Perceptual similarity modulates effects of learning from variability on face recognition

Face recognition is a challenging classification task that humans perform effortlessly for familiar faces. Recent studies have emphasized the importance of exposure to high variability appearances of the same identity to perform this task. However, these studies did not explicitly measure the perceptual similarity between the learned images and the images presented at test, which may account for the advantage of learning from high variability. Particularly, randomly selected test images are more likely to be perceptually similar to learned high variability images, and dissimilar to learned low variability images. Here we dissociated effects of learning from variability and study-test perceptual similarity, by collecting human similarity ratings for the study and test images. Using these measures, we independently manipulated the variability between the learning images and their perceptual similarity to the test images. Different groups of participants learned face identities from a low or high variability set of images. The learning phase was followed by a face matching test (Experiment 1) or a face recognition task (Experiment 2) that presented novel images of the learned identities that were perceptually dissimilar or similar to the learned images. Results of both experiments show that perceptual similarity between study and test, rather than image variability at learning per se, predicts face recognition. We conclude that learning from high variability improves face recognition for perceptually similar but not for perceptually dissimilar images. These findings may not be specific to faces and should be similarly evaluated for other domains.