The surprisingly high human efficiency at learning to recognize faces.

ABSTRACT: We investigated the ability of humans to optimize face recognition performance through rapid learning of individual relevant features. We created artificial faces with discriminating visual information heavily concentrated in single features (nose, eyes, chin or mouth). In each of 2500 learning blocks a feature was randomly selected and retained over the course of four trials, during which observers identified randomly sampled, noisy face images. Observers learned the discriminating feature through indirect feedback, leading to large performance gains. Performance was compared to a learning Bayesian ideal observer, resulting in unexpectedly high learning compared to previous studies with simpler stimuli. We explore various explanations and conclude that the higher learning measured with fa

SUBMITTER: Peterson MF 

PROVIDER: S-EPMC2730895 | biostudies-literature | 2009 Feb

REPOSITORIES: biostudies-literature