Exemplar Variance Supports Robust Learning of Facial Identity

Differences in the visual processing of familiar and unfamiliar faces have prompted considerable interest in face learning, the process by which unfamiliar faces become familiar. Previous work indicates that face learning is determined in part by exposure duration; unsurprisingly, viewing faces for longer affords superior performance on subsequent recognition tests. However, there has been further speculation that exemplar variation, experience of different exemplars of the same facial identity, contributes to face learning independently of viewing time. Several leading accounts of face learning, including the averaging and pictorial coding models, predict an exemplar variation advantage. Nevertheless, the exemplar variation hypothesis currently lacks empirical support. The present study therefore sought to test this prediction by comparing the effects of unique exemplar face learning—a condition rich in exemplar variation—and repeated exemplar face learning—a condition that equates viewing time, but constrains exemplar variation. Crucially, observers who received unique exemplar learning displayed better recognition of novel exemplars of the learned identities at test, than observers in the repeated exemplar condition. These results have important theoretical and substantive implications for models of face learning and for approaches to face training in applied contexts.