Race-specific norms for coding face identity and a functional role for norms.

Models of face perception often adopt a framework in which faces are represented as points or vectors in a multidimensional space, relative to the average face that serves as a norm for encoding. Since faces are very similar in their configuration and share many visual properties, they could be encoded in one common space against one norm. However, certain face properties may result in grouping and "subclassification" of similar faces. We studied the processing of faces of different races, using high-level aftereffects, where exposure to one face systematically distorts the perception of a subsequently viewed face toward the "opposite" identity in face space. We measured identity aftereffects for adapt-test pairs that were opposite relative to race-specific (Asian and Caucasian) averages and pairs that were opposite relative to a "generic" average (both races morphed together). Aftereffects were larger for race-specific compared to mixed-race adapt-test pairs. These results suggest that race-specific norms are used to code identity because aftereffects are generally larger for adapt-test pairs drawn from trajectories passing through the norm (opposite pairs) than for those that do not. We also found that identification thresholds were lower when targets were distributed around race-specific averages than around the mixed-race average, suggesting that norm-based face encoding may play a functional role in facilitating identity discrimination.