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    We introduce the Unified Model of Saliency and Scanpaths (UMSS), a new AI model predicting visual attention and eye movement sequences on information visualizations. UMSS enhances understanding of user behavior without eye-tracking equipment.

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    Area of Science:

    • Computer Vision
    • Human-Computer Interaction
    • Cognitive Science

    Background:

    • Scanpaths offer insights into visual exploration of information visualizations.
    • Previous models primarily predicted aggregated attention statistics like saliency.
    • Limited understanding of element-specific gaze dynamics in visualizations.

    Purpose of the Study:

    • To develop a model predicting multi-duration saliency and scanpaths on information visualizations.
    • To analyze gaze behavior across different visualization elements.
    • To improve computational models of visual attention for information visualization.

    Main Methods:

    • Developed the Unified Model of Saliency and Scanpaths (UMSS).
    • Analyzed gaze behavior on the MASSVIS dataset, focusing on elements like Title, Label, and Data.
    • UMSS predicts element-level saliency maps and probabilistically samples scanpaths.

    Main Results:

    • Gaze patterns show consistency across visualizations but structural differences for specific elements.
    • UMSS significantly outperforms state-of-the-art methods in scanpath and saliency prediction.
    • Achieved 11.5% relative improvement in scanpath sequence score and up to 23.6% in saliency prediction (Pearson correlation).

    Conclusions:

    • UMSS provides a more accurate simulation of visual attention on information visualizations.
    • The findings suggest potential for richer user models and attention simulations.
    • Enables advanced visualization analysis without requiring eye-tracking data.