Modeling the Other-race Advantage with PCA

Modeling the Other-race Advantage with PCA AfmZakaria Haque (ahaque@cs.ucsd.edu) Department of Computer Science & Engineering, 9500 Gilman Dr. La Jolla, CA, 92093 Garrison W. Cottrell (gary@ucsd.edu) Department of Computer Science & Engineering, 9500 Gilman Dr. La Jolla, CA, 92093 still leaves the question that how the need for individuation can cause the ORE. In this section we will discuss two dominant hypotheses of the ORE. Both of them concentrate on the feature selection scheme humans use for processing faces of same and different races. Abstract The Other-race effect (ORE) refers to the well-known phenomenon of people being less accurate in recognizing faces of a different race. One popular hypothesis is that we learn to use face-features that are optimal for individuating faces of our own race; thus reducing the recognition accuracy for faces of a different race. However this hypothesis has not been able to explain some advantages other-race faces have in certain tasks. For example, some recent experiments showed that in a visual search task other-race faces are found faster than same race faces when the subjects show the ORE. A race based feature selection hypothesis where deviation from the familiar race is treated as an explicit part of the encoding has been proposed to explain this other-race advantage. In this paper, we argue that the other-race advantage can be explained without this assumption. We present the results from our simulations that suggest that the other-race advantage is an inherent characteristic of an optimal feature selection model. Optimal Feature Selection Optimal feature selection treats the ORE as people’s inability to generalize their feature selection from the same race faces to the other-race faces. People select face-features that are optimal for identifying each individual. Since generally people are most exposed to faces of their own race, their feature selection is biased towards optimizing the recognition of this class of faces. Assuming faces of different races vary along different dimensions, their feature selection does not capture the variations of other-race faces well, reducing their accuracy in other-race face recognition. In some ways this hypothesis is a closely related with perceptual expertise. The hypothesis can be interpreted as an effect of specialization for same race faces within the domain of face expertise (Tanaka et al., in press). Several studies (O'Toole et al., 1991, 1994) have used Principal component analysis (Jolliffe, 1986) to model optimal feature selection. PCA finds a linear transformation to a new set of dimensions that maximizes the variance of the data. Linearity might be an undesirable constraint, but the simplified computation is well worth the compromise. Moreover PCA is a neurobiologically plausible means of feature selection since simple networks employing hebbian learning can learn to extract equivalent features (Sanger, 1989). In our simulation we modeled optimal feature selection with PCA. Introduction It has long been known that people recognize faces from their own racial group with greater accuracy than faces from another racial group. This is known as the other-race effect (ORE), cross-race effect or own-race bias. Several meta- analyses of large number of studies in face recognition have found a strong ORE (Bothwell, Brigham, & Malpass, 1989; Shapiro & Penod, 1986). There is naturally a strong agreement that the ORE is somehow caused by the learning history of each individual. Chance et al (1982) found that six year old children did not show a significant other-race effect; but for older participants the degree of the ORE increased with age. Although there is no conclusive answer to the question of how experience with faces can cause the ORE, there are two dominant hypotheses. Race Dependent Feature Selection The race dependent feature selection hypothesis assumes an asymmetric feature selection scheme for faces. Levin (2000) proposed that for other-race faces race specifying information is encoded at the expense of individuating information. Loss of individuating information reduces recognition accuracy for other-race faces, causing the ORE. In this hypothesis, for other-race faces, feature selection is optimal for classification by race and not for identity. Race specifying information, which can be treated as the What Causes the ORE Feingold (1914) suggested that other things being equal, the other-race effect depends on the contact with people of the other race. However, several studies (Shepard, 1981; Valentine, Chiroro, & Dixon, 1995) argued against this contact hypothesis. They suggested that contact for individuation could be more significant than mere contact. It