Ann M. Bisantz and Dapeng Cao and Michael Jenkins and Priyadarshini R. Pennathur and Michael Farry and Emilie Roth and Scott S. Potter and Jonathan Pfautz.
Comparing Uncertainty Visualizations for a Dynamic Decision-Making Task.
In Journal of Cognitive Engineering and Decision Making, vol. 5, no. 3, pp. 277-293, 2011.

Links:

Abstract:

Supporting complex decision making requires conveying relevant information characteristics or qualifiers. The authors tested transparency and numeric annotation for displaying uncertainty about object identity. Participants performed a "missile defense" game in which they decided whether to destroy moving objects (which were either threatening missiles or nonthreatening birds and planes) before they reached a city. Participants were provided with uncertain information about the objects' classifica-tions. Uncertainty was represented through the transparency of icons representing the objects and/or with numeric annotations. Three display methods were created. Icons represented the most likely object classification (with solid icons), the most likely object classification (with icons whose transparency represented the level of uncertainty), or the probability that the icon was a missile (with transparency). In a fourth condition, participants could choose among the representations. Icons either were or were not annotated with numeric probability labels. Task performance was highest when participants could toggle the displays, with little effect of numeric annotation. In conditions in which probabilities were available graphically or numerically, participants chose to engage objects when they were farther from the city and had a lower probability of being a missile. Results provided continued support for the use of graphical uncertainty representations, even when numeric representations are present.

Bibtex:

@Article{        bisantz:2011:CUVD,
  author = 	 {Ann M. Bisantz and Dapeng Cao and Michael Jenkins and Priyadarshini R. Pennathur and Michael Farry and Emilie Roth and Scott S. Potter and Jonathan Pfautz},
  title = 	 {Comparing Uncertainty Visualizations for a Dynamic Decision-Making Task},
  journal = 	 {Journal of Cognitive Engineering and Decision Making},
  year = 	 {2011},
  volume = 	 {5},
  number = 	 {3},
  pages = 	 {277-293},
  month = 	 {September},
}

Images:

References:

Aerts, J. Clarke, K., & Keuper, A. (2003). Testing popular visualization techniques for representing model uncertainty. Cartography and Geographic Information Science, 30, 249-261.
Aigner, W., Miksch, S., Thurnher, B., & Biffl, S. (2005). PlanningLines: Novel glyphs for representing temporal uncertainties and their evaluation. In Proceedings of the Ninth International Conference on Information Visualisation (pp. 457-463). New York, NY: IEEE.
Bisantz, A. M., Marsiglio, S., & Munch, J. L. (2005). The effects of format and level of specificity for displaying uncertainty. Human Factors, 47, 777-798.
Bisantz, A. M., Stone, R., Pfautz, J., Fouse, A., Farry, M., Roth, E. M., . . . Thomas, G. (2009). Visual representations of meta information. Journal of Cognitive Engineering and Decision Making, 3, 61-91.
Botchen, R. P., Weiskopf, D., & Ertl, T. (2005). Texture-based visualization of uncertainty in flow fields. In Proceedings of the IEEE Visualization Conference (pp. 82). Piscataway, NJ: IEEE.
Brown, R. (2004). Animated visual vibrations as an uncertainty visualisation technique. In Proceedings of the International Conference on Computer Graphics and Interactive Techniques in Australasia and South East Asia (pp. 84-89). Singapore: IEEE.
Drecki, I. (2002). Visualisation of uncertainty in geographical data. In W. Shi, P. Fisher, & M. Goodchild (Eds.), Spatial data quality (pp. 140-159). London, UK: Taylor and Francis.
Ehlschlaeger, C. R., Shortridge, A. M., & Goodchild, M. F. (1997). Visualizing spatial data uncertainty using animation. Computers and Geosciences, 23, 387-395.
Finger, R., & Bisantz, A. M. (2002). Utilizing graphical formats to convey uncertainty in a decision making task. Theoretical Issues in Ergonomics Science, 3(1), 1-25.
Gershon, N. (1998). Visualization of an imperfect world. IEEE Computer Graphics and Applications, 18(4), 43-45.
Hart, S. G., & Staveland, L. E. (1988). Development of a multi-dimensional workload rating scale: Results of empirical and theoretical research. In P. A. Hancock & N. Meshkati (Eds.), Human mental workload (pp. 139-183). Amsterdam, Netherlands: Elsevier.
Howard, D., & MacEachren, A. M. (1996). Interface design for geographic visualization: Tools for representing reliability. Cartography and Geographic Information Systems, 23, 59-77.
Interrante, V. (2000). Harnessing natural textures for multivariate visualization. IEEE Computer Graphics and Applications, 20(6), 6-11.
Kahneman, D., & Tversky, A. (1984). Choices, values, and frames. American Psychologist, 39, 341-350.
MacEachren, A. M. (1992). Visualizing uncertain information. Cartographic Perspective, 13, 10-19.
MacEachren, A. M., Robinson, A., Hopper, S., Gardner, S., Murray, R., Gahegan, M., & Hetzler, E. (2005). Visualizing geospatial information uncertainty: What we know and what we need to know. Cartography and Geographic Information Science, 32, 139-160.
Nadav-Greenberg, L., Joslyn, S. L., & Taing, M. U. (2008). The effect of uncertainty visualizations on decision making in weather forecasting. Journal of Cognitive Engineering and Decision Making, 2, 24-37.
Pang, A. T., Wittenbrink, C. M., & Lodha, S. K. (1997). Approaches to uncertainty visualization. Visual Computing, 13, 370-390.
Pfeiffer, J. J. (2002). Using brightness and saturation to visualize belief and uncertainty. In M. Hegarty, B. Meyer, & N. H. Narayanan (Eds.), Diagrams 2002 (pp. 272-289). Heidelberg, Germany: Springer-Verlag.
Slocum, T. A., Cliburn, D. C., Feddema, J. J., & Miller, J. R. (2003). Evaluating the usability of a tool for visualizing the uncertainty of the future global water balance. Cartography and Geographic Information Science, 30, 299-317.
Urness, T., Interrante, V., Marusic, I., Longmire, E., & Ganapathishubramani, B. (2003). Effectively visualizing multi-valued flow data using color and texture. In IEEE Visualization 2003 (pp. 115-121). New York, NY: IEEE.
Wallsten, T. S. (1990). The costs and benefits of vague information. In R. M. Hogarth (Ed.), Insights in decision making (pp. 28-43). Chicago, IL: University of Chicago.
Wallsten, T. S., & Budescu, D. V. (1995). A review of human linguistic probability processing: General principles and empirical evidence. Knowledge Engineering Review, 10, 43-62.
Wickens, C. D., & Andre, A. D. (1990). Proximity compatibility and information display: Effects of color, space, and objectness of information integration. Human Factors, 32, 61-77.
Wittenbrink, C. M., Saxon, E., Furman, J. J., Pang, A. T., & Lodha, S. K. (1996). Glyphs for visualizing uncertainty in environmental vector fields. IEEE Transactions on Visualization and Computer Graphics, 2, 266-279.