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Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior
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Patterns and Pace: Quantifying Diverse Exploration Behavior with Visualizations on the Web.

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    This summary is machine-generated.

    New metrics reveal novel aspects of how people explore interactive visualizations. These metrics capture exploration uniqueness and pacing, offering deeper insights than traditional measures.

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    Area of Science:

    • Human-Computer Interaction
    • Information Visualization
    • Data Science

    Background:

    • The web hosts diverse interactive visualizations for data analysis.
    • Current metrics inadequately capture the full scope of user exploration in visualizations.
    • Understanding user interaction is crucial for effective visualization design.

    Purpose of the Study:

    • To identify needs for measuring visualization interaction behavior.
    • To develop new metrics for analyzing open-ended user explorations.
    • To propose metrics that capture exploration uniqueness and pacing.

    Main Methods:

    • Identified needs and developed candidate features from user interaction data.
    • Proposed novel metrics: exploration uniqueness and exploration pacing.
    • Evaluated new metrics against existing ones using data from prior studies.

    Main Results:

    • New metrics reveal previously uncharacterized aspects of visualization use.
    • The proposed metrics can statistically differentiate between visualization designs.
    • These novel metrics are independent of previously established visualization research metrics.

    Conclusions:

    • The developed metrics offer a more comprehensive understanding of user interaction with visualizations.
    • These metrics have potential applications in analyzing visualization interaction and design.
    • Further research is needed to refine and select metrics for depicting visualization explorations.