research-article

A model of symbol size discrimination in scatterplots

Authors:

Jean-Bernard Martens,

Jarke J. van WijkAuthors Info & Claims

CHI '10: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Pages 2553 - 2562

https://doi.org/10.1145/1753326.1753714

Published: 10 April 2010 Publication History

Abstract

Symbols are used in scatterplots to encode data in a way that is appropriate for perception through human visual channels. Symbol size is believed to be the second dominant channel after color. We study symbol size perception in scatterplots in the context of analytic tasks requiring size discrimination. More specifically, we performed an experiment to measure human performance in three visual analytic tasks. Circles are used as the representative symbol, with eight, linearly varying radii; 24 persons, divided across three groups, participated; and both objective and subjective measures were obtained. We propose a model to describe the results. The perception of size is assumed to be an early step in the complex cognitive process to mediate discrimination, and psychophysical laws are used to describe this perceptual mapping. Different mapping schemes are compared by regression on the experimental data. The results show that approximate homogeneity of size perception exists in our complex tasks and can be closely described by a power law transformation with an exponent of 0.4. This yields an optimal scale for symbol size discrimination.

References

[1]

Christ, R.E. Review and analysis of color coding research for visual displays. Human Factors, 17(6):542--570, 1975.

[2]

Stevens, S.S. On the theory of scales of measurement. Science, 103(2684):677--680, 1946.

[3]

Stevens, S.S. The psychophysics of sensory function. Amer. Scientist, 48, 226--253, 1960.

[4]

Stevens, S.S. and Guirao, M. Subjective scaling of length and area and the matching of length to loudness and brightness. Journal of Experimental Psychology, 66, 177--186, 1963.

[5]

Stevens, S.S. Matching functions between loudness and ten other continua. Perception and Psychophysics, 1, 5--8, 1965.

[6]

Laming, D. The Measurement of Sensation. Oxford University Press, NY, USA, 1997.

[7]

Macmillan, N.A., Moschetto, C.F., Bialostozky, F.M. and Engel, L. Size judgment: The presence of a standard increases the exponent of the power law. Perception and Psychophysics, 16(2), 340--346, 1974.

[8]

Wolfe, J.M. and Cave, K.R. Deploying visual attention: The Guided Search model. In AI and the Eye, Troscianko, T. and Blake, A., eds., John Eiley & Sons, UK, 79--103, 1989.

[9]

Wolfe, J.M. Guided Search 2.0: A revised model of visual search. Psychomonic Bulletin & Review, 1(2):202--238, 1994.

[10]

Li, J., van Wijk, J.J. and Martens, J.B. Evaluation of symbol contrast in scatterplots. Proc. PacificVis2009, 97--104, 2009.

Digital Library

[11]

Miller, A.L., Pedersen, V.M. and Sheldon, R.W. Magnitude estimation of average length: A follow-up. American Journal of Psychology, 83, 95--102, 1970.

[12]

Weiss, D.J. Averaging: An empirical validity criterion for magnitude estimation. Perception and Psychophysics, 12, 385--388, 1972.

[13]

Ariely, D. Seeing sets: representation by statistical properties. Psychological Science, 12(2):157--162, 2001.

[14]

Narens, L. A theory of ratio magnitude estimation. Journal of Mathematical Psychology, 40, 109--129, 1996.

Digital Library

[15]

Ware, C. Information Visualization: Perception for Design, 2nd Edition, Morgan Kaufmann, SF, USA, 2004.

Digital Library

[16]

Cleveland, W.S. and McGill, R. Graphical perception: theory, experimentation, and application to the development of graphical methods. Journal of the American Statistical Association, 79(387):531--554, 1984.

[17]

Nowell, L.T. Graphical Encoding for Information Visualization: using icon color, shape and size to convey nominal and quantitative data. PhD Dissertation, Dep. of Computer Science, Faculty of the Virginia Polytechnic Institute and State University, Digital Library, 1997.

Digital Library

[18]

Lewandowsky, S. and Spence, I. Discriminating strata in scatterplots. Journal of the American Statistical Association, 84(407):682--699, 1989.

[19]

Tremmel, L. The visual separability of plotting symbols in scatterplots. Journal of Computational and Graphical Statistics, 4(2):101--112, 1995.

[20]

Amar, R., Eagan, J. and Stasko, J.Low-level components of analytic activity in Information Visualization. Proc. IEEE InfoVis: 112--119, 2005.

Digital Library

[21]

Martens, J.B. Image Technology Design: A Perceptual Approach. Kluwer Academic Publishers: Dordrecht, NL, 2003.

[22]

Green, P.E., Carmone Jr. F.J. and Smith, S.M. Multidimensional Scaling, concepts and Applications. Allyn & Bacon, Massachusetts, USA, 1989.

[23]

Uusipaikka, E. Confidence Intervals in Generalized Regression Models. STATISTICS: Textbooks and Monographs. CRC Press: Finland, 2009.

[24]

Burnham, K.P. and Anderson, D.R. Multimodel Inference -- Understanding AIC and BIC in Model Selection. Sociological Methods & Research, 33(2):261--304, 2004.

