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    This study introduces a multidimensional framework for understanding trust in data visualization, moving beyond single-item measures. Findings highlight the impact of cognitive and affective factors on user trust in science visualizations.

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    Area of Science:

    • Human-Computer Interaction
    • Information Visualization
    • Cognitive Science

    Background:

    • Trust is critical for data visualization interpretation and decision-making.
    • Existing research often uses simplified measures of trust in visualizations.
    • Social science theories offer insights into multidimensional trust formation.

    Purpose of the Study:

    • To propose a comprehensive, multidimensional conceptualization and operationalization of trust in data visualization.
    • To extend existing work by integrating social science trust theories and visualization-specific factors.
    • To investigate the role of visual complexity in establishing trust in science visualizations.

    Main Methods:

    • Developed a two-dimensional framework distinguishing cognitive/affective and visualization/data-specific trust.
    • Designed and conducted a large crowd-sourced study to test the framework.
    • Quantified the influence of visual complexity on trust in science visualizations.

    Main Results:

    • Provided empirical support for the proposed multidimensional trust framework.
    • Demonstrated the significant impact of cognitive appraisal on visualization trust.
    • Highlighted the influence of affective responses and individual differences in building trust.
    • Showcased the role of visual complexity in shaping user trust.

    Conclusions:

    • Trust in data visualization is multidimensional, encompassing cognitive and affective elements.
    • A comprehensive framework is necessary for accurately measuring and understanding visualization trust.
    • Future research should consider cognitive, affective, and individual factors when assessing trust in visualizations.