Ranking visualizations of correlation using Weber's law

Lane Harrison*, Fumeng Yang, Steven Franconeri, Remco Chang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

85 Scopus citations

Abstract

Despite years of research yielding systems and guidelines to aid visualization design, practitioners still face the challenge of identifying the best visualization for a given dataset and task. One promising approach to circumvent this problem is to leverage perceptual laws to quantitatively evaluate the effectiveness of a visualization design. Following previously established methodologies, we conduct a large scale (n=1687) crowdsourced experiment to investigate whether the perception of correlation in nine commonly used visualizations can be modeled using Weber's law. The results of this experiment contribute to our understanding of information visualization by establishing that: 1) for all tested visualizations, the precision of correlation judgment could be modeled by Weber's law, 2) correlation judgment precision showed striking variation between negatively and positively correlated data, and 3) Weber models provide a concise means to quantify, compare, and rank the perceptual precision afforded by a visualization.

Original languageEnglish (US)
Article number6875978
Pages (from-to)1943-1952
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Volume20
Issue number12
DOIs
StatePublished - Dec 31 2014

Keywords

  • Evaluation
  • Perception
  • Visualization

ASJC Scopus subject areas

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

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