Abstract
Visualization approaches for eye movement analysis suffers from two limitations: (1) inability to handle the stochasticity of both number and position of moving areas of interests (AOIs), and (2) absence of quantitative metrics to analyze eye movement data. We adapted the directed weighted network (DWN) and associated “centrality” metrics to support the visualization of the complex eye movement data. A case study was performed using a realistic air traffic control task environment. Promising results were found as we were able identify important targets (aircraft) interrogated by an air traffic controller based on different time frames. This case study serves as a foundation to develop effective data visualization methods and quantitative metrics for analyzing complex eye movements for a multi-element tracking task.
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