Facial Memory Is Kernel Density Estimation (Almost)

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) MMOM, 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 modelin MDS space predicts the observed "morph familiarity inversion" effect, in which the subjects' false alarm rate for morphs between similarfaces is higher than their hit rate for many of the studied faces. This evidence is consistent with the hypothesis that human memory forfaces is a kernel density estimation task, with the caveat that distinctive facesrequire larger kernels than do typical faces.