Humans' extreme face recognition abilities challenge the well-established familiarity effect.

Classification performance is better for learned than unlearned stimuli. This was also reported for faces, where identity matching of unfamiliar faces is worse than for familiar faces. This familiarity advantage led to the conclusion that variability across appearances of the same identity is partly idiosyncratic and cannot be generalized from familiar to unfamiliar identities. Recent advances in machine vision challenge this claim by showing that the performance for untrained (unfamiliar) identities reached the level of trained identities as the number of identities that the algorithm is trained with increases. We therefore asked whether humans who reportedly can identify a vast number of identities, such as super recognizers, may close the gap between familiar and unfamiliar face classification. Consistent with this prediction, super recognizers classified unfamiliar faces just as well as typical participants who are familiar with the same faces, on a task that generates a sizable familiarity effect in controls. Additionally, prosopagnosics' performance for familiar faces was as bad as that of typical participants who were unfamiliar with the same faces, indicating that they struggle to learn even identity-specific information. Overall, these findings demonstrate that by studying the extreme ends of a system's ability we can gain novel insights into its actual capabilities.