Biased Average Position Estimates in Line and Bar Graphs: Underestimation, Overestimation, and Perceptual Pull

Cindy Xiong, Cristina R. Ceja, Casimir J.H. Ludwig, Steven Franconeri

Research output: Contribution to journalArticle (Academic Journal)

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Abstract

In visual depictions of data, position (i.e., the vertical height of a line or a bar) is believed to be the most precise way to encode information compared to other encodings (e.g., hue). Not only are other encodings less precise than position, but they can also be prone to systematic biases (e.g., color category boundaries can distort perceived differences between hues). By comparison, position's high level of precision may seem to protect it from such biases. In contrast, across three empirical studies, we show that while position may be a precise form of data encoding, it can also produce systematic biases in how values are visually encoded, at least for reports of average position across a short delay. In displays with a single line or a single set of bars, reports of average positions were significantly biased, such that line positions were underestimated and bar positions were overestimated. In displays with multiple data series (i.e., multiple lines and/or sets of bars), this systematic bias still persisted. We also observed an effect of 'perceptual pull', where the average position estimate for each series was 'pulled' toward the other. These findings suggest that, although position may still be the most precise form of visual data encoding, it can also be systematically biased.

Original languageEnglish
Article number8805427
Pages (from-to)301-310
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Volume26
Issue number1
Early online date19 Aug 2019
DOIs
Publication statusPublished - 1 Jan 2020

Structured keywords

  • Cognitive Science
  • Visual Perception

Keywords

  • bar graphs
  • cue combination
  • line graphs
  • perception and cognition
  • Perceptual biases
  • position estimation

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