Readings in information visualization: using vision to think
Readings in information visualization: using vision to think
Empirical evaluation of information visualizations: an introduction
International Journal of Human-Computer Studies - Empirical evaluation of information visualizations
Graph Drawing: Algorithms for the Visualization of Graphs
Graph Drawing: Algorithms for the Visualization of Graphs
Which Aesthetic has the Greatest Effect on Human Understanding?
GD '97 Proceedings of the 5th International Symposium on Graph Drawing
Cognitive measurements of graph aesthetics
Information Visualization
Human Factors in Visualization Research
IEEE Transactions on Visualization and Computer Graphics
Using games to investigate movement for graph comprehension
Proceedings of the working conference on Advanced visual interfaces
A Comparison of the Readability of Graphs Using Node-Link and Matrix-Based Representations
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Layout effects on sociogram perception
GD'05 Proceedings of the 13th international conference on Graph Drawing
The Effectiveness Of Graphic And Tabular Presentation Under Time Pressure And Task Complexity
Information Resources Management Journal
Review: Integrating cognitive load theory and concepts of human-computer interaction
Computers in Human Behavior
Exploring the relative importance of crossing number and crossing angle
Proceedings of the 3rd International Symposium on Visual Information Communication
Human-centered visualization environments
Human-centered visualization environments
| Hi-index | 0.00 |
Performance and preference measures are commonly used in the assessment of visualization techniques. This is important and useful in understanding differences in effectiveness between different treatments. However, these measures do not answer how and why the differences are caused. And sometimes, performance measures alone may not be sensitive enough to detect differences. In this paper, we introduce a cognitive approach for visualization effectiveness and efficiency assessment. A model of user performance, mental effort and cognitive load (memory demand) is proposed and further mental effort and visualization efficiency measures are incorporated into our analysis. It is argued that 1) combining cognitive measures with traditional methods provides us new insights and practical guidance in visualization assessment. 2) analyzing human cognitive process not only helps to understand how viewers interact with visualizations, but also helps to predict user performance in initial stage. 3) keeping cognitive load induced by a visualization low allows more memory resources to be available for high level complex cognitive activities. A case study conducted supports our arguments.