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Isabel Gauthier, Rankin McGugin, Jennifer Richler, Grit Herzmann, Magen Speegle, Ana Van Gulick; Experience with objects moderates the overlap between object and face recognition performance, suggesting a common ability. Journal of Vision 2013;13(9):982. doi: https://doi.org/10.1167/13.9.982.
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© ARVO (1962-2015); The Authors (2016-present)
Recognizing faces is a crucially important skill in our social environment. Recent work suggests that face recognition constitutes a special human ability that is relatively independent from domain-general factors such as general cognitive ability, motivation or attention. We present evidence questioning the distinction between face recognition and abilities to recognize other objects. Our data suggest that face and object recognition performance reflect a common ability that more generally supports the acquisition of skills in discriminating visually similar objects and patterns. We show that the overlap between face and object recognition depends on experience with objects. In 255 participants, we measured face recognition using the Cambridge Face Memory Test (CFMT), object recognition for 8 categories using the Vanderbilt Expertise Test (VET), and self-reported experience (EXP) for the 8 object categories plus faces. Overall performance on VET and CFMT showed a significant correlation (r(254)=0.36 p=.003), whereas EXP correlated with neither the CFMT (r(254)=-.02, n.s.) nor VET (r(254)=-.03, n.s.). However, in a multiple regression on CFMT, the interaction between EXP and VET was significant (t253 = 2.15, p =.03). At the extreme, for participants with EXP more than 2 SDs above the mean, the shared variance between CFMT and VET was 59% (corrected for reliability of tests: 73% (95 CI: 49.5- 100)). This is consistent with object and face performance tapping into a single domain-general ability, given enough experience. The existence of such a domain-general ability may have broad-ranging implications. Beyond influencing aptitude in social interactions, this ability would constrain performance in other domains, such as learning to play chess, identifying tumors in X-rays or MRI pictures, identifying fingerprints or reading airport security displays. Because many domains of human activity benefit from skilled perception, measuring individual differences that may explain variability separate from that accounted for by general cognitive ability could greatly increase predictions about performance.
Meeting abstract presented at VSS 2013
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