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Robert Luedeman, Ken Nakayama; Transferring localized facial learning across all of face space. Journal of Vision 2008;8(6):182. doi: https://doi.org/10.1167/8.6.182.
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To accomplish the representation of the vast number of known faces, Valentine (1991) proposed that the brain codes faces as points in a multi-dimensional face space, where the axes correspond to facial attributes. Later researchers (O'Toole, Abdi, Defenbacher, & Valentin, 1993; Wilson & Diasconescu, 2006) have suggested that the axes are formed by extracting the principal components (PC) from a population of faces. As this theory has taken hold, various properties of face space have been examined. For instance, Wilson (2006) showed that learning could have an effect on the properties of face space. Recognition thresholds were significantly better in the regions surrounding learned faces than they were in the regions surrounding novel faces. This study demonstrates a similar effect, wherein participants were shown faces that consistently varied along a particular dimension in face space. Differences between pre- and post-learning thresholds for other faces randomly scattered about face space confirmed that it is indeed possible to transfer increased perceptual discrimination abilities not just to nearby faces, but across all of face space.
O'Toole, A. J., Abdi, H., Deffenbacher, K. A., & Valentin, D. (1993). Low-dimensional representation of faces in higher dimensions of the face space. Journal of the Optical Society of America, 10, 405-411
Valentine, T. (1991). A unified account of the effects of distinctiveness, inversion, and race in face recognition. Quarterly Journal of Experimental Psychology, 43A, 161-204.
Wilson, Hugh R., Diaconescu, Andreea (2006). Learning alters local face space geometry. Vision Reasearch, 46, 4143-4151.
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