A cost-efficient face learning mechanism: The impact of stability in appearance on the resolution of facial representations

The way faces become familiar and what information is represented as familiarity develops has puzzled researchers in the field of human face recognition for decades. In this paper, we propose a cost-efficient mechanism of face learning to describe how facial representations form over time and that explains why recognition errors occur. Encoding of diagnostic facial information would follow a coarse-to-fine trajectory, modulated by the intrinsic stability in individual faces’ appearance. In four experiments, we draw on a robust and ecological method using a proxy of exposure to famous faces in the real world to test hypotheses generated by the model and we manipulate test images to probe the nature of facial representations. We consistently show that stable facial appearances help create more reliable representation in early stages of familiarisation but that their resolution remains relatively low and therefore less discriminative over time. In contrast, variations in appearance hinder recognition at first but encourage refinement of representations with further exposure. Consistent with the cost-efficient face learning mechanism we propose, facial representations built on a foundation of large-scale coarse information. When coarse information loses its diagnostic value through the experience of variations across encounters, facial details and their spatial relationships receive additional representational weights.