Contextualization or Rationalization? The Effect of Causal Priors on Data Visualization Interpretation.

2026-02-08

IEEE Transactions on Visualization and Computer Graphics

AI Summary

This study investigates how causal prior beliefs shape the perception and interpretation of patterns—such as trends and clusters—in ambiguous scatterplots. Employing a mixed-methods approach that combines large-scale online experiments with think-aloud interviews, and integrating visualization analysis with qualitative coding, this work reveals for the first time the critical role of causal priors in higher-order visual data interpretation. The findings show that when data align with participants’ prior beliefs, they tend to “contextualize” the information by reinforcing existing causal explanations; conversely, when data conflict with their priors, they engage in “rationalization” by invoking external factors—such as confounding variables—to reconcile the discrepancy. The study proposes a “contextualization–rationalization” dual-pathway framework, offering a novel perspective on the interplay between belief systems and data-driven reasoning in visual analytics.

Technology Category

• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Preferences
• Machine Learning: Causal Learning

Application Category

• Web Mining and Content Analysis: Web data visualization
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Security and Privacy: Data transparency and provenance

Abstract

Understanding how individuals interpret charts is a crucial concern for visual data communication. This imperative has motivated a number of studies, including past work demonstrating that causal priors-a priori belief about causal relationships between concepts-can have significant influences on the perceived strength of variable relationships inferred from visualizations. This paper builds on these previous results, demonstrating that causal priors can also influence the types of patterns that people perceive as the most salient within ambiguous scatterplots that have roughly equal evidence for trend and cluster patterns. Using a mixed-design approach that combines a largescale online experiment for breadth of findings with an in-person think-aloud study for analytical depth, we investigated how users' interpretations are influenced by the interplay between causal priors and the visualized data patterns. Our analysis suggests two archetypal reasoning behaviors through which people often make their observations: contextualization, in which users accept a visual pattern that aligns with causal priors and use their existing knowledge to enrich interpretation, and rationalization, in which users encounter a pattern that conflicts with causal priors and attempt to explain away the discrepancy by invoking external factors, such as positing confounding variables or data selection bias. These findings provide initial evidence highlighting the critical role of causal priors in shaping high-level visualization comprehension, and introduce a vocabulary for describing how users reason about data that either confirms or challenges prior beliefs of causality.

Problem

• causal priors
• data visualization
• interpretation
• scatterplots
• cognitive bias

Innovation

• causal priors
• visualization interpretation
• contextualization
• rationalization
• scatterplot perception