Cognitive models of the influence of color scale on data visualization tasks.
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.


METHODS
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.


CONCLUSIONS
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.


CONCLUSIONS
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.
Breslow
Leonard A
LA
Naval Research Laboratory, Code 5515, 4555 Overlook Ave. SW, Washington, DC, 20375, USA. breslow@aic.nrl.navy.mil
Ratwani
Raj M
RM
Trafton
J Gregory
JG
D003118:Color Perception / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D010364:Pattern Recognition, Visual / Q000502:physiology*; D013647:Task Performance and Analysis