Abstract
Several studies (e.g., Bernstein et al., 2007) have demonstrated that social categorization in the absence of physical differences is sufficient to elicit recognition biases mimicking the other-race effect, thus suggesting that in-group biases alone underlie race-based deficits in face processing. If this is the case, then social categories alone should elicit an in-group advantage in another task that shows a strong benefit for own-race faces: a sorting task in which participants are asked to recognize the same identity across superficial changes (e.g., hairstyle, expression). Laurence et al. (2015) recently reported that when asked to sort images into piles representing different identities, participants sort photographs of two other-race identities into more piles than two own-race identities. In the present study, we examined whether this finding would replicate for faces that differed only in terms of social category. Caucasian participants (n = 48) were shown 40 photographs of two unfamiliar Caucasian identities (20 photographs/model) and asked to sort them into piles based on the number of identities they believed were present. Half of the participants were told that the faces were those of students currently attending their private university (a social in-group), whereas half were told that the faces were those of students currently attending a public university located out of province (a social out-group). Despite indicating that they strongly identified with their university affiliation, participants sorted the photographs into a comparable number of identities for in-group (M = 9.00, median = 8.50, range = 2-20) and out-group (M = 9.87, median = 9.50, range = 2-21) faces, p > .50, thus showing no in-group advantage in tasks examining perceptions of within-person variability. These results suggest that social categorical models of the other-race effect have limited explanatory power, as they cannot account for race-based biases in tasks outside of recognition memory.
Meeting abstract presented at VSS 2016