- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
People recognize faces of their own race more accurately than faces of other races—a phenomenon known as the “Other-Race Effect” (ORE). Previous studies show that training with multiple variable images improves face recognition. Building on multi-image training, we take a novel approach to improving own- and other-race face recognition by testing the role of learning context on accuracy. Learning context was either contiguous, with multiple images of each identity seen in sequence, or distributed, with multiple images of an identity randomly interspersed among different identities. In two experiments, East Asian and Caucasian participants learned ownand other-races faces either in a contiguous or distributed order. In Experiment 1, people learned each identity from four highly variable face images. In Experiment 2, identities were learned from one image, repeated four times. In both experiments we found a robust other-race effect. The effect of learning context, however, differed depending on the variability of the learned images. The distributed presentation yielded better recognition when people learned from single repeated images (Exp. 1), but not when they learned from multiple variable images (Exp. 2). Overall, performance was better with multiple-image training than repeated single image training. We conclude that multiple-image training and distributed learning can both improve recognition accuracy, but via distinct processes. The former broadens perceptual tolerance for image variation from a face, when there are diverse images available to learn. The latter effectively strengthens the representation of differences among similar faces, when there is only a single learning image.
The primary, novel finding of this study was that learning context (contiguous and distributed) affected recognition accuracy for both own- and other-race faces. Notably, distributed learning yielded greater recognition accuracy, but only when the same learning image was repeated. There was no effect of presentation type when the learning images were highly diverse. Combined with Roark (2007), we conclude that a pre-requisite factor in the utility of distributed learning is the ability of participants to perceive that the images that are repeated in a distributed sequence picture the same person (i.e., limited variability). In other words, the benefits of distributed learning may apply only when the associated images (identical or moderately diverse) are easily “seen together” as a unique identity. As suggested by Roark (2007), one possible explanation of the distributed advantage is the multiple trace theory (Crowder, 1976). This theory suggests that the formation of multiple memory traces benefits recognition. By this account, distributed presentation provides multiple traces of the experience of seeing a face, whereas contiguous presentation creates a single episodic memory trace for an identity.
The second novel finding was that multi-image learning also benefits recognition accuracy for other-race faces. This complements the benefits of multi-image learning for own-race faces (Dowsett & Burton, 2015; Jenkins et al., 2011; Longmore et al., 2008; Murphy et al., 2015; Ritchie & Burton, 2016), which we replicate here as well. Thus, our findings show that multi-image learning is a promising tool for improving other-race recognition. Data consistent with the utility of multiimage learning for other-race faces were reported in Matthews and Mondloch (2017), but in a design that tested participants of one race, with face stimuli of another race. Results from our cross-experimental analysis demonstrate that this effect applies generally as a cross-race effect. Notably, we found no interaction effects with face and participant race across experiments. This suggests that the benefits of multiimage learning apply equally to faces of own- and other-races, with no indication of qualitatively different effects.