title: Simulating the 'other-race' effect with autoassociative neural networks: further evidence in favor of the face-space model.
abstract: Other-race (OR) faces are less accurately recognized than same-race (SR) faces, but faster classified by race. This phenomenon has often been reported as the 'other-race' effect (ORE). Valentine (1991 Quarterly Journal of Experimental Psychology A: Human Experimental Psychology 43 161-204) proposed a theoretical multidimensional face-space model that explained both of these results, in terms of variations in exemplar density between races. According to this model, SR faces are more widely distributed across the dimensions of the space than OR faces. However, this model does not quantify nor state the dimensions coded within this face space. The aim of the present study was to test the face-space explanation of the ORE with neural network simulations by quantifying its dimensions. We found the predicted density properties of Valentine's framework in the face-projection spaces of the autoassociative memories. This was supported by an interaction for exemplar density between the race of the learned face set and the race of the faces. In addition, the elaborated face representations showed optimal responses for SR but not for OR faces within SR face spaces when explored at the individual level, as gender errors occurred significantly more often in OR than in SR face-space representations. Altogether, our results add further evidence in favor of a statistical exemplar density explanation of the ORE as suggested by Valentine, and question the plausibility of such coding for faces in the framework of recent neuroimaging studies.
authors.affiliation: Department of Psychology, University of Glasgow, 58 Hillhead Street, Glasgow G12 8QB, Scotland, UK. r.caldara@psy.gla.ac.uk
authors.forename: Roberto
authors.initials: R
authors.lastname: Caldara
authors.forename: Abdi
authors.initials: A
authors.lastname: Hervé
mesh_terms: D044466:Asian People; D003198:Computer Simulation; D004193:Discrimination Learning; D004867:Equipment Design; D005145:Face; D005260:Female; D006801:Humans; D008297:Male; D008960:Models, Psychological; D016571:Neural Networks, Computer; D025341:Principal Component Analysis / Q000379:methods; D044469:Racial Groups; D021641:Recognition, Psychology; D012737:Sex Factors; D044465:White People