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Matthew Dailey

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2 papers

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NeurIPS Conference 1998 Conference Paper

Facial Memory Is Kernel Density Estimation (Almost)

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.

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NeurIPS Conference 1997 Conference Paper

Task and Spatial Frequency Effects on Face Specialization

Matthew Dailey
Garrison Cottrell

There is strong evidence that face processing is localized in the brain. The double dissociation between prosopagnosia, a face recognition deficit occurring after brain damage, and visual object agnosia, difficulty recognizing otber kinds of complex objects, indicates tbat face and non(cid: 173) face object recognition may be served by partially independent mecha(cid: 173) nisms in the brain. Is neural specialization innate or learned? We sug(cid: 173) gest that this specialization could be tbe result of a competitive learn(cid: 173) ing mechanism that, during development, devotes neural resources to the tasks they are best at performing. Furtber, we suggest that the specializa(cid: 173) tion arises as an interaction between task requirements and developmen(cid: 173) tal constraints. In this paper, we present a feed-forward computational model of visual processing, in which two modules compete to classify input stimuli. When one module receives low spatial frequency infor(cid: 173) mation and the other receives high spatial frequency information, and the task is to identify the faces while simply classifying the objects, the low frequency network shows a strong specialization for faces. No otber combination of tasks and inputs shows this strong specialization. We take these results as support for the idea that an innately-specified face processing module is unnecessary.

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