The Attraction Effect in Information Visualization
Open accessAbstract
The attraction effect is a well-studied cognitive bias in decision making research, where one's choice between two alternatives is influenced by the presence of an irrelevant (dominated) third alternative. We examine whether this cognitive bias, so far only tested with three alternatives and simple presentation formats such as numerical tables, text and pictures, also appears in visualizations. Since visualizations can be used to support decision making - e.g., when choosing a house to buy or an employee to hire - a systematic bias could have important implications. In a first crowdsource experiment, we indeed partially replicated the attraction effect with three alternatives presented as a numerical table, and observed similar effects when they were presented as a scatterplot. In a second experiment, we investigated if the effect extends to larger sets of alternatives, where the number of alternatives is too large for numerical tables to be practical. Our findings indicate that the bias persists for larger sets of alternatives presented as scatterplots. We discuss implications for future research on how to further study and possibly alleviate the attraction effect.
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In higher-level decision tasks, visualization design influences data interpretation and decision making.
“In higher-level decision tasks, visualization design also influences data interpretation and decision making [9, 4].”
From Illusion of Causality in Visualized Data (2020) · cites this paper as [9]
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‘Three is better than two’: Increasing donations with the attraction effect
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Impacts of Visualizations on Decoy Effects
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A Novel Lens on Metacognition in Visualization
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A Review of Visualization Assessment in Terms of User Performance and Experience
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Adaptive and Personalized Visualization
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Juxtaposing Controlled Empirical Studies in Visualization with Topic Developments in PsychologyPreprint
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The phantom effect in information visualization
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Insightful Visualization of Remote Sensing Images
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A registered report on presentation factors that influence the attraction effect
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Cognitive Biases and Data Visualization
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Self-citation
- Authors
- Dimara, E., Bezerianos, A., & Dragicevic, P.
- Affiliations
- Université Paris-Sud (FR)
- Institut national de recherche en sciences et technologies du numérique (FR)
- Venue
- IEEE Transactions on Visualization and Computer Graphics
- Published
- 2017-01
- Volume
- 23
- Issue
- 1
- Pages
- 471-480
- ISSN
- 1077-2626 (ISSN-L)
- OpenAlex
- W2513374982
- Cited by
- 89
- References
- 62
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 3 · 2023: 8 · 2022: 9 · 2021: 15 · 2020: 6 · 2019: 15 · 2018: 9 · 2017: 13 · 2016: 1
- FWCI
- 8.61
- Type
- journal article
- Language
- en
- Open access
- green
- inria.hal.science
- Locations
- IEEE Transactions on Visualization and Computer Graphics (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Keywords
- attraction effect, cognitive biases, decision-making, information visualization, scatter plots, mathematical tables
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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