The Cognition of Complex Visualizations

The Cognition of Complex Visualizations J. Gregory Trafton (trafton@itd.nrl.navy.mil) Naval Research Laboratory Priti Shah (priti@umich.edu) and Eric G. Freedman (freedman@umflint.edu) Department of Psychology, University of Michigan Susan Kirschenbaum (KirschenbaumSS@npt.nuwc.navy.mil) Naval Undersea Warfare Center Peter C-H. Cheng (peter.cheng@nottingham.ac.uk) University of Nottingham Discussant: Mary Hegarty ( hegarty@psych.ucsb.edu ) Department of Psychology, University of California, Santa Barbara Abstract We explore current research on how complex visualizations are perceived, comprehended, used, and taught. Introduction How do people perceive, comprehend, and use complex visualizations, and when are they needed? Many domains (meteorology, scientific visualization, stock market analyses) deal with very complex data that must be displayed and used in novel ways. Unfortunately, very little is known about how these complex displays are used, how to best display complex graphical information, or how to design good complex visualizations for teaching purposes. This symposium will examine: • how people understand and use complex visualizations; • how people gain expertise in using complex visualizations; • how to teach complex domains by using graphs and visualizations; • how to visualize uncertainty across many variables; • why a visualization is hard or easy to use; and • how current models of graph comprehension scale up to more complexity. Building Qualitative Mental Models Greg Trafton How do people use a complex visualization? Most current theories predict a straightforward process of reading off specific information, typically at the request of an experimenter. Many complex domains, however (many areas of scientific visualization, meteorology, etc.) need to deal with multi-dimensional data with complex interactions and anomalies. I will present several recent studies that show that while experts mostly conform to the standard models of graph comprehension, there are some glaring holes in current theories. Specifically, experts do more than simply read off information. First, they extract primarily qualitative information from complex visualizations (e.g., The wind is fast over San Diego ) even when quantitative information is available and needed later. With this qualitative information, they build a complex mental representation (which we call a qualitative mental model, or QMM) to reason with. I will present data that shows how experts build these complex mental structures by looking at complex visualizations. I will also present evidence from eye- tracking and protocol studies of experts and novices working in their own domain, showing how novices seem to conform to the standard graph comprehension models while experts do not. The Role of Prior Knowledge in Complex Data Comprehension Priti Shah & Eric G. Freedman People are increasingly faced with the task of interpreting complex˚multivariate quantitative data sets. Unfortunately, much research on graph interpretation has focused on how novice (college undergraduate) viewers use common formats˚ (e.g., bar and line graphs) for simple tasks (e.g., read a data point or describe a trend) and sparse (2-3 variables and few data points) and meaningless (axes labeled x and y) data.˚ In our presentation, we argue that models based on this research may not scale up to account for more complex data interpretation, which differs in several key features. Complex data interpretation usually refers to tasks involving many variables, complex interactions between the variables, and a large number of data points. Complexity extends beyond simply data complexity, however. Dealing with complex data coincides with complex tasks (e.g., making decisions or explaining data) rather than fact retrieval.˚ Complex data also involves the extensive use of prior knowledge and viewers with data interpretation skills (experts use complex data, not novices).˚ Finally, complex data is often presented via