Cited By

Quadri GWang AWang ZAdorno JRosen PSzafir D(2024)Do You See What I See? A Qualitative Study Eliciting High-Level Visualization ComprehensionProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642813(1-26)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642813
Hilasaca GMarcílio-Jr WEler DMartins RPaulovich F(2024)A Grid-Based Method for Removing Overlaps of Dimensionality Reduction Scatterplot LayoutsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.330994130:8(5733-5749)Online publication date: Aug-2024
https://doi.org/10.1109/TVCG.2023.3309941
Tseng CQuadri GWang ZSzafir D(2023)Measuring Categorical Perception in Color-Coded ScatterplotsProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581416(1-14)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581416
Show More Cited By

Index Terms

A model of symbol size discrimination in scatterplots
1. Computing methodologies
  1. Artificial intelligence
    1. Philosophical/theoretical foundations of artificial intelligence
      1. Cognitive science
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI theory, concepts and models
  2. Interaction design
    1. Interaction design theory, concepts and paradigms

Recommendations

A Nested Hierarchy of Localized Scatterplots
SIBGRAPI '14: Proceedings of the 2014 27th SIBGRAPI Conference on Graphics, Patterns and Images

The simplicity and visual clarity of scatterplots makes them one of the most widely-used visualization techniques for multivariate data. In complex data sets the important information can be hidden in subsets of the data, often obscured in the typical ...
On the perceptual influence of shape overlap on data-comparison using scatterplots▪
Highlights
- A measurement for overlap in multiclass scatterplots based on human perception.
Graphical abstract

Display Omitted

Abstract
Scatterplots can be used for a wide range of visual analysis tasks, for example comparing correlations or variances of clusters across potentially multiple classes of data, in order to find answers to higher-level questions. Comparing ...
On the Benefits of Using Constant Visual Angle Glyphs in Interactive Exploration of 3D Scatterplots

Visual exploration of clouds of data points is an important application of virtual environments. The common goal of this activity is to use the strengths of human perception to identify interesting structures in data, which are often not detected using ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CHI '10: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

April 2010

2690 pages

ISBN:9781605589299

DOI:10.1145/1753326

General Chair:
Elizabeth Mynatt
Georgia Institute of Technology
,
Program Chairs:
Geraldine Fitzpatrick
Vienna University of Technology
,
Scott Hudson
Carnegie Mellon University
,
Keith Edwards
Georgia Tech
,
Tom Rodden
University of Nottingham

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 April 2010

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CHI '10

Sponsor:

SIGCHI

CHI '10: CHI Conference on Human Factors in Computing Systems

April 10 - 15, 2010

Georgia, Atlanta, USA

Acceptance Rates

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

Upcoming Conference

CHI 2025

Sponsor:
sigchi

ACM CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2025

Yokohama , Japan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

33
Total Citations
View Citations
662
Total Downloads

Downloads (Last 12 months)46
Downloads (Last 6 weeks)4

Reflects downloads up to 11 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Quadri GWang AWang ZAdorno JRosen PSzafir D(2024)Do You See What I See? A Qualitative Study Eliciting High-Level Visualization ComprehensionProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642813(1-26)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642813
Hilasaca GMarcílio-Jr WEler DMartins RPaulovich F(2024)A Grid-Based Method for Removing Overlaps of Dimensionality Reduction Scatterplot LayoutsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.330994130:8(5733-5749)Online publication date: Aug-2024
https://doi.org/10.1109/TVCG.2023.3309941
Tseng CQuadri GWang ZSzafir D(2023)Measuring Categorical Perception in Color-Coded ScatterplotsProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581416(1-14)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581416
van Onzenoodt CVázquez PRopinski T(2023)Out of the Plane: Flower versus Star Glyphs to Support High-Dimensional Exploration in Two-Dimensional EmbeddingsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.321691929:12(5468-5482)Online publication date: Dec-2023
https://doi.org/10.1109/TVCG.2022.3216919
Müller GHollenstein DÇöltekin ABleisch S(2023)Square-glyphs: assessing the readability of multidimensional spatial data visualized as square-glyphsInternational Journal of Cartography10.1080/23729333.2023.22354929:3(449-465)Online publication date: 21-Jul-2023
https://doi.org/10.1080/23729333.2023.2235492
Matzen LRogers AHowell B(2023)Visualizing Uncertainty in Different Domains: Commonalities and Potential Impacts on Human Decision-MakingVisualization Psychology10.1007/978-3-031-34738-2_14(331-369)Online publication date: 7-Nov-2023
https://doi.org/10.1007/978-3-031-34738-2_14
Presnov DKolb A(2022)Perception and Quantization Model for Periodic Contour ModificationsJournal of Imaging10.3390/jimaging81103118:11(311)Online publication date: 21-Nov-2022
https://doi.org/10.3390/jimaging8110311
Li ZShi RLiu YLong SGuo ZJia SZhang J(2022)Dual Space Coupling Model Guided Overlap-Free ScatterplotIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.3209459(1-11)Online publication date: 2022
https://doi.org/10.1109/TVCG.2022.3209459
Quadri GRosen P(2022)A Survey of Perception-Based Visualization Studies by TaskIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.309824028:12(5026-5048)Online publication date: 1-Dec-2022
https://doi.org/10.1109/TVCG.2021.3098240
Hou YZhu HLiang HYu L(2022)A study of the effect of star glyph parameters on value estimation and comparisonJournal of Visualization10.1007/s12650-022-00888-x26:2(493-507)Online publication date: 3-Nov-2022
https://doi.org/10.1007/s12650-022-00888-x
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents