An own-age bias in face recognition for children and older adults

A framework is outlined in which individual faces are assumed to be encoded as a point in a multidimensional space, defined by dimensions that serve to discriminate faces. It is proposed that such a framework can account for the effects of distinctiveness, inversion, and race on recognition of faces. Two specific models within this framework are identified: a norm-based coding model, in which faces are encoded as vectors from a population norm or prototype, and a purely exemplar-based model. Both models make similar predictions, albeit in different ways, concerning the interactions between the effects of distinctiveness, inversion and race. These predictions were supported in five experiments in which photographs of faces served as stimuli. The norm-based coding version and the exemplar-based version of the framework cannot be distinguished on the basis of the experiments reported, but it is argued that a multidimensional space provides a useful heuristic framework to investigate recognition of faces. Finally, the relationship between the specific models is considered and an implementation in terms of parallel distributed processing is briefly discussed.

One of the most familiar empirical phenomena associated with face recognition is the cross-race (CR) recognition deficit whereby people have difficulty recognizing members of a race different from their own. Most researchers assume that the CR deficit is caused by failure to generalize perceptual encoding expertise from same-race (SR) faces to CR faces. However, this explanation ignores critical differences in the social cognitions and feature coding priorities associated with SR and CR faces. On the basis of data from visual search and perceptual discrimination tasks, it appears that the deficit occurs because people emphasize visual information specifying race at the expense of individuating information when recognizing CR faces. In particular, it is possible to observe a paradoxical improvement in both detection and perceptual discrimination accuracy for CR faces that is limited to those who recognize them poorly. These findings support a new explanation for the CR recognition deficit based on feature coding differences between CR and SR faces, and appear incompatible with similarity-based models of face categories.