Readers can have different goals with respect to the text they are reading. Can these goals be decoded from the pattern of their eye movements over the text? In this work, we examine for the first time whether it is possible to decode two types of reading goals that are common in daily life: information seeking and ordinary reading. Using large scale eye-tracking data, we apply to this task a wide range of state-of-the-art models for eye movements and text that cover different architectural and data representation strategies, and further introduce a new model ensemble. We systematically evaluate these models at three levels of generalization: new textual item, new participant, and the combination of both. We find that eye movements contain highly valuable signals for this task. We further perform an error analysis which builds on prior empirical findings on differences between ordinary reading and information seeking and leverages rich textual annotations. This analysis reveals key properties of textual items and participant eye movements that contribute to the difficulty of the task.

Head mounted displays bring eye tracking into daily use and this raises privacy concerns for users. Privacy-preservation techniques such as differential privacy mechanisms are recently applied to the eye tracking data obtained from such displays; however, standard differential privacy mechanisms are vulnerable to temporal correlations in the eye movement features. In this work, a transform coding based differential privacy mechanism is proposed for the first time in the eye tracking literature to further adapt it to statistics of eye movement feature data by comparing various low-complexity methods. Fourier Perturbation Algorithm, which is a differential privacy mechanism, is extended and a scaling mistake in its proof is corrected. Significant reductions in correlations in addition to query sensitivities are illustrated, which provide the best utility-privacy trade-off in the literature for the eye tracking dataset used. The differentially private eye movement data are evaluated also for classification accuracies for gender and document-type predictions to show that higher privacy is obtained without a reduction in the classification accuracies by using proposed methods.

In this paper, we adopt a multimodal emotion recognition framework by combining eye movements and electroencephalography (EEG) to enhance emotion recognition. The main contributions of this paper are twofold. a) We investigate sixteen eye movements related to emotions and identify the intrinsic patterns of these eye movements for three emotional states: positive, neutral and negative. b) We examine various modality fusion strategies for integrating users external subconscious behaviors and internal cognitive states and reveal that the characteristics of eye movements and EEG are complementary to emotion recognition. Experiment results demonstrate that modality fusion could significantly improve emotion recognition accuracy in comparison with single modality. The best accuracy achieved by fuzzy integral fusion strategy is 87.59%, whereas the accuracies of solely using eye movements and EEG data are 77.80% and 78.51%, respectively.