Previous research has identified many effective methods to visualize different types of meta-information, or information qualifiers; however, these methods are often incorporated without understanding how the graphical codes are perceived and how the encoded information is interpreted by display users. This results in display designers selecting graphical codes to represent meta-information without empirical evidence to determine the appropriateness of these selections. To help address this lack of guidance, this paper presents a systematic study of how people perceive two graphical codes (saturation and opacity) and relate those codes to different types of meta-information. Results were generated using psychophysical scaling methods, and provide visualization designers with a means to more appropriately design meta-information representations.
Get full access to this article
View all access options for this article.
References
1.
AignerW.MikschS.ThurnherB.BifflS. (2005). Planninglines: Novel glyphs for representing temporal uncertainties and their evaluation. 9th International Conference on Information Visualisation, IEEE, p. 457–463.
2.
BedfordR.WyszeckiG. (1958). Wavelength discrimination for point sources. JOSA, 48(2), 129-130.
3.
BisantzA. M.CaoD.JenkinsM.PennathurP.FarryM.RothE.PotterS.PfautzJ. (2011). Comparing uncertainty visualizations for a dynamic decision-making task. Journal of Cognitive Engineering and Decision-Making. 5, 3, 277–295.
4.
BisantzA. M.MarsiglioS. S.MunchJ. (2005). Displaying uncertainty: Investigating the effects of display format and specificity. Human Factors, 47(4), 777.
5.
BisantzA. M.StoneR. T.PfautzJ.FouseA.FarryM.RothE.NagyA.DanielsG. (2009). Visual Representations of Meta-Information. J. of Cognitive Engineering and Decision Making, 3(1), 67-91.
6.
BotchenR. P.WeiskopfD.ErtlT.(2005). Texture-based visualization of uncertainty in flow fields. IEEE Visualization2005.
7.
BudescuD. V.WeinbergS.WallstenT. S. (1988). Decisions based on numerically and verbally expressed uncertainties. Journal of Experimental Psychology: Human Perception and Performance, 14(2), 281.
8.
ChristR. E. (1975). Review and analysis of color coding research for visual displays. Human Factors, 17(6), 542-570.
9.
ChristR. E. (1984). Research for evaluating visual display codes: An emphasis on colour coding. Information Design, NY: John Wiley, 209-228.
10.
DreckiI. (2002). Visualisation of uncertainty in geographical data. Spatial data quality, 140–159.
11.
EvansR. M.SwenholtB. K. (1968). Chromatic Strengths of Colors, Part II. The Munsell System. JOSA, 58(4), 580-583.
12.
FingerR.BisantzA. M. (2002). Utilizing graphical formats to convey uncertainty in a decision-making task. Theoretical Issues in Ergonomics Science, 3(1), 1-25.
13.
GershonN. (1998). Visualization of an imperfect world. IEEE Computer Graphics and Applications, 18(4), 43-45.
14.
GuilfordJ. P. (1954). Psychometric methods. NY: McGraw-Hill.
15.
HuntR. W. G. (1952). Light and dark adaptation and the perception of color. JOSA, 42(3), 190-199.
16.
HurvichL. M.JamesonD. (1955). Some quantitative aspects of an opponent-colors theory. II. Brightness, saturation, and hue in normal and dichromatic vision. JOSA, 45(8), 602-616.
17.
IndowT. (1967). Saturation scales for red. Vision research, 7, 481-5.
18.
InterranteV. (2000). Harnessing natural textures for multivariate visualization. Computer Graphics and Applications, IEEE, 20(6), 6-11.
19.
LeitnerM.ButtenfieldB. P. (2000). Guidelines for the display of attribute certainty. Cartography and GIS, 27(1), 3-14.
20.
MacEachrenA. M. (1992). Visualizing uncertain information. Cartographic Perspective, 13(3), 10-19.
21.
MacEachrenA. M.BrewerC. A.PickleL. W. (1998). Visualizing georeferenced data: representing reliability of health statistics. Environment and Planning A, 30, 1547-1562.
22.
PanekW.StevensS.S. (1966). Saturation of red: A prothetic continuum. Perception & Psychophysics, 1 (1), 59-66.
23.
PangA. T.WittenbrinkC. M.LodhaS. K. (1997). Approaches to uncertainty visualization. The Visual Computer, 13(8), 370-390.
24.
PfautzJ.FouseA.FichtlT.BisantzA.RothE.MaddenS. (2005). The impact of meta-information on decision-making in intelligence operations. 49th Annual HFES Annual Meeting, 214-218.
25.
PokornyJ.SmithV. (2004). Chromatic discrimination. The visual neuroscience, 2, 908-923.
26.
PurdyD. (1931). Spectral hue as a function of intensity. American Journal of Psychology, 43, 541-559.
27.
SchweizerD. M.GoodchildM. F. (1992). Data quality and choropleth maps. GIS/LIS, San Jose, CA.
28.
StevensJ.C. (1967). Brightness inhibition re size of surround. Perception & Psychophysics, 2, 189-192.
29.
StevensS.S. (1974). Notes for a life story. In LindzeyG. (ed.), A history of psychology in autobiography. Volume 6 (pp. 395-420). Englewood Cliffs, NJ: Prentice Hall.
30.
TorgersonW. S. (1958). Theory and methods of scaling. New York: John Wiley & Sons, Inc.
31.
UrnessT.InterranteV.MarusicI.LongmireE.GanapathisubramaniB. (2003). Effectively visualizing multi-valued flow data using color and texture. Paper presented at the Proceedings IEEE Visualization ’03.
32.
WallstenT. S.BudescuD. V. (1995). A review of human linguistic probability processing: General principles and empirical evidence. Knowledge Engineering Review, 10(1), 43-62.
33.
WittenbrinkC. M.PangA. T.LodhaS. K. (1996). Glyphs for visualizing uncertainty in vector fields. Visualization and Computer Graphics, 2(3), 266-279.
34.
WyszeckiStilesW. S. (1982). Color science: concepts and methods, quantitative data and formulae: John Wiley & Sons.
