Learning context and the other-race effect: Strategies for improving face recognition

Abstract

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 own- and 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.

Keywords: Face recognition; Learning context; Other-race effect; Training.

Fig. 1.

Experiment 1 stimuli examples. East Asian (a) and Caucasian (b) learning images. East Asian (left) and Caucasian (right) testing images (c).

Fig. 2.

No effect of learning context on face recognition accuracy using multiple varying images. Results show that there was no difference in accuracy for learning context (a), participant race (b), or race of face stimuli (c). However, we found a robust other-race effect (d). Error bars show 95% CI.

Fig. 3.

Effect of learning context on face recognition criterion bias using multiple varying images. Contiguous learning yielded more conservative responses than distributed learning (a). East Asians and Caucasians did not differ in response bias (b), however participants responded more conservatively to Caucasian faces (c). There was a trend indicating that Caucasians responded more conservatively to Caucasian faces (d). Error bars show 95% CI.

Fig. 4.

Example of diverse learning images in Roark (2007).

Fig. 5.

Experiment 2 stimuli examples. East Asian (a) and Caucasian (b) learning images.

Fig. 6.

Effect of learning context on face recognition accuracy using single repeated images. Recognition accuracy was greater for contiguous learning than distributed learning (a). East Asians and Caucasians participants did not differ in recognition accuracy (b), however participants responded more conservatively to Caucasian faces (c). There was robust other-race effect (d). Error bars show 95% CI.

Fig. 7.

No effect of learning context on criterion bias using single repeated images. Results revealed no difference in response bias for learning context (a) or participant race (b). However, there was a greater conservative response bias for Caucasian faces (c) and a trend indicating that Caucasians responded more conservatively to Caucasian faces (d). Error bars show 95% CI.

Fig. 8.

Effect of image variability on face recognition accuracy. Results demonstrate greater recognition accuracy for multiple varying images compared to single repeated images (a), but no interaction between learning context and image variability (b). Recognition accuracy was greater for East Asian faces compared to Caucasian faces (c). A robust other-race effect was found (d). Error bars show 95% CI.