  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.
