Designers often create visualizations to achieve specific high-level analytical or communication goals. These goals require people to naturally extract complex, contextualized, and interconnected patterns in data. While limited prior work has studied general high-level interpretation, prevailing perceptual studies of visualization effectiveness primarily focus on isolated, predefined, low-level tasks, such as estimating statistical quantities. This study more holistically explores visualization interpretation to examine the alignment between designers' communicative goals and what their audience sees in a visualization, which we refer to as their comprehension. We found that statistics people effectively estimate from visualizations in classical graphical perception studies may differ from the patterns people intuitively comprehend in a visualization. We conducted a qualitative study on three types of visualizations -- line graphs, bar graphs, and scatterplots -- to investigate the high-level patterns people naturally draw from a visualization. Participants described a series of graphs using natural language and think-aloud protocols. We found that comprehension varies with a range of factors, including graph complexity and data distribution. Specifically, 1) a visualization's stated objective often does not align with people's comprehension, 2) results from traditional experiments may not predict the knowledge people build with a graph, and 3) chart type alone is insufficient to predict the information people extract from a graph. Our study confirms the importance of defining visualization effectiveness from multiple perspectives to assess and inform visualization practices.

翻译：设计师通常创建可视化以实现特定的高层次分析或交流目标。这些目标要求人们自然地提取数据中复杂、情境化且相互关联的模式。尽管先前有限的研究探讨了一般性高层次解读，但当前关于可视化有效性的感知研究主要集中在孤立的、预定义的、低层次任务上，例如估计统计量。本研究更全面地探索可视化解读，以检验设计师的交流目标与其受众在可视化中所看到的内容（我们称之为理解）之间的一致性。我们发现，在经典图形感知研究中，人们从可视化中有效估计的统计量，可能不同于他们直观理解的可视化模式。我们针对三种可视化类型——折线图、条形图和散点图——开展了一项定性研究，以探究人们自然从可视化中提取的高层次模式。参与者使用自然语言和出声思维法描述了一系列图形。我们发现，理解会随着图形复杂性和数据分布等多种因素而变化。具体而言：1）可视化的既定目标常常与人们的理解不一致；2）传统实验的结果可能无法预测人们从图形中构建的知识；3）仅凭图表类型不足以预测人们从图形中提取的信息。本研究证实，从多角度定义可视化有效性对于评估和指导可视化实践具有重要意义。