Fast and robust visual object recognition in young children

Abstract

By adulthood, humans rapidly identify objects from sparse visual displays and across significant disruptions to their appearance. What are the minimal conditions needed to achieve robust recognition abilities and when might these abilities develop? To test this question, we investigated the upper-limits of children's object recognition abilities. We found that children as young as 3-years-of-age successfully identified objects at speeds of 100 ms (both forward and backward masked) under sparse and disrupted viewing conditions. By contrast, a range computational models implemented with biologically plausible properties or optimized for visual recognition did not reach child-level performance. Models only matched children if they were trained with more data than children are capable of experiencing. These findings highlight the robustness of the human visual system in the absence of extensive experience and identify important developmental constraints for building biologically plausible machines.