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Contextualization or Rationalization? The Effect of Causal Priors on Data Visualization Interpretation
IEEE Transactions on Visualization and Computer Graphics
|February 9, 2026
Summary
People
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.
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