Modeling Interaction Patterns in Visualizations with Eye-Tracking: A Characterization of Reading and Information Styles

In data visualization, users' scanning patterns are as crucial as their reading patterns in text-based media. Yet, no systematic attempt exists to characterize this activity with basic features, such as reading speed and scanpaths, nor to relate them to data complexity and information disposition. To fill this gap, this paper proposes a model-based method to analyze and interpret those features from eye-tracking data. To this end, the bias-noise model is applied to a data visualization eye-tracking dataset available online, and enriched with areas of interest labels. The positive results of this method are as follows: (i) the identification of users' reading styles like meticulous, systematic, and serendipitous; (ii) the characterization of information disposition as gathered or scattered, and of information complexity as more or less dense; (iii) the discovery of a behavioural pattern of efficiency, given that the more visualizations were read by a participant, the greater their reading speed, consistency, and predictability of reading; (iv) the identification of encoding and title areas of interest as the primary loci of attention in visualizations, with a peculiar back-and-forth reading pattern; (v) the identification of the encoding area of interest as the fastest to read in less dense visualization types, such as bars, circles, and lines charts. Future experiments involving participants from diverse cultural backgrounds could not only validate the observed behavioural patterns, but also enrich the experimental framework with additional perspectives.