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Hillary Hadley, Erik Arnold, James Tanaka, Tim Curran, Lisa Scott; Multidimensional-expertise space: Multidimensional scaling changes after expertise training with objects. Journal of Vision 2015;15(12):1141. doi: 10.1167/15.12.1141.
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© ARVO (1962-2015); The Authors (2016-present)
Perceptual experts are defined by their ability to make fast and accurate identifications of objects within their domain of expertise at specific levels of categorization (Tanaka & Taylor, 1991). Researchers have previously applied a multidimensional space (MDS) framework to better understand the psychological similarities of members of the same object or face category. For example, difficulties in recognizing other-race faces have been attributed to a more densely clustered MDS space of other-race versus own-race faces (e.g., Byatt & Rhodes, 2004; Papesh & Goldinger, 2010). Here, we used MDS analyses to examine whether 9 hours of expertise training with computer-generated objects influenced density of clustering and whether this differed for an untrained control group. Adults completed similarity ratings and training with two “families” of objects, one trained at a basic level (multiple exemplars of 10 species all labeled “Other”) and one trained at a subordinate level (multiple exemplars of 10 species, labeled “A” through “J”). MDS plots were constructed based on dissimilarity ratings between object species before and after training. For the pre-training group, and for an untrained control group, MDS results show two distinct clusters of species that fall in line with the two family distinctions. After subordinate-level training, the density of clusters changed along two hypothesized dimensions, family membership and feature distinctiveness. In contrast, basic-level training only led to changes along the dimension of family membership. These results suggest that basic-level and subordinate-level expertise training leads to qualitatively different changes in the density and specificity of psychological object representations.
Meeting abstract presented at VSS 2015
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