Ranking Visualizations of Correlation Using Weber's Law
Abstract
Despite years of research yielding systems and guidelines to aid visualization design, practitioners still face the challenge of identifying the best visualization for a given dataset and task. One promising approach to circumvent this problem is to leverage perceptual laws to quantitatively evaluate the effectiveness of a visualization design. Following previously established methodologies, we conduct a large scale (n=1687) crowdsourced experiment to investigate whether the perception of correlation in nine commonly used visualizations can be modeled using Weber's law. The results of this experiment contribute to our understanding of information visualization by establishing that: (1) for all tested visualizations, the precision of correlation judgment could be modeled by Weber's law, (2) correlation judgment precision showed striking variation between negatively and positively correlated data, and (3) Weber models provide a concise means to quantify, compare, and rank the perceptual precision afforded by a visualization.
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Conducting behavioral research on Amazon’s Mechanical Turk
Mason, W., & Suri, S. (2012) · Behavior Research Methods · doi:10.3758/s13428-011-0124-6 · Cited by 3,172 · see citing works
Cleveland, W. S., & McGill, R. (1984) · Journal of the American Statistical Association · doi:10.1080/01621459.1984.10478080 · Cited by 1,797 · see citing works
Cleveland, W. S., & McGill, R. (1984) · Journal of the American Statistical Association · doi:10.2307/2288400 · Cited by 1,796 · see citing works
Automating the design of graphical presentations of relational information
Mackinlay, J. D. (1986) · ACM Transactions on Graphics · doi:10.1145/22949.22950 · Cited by 1,631 · see citing works
Semiology of Graphics: Diagrams, Networks, Maps
Bertin, J., & Berg, W. J. (2010) · Cited by 1,565 · see citing works
ColorBrewer.org: An Online Tool for Selecting Colour Schemes for Maps
Harrower, M., & Brewer, C. A. (2003) · The Cartographic Journal · doi:10.1179/000870403235002042 · Cited by 1,134 · see citing works
A Nested Model for Visualization Design and Validation
Munzner, T. (2009) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2009.111 · Cited by 882 · see citing works
Crowdsourcing graphical perception
Heer, J., & Bostock, M. (2010) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1753326.1753357 · Cited by 819 · see citing works
Digital Color Imaging Handbook
Sharma, G. (2011) · CRC Press eBooks · doi:10.1201/9781420041484 · Cited by 651 · see citing works
Empirical Studies in Information Visualization: Seven Scenarios
Lam, H., Bertini, E., Isenberg, P., Catherine Plaisant, B., & Carpendale, S. (2011) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2011.279 · Cited by 625 · see citing works
Show Me: Automatic Presentation for Visual Analysis
Mackinlay, J. D., Hanrahan, P., & Stolte, C. (2007) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2007.70594 · Cited by 499 · see citing works
Coren, S., Porac, C., & Ward, L. M. (1979) · Optometry and Vision Science · doi:10.1097/00006324-197905000-00008 · Cited by 495 · see citing works
Sharma, G., & Trussell, H. J. (1997) · IEEE Transactions on Image Processing · doi:10.1109/83.597268 · Cited by 440 · see citing works
Evaluating Information Visualizations
Carpendale, S. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-70956-5_2 · Cited by 367 · see citing works
Little, G., Chilton, L. B., Goldman, M., & Miller, R. C. (2010) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/1866029.1866040 · Cited by 307 · see citing works
Bars and lines: A study of graphic communication
Zacks, J., & Tversky, B. (1999) · Memory & Cognition · doi:10.3758/bf03201236 · Cited by 300 · see citing works
THE VISUAL DISCRIMINATION OF INTENSITY AND THE WEBER-FECHNER LAW
Hecht, S. (1924) · The Journal of General Physiology · doi:10.1085/jgp.7.2.235 · Cited by 285 · see citing works
Banich, M. T., & Compton, R. J. (2023) · Cognitive Neuroscience · doi:10.1017/9781108923361.006 · Cited by 272 · see citing works
Selecting Semantically‐Resonant Colors for Data Visualization
Lin, S., Fortuna, J., Kulkarni, C. E., Stone, M., & Heer, J. (2013) · Computer Graphics Forum · doi:10.1111/cgf.12127 · Cited by 188 · see citing works
The Perception of Correlation in Scatterplots
Rensink, R. A., & Baldridge, G. (2010) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2009.01694.x · Cited by 182 · see citing works
Variables on Scatterplots Look More Highly Correlated When the Scales Are Increased
Cleveland, W. S., Diaconis, P. W., & McGill, R. (1982) · Science · doi:10.1126/science.216.4550.1138 · Cited by 177 · see citing works
Judging Correlation from Scatterplots and Parallel Coordinate Plots
Li, J., Martens, J.-B., & van Wijk, J. J. (2008) · Information Visualization · doi:10.1057/ivs.2008.13 · Cited by 115 · see citing works
The Time of Perception as a Measure of Differences in Sensations
Henmon, V. A. C. (2009) · Medical Entomology and Zoology · Cited by 99 · see citing works
On the Prospects for a Science of Visualization
Rensink, R. A. (2013) · Springer eBooks · doi:10.1007/978-1-4614-7485-2_6 · Cited by 48 · see citing works
Visual Embedding: A Model for Visualization
Demiralp, Ç., Scheidegger, C., Kindlmann, G., et al. (2014) · IEEE Computer Graphics and Applications · doi:10.1109/mcg.2014.18 · Cited by 44 · see citing works
Enumeration of small collections violates Weber’s law
Choo, H., & Franconeri, S. (2013) · Psychonomic Bulletin & Review · doi:10.3758/s13423-013-0474-4 · Cited by 42 · see citing works
Self-citationSelecting the Aspect Ratio of a Scatter Plot Based on Its Delaunay Triangulation
Fink, M., Haunert, J., Spoerhase, J., & Wolff, A. (2013) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2013.187 · Cited by 37 · see citing works
The Time of Perception as a Measure of Differences in Sensations
Nakashima, T. (1908) · The Journal of Philosophy Psychology and Scientific Methods · doi:10.2307/2010970 · Cited by 19 · see citing works
Scott, N., & Liu, B. (2024) · Tourism social science series · doi:10.1108/s1571-504320240000027006 · Cited by 11 · see citing works
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Xiong, C., Shapiro, J., Hullman, J., & Franconeri, S. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2934399 · Cited by 71 · see citing works
Self-citationConstantin, R., Duck, M., Alexandrov, A., Matosevic, P., Keidar, D., & El-Assady, M. (2022) · 2022 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX) · doi:10.1109/trex57753.2022.00005 · Cited by 3 · see citing works
- Authors
- Harrison, L., Yang, F., Franconeri, S., & Chang, R.
- Affiliations
- Tufts University (US)
- Northwestern University (US)
- Venue
- IEEE Transactions on Visualization and Computer Graphics
- Published
- 2014-12-31
- Volume
- 20
- Issue
- 12
- Pages
- 1943-1952
- ISSN
- 1077-2626 (ISSN-L)
- OpenAlex
- W2043523210
- Cited by
- 212
- References
- 29
- Citations by year
- 2026: 7 · 2025: 15 · 2024: 20 · 2023: 22 · 2022: 17 · 2021: 25 · 2020: 20 · 2019: 20 · 2018: 22 · 2017: 20 · 2016: 17 · 2015: 6 · 2014: 1
- FWCI
- 13.12
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Visualization and Computer Graphics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- NSF (IIS-1218170)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Keywords
- correlation visualization, Weber's law, perceptual precision, perceptual models, negative correlation, positive correlation, crowdsourced experiments
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