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

TL;DR

This study explores how causal priors influence the interpretation of ambiguous scatterplots, revealing two main reasoning behaviors—contextualization and rationalization—that shape data comprehension.

Contribution

It demonstrates that causal priors affect not only perceived relationships but also the salience of patterns, introducing a new vocabulary for understanding reasoning in data visualization interpretation.

Findings

Causal priors influence pattern salience in scatterplots.

Two reasoning archetypes: contextualization and rationalization.

Causal priors shape high-level visualization comprehension.

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 beliefs 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 large-scale 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.