Truth or Square: Aspect Ratio Biases Recall of Position Encodings
Cristina R. Ceja, Caitlyn M. McColeman, Cindy Xiong, Steven L. Franconeri
Abstract
Bar charts are among the most frequently used visualizations, in part because their position encoding leads them to convey data values precisely. Yet reproductions of single bars or groups of bars within a graph can be biased. Curiously, some previous work found that this bias resulted in an overestimation of reproduced data values, while other work found an underestimation. Across three empirical studies, we offer an explanation for these conflicting findings: this discrepancy is a consequence of the differing aspect ratios of the tested bar marks. Viewers are biased to remember a bar mark as being more similar to a prototypical square, leading to an overestimation of bars with a wide aspect ratio, and an underestimation of bars with a tall aspect ratio. Experiments 1 and 2 showed that the aspect ratio of the bar marks indeed influenced the direction of this bias. Experiment 3 confirmed that this pattern of misestimation bias was present for reproductions from memory, suggesting that this bias may arise when comparing values across sequential displays or views. We describe additional visualization designs that might be prone to this bias beyond bar charts (e.g., Mekko charts and treemaps), and speculate that other visual channels might hold similar biases toward prototypical values.
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Cited by top-tier papers4
- Rethinking the Ranks of Visual ChannelsCaitlyn M. McColeman, Fumeng Yang, Timothy F. Brady, Steven FranconeriIEEE VIS 2021 · 30 citations
- A Review and Collation of Graphical Perception Knowledge for Visualization RecommendationZehua Zeng, Leilani BattleCHI 2023 · 23 citations
- No mark is an island: Precision and category repulsion biases in data reproductionsCaitlyn M. McColeman, Lane Harrison, Mi Feng, Steven FranconeriIEEE VIS 2020 · 15 citations
- How Do Viewers Synthesize Conflicting Information from Data Visualizations?Prateek Mantri, Hariharan Subramonyam, Audrey L. Michal, Cindy XiongIEEE VIS 2022 · 13 citations
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