Partitioning natural face image variability emphasises within-identity over between-identity representation for understanding accurate recognition.

Accurately recognising faces enables social interactions. In recent years it has become clear that people's accuracy differs markedly depending on viewer's familiarity with a face and their individual skill, but the cognitive and neural bases of these accuracy differences are not understood. We examined cognitive representations underlying these accuracy differences by measuring similarity ratings to natural facial image variation. Natural variation was sampled from uncontrolled images on the internet to reflect the appearance of faces as they are encountered in daily life. Using image averaging, and inspired by the computation of Analysis of Variance, we partitioned this variation into differences between faces (between-identity variation) and differences between photos of the same face (within-identity variation). This allowed us to compare modulation of these two sources of variation attributable to: (i) a person's familiarity with a face and, (ii) their face recognition ability. Contrary to prevailing accounts of human face recognition and perceptual learning, we found that modulation of within-identity variation - rather than between-identity variation - was associated with high accuracy. First, familiarity modulated similarity ratings to within-identity variation more than to between-face variation. Second, viewers that are extremely accurate in face recognition - 'super-recognisers' - differed from typical perceivers mostly in their ratings of within-identity variation, compared to between-identity variation. In a final computational analysis, we found evidence that transformations of between- and within-identity variation make separable contributions to perceptual expertise in face recognition. We conclude that inter- and intra-individual accuracy differences primarily arise from differences in the representation of within-identity image variation.