We are usually able to recognise novel instances of familiar faces with little difficulty, yet recognition of unfamiliar faces can be dramatically impaired by natural within-person variability in appearance. In a card-sorting task for facial identity, different photos of the same unfamiliar face are often seen as different people (Jenkins, White, van Montfort & Burton, 2011). Here we report two card sorting experiments in which we manipulate whether participants know the number of identities present. Without constraints, participants sort faces into many identities. However, when told the number of identities present, they are highly accurate. This minimal contextual information appears to support viewers in 'telling faces together'. In Experiment 2 we show that exposure to within-person variability in the sorting task improves performance in a subsequent face-matching task.This appears to offer a fast route to learning generalizable representations of new faces.

First impressions from faces emerge quickly and shape subsequent behaviour. Given that different pictures of the same face evoke different impressions, we asked whether presentation order affects the overall impression of the person. In three experiments, we presented naturally varying photos of a person’s face in ascending (low-to-high) or descending (high-to-low) order of attractiveness. We found that attractiveness ratings for a subsequent test item were higher for the descending condition than for the ascending condition (Experiment 1), consistent with anchoring effects. In Experiment 2, we ruled out contrast between the final item and the test item as the cause of the effect by demonstrating anchoring within the sequence itself. In Experiment 3, we found that order of image presentation also affected dating decisions. Our findings demonstrate that first impressions from faces depend not only on visual information but also on the order in which that information is received. We suggest that models of impression formation and learning of individual faces could be improved by considering temporal order of encounters.