Journal ArticleDOI
Multidimensional Similarity of Letters
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‘Vertical linearity’ and ‘Roundness’ are the most important of these factors and account for a considerable portion of the similarity among many letters.Abstract:
Summary.-28 lower-case letters of the Swedish alphabet were studied by the method of multidimensional similarity analysis. 57 Ss participated in the experiment. 9 factors were found. Factor I is called 't' or 'Vertical linearity,' Factor 11: '0' or 'Roundness,' Factor 111: 'n' or 'Parallel vertical linearity,' Factor IV: 'i' or 'Vertical linearity with dot,' Factor V: 'p' or 'Roundness attached to vertical linearity,' Factor VI: 'k' or 'Vertical linearity with crossness,' Factor VII: 'a' or 'Roundness attached to a hook,' Factor VIII: 'v' or 'Angularity open upward' and Factor IX: '2' or 'Zigzaggedness.' 'Vertical linearity' and 'Roundness' are the most important of these factors and account for a considerable portion of the similarity among many letters. In previous experiments with nine capital letters (Kuennapas, 1966, 1967, 1968a) it was shown that multidimensional similarity analysis (Ekman, 1965, 1968) could be used successfully for the study of the perceived forms of these capitals. The factors revealed by the analyses could be interpreted easily and meaningfully as 'Rectangularity,' 'Roundness' and 'Vertical linearity.' The purposes of the present study were (a) to extend the multidimensional similarity analysis to the whole alphabet of lower-case letters, and (b) to reveal the main perceptual dimensions of visual forms of these letters.read more
Citations
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Journal ArticleDOI
Features of Similarity
TL;DR: The metric and dimensional assumptions that underlie the geometric representation of similarity are questioned on both theoretical and empirical grounds and a set of qualitative assumptions are shown to imply the contrast model, which expresses the similarity between objects as a linear combination of the measures of their common and distinctive features.
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Additive similarity trees
Shmuel Sattath,Amos Tversky +1 more
TL;DR: A computer program, ADDTREE, for the construction of additive trees is described and applied to several sets of data, and some empirical and theoretical advantages of tree representations over spatial representations of proximity data are illustrated.
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A least squares algorithm for fitting additive trees to proximity data
TL;DR: A least squares algorithm for fitting additive trees to proximity data using a penalty function to enforce the four point condition on the estimated path length distances is described.
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Visual recognition of isolated lower-case letters
TL;DR: Perceptual cues prevail over bias effects in eccentric vision, and bias effects occur towards letters that occur frequently in the printed language, but they are restricted to confusions.
Journal ArticleDOI
Nearest neighbor analysis of psychological spaces.
TL;DR: The analysis of 100 data sets shows that most perceptual data satisfy the geometric-statistical bound whereas many conceptual data sets exceed it, and the most striking discrepancies between the data and their multidimensional representations arise in semantic fields.
References
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TL;DR: The quantitative similarity of nine capital letters was studied by the direct multidimensional ratio scaling method and by the method of similarity analysis and three factors, ‘E’, ’O’ and T, were found.
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Visual memory of capital letters: multidimensional ratio scaling and multidimensional similarity.
TL;DR: The similarity of 9 capital letters, as judged on the basis of visual memory, was studied by direct multidimensional ratio scaling and by the method of similarity analysis.
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Acoustic perception and acoustic memory of letters. Multidi mensional ratio scaling and multidimensional similarity.
TL;DR: Three factors were found which were exactly the same in respect of both perception and memory of these letters of the alphabet, and with nearly identical loadings.