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Unifying Effects of Direct and Relational Associations for Visual Communication.

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    People infer how colors map to data concepts in visualizations. This study unites direct and relational associations, showing both independently influence color-concept mapping for better data visualization design.

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    Area of Science:

    • Cognitive Science
    • Information Visualization
    • Human-Computer Interaction

    Background:

    • Expectations about color-concept mappings improve visualization interpretation.
    • Previous research separated categorical (direct associations) and continuous (relational associations) data mapping factors.

    Purpose of the Study:

    • Unify categorical and continuous data mapping into a single assignment inference framework.
    • Extend assignment inference to continuous data by incorporating relational associations.
    • Develop a predictive model for inferred color-concept mappings.

    Main Methods:

    • Developed a unified assignment inference model.
    • Broadened the "merit" concept to include relational associations.
    • Tested the model on colormap visualizations of environmental data (e.g., wildfire, ocean water).

    Main Results:

    • Both direct and relational associations independently contribute to inferred color-concept mappings.
    • The proposed model successfully predicts inferred mappings.
    • Demonstrated the interplay between different types of associations in visualization interpretation.

    Conclusions:

    • Assignment inference provides a unified framework for understanding color-concept mappings.
    • Optimizing visualization design requires considering both direct and relational associations.
    • Findings facilitate the creation of more effective and intuitive data visualizations.