Developing Race Categories in Infancy via Bayesian Face Recognition.

The other-race effect emerges during infancy following the perceptual narrowing of face recognition. Other-race faces that were previously discriminable in early infancy cannot be distinguished by older infants. Presently, I discuss a Bayesian model of this process that posits that the other-race effect may be a consequence of learning to distinguish between intra-personal variation (changes to face appearance that preserve identity) and extra-personal variation (changes that do not preserve identity) in a visual environment in which a subset of race categories dominate. I demonstrate that race categories, which I have previously argued are a critical pre-cursor to the emergence of the other-race effect in infancy, are a natural by-product of this model. Perceptual narrowing for race may thus be a natural consequence of visual experience and the estimation of face variability based on a growing number of exemplars. I describe the basic architecture of the model, its applicability to a range of visual learning scenarios, and identify critical choices one faces in applying the model to a specific perceptual task. Despite the success of the model in accounting for these behavioral results, I conclude by identifying important shortcomings of the model and describe important challenges for future efforts to characterize the development of the other-race effect computationally.