Inferring user cognitive abilities from eye-tracking data

User-adaptive visualization can provide intelligent personalization to aid the user in the information processing. The adaptations, in the form as simple as helpful highlighting, are applied based on user’s characteristics and preferences inferred by the system. Previous work has shown that binary labels of user’s cognitive abilities relevant for processing information visualizations could be predicted in real time, by leveraging user gaze patterns collected via a non-intrusive eye-tracking device. The classification accuracies reported were in the 59–65% range, which is statistically more accurate than a majority-class classifier, but not of great practical significance. In this thesis, we expand on previous work by showing that significantly higher accuracies can be achieved by leveraging summative statistics on a user’s pupil size and head distance to the screen measurements, also collected by an eye tracker. Our experiments show that these results hold for two datasets, providing evidence of the generality of our findings. We also explore the sequential nature of gaze movement by extracting common substring patterns and using the frequency of these patterns as features for classifying user’s cognitive abilities. Our sequence features are able to classify more accurately than the majority-class baseline, but unable to outperform our best classification model with the summative eye-tracking features.Science, Faculty ofComputer Science, Department ofGraduat