Do You See What I See? A Qualitative Study Eliciting High-Level Visualization Comprehension
<|reference_start|>Do You See What I See? A Qualitative Study Eliciting High-Level Visualization Comprehension: 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.<|reference_end|>
@article{quadri2024do,
    title={Do You See What I See? A Qualitative Study Eliciting High-Level
  Visualization Comprehension},
    author={Ghulam Jilani Quadri, Arran Zeyu Wang, Zhehao Wang, Jennifer Adorno,
  Paul Rosen, Danielle Albers Szafir},
    journal={In Proceedings of the CHI Conference on Human Factors in Computing
  Systems (CHI '24), May 11-16, 2024, Honolulu, HI, USA},
    year={2024},
    doi={10.1145/3613904.3642813},
    archivePrefix={arXiv},
    eprint={2402.15605},
    primaryClass={cs.HC}
}