Learning faces from variability

Abstract

Research on face learning has tended to use sets of images that vary systematically on dimensions such as pose and illumination. In contrast, we have proposed that exposure to naturally varying images of a person may be a critical part of the familiarization process. Here, we present two experiments investigating face learning with “ambient images”—relatively unconstrained photos taken from internet searches. Participants learned name and face associations for unfamiliar identities presented in high or low within-person variability—that is, images of the same person returned by internet search on their name (high variability) versus different images of the same person taken from the same event (low variability). In Experiment 1 we show more accurate performance on a speeded name verification task for identities learned in high than in low variability, when the test images are completely novel photos. In Experiment 2 we show more accurate performance on a face matching task for identities previously learned in high than in low variability. The results show that exposure to a large range of within-person variability leads to enhanced learning of new identities.