No abstract

We tested four people who claimed to have significantly better than ordinary face recognition ability. Exceptional ability was confirmed in each case. On two very different tests of face recognition, all four experimental subjects performed beyond the range of control subject performance. They also scored significantly better than average on a perceptual discrimination test with faces. This effect was larger with upright than inverted faces, and the four subjects showed a larger ‘inversion effect’ than control subjects, who in turn showed a larger inversion effect than developmental prosopagnosics. This indicates an association between face recognition ability and the magnitude of the inversion effect. Overall, these ‘super-recognizers’ are about as good at face recognition and perception as developmental prosopagnosics are bad. Our findings demonstrate the existence of people with exceptionally good face recognition ability, and show that the range of face recognition and face perception ability is wider than previously acknowledged.

The Cambridge Face Memory Test (CFMT) and Cambridge Face Perception Test (CFPT) have provided the first theoretically strong clinical tests for prosopagnosia based on novel rather than famous faces. Here, we assess the extent to which norms for these tasks must take into account ageing, sex, and testing country. Data were from Australians aged 18 to 88 years (N = 240 for CFMT; 128 for CFPT) and young adult Israelis (N = 49 for CFMT). Participants were unselected for face recognition ability; most were university educated. The diagnosis cut-off for prosopagnosia (2 SDs poorer than mean) was affected by age, participant-stimulus ethnic match (within Caucasians), and sex for middle-aged and older adults on the CFPT. We also report internal reliability, correlation between face memory and face perception, correlations with intelligence-related measures, correlation with self-report, distribution shape for the CFMT, and prevalence of developmental prosopagnosia.

With the increasing sophistication and ubiquity of the Internet, behavioral research is on the cusp of a revolution that will do for population sampling what the computer did for stimulus control and measurement. It remains a common assumption, however, that data from self-selected Web samples must involve a trade-off between participant numbers and data quality. Concerns about data quality are heightened for performance-based cognitive and perceptual measures, particularly those that are timed or that involve complex stimuli. In experiments run with uncompensated, anonymous participants whose motivation for participation is unknown, reduced conscientiousness or lack of focus could produce results that would be difficult to interpret due to decreased overall performance, increased variability of performance, or increased measurement noise. Here, we addressed the question of data quality across a range of cognitive and perceptual tests. For three key performance metrics-mean performance, performance variance, and internal reliability-the results from selfselected Web samples did not differ systematically from those obtained from traditionally recruited and/or lab-tested samples. These findings demonstrate that collecting data from uncompensated, anonymous, unsupervised, self-selected participants need not reduce data quality, even for demanding cognitive and perceptual experiments.