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Ana Van Gulick, Isabel Gauthier; Measurement of visual and semantic knowledge for cars and estimation of experience. Journal of Vision 2013;13(9):984. doi: 10.1167/13.9.984.
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© ARVO (1962-2015); The Authors (2016-present)
The study of individual differences in object recognition is complicated by experience. The ability underlying object recognition cannot be observed directly: it is expressed in performance, which is the product of ability and experience. For faces, experience is high and varies little, and performance therefore reflects ability. For object categories, experience may vary greatly and estimating ability requires precise quantification of experience. We recently found that the correlation between performance for objects and faces increases with self-reports of experience with objects (Gauthier et al., submitted). However, the reliability and validity of self-reports is difficult to assess. Here, we aim to estimate experience independently of self-report. We assume, based on research with faces, that visual ability does not correlate with fluid intelligence, and that the latter determines the acquisition of verbal knowledge. Accordingly, the shared variance between visual and semantic performance for a given domain should reflect experience, which we eventually plan to estimate through a bi-factor binary response confirmatory factor analysis. As a first step, we designed a test of verbal knowledge for cars, the Semantic Vanderbilt Expertise Test (SVET-car), in which subjects identify real car names among foils. In 174 subjects (100 male) the SVET-car was reliable (a=.89) and correlated with visual car performance (VET-car, McGugin et al., 2012; r=.416, p<.0001). Using multiple regression, we found independent contributions of the VET-car and the SVET-car to self-report of car experience, confirming that self-report may be problematic for assessing experience because one’s impression of one’s knowledge and performance may influence reports. The interaction between VET-car and SVET-car was significant in predicting self-reports of experience (p=.018), with VET-car a better predictor of self-reports for those with greater semantic knowledge of cars. Estimation of object experience through performance in visual and semantic tasks should help us assess underlying abilities in object recognition.
Meeting abstract presented at VSS 2013
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