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.

翻译：理解个体如何解读图表是视觉数据传播的关键问题。这一需求催生了多项研究，包括已有工作表明因果先验——即关于概念间因果关系的先验信念——能显著影响人们从可视化中推断出的变量关系感知强度。本文基于这些先前发现，进一步证明在趋势模式与聚类模式证据大致相当的模糊散点图中，因果先验同样会影响人们感知到的最显著模式类型。通过采用混合设计方法——结合大规模在线实验以获取发现广度，以及面对面有声思维研究以获取分析深度——我们探究了用户解读如何受到因果先验与可视化数据模式间相互作用的影响。我们的分析揭示了两种典型的推理行为模式：语境化（用户接受与因果先验一致的可视模式，并运用既有知识丰富解读）与合理化（用户遇到与因果先验冲突的模式时，试图通过援引外部因素——如假定混杂变量或数据选择偏误——来解释差异）。这些发现为阐明因果先验在塑造高层次可视化理解中的关键作用提供了初步证据，并建立了一套术语体系，用以描述用户如何对证实或挑战既有因果信念的数据进行推理。