{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/20786"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/20786","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Perceptual similarity of closed contours","abstract":"A model is presented for characterizing the perceptual similarities of closed contours, utilizing a Fourier analysis on the cumulative angular bend of the curve. The model is tested in four experiments, using both ratings of similarity and accuracy of discrimination. The results indicate that in cases where the underlying Fourier dimensionality is low, the model is quite successful, as indicated by consistently high, significant correlations between the predicted and judged similarities, and by the fit of Fourier components to the dimensions recovered from multidimensional scaling solutions. When the dimensionality in the frequency space is high, the model is also shown to account for measures of perceptual similarity, albeit to a lesser degree. Implications of the work, and suggestions for alternative versions of the model, are discussed.","abstract_html":"A model is presented for characterizing the perceptual similarities of closed contours, utilizing a Fourier analysis on the cumulative angular bend of the curve. The model is tested in four experiments, using both ratings of similarity and accuracy of discrimination. The results indicate that in cases where the underlying Fourier dimensionality is low, the model is quite successful, as indicated by consistently high, significant correlations between the predicted and judged similarities, and by the fit of Fourier components to the dimensions recovered from multidimensional scaling solutions. When the dimensionality in the frequency space is high, the model is also shown to account for measures of perceptual similarity, albeit to a lesser degree. 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The model is tested in four experiments, using both ratings of similarity and accuracy of discrimination. The results indicate that in cases where the underlying Fourier dimensionality is low, the model is quite successful, as indicated by consistently high, significant correlations between the predicted and judged similarities, and by the fit of Fourier components to the dimensions recovered from multidimensional scaling solutions. When the dimensionality in the frequency space is high, the model is also shown to account for measures of perceptual similarity, albeit to a lesser degree. Implications of the work, and suggestions for alternative versions of the model, are discussed.","Made available in DSpace on 2011-05-07T12:49:14Z (GMT). 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