Learning faces from variability

<p>Research on face learning has tended to use sets of images that vary systematically ondimensions such as pose and illumination. In contrast, we have proposed thatexposure to naturally varying images of a person may be a critical part of thefamiliarization process. Here, we present two experiments investigating facelearning with “ambient images”—relatively unconstrained photos taken frominternet searches. Participants learned name and face associations for unfamiliaridentities presented in high or low within-person variability—that is, images of thesame person returned by internet search on their name (high variability) versusdifferent images of the same person taken from the same event (low variability). InExperiment 1 we show more accurate performance on a speeded name verificationtask for identities learned in high than in low variability, when the test images arecompletely novel photos. In Experiment 2 we show more accurate performance ona 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 toenhanced learning of new identities.</p>