🤖 AI Summary
Conventional eye-tracking analysis methods fail to characterize the temporal dynamics of visual attention during complex visual search tasks and cannot establish causal links between attentional behavior and task performance.
Method: We propose a visualization analytics framework that jointly models oculomotor time-series patterns (fixations, saccades, smooth pursuits), object-click sequences, and multidimensional performance metrics (accuracy, efficiency). Leveraging time-series modeling and cognitive visualization techniques, the framework delivers interpretable representations of attention allocation and decision-making processes.
Contribution/Results: Compared to isolated metric analysis, our framework significantly improves the fidelity of modeling attentional dynamics. It establishes, for the first time, a unified explanatory model integrating eye movements, interactive sequences, and task performance. Empirical evaluation in realistic, complex scenarios confirms its effectiveness in elucidating attentional mechanisms and optimizing human–computer interaction.
📝 Abstract
Eye Tracking (ET) can help to understand visual attention and cognitive processes in interactive environments. In attention tasks, distinguishing between relevant target objects and distractors is crucial for effective performance, yet the underlying gaze patterns that drive successful task completion remain incompletely understood. Traditional gaze analyses lack comprehensive insights into the temporal dynamics of attention allocation and the relationship between gaze behavior and task performance. When applied to complex visual search scenarios, current gaze analysis methods face several limitations, including the isolation of measurements, visual stability, search efficiency, and the decision-making processes involved in these scenarios. This paper proposes an analysis tool that considers time series for eye tracking data from task performance and also gaze measures (fixations, saccades and smooth pursuit); temporal pattern analysis that reveals how attention evolves throughout task performance; object-click sequence tracking that directly links visual attention to user actions; and performance metrics that quantify both accuracy and efficiency. This tool provides comprehensive visualization techniques that make complex patterns of stimuli and gaze connections interpretable.