Interaction graphs: visual analysis of eye movement data from interactive stimuli

Eye tracking studies have been conducted to understand the visual attention in different scenarios like, for example, how people read text, which graphical elements in a visualization are frequently attended, how they drive a car, or how they behave during a shopping task. All of these scenarios - either static or dynamic - show a visual stimulus in which the spectators are not able to change the visual content they see. This is different if interaction is allowed like in (graphical) user interfaces (UIs), integrated development environments (IDEs), dynamic web pages (with different user-defined states), or interactive displays in general as in human-computer interaction, which gives a viewer the opportunity to actively change the stimulus content. Typically, for the analysis and visualization of time-varying visual attention paid to a web page, there is a big difference for the analytics and visualization approaches - algorithmically as well as visually - if the presented web page stimulus is static or dynamic, i.e. time-varying, or dynamic in the sense that user interaction is allowed. In this paper we discuss the challenges for visual analysis concepts in order to analyze the recorded data, in particular, with the goal to improve interactive stimuli, i.e., the layout of a web page, but also the interaction concept. We describe a data model which leads to interaction graphs, a possible way to analyze and visualize this kind of eye movement data.