Becoming Familiar With a Newly Encountered Face: Evidence of an Own-Race Advantage

Abstract

Adults' ability to match identity in images of unfamiliar faces is impaired for other- compared with own-race faces; their ability to match identity in images of familiar faces is independent of face race. Exposure to within-person variability in appearance plays a key role in face learning. Past research suggests that children need exposure to higher levels of variability than adults to learn a new face-a difference that has been attributed to experience. We predicted that adults' limited experience with other-race faces would result in their needing exposure to higher levels of variability when learning other- compared with own-race faces. We introduced adults to four new identities (two own-race; two other-race) in one of the three conditions: a single image, a low-variability video (filmed on 1 day), or a high-variability video (filmed across 3 days). Adults' ability to recognize new instances of learned identities improved in the low-variability condition for own-race faces but only in the high-variability condition for other-race faces. We discuss learning mechanisms that might drive this difference-a difference we attribute to experience.

Learning own- and other-race facial identities: Testing implicit recognition with event-related brain potentials

“…These findings suggest an own-race advantage in identity learning from multiple, highly variable images (see also Hayward et al, 2017;Zhou et al, 2018).…”

“…In line with previous findings (e.g., Laurence et al, 2016), we expected better sorting of own-than other-race identities. Based on recent findings that other-race faces are harder to learn than own-race faces (e.g., Zhou et al, 2018), we also expected overall better matching accuracy with own-race faces and a stronger performance increase over blocks for this face category. With respect to ERPs, we expected to replicate the findings of Andrews et al 2017for own-race faces.…”

“…First, Hayward and colleagues found that participants learned otherrace identities less efficiently than own-race identities, and that training generalised more poorly to novel exemplars of the learnt other-relative to own-race identities (Hayward, Favelle, Oxner, Chu, & Lam, 2017). Second, better learning of own-relative to other-race identities has also been observed by Zhou, Matthews, Baker, & Mondloch (2018). These authors found that a higher degree of variability during learning was needed for later imageindependent recognition of other-as compared to own-race identities.…”

“…These authors found that a higher degree of variability during learning was needed for later imageindependent recognition of other-as compared to own-race identities. These learning difficulties associated with other-race faces have been interpreted to reflect reduced perceptual expertise with the other-race category (e.g., Proietti, Laurence, Matthews, Zhou, & Mondloch, 2018;Zhou et al, 2018). At the same time, Cavazos, Noyes, & O'Toole (2018) found that own-and other-race faces equally benefitted from multi-image training.…”

Learning own- and other-race facial identities from natural variability

“…In this study, a name identification test with new images of the learnt identities revealed higher accuracies for identifying own-race compared to other-race identities. Similarly, Zhou, Matthews, Baker, and Mondloch (2018) showed an own-race advantage in a paradigm where identities were learnt from a single image, a low variability video, or a high variability video.…”