larger kernels than do typical faces.
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