Cognitive Models of the Influence of Color Scale on Data Visualization Tasks

Abstract

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.