The role of variability in appearance, exposure and learning procedure in dynamic face learning

Research suggests that humans recognize familiar faces reliably but struggle to learn new ones. Uncontrolled variations—in facial appearance and viewing conditions—facilitate learning new faces in lab-based studies. Conversely, stability in appearance seems to support early learning stages in the real-world and changes in appearance disrupt recognition regardless of familiarity. To reconcile these findings, we examined the specific role of variability in appearance, based on a cost-efficient learning framework in which stability in appearance would shape initial coarse representations, while variability would refinement over time. We conducted five pre-registered experiments manipulating appearance and exposure, using strictly controlled but ecological stimuli. Participants in different exposure groups learned two stable and two variable faces (3, 6, 9, or 12 videos/face), via interleaved learning episodes (Experiments 1, 2 and 5) or in a blocked manner (Experiments 3 and 4). The correspondence between test and learning materials varied across experiments. Stable faces were recognized better than variable ones, but only when test images corresponded to stable learning material. Recognition improved with additional exposure but only in interleaved learning experiments. This set of studies highlights multiple factors that affect face learning and open new research avenues to refine current theoretical accounts of face learning.