Which emphasis technique to use? Perception of emphasis techniques with varying distractors, backgrounds, and visualization types.

Data visualization empirical evaluation experimental studies human-computer interaction perception

Journal

Information visualization
ISSN: 1473-8716
Titre abrégé: Inf Vis
Pays: United States
ID NLM: 101232636

Informations de publication

Date de publication:
Apr 2022
Historique:
entrez: 18 2 2022
pubmed: 19 2 2022
medline: 19 2 2022
Statut: ppublish

Résumé

Emphasis effects are visual changes that make data elements distinct from their surroundings. Designers may use computational saliency models to predict how a viewer's attention will be guided by a specific effect; however, although saliency models provide a foundational understanding of emphasis perception, they only cover specific visual effects in abstract conditions. To address these limitations, we carried out crowdsourced studies that evaluate emphasis perception in a wider range of conditions than previously studied. We varied effect magnitude, distractor number and type, background, and visualization type, and measured the perceived emphasis of 12 visual effects. Our results show that there are perceptual commonalities of emphasis across a wide range of environments, but also that there are limitations on perceptibility for some effects, dependent on a visualization's background or type. We developed a model of emphasis predictability based on simple scatterplots that can be extended to other viewing conditions. Our studies provide designers with new understanding of how viewers experience emphasis in realistic visualization settings.

Identifiants

pubmed: 35177955
doi: 10.1177/14738716211045354
pii: 10.1177_14738716211045354
pmc: PMC8841630
doi:

Types de publication

Journal Article

Langues

eng

Pagination

95-129

Informations de copyright

© The Author(s) 2021.

Références

IEEE Trans Vis Comput Graph. 2014 Dec;20(12):1933-42
pubmed: 26356907
IEEE Trans Vis Comput Graph. 2014 Dec;20(12):2456-65
pubmed: 26356959
Hum Factors. 1988 Apr;30(2):127-42
pubmed: 3384442
J Vis. 2007 Aug 16;7(2):17.1-22
pubmed: 18217832
IEEE Trans Vis Comput Graph. 2020 Jan;26(1):1215-1225
pubmed: 31425090
IEEE Trans Vis Comput Graph. 2017 Jan;23(1):341-350
pubmed: 27875150
IEEE Trans Vis Comput Graph. 2012 Jul;18(7):1170-88
pubmed: 21788672
IEEE Trans Vis Comput Graph. 2007 Nov-Dec;13(6):1240-7
pubmed: 17968070
IEEE Trans Vis Comput Graph. 2018 Jan;24(1):392-401
pubmed: 28866544
Nat Rev Neurosci. 2004 Jun;5(6):495-501
pubmed: 15152199
Cogn Psychol. 1980 Jan;12(1):97-136
pubmed: 7351125
IEEE Trans Vis Comput Graph. 2010 Nov-Dec;16(6):1139-48
pubmed: 20975152
Trends Cogn Sci. 2012 Aug;16(8):437-43
pubmed: 22795563
IEEE Trans Vis Comput Graph. 2009 Nov-Dec;15(6):1523-30
pubmed: 19834229
IEEE Trans Vis Comput Graph. 2015 Mar;21(3):339-49
pubmed: 26357066
IEEE Trans Pattern Anal Mach Intell. 2013 Jan;35(1):185-207
pubmed: 22487985
IEEE Trans Vis Comput Graph. 2019 Mar;25(3):1474-1488
pubmed: 29993809
J Exp Psychol Gen. 2010 May;139(2):247-65
pubmed: 20438251
IEEE Trans Vis Comput Graph. 2012 Dec;18(12):2402-10
pubmed: 26357148
Behav Res Methods. 2018 Dec;50(6):2586-2596
pubmed: 29542063
Psychol Rev. 1989 Jul;96(3):433-58
pubmed: 2756067
Atten Percept Psychophys. 2019 Apr;81(3):637-653
pubmed: 30603990
IEEE Trans Vis Comput Graph. 2011 Dec;17(12):2301-9
pubmed: 22034350
Percept Psychophys. 2001 Nov;63(8):1293-313
pubmed: 11800458
IEEE Trans Vis Comput Graph. 2012 Dec;18(12):2496-505
pubmed: 26357158
Vision Res. 2005 Aug;45(18):2397-416
pubmed: 15935435
IEEE Trans Vis Comput Graph. 2018 Jan;24(1):563-573
pubmed: 28866504
IEEE Trans Vis Comput Graph. 2021 Feb;27(2):1117-1127
pubmed: 33090954
IEEE Trans Vis Comput Graph. 2012 Dec;18(12):2769-78
pubmed: 26357186
Science. 1982 Jun 4;216(4550):1138-41
pubmed: 17808503
IEEE Trans Vis Comput Graph. 2019 Jul;25(7):2505-2512
pubmed: 29994001
IEEE Trans Vis Comput Graph. 2013 Dec;19(12):2306-15
pubmed: 24051797
Front Psychol. 2012 Feb 06;3:13
pubmed: 22347200
Behav Res Methods. 2012 Mar;44(1):1-23
pubmed: 21717266

Auteurs

Aristides Mairena (A)

University of Saskatchewan, Saskatoon, SK, Canada.

Carl Gutwin (C)

University of Saskatchewan, Saskatoon, SK, Canada.

Andy Cockburn (A)

University of Canterbury, Christchurch, New Zealand.

Classifications MeSH