Perceptual Biases in Font Size as a Data Encoding

Eric Carlson Alexander*, Chih Ching Chang, Mariana Shimabukuro, Steven Franconeri, Christopher Collins, Michael Gleicher

*Corresponding author for this work

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

Many visualizations, including word clouds, cartographic labels, and word trees, encode data within the sizes of fonts. While font size can be an intuitive dimension for the viewer, using it as an encoding can introduce factors that may bias the perception of the underlying values. Viewers might conflate the size of a word's font with a word's length, the number of letters it contains, or with the larger or smaller heights of particular characters ('o' versus 'p' versus 'b'). We present a collection of empirical studies showing that such factors - which are irrelevant to the encoded values - can indeed influence comparative judgements of font size, though less than conventional wisdom might suggest. We highlight the largest potential biases, and describe a strategy to mitigate them.

Original languageEnglish (US)
Pages (from-to)2397-2410
Number of pages14
JournalIEEE Transactions on Visualization and Computer Graphics
Volume24
Issue number8
DOIs
StatePublished - Aug 1 2018

Keywords

  • Text and document data
  • cognitive and perceptual skill
  • quantitative evaluation

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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