Recent studies of eye movements in reading and other information processing tasks, such as music reading, typing, visual search, and scene perception, are reviewed. The major emphasis of the review is on reading as a specific example of cognitive processing. Basic topics discussed with respect to reading are (a) the characteristics of eye movements, (b) the perceptual span, (c) integration of information across saccades, (d) eye movement control, and (e) individual differences (including dyslexia). Similar topics are discussed with respect to the other tasks examined. The basic theme of the review is that eye movement data reflect moment-to-moment cognitive processes in the various tasks examined. Theoretical and practical considerations concerning the use of eye movement data are also discussed. Many studies using eye movements to investigate cognitive processes have appeared over the past 20 years. In an earlier review, I (Rayner, 1978b) argued that since the mid-1970s we have been in a third era of eye movement research and that the success of research in the current era would depend on the ingenuity of researchers in designing interesting and informative

Motivated by and grounded in observations of eye-gaze patterns in human-human dialogue, this study explores using eye-gaze patterns in managing human-computer dialogue. We developed an interactive system, iTourist, for city trip planning, which encapsulated knowledge of eye-gaze patterns gained from studies of human-human collaboration systems. User study results show that it was possible to sense users ’ interest based on eye-gaze patterns and manage computer information output accordingly. Study participants could successfully plan their trip with iTourist and positively rated their experience of using it. We demonstrate that eyegaze could play an important role in managing future multimodal human-computer dialogues.

This article describes and evaluates a class of methods for performing automated analysis of eye-movement protocols. Although eye movements have become increasingly popular as a tool for investigating user behavior, they can be extremely difficult and tedious to analyze. In this article we propose an approach to automating eye-movement protocol analysis by means of tracing—relating observed eye movements to the sequential predictions of a process model. We present three tracing methods that provide fast and robust analysis and alleviate the equipment noise and individual variability prevalent in typical eye-movement protocols. We also describe three applications of the tracing methods that demonstrate how the methods facilitate the use of eye movements in the study of user behavior and the inference of user intentions. 1.

We discuss a novel type of interface, the intelligent gazeadded interface, and describe the design and evaluation of a sample gaze-added operating-system interface. Gaze-added interfaces, like current gaze-based systems, allow users to execute commands using their eyes. However, while most gaze-based systems replace the functionality of other inputs with that of gaze, gaze-added interfaces simply add gaze functionality that the user can employ if and when desired. Intelligent gaze-added interfaces utilize a probabilistic algorithm and user model to interpret gaze focus and alleviate typical problems with eye-tracking data. We extended a standard WIMP operating-system interface into a new interface, IGO, that incorporates intelligent gazeadded input. In a user study, we found that users quickly adapted to the new interface and utilized gaze effectively both alone and with other inputs.

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This paper presents a multiple camera system to determine the head pose of people in an indoor setting. Our approach extends current eye tracking techniques from a single camera system to a multiple camera system. The head pose of a person is determined by triangulating multiple facial features that are obtained in real-time from eye trackers. Our work is unique in that it allows us to observe user head orientation in real-time using several cameras over a much larger space than covered by a single camera. We demonstrate the viability of this system by experimenting with several people under di#erent lighting conditions performing head movements.

In the present paper, we present a novel gaze-controlled interface. It allows the user to magnify and inspect any part of an image by just looking at the part in question and subsequently shifting gaze to another window. No manual input is required to control this process. The interface was empirically evaluated in a multi-session experiment employing a comparative visual search tasks that required several steps of zooming in and out of a search display. Each participant's performance was assessed separately for using gaze control and using a mouse as the input device and compared between conditions and across sessions. The results demonstrate that participants' performance with the gaze-control interface is quite comparable with a standard mouse input device and that using the gaze-control interface can be learned very quickly.

We present a multi-camera vision-based eye tracking method to robustly locate and track user's eyes as they interact with an application. We propose enhancements to various visionbased eye-tracking approaches, which include (a) the use of multiple cameras to estimate head pose and increase coverage of the sensors and (b) the use of probabilistic measures incorporating Fisher's linear discriminant to robustly track the eyes under varying lighting conditions in real-time. We present experiments and quantitative results to demonstrate the robustness of our eye tracking in two application prototypes. Categories and Subject Descriptors I.4.m [Image Processing and Computer Vision]: Miscellaneous; I.4.0 [Image Processing and Computer Vision ]: General---Image Processing Software ; G.3 [Probability and Statistics]: Probabilistic algorithms General Terms Algorithms, Human Factors, Design Keywords Eye Tracking, Multiple Cameras, Fisher's Discriminant Analysis, Computer Vision, Human Computer Interaction 1.

Advances in the measurement of eye movements make it possible to construct gaze-controlled interfaces for physically challenged people and for enhanced human-computer interaction. The use of hybrid interfaces that combine manual and gaze control has the advantage of being effortless, quick, and intuitive: The user selects an item by looking at it and confirms this selection by pressing a button. Earlier studies have shown task completion times to be slightly longer for gaze-controlled interfaces compared to mouse-controlled interfaces. From these results we derived the hypothesis that the efficiency of conventional hybrid interfaces is limited by the fact that the users are already looking for the next object when pressing the button, causing an unintended selection. In the first experiment we investigated if such an eye-hand span really exists and which factors affect its size. In the second experiment we modified the gaze-controlled interface and showed that compensating for the eye-hand span substantially improves the subjects' performance.