Inferring Cognitive Style from Eye Gaze Behavior During Information Visualization Usage

Information Visualization is a key technique to assist users in data analysis tasks, by creating visual representations of data to amplify human cognition. However, while human cognitive abilities and styles have been shown to differ significantly, Information Visualizations have traditionally been designed in a manner that does not consider such individual user differences. Recent research has started to address this issue, by identifying individual user characteristics that influence individual users' interactions with Information Visualizations, as well as developing novel Information Visualization systems that provide more personalized support. This paper presents a set of experiments aimed towards building such User-Adaptive Information Visualization systems, by studying the extent to which a user's cognitive style can be inferred from a user's interaction with an Information Visualization system. Results show that a user's eye gaze data can be used to infer a user's cognitive style during information visualization usage with up to 86% accuracy, and that the most informative features relate to a user's saccade angles and fixation durations.