Simulating the “other-race effect” as a problem in perceptual learning

We report a series of simulations on the well-known “other-race effect.” We trained an autoassociative network on a majority and a minority race of faces, and tested the model’s ability to process faces from the two races in different ways. First, the model was better able to reconstruct unlearned majority faces than minority faces. Secondly, the average inter-face similarity was higher for the reconstructed minority faces than for reconstructed majority faces, indicating that the model was coding the majority faces more distinctively than the minority faces. These results held for Caucasian faces as the majority race and Japanese faces as the minority race and vice versa. Thirdly, we simulated a recognition task for sameand other-race faces by using a face history matrix and a recognition task matrix with equal numbers of Caucasian and Japanese faces, and reconstructing these faces as a weighted combination of the two matrices. Using Caucasian faces as the majority race, the model was better able to discriminate learned from new Caucasian faces than learned from new Japanese faces. We discuss the results in terms of perceptual tuning to information useful for processing faces of a single race.