Bars and lines: A study of graphic communication
Abstract
Interpretations of graphs seem to be rooted in principles of cognitive naturalness and information processing rather than arbitrary correspondences. These predict that people should more readily associate bars with discrete comparisons between data points because bars are discrete entities and facilitate point estimates. They should more readily associate lines with trends because lines connect discrete entities and directly represent slope. The predictions were supported in three experiments--two examining comprehension and one production. The correspondence does not seem to depend on explicit knowledge of rules. Instead, it may reflect the influence of the communicative situation as well as the perceptual properties of graphs.
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Bar graphs facilitate finding clusters, line graphs facilitate finding correlations and scatter plots facilitate finding outliers.
“Bar graphs facilitate finding clusters, line graphs facilitate finding correlations and scatter plots facilitate finding outliers [41, 50].”
From Illusion of Causality in Visualized Data (2020) · cites this paper as [50]
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- Authors
- Zacks, J., & Tversky, B.
- Affiliations
- Stanford University (US)
- ORCID
- Tversky, Barbara
- Venue
- Memory & Cognition
- Published
- 1999-11
- Volume
- 27
- Issue
- 6
- Pages
- 1073-1079
- ISSN
- 0090-502X (ISSN-L)
- 1532-5946
- OpenAlex
- W2095738647
- Cited by
- 300
- References
- 17
- Citations by year
- 2026: 7 · 2025: 19 · 2024: 13 · 2023: 13 · 2022: 14 · 2021: 17 · 2020: 14 · 2019: 12 · 2018: 19 · 2017: 11 · 2016: 15 · 2015: 13 · 2014: 15 · 2013: 12 · 2012: 12
- FWCI
- 5.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Memory & Cognition (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Physical Sciences › Engineering › Automotive Engineering › Spatial Cognition and Navigation
- Keywords
- graph interpretation, bar charts, line graphs, graphic communication, information processing, point estimates, communicative situations
- MeSH terms
- Adult, Communication, Concept Formation, Female, Humans, Male, Mathematics, Pattern Recognition, Visual, Problem Solving
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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