Applying Points-of-View Analysis to Individual Variations in Colour Sorting Data

in Journal of Cognition and Culture
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“Points-of-View” analysis has been promoted as an appropriate analysis for similarity data collected with the Method of Sorting. It can be regarded as an extension of Cultural Consensus Analysis (cca). The latter assumes that subjects all base their responses on a single shared ‘model’ of the items to be sorted (while varying in the level of reliability with which they consult that model). Conversely, the titular “points-of-view” are multiple models, sampled singularly by some subjects’ responses, while other subjects combine the models in various proportions. The analysis appears to be comparatively insensitive to the artefacts to which sorting data are prone, which affect how easily they can be interpreted with (for instance) multidimensional scaling (mds). Here we apply the Points-of-View approach to two sets of data from the colour domain, a sensory modality well-suited for the sorting procedure. One study extracted three viewpoints – i.e., factors, prototypal ways of organising the stimuli – with one viewpoint specific to colour-vision-deficient observers, capturing independently-measured differences among the subjects. In the second study, two viewpoints proved to be appropriate: one arranged the items by two dimensions, lightness/saturation as well as hue, while the second was essentially a one-dimensional hue-based arrangement. This distinction could not be recovered from the raw data by applying mds with the weighted-Euclidean model of individual difference. We discuss some demographic factors which might dispose subjects to attend to hue only or saturation/lightness as well.

Applying Points-of-View Analysis to Individual Variations in Colour Sorting Data

in Journal of Cognition and Culture

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Figures

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    Hierarchical sorting data for 16 colour caps: 2D weighted-Euclidean mds solution. (Top) Group configuration; (bottom) individual dimensional weights (cvd subjects shown by stars).
  • View in gallery
    pca of hierarchical sorting data: Loading plot for three rotated principal components (pcs), showing subjects’ pc1 and pc3 loadings, with the pc2 loadings on an axis perpendicular to the page. cvd subjects shown by stars.
  • View in gallery
    Sorting data for 16 colours: three viewpoints are shown as 2D mds solutions.
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    Sorting data for 51 Color-aid tiles: 2D weighted-Euclidean mds solution. Symbols indicate the most frequent colour name assigned to each stimulus.
  • View in gallery
    pca of sorting data: Loading plot for two rotated pcs. Subjects identified by Italian dialect, if known.
  • View in gallery
    First viewpoint (pc1) for 51 Color-aid tiles, summarised as (a) 2D mds solution and (b) hierarchical clustering. Stimulus symbols as in Figure 4.
  • View in gallery
    Second viewpoint (pc2) for Color-aid tiles, summarised as (a) 2D mds solution and (b) hierarchical clustering.
  • View in gallery
    Loading plot for two rotated pcs, for nine subjects who sorted the stimuli twice, with lines linking the pairs of points.

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