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    Area of Science:

    • Human-Computer Interaction
    • Data Visualization
    • Cognitive Psychology

    Background:

    • Causal priors influence perceived relationships in visualizations.
    • Understanding chart interpretation is vital for effective data communication.

    Purpose of the Study:

    • Investigate how causal priors affect pattern salience in ambiguous scatterplots.
    • Explore user reasoning when data conflicts with or confirms prior beliefs.

    Main Methods:

    • Mixed-design approach: large-scale online experiment and in-person think-aloud study.
    • Analysis of user interpretations influenced by causal priors and visual patterns.

    Main Results:

    • Causal priors shape perceived patterns in ambiguous scatterplots.
    • Identified two reasoning behaviors: contextualization and rationalization.
    • Contextualization: aligning patterns with priors; Rationalization: explaining discrepancies.

    Conclusions:

    • Causal priors critically shape high-level visualization comprehension.
    • Introduced a vocabulary for user reasoning about data confirming or challenging causal beliefs.