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Learning Perceptual Kernels for Visualization Design

Çağatay Demiralp, Michael Bernstein, Jeffrey Heer. IEEE Trans. Visualization & Comp. Graphics (Proc. InfoVis), 2014
Figure for Learning Perceptual Kernels for Visualization Design
A crowd-estimated perceptual kernel for a shape palette, obtained using ordinal triplet matching (left). A two-dimensional projection of the palette shapes obtained via multidimensional scaling of the perceptual kernel (right).
Visualization design can benefit from careful consideration of perception, as different assignments of visual encoding variables such as color, shape and size affect how viewers interpret data. In this work, we introduce perceptual kernels: distance matrices derived from aggregate perceptual judgments. Perceptual kernels represent perceptual differences between and within visual variables in a reusable form that is directly applicable to visualization evaluation and automated design. We report results from crowd- sourced experiments to estimate kernels for color, shape, size and combinations thereof. We analyze kernels estimated using five different judgment types - including Likert ratings among pairs, ordinal triplet comparisons, and manual spatial arrangement - and compare them to existing perceptual models. We derive recommendations for collecting perceptual similarities, and then demonstrate how the resulting kernels can be applied to automate visualization design decisions.
  title = {Learning Perceptual Kernels for Visualization Design},
  author = {Demiralp, \c{C}a\u{g}atay AND Bernstein, Michael AND Heer, Jeffrey},
  journal = {IEEE Trans. Visualization \& Comp. Graphics (Proc. InfoVis)},
  year = {2014},
  url = {https://idl.uw.edu/papers/perceptual-kernels},
  doi = {10.1109/TVCG.2014.2346978}