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Joseph Houpt, Robert Hawkins, Devin Burns, James Townsend; Measuring Configural Superiority with the Capacity Coefficient. Journal of Vision 2013;13(9):73. doi: 10.1167/13.9.73.
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© ARVO (1962-2015); The Authors (2016-present)
Configural superiority effects are an important component of our understanding of visual perception of many types of stimuli. We propose the capacity coefficient as common framework for measuring configural superiority across a wide range of stimulus types. This measure has a number of advantages. The coefficient is based on a comparison of responses to the configuration with a baseline of unlimited-capacity, independent, parallel processing of each of the parts. Response times for processing the parts in isolation are used to estimate that baseline performance. Better than baseline performance, or better than unlimited-capacity, independent parallel processing, of a configuration of parts, indicates configural superiority. Furthermore, because the capacity coefficient accounts for the difficulty of processing each part, the capacity coefficient for one type of configuration can be compared to the capacity coefficient of another configuration, even if the parts are not exactly the same. We applied the capacity coefficient to three domains in which configural superiority effects have been previously demonstrated: the orientation of a pair of dots, words, and faces. We found that participants had better than baseline performance for detecting differences in the location of dots relative to reference points if there was also a difference in the orientation. The capacity coefficient was much higher than when there was not a difference in orientation; in fact, when there was no difference in orientation, the capacity coefficient indicated worse than baseline performance. Likewise, we found that participants performed better than baseline with words. Participants’ capacity coefficients were higher for words than random consonant sequences, which tended to have equal to or worse than baseline capacity coefficients. Finally, we found that participants had better than baseline performance with aligned upper and lower face halves, but lower capacity coefficients with misaligned face halves, usually below baseline.
Meeting abstract presented at VSS 2013
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