Individual differences in a spatial-semantic virtual environment
Journal of the American Society for Information Science - Special topic issue: individual differences in virtual environments
The Impact of Experience and Time on the Use of Data Quality Information in Decision Making
Information Systems Research
IEEE Computer Graphics and Applications
Exploring the role of individual differences in information visualization
AVI '08 Proceedings of the working conference on Advanced visual interfaces
Visualization of Uncertainty and Reasoning
SG '07 Proceedings of the 8th international symposium on Smart Graphics
Recovering Reasoning Processes from User Interactions
IEEE Computer Graphics and Applications
A Nested Model for Visualization Design and Validation
IEEE Transactions on Visualization and Computer Graphics
Useful junk?: the effects of visual embellishment on comprehension and memorability of charts
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Measuring effectiveness of graph visualizations: a cognitive load perspective
Information Visualization
Laws of Attraction: From Perceptual Forces to Conceptual Similarity
IEEE Transactions on Visualization and Computer Graphics
Pair Analytics: Capturing Reasoning Processes in Collaborative Visual Analytics
HICSS '11 Proceedings of the 2011 44th Hawaii International Conference on System Sciences
Benefitting InfoVis with Visual Difficulties
IEEE Transactions on Visualization and Computer Graphics
Brainput: enhancing interactive systems with streaming fnirs brain input
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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The effects of individual differences on user interaction is a topic that has been explored for the last 25 years in HCI. Recently, the importance of this subject has been carried into the field of information visualization and consequently, there has been a wide range of research conducted in this area. However, there has been no consensus on which evaluation methods best answer the unique needs of information visualization. In this position paper we propose that individual differences are evaluated in three dominant dimensions: cognitive traits, cognitive states and experience/bias. We believe that this is a first step in systematically evaluating the effects of users' individual differences on information visualization and visual analytics.