Dataset Information

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 faces cannot be driven by adaptive eye movement strategies but can be mostly accounted for by suboptimalities in human face discrimination when observers are uncertain about the discriminating feature. We show that an initial bias of humans to use specific features to perform the task even though they are informed that each of four features is equally likely to be the discriminatory feature would lead to seemingly supra-optimal learning. We also examine the possibility of inefficient human integration of visual information across the spatially distributed facial features. Together, the results suggest that humans can show large performance improvement effects in discriminating faces as they learn to identify the feature containing the discriminatory information.

SUBMITTER: Peterson MF

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

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The surprisingly high human efficiency at learning to recognize faces.

Peterson Matthew F MF Abbey Craig K CK Eckstein Miguel P MP

Vision research 20081216 3

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 thro ...[more]

PMID: 19000918

Similar Datasets

Project description:BackgroundWhile humans (like other primates) communicate with facial expressions, the evolution of speech added a new function to the facial muscles (facial expression muscles). The evolution of speech required the development of a coordinated action between visual (movement of the lips) and auditory signals in a rhythmic fashion to produce "visemes" (visual movements of the lips that correspond to specific sounds). Visemes depend upon facial muscles to regulate shape of the lips, which themselves act as speech articulators. This movement necessitates a more controlled, sustained muscle contraction than that produced during spontaneous facial expressions which occur rapidly and last only a short period of time. Recently, it was found that human tongue musculature contains a higher proportion of slow-twitch myosin fibers than in rhesus macaques, which is related to the slower, more controlled movements of the human tongue in the production of speech. Are there similar unique, evolutionary physiologic biases found in human facial musculature related to the evolution of speech?Methodology/prinicipal findingsUsing myosin immunohistochemistry, we tested the hypothesis that human facial musculature has a higher percentage of slow-twitch myosin fibers relative to chimpanzees (Pan troglodytes) and rhesus macaques (Macaca mulatta). We sampled the orbicularis oris and zygomaticus major muscles from three cadavers of each species and compared proportions of fiber-types. Results confirmed our hypothesis: humans had the highest proportion of slow-twitch myosin fibers while chimpanzees had the highest proportion of fast-twitch fibers.Conclusions/significanceThese findings demonstrate that the human face is slower than that of rhesus macaques and our closest living relative, the chimpanzee. They also support the assertion that human facial musculature and speech co-evolved. Further, these results suggest a unique set of evolutionary selective pressures on human facial musculature to slow down while the function of this muscle group diverged from that of other primates.

Dataset Information

The surprisingly high human efficiency at learning to recognize faces.

Publications

The surprisingly high human efficiency at learning to recognize faces.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets