Facial Memory Is Kernel Density Estimation (Almost)

Part of Advances in Neural Information Processing Systems 11 (NIPS 1998)

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Matthew Dailey, Garrison Cottrell, Thomas Busey


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 mem(cid:173) ory experiment. The models are 1) the Generalized Context Model, 2) SimSample, a probabilistic sampling model, and 3) MMOM, a novel model related to kernel density estimation that explicitly encodes stim(cid:173) ulus 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 distinc(cid:173) tiveness model in MDS space predicts the observed "morph familiarity inversion" effect, in which the subjects' false alarm rate for morphs be(cid:173) tween similar faces is higher than their hit rate for many of the studied faces. This evidence is consistent with the hypothesis that human mem(cid:173) ory for faces is a kernel density estimation task, with the caveat that dis(cid:173) tinctive faces require larger kernels than do typical faces.