An algorithm was developed to characterize, compare, and analyze eye movement sequences that occur during visual tracking of multiple moving targets. When individuals perform a task requiring interrogating multiple moving targets, complex and long eye movement sequences occur, making sequence comparisons difficult in whole and in part. The developed algorithm characterizes a sequence by hierarchically clustering the targets that an individual interrogated through an unordered transition matrix created from the frequencies of eye fixation transitions among the targets. Then, the resulting sets of clustered targets, which we define as multilevel visual groupings (VGs), can be compared with analyze performance. The algorithm was applied to an aircraft conflict detection task. Eye movement data were collected from 25 expert air traffic controllers and 40 novices. The task was to detect air traffic conflicts for easy, moderate, and hard difficulty scenarios on simulated radar display. Experts' and novices' multilevel (level one composed of pairs, and level two composed of three or four targets) VGs were aggregated and visualized. Chisquare tests confirmed that there were significant differences for easy (level one: p < 0.001, level two: p = 0.004), moderate (level two: p = 0.047), and hard (level two: p < 0.001) difficulty scenarios. The algorithm supported identifying different eye movement characteristics between experts and novices. Scans of the experts had multilevel VGs around the conflict pairs, whereas those of the novices included different aircraft. The results show promise for using the compact representation of eye movements for performance analysis.

No abstract

No abstract

-Subjects performing visual target tracking tasks have been shown to utilize perceptual organization. This organization has both Gestalt and goal-oriented features. Previous studies have attempted to use memory recall techniques to examine potential cognitive groupings in air traffic control (ATC), which is, in part, a complex target tracking task. In the present research, a special form of cluster analysis was successful in revealing cognitive groupings having appreciable influence on task performance in a targettracking task designed to resemble ATC. The method of cluster analysis was derived from the "virtual associative network" model of memory organization, and applied to the record of eye fixations in the course of task performance. Results using inexperienced subjects showed fixation clustering consistent with Gestalt factors. Task objectives (such as conflict detection) did not seem to affect grouping as much. The subjects' recall was generally poor, except where direct manipulation of targets occurred. We conclude that a) cognitive grouping influences performance, b) recall techniques may not be able to elicit subjects' cognitive groupings, and c) such groupings can be determined via analysis of eye fixations. These findings have implications for studying workload assessment and information structuring in complex visual scanning tasks.

Implementing experts' scanpaths into novices' active learning process shows promise in enhancing training effectiveness and reducing training time.

The Traffic Management Advisor (TMA) is an air traffic control automation system currently in use in seven Air Route Traffic Control Centers (ARTCCs) to enable time based metering to busy airports within their airspace. However, this system is limited to operation within a single ARTCC, within about a 200 nautical mile radius of the airport, and on relatively simple streams of traffic. The need for coordinated metering within a greater (300+ nautical mile) radius of an airport, on streams of traffic with significant branching, and across ARTCC boundaries, has been identified.Early tests revealed that TMA could not simply be scaled up to handle such a problem. Instead, a loosely coupled hierarchy of schedules, in which constraints from downstream schedules are passed upstream, is required. Such an architecture reduces the reliance on distant projections of arrival times, making schedules robust to changes in sequence and to additions of aircraft (such as aircraft departing inside the system's scheduling horizon). This architecture is also scaleable, easily reconfigurable, and can be networked together. As such, it can be adapted for use in any size or configuration of airspace and with any number of airports delivering restrictions. An implementation of this distributed scheduling architecture is currently undergoing testing in the TMA-Multi Center system. This paper describes the architecture and its motivation.

No abstract

Interactive visualizations have the potential to greatly enhance our ability to analyze data. Although the user is central to the use of such environments, perceptual and cognitive processes are not well understood within the context of interactive visualization. Although cognitive psychology has provided a greater understanding of visual perception and cognition, a theoretical framework that links this knowledge with the interactive visualization process is needed. This article aims to fill this gap by introducing a visual information processing model that facilitates the understanding of how users interact with interactive visualization environments.