Objective: Computational models of identification and relative comparison tasks performed on color-coded data visualizations were presented and evaluated against two experiments. In this context, the possibility of a dual-use color scale, useful for both tasks, was explored, and the use of the legend was a major focus. Background: Multicolored scales are superior to ordered brightness scales for identification tasks, such as determining the absolute numeric value of a represented item, whereas ordered brightness scales are superior for relative comparison tasks, such as determining which of two represented items has a greater value. Method: Computational models were constructed for these tasks, and their predictions were compared with the results of two experiments. Results: The models fit the experimental results well. A multicolored, brightness-ordered dual-use scale supported high accuracy on both tasks and fast responses on a comparison task but relatively slower responses on the identification task. Conclusion: Identification tasks are solved by a serial visual search of the legend, whose speed and accuracy are a function of the discriminability of the color scales. Comparison tasks with multicolored scales are performed by a parallel search of the legend; with brightness scales, comparison tasks are generally solved by a direct comparison between colors on the visualization, without reference to the legend. Finally, it is possible to provide users a dual-use color scale effective on both tasks. Application: Trade-offs that must typically be made in the design of color-coded visualizations between speed and accuracy or between identification and comparison tasks may be mitigated.
Anderson, J.R. ( 2007). How can the human mind occur in the physical universe?New York: Oxford University Press .
2.
Anderson, J.R., Bothell, D., Byrne, M.D., Lebiere, C., & Qin, Y. ( 2004). An integrated theory of the mind. Psychological Review, 111, 1036-1060.
3.
Braithwaite, J.J., Humphreys, G.W., & Hodsoll, J. ( 2003). Color grouping in space and time: Evidence from negative color-based carryover effects in preview search. Journal of Experimental Psychology: Human Perception and Performance, 29, 758-778.
4.
Breslow, L.A., Trafton, J.G., McCurry, J.M., & Ratwani, R.M. (in press). An algorithm for generating color scales for both categorical and ordinal coding. Color Research and Application.
5.
Breslow, L.A., Trafton, J.G., & Ratwani, R.M. ( 2009). A perceptual process approach to selecting color scales for complex visualizations. Journal of Experimental Psychology: Applied, 15, 25-34.
6.
Carpenter, P.A., & Shah, P. ( 1998). A model of the perceptual and conceptual processes in graph comprehension. Journal of Experimental Psychology: Applied , 4, 75-100.
7.
Carter, R.C. ( 1982). Visual search with color. Journal of Experimental Psychology: Human Perception and Performance, 8, 127-136.
8.
CIE. (2001). Improvements to industrial colour-difference evaluation (Publication CIE 142-2001). Vienna: Commission Internationale de l’Eclairage .
9.
Derefeldt, G., Swartling, T., Berggrund, U., & Bodrogi, P. ( 2004). Cognitive color. Color Research and Application , 29, 7-19.
10.
Fairchild, M.D., & Pirrotta, E. ( 1991). Predicting the lightness of chromatic object colors using CIELAB. Color Research and Application, 16, 385-393.
Hoffman, R.R., Detweiler, M.A., Lipton, K., & Conway, J.A. ( 1993). Considerations in the use of color in meteorological displays. Weather and Forecasting, 8, 505-518.
13.
Merwin, D.H., & Wickens, C.D. (1993). Comparison of eight color and gray scales for displaying continuous 2D data . In Proceedings of the Human Factors and Ergonomics Society 37th Annual Meeting (pp. 1330-1334). Santa Monica, CA: Human Factors and Ergonomics Society.
14.
Nagy, A.L. ( 1999). Interactions between achromatic and chromatic mechanisms in visual search. Vision Research, 39, 3253-3326.
15.
Nagy, A.L., & Sanchez, R.R. ( 1992). Chromaticity and luminance as coding dimensions in visual search. Human Factors, 34, 601-614.
16.
Peebles, D., & Cheng, P.C.H. ( 2003). Modeling the effect of task and graphical representation on response latency in a graph reading task. Human Factors , 45, 28-46.
17.
Phillips, R.J. ( 1982). An experimental investigation of layer tints for relief maps in school atlases. Ergonomics, 25, 1143-1154.
18.
R Development Core Team. (2007). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.
Schneider, W., Eschman, A., & Zuccolotto, A. (2002). E-Prime user’s guide . Pittsburgh, PA: Psychology Software Tools.
21.
Schunn, C.D., & Wallach, D. ( 2005). Evaluating goodness-of-fit in comparison of models to data. In W. Tack (Ed.), Psychologie der Kognition: Reden und Vorträge anlässlich der Emeritierung von Werner Tack (pp. 115-154). Saarbrueken , Germany: University of Saarland Press.
22.
Sharma, G., Wu, W., & Dalal, E.N. ( 2005). The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations. Color Research and Application, 30, 21-30.
23.
Smallman, H.S., & Boynton, R.M. ( 1990). Segregation of basic colors in an information display . Journal of the Optical Society of America A, 7, 1985-1994.
24.
Spence, I., Kutlesa, N., & Rose, D.L. ( 1999). Using color to code quantity in spatial displays . Journal of Experimental Psychology: Applied, 5, 393-412.
25.
Sperling, G. ( 1960). The information available in brief visual presentations . Psychological Monographs: General and Applied, 74(11), 1-30.
26.
Trafton, J.G., Kirschenbaum, S.S., Tsui, T.L., Miyamoto, R.T., Ballas, J.A., & Raymond, P.D. ( 2000). Turning pictures into numbers: Extracting and generating information from complex visualizations. International Journal of Human Computer Studies, 53, 827-850.
27.
Trafton, J.G., Marshall, S., Mintz, F.E., & Trickett, S.B. (2002). Extracting explicit and implicit information from complex visualizations. In B. Meyer & H. Narayanan (Eds.), Diagramatic representation and inference (pp. 206-220). Berlin: Springer-Verlag.
28.
Trafton, J.G., Trickett, S.B., & Mintz, F.E. ( 2005). Connecting internal and external representations: Spatial transformations of scientific visualizations. Foundations of Science, 10, 89-106.
29.
Trickett, S.B., & Trafton, J.G. ( 2007). The use of spatial cognition in graph interpretation . In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 1563-1568). Austin, TX : Cognitive Science Society. Retrieved April 15, 2009, from http://www.cogsci.rpi.edu/csjarchive/proceedings/2007/docs/p1563.pdf
30.
Vandenbeld, L.A., & Rensink, R.A. ( 2003). The decay characteristics of size, color, and shape information in visual short-term memory. Journal of Vision, 3, 682.
31.
Yokoi, K., & Uchikawa, K. ( 2005). Color category influences heterogeneous visual search for color. Optical Society of America A, 22, 2309-2317.
