No mark is an island: Precision and category repulsion biases in data reproductions
Caitlyn M. McColeman, Lane Harrison, Mi Feng, Steven Franconeri
Abstract
Data visualization is powerful in large part because it facilitates visual extraction of values. Yet, existing measures of perceptual precision for data channels (e.g., position, length, orientation, etc.) are based largely on verbal reports of ratio judgments between two values (e.g., [7]). Verbal report conflates multiple sources of error beyond actual visual precision, introducing a ratio computation between these values and a requirement to translate that ratio to a verbal number. Here we observe raw measures of precision by eliminating both ratio computations and verbal reports; we simply ask participants to reproduce marks (a single bar or dot) to match a previously seen one. We manipulated whether the mark was initially presented (and later drawn) alone, paired with a reference (e.g. a second '100%' bar also present at test, or a y-axis for the dot), or integrated with the reference (merging that reference bar into a stacked bar graph, or placing the dot directly on the axis). Reproductions of smaller values were overestimated, and larger values were underestimated, suggesting systematic memory biases. Average reproduction error was around 10% of the actual value, regardless of whether the reproduction was done on a common baseline with the original. In the reference and (especially) the integrated conditions, responses were repulsed from an implicit midpoint of the reference mark, such that values above 50% were overestimated, and values below 50% were underestimated. This reproduction paradigm may serve within a new suite of more fundamental measures of the precision of graphical perception.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 0e7205f4-e11e-4b9f-afe3-6426114e3228Cited by top-tier papers5
- Investigating Perceptual Biases in Icon ArraysCindy Xiong, Ali Sarvghad, Daniel G. Goldstein, Jake M. Hofman et al.CHI 2022 · 35 citations
- Rethinking the Ranks of Visual ChannelsCaitlyn M. McColeman, Fumeng Yang, Timothy F. Brady, Steven FranconeriIEEE VIS 2021 · 30 citations
- Truth or Square: Aspect Ratio Biases Recall of Position EncodingsCristina R. Ceja, Caitlyn M. McColeman, Cindy Xiong, Steven L. FranconeriIEEE VIS 2020 · 20 citations
- How Do Viewers Synthesize Conflicting Information from Data Visualizations?Prateek Mantri, Hariharan Subramonyam, Audrey L. Michal, Cindy XiongIEEE VIS 2022 · 13 citations
- Beware of Validation by Eye: Visual Validation of Linear Trends in ScatterplotsDaniel Braun, Remco Chang, Michael Gleicher, Tatiana von LandesbergerIEEE VIS 2024 · 2 citations
Builds on1
Related papers
- Taking Truncation to Task: A Task-Based Exploration of Axis Truncation in Bar Charts: A Task-Based Exploration of Axis Truncation in Bar ChartsOen G. McKinley, Alvitta OttleyCHI 2026 · 1 citation
- Revealing Perceptual Proxies with Adversarial ExamplesBrian D. Ondov, Fumeng Yang, Matthew Kay, Niklas Elmqvist et al.IEEE VIS 2020 · 22 citations
- Confirmation Bias: The Double-Edged Sword of Data Facts in Visual Data CommunicationShiyao Li, Thomas James Davidson, Cindy Xiong Bearfield, Emily WallCHI 2025 · 16 citations
- Graphical Perception of Icon Arrays versus Bar Charts for Value Comparisons in Health Risk CommunicationJade Kandel, Jiayi Liu, Arran Zeyu Wang, Chin Tseng et al.IEEE VIS 2025 · 3 citations
- Seeing What You Believe or Believing What You See? Belief Biases Correlation EstimationCindy Xiong, Chase Stokes, Yea-Seul Kim, Steven FranconeriIEEE VIS 2022 · 49 citations
