Further Results on Predicting Cognitive Abilities for Adaptive Visualizations

Cristina Conati, Sébastien Lallé, Md. Abed Rahman, Dereck Toker

Abstract: Previous work has shown that some user cognitive abilities relevant for processing information visualizations can be predicted from eye tracking data. Performing this type of user modeling is important for devising user-adaptive visualizations that can adapt to a user’s abilities as needed during the interaction. In this paper, we contribute to previous work by extending the type of visualizations considered and the set of cognitive abilities that can be predicted from gaze data, thus providing evidence on the generality of these findings. We also evaluate how quality of gaze data impacts prediction.