Abstract
Information visualizations can be regarded as one of the most powerful cognitive tools to significantly amplify human cognition. However, traditional information visualization systems have been designed in a manner that does not consider individual user differences, even though human cognitive abilities and styles have been shown to differ significantly. In order to address this research gap, novel adaptive systems need to be developed that are able to (1) infer individual user characteristics and (2) provide an adaptation mechanism to personalize the system to the inferred characteristic. This paper presents a first step toward this goal by investigating the extent to which a user's cognitive style can be inferred from their behavior with an information visualization system. In particular, this paper presents a series of experiments that utilize features calculated from user eye gaze data in order to infer a user's cognitive style. Several different data and feature sets are presented, and results overall show that a user's eye gaze data can be used successfully to infer a user's cognitive style during information visualization usage.
Highlights
With the proliferation of large quantities of data across all aspects of our daily lives, it has become paramount to research new paradigms to help users deal with such data efficiently and effectively
This paper has presented an initial step toward building information visualization systems that can adaptively support users based on their individual cognitive style
Steichen and Fu (2019) had found that different visualization overlays were preferred by users with different cognitive styles, for example, added data values being preferred by field-dependent users, and our inference system could be combined with such overlays to dynamically change the current visualization to best suit individual users
Summary
With the proliferation of large quantities of data across all aspects of our daily lives (ranging from reading news articles to reaching critical business decisions), it has become paramount to research new paradigms to help users deal with such data efficiently and effectively. A key reason for the success of such information visualizations is the fact that they are making use of “the highest bandwidth channel from the computer to the human” (Ware, 2004), namely, the human visual system. While information visualization systems have largely been successful in helping humans perceive and analyze information, they have typically been designed in a non-personalized manner, i.e., each individual user/viewer is being shown the same visualization in the same form. This nonadaptive nature of systems assumes that cognitive processing is mostly identical across humans and, that all users would benefit from the same visualization. Examples of cognitive abilities include perceptual speed (“a measure of speed when performing simple perceptual tasks”) and verbal/visual working memory
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