A Bayesian Cognition Approach to Improve Data Visualization
Open accessAbstract
People naturally bring their prior beliefs to bear on how they interpret the new information, yet few formal models exist for accounting for the influence of users' prior beliefs in interactions with data presentations like visualizations. We demonstrate a Bayesian cognitive model for understanding how people interpret visualizations in light of prior beliefs and show how this model provides a guide for improving visualization evaluation. In a first study, we show how applying a Bayesian cognition model to a simple visualization scenario indicates that people's judgments are consistent with a hypothesis that they are doing approximate Bayesian inference. In a second study, we evaluate how sensitive our observations of Bayesian behavior are to different techniques for eliciting people subjective distributions, and to different datasets. We find that people don't behave consistently with Bayesian predictions for large sample size datasets, and this difference cannot be explained by elicitation technique. In a final study, we show how normative Bayesian inference can be used as an evaluation framework for visualizations, including of uncertainty.
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Persuasiveness of visualized data depends on both context and visualization designs.
“Prior work suggests that persuasiveness of visualized data depends on both context (does the topic align with the viewers' prior beliefs?) and visualization designs (tabular design or bar graphs) [25, 26, 34].”
From Illusion of Causality in Visualized Data (2020) · cites this paper as [26]
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- Authors
- Kim, Y.-S., Walls, L. A., Krafft, P., & Hullman, J.
- Affiliations
- University of Washington (US)
- Northwestern University (US)
- Venue
- Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2019-05-02
- Pages
- 1-14
- OpenAlex
- W2908972697
- Cited by
- 76
- References
- 60
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 10 · 2023: 9 · 2022: 13 · 2021: 16 · 2020: 13 · 2019: 5
- FWCI
- 3.83
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1749266)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Keywords
- data visualization, prior beliefs, approximate Bayesian inference, visualization evaluation, uncertainty visualization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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