Super-recognizers sample visual information of superior computational value for facial recognition.

Super-recognizers-individuals with exceptionally high face recognition abilities-are a key exemplar of biological visual expertise. Recent eye-tracking evidence suggests that their expertise may be driven by exploratory viewing behaviour during learning, but it remains unclear whether this perceptual sampling is functional for face identity processing. Here, we develop a novel approach to quantify the computational value of face information samples and test the utility of information sampling in super-recognizers. Using measurements of eye gaze behaviour, we reconstructed the retinal information that participants acquired while learning new faces. We then evaluated the computational value of this information for face identity processing using nine deep neural networks (DNNs) optimized for this task. Identity matching accuracy improved across all DNNs when using visual information sampled by super-recognizers compared with typical viewers. Interestingly, this advantage could not be explained by the greater quantity of information alone, and so differences in both the quantity and quality of face information encoded on the retina contribute to individual differences in face processing ability. These findings support accounts of visual expertise that emphasize attentional mechanisms and the role of active visual exploration in learning.