51 citations · 87 across the 11 of their papers we have counts for
12 papers
The Arrangement of Marks Impacts Afforded Messages: Ordering, Partitioning, Spacing, and Coloring in Bar Charts
Racquel Fygenson, Steven Franconeri, Enrico Bertini
Data visualizations present a massive number of potential messages to an observer. One might notice that one group's average is larger than another's, or that a difference in value…
Average Estimates in Line Graphs Are Biased Toward Areas of Higher Variability
Dominik Moritz, Lace M. Padilla, Francis Nguyen +1
We investigate variability overweighting, a previously undocumented bias in line graphs, where estimates of average value are biased toward areas of higher variability in that line…
Seeing What You Believe or Believing What You See? Belief Biases Correlation Estimation
Cindy Xiong, Chase Stokes, Yea-Seul Kim +1
When an analyst or scientist has a belief about how the world works, their thinking can be biased in favor of that belief. Therefore, one bedrock principle of science is to minimiz…
Jurassic Mark: Inattentional Blindness for a Datasaurus Reveals that Visualizations are Explored, not Seen
Tal Boger, Steven B. Most, Steven L. Franconeri
Graphs effectively communicate data because they capitalize on the visual system's ability to rapidly extract patterns. Yet, this pattern extraction does not occur in a single glan…
Visual Arrangements of Bar Charts Influence Comparisons in Viewer Takeaways
Cindy Xiong, Vidya Setlur, Benjamin Bach +3
Well-designed data visualizations can lead to more powerful and intuitive processing by a viewer. To help a viewer intuitively compare values to quickly generate key takeaways, vis…
Rethinking the Ranks of Visual Channels
Caitlyn M. McColeman, Fumeng Yang, Steven Franconeri +1
Data can be visually represented using visual channels like position, length or luminance. An existing ranking of these visual channels is based on how accurately participants coul…
Truth or Square: Aspect Ratio Biases Recall of Position Encodings
Cristina R. Ceja, Caitlyn M. McColeman, Cindy Xiong +1
Bar charts are among the most frequently used visualizations, in part because their position encoding leads them to convey data values precisely. Yet reproductions of single bars o…
How to evaluate data visualizations across different levels of understanding
Alyxander Burns, Cindy Xiong, Steven Franconeri +2
Understanding a visualization is a multi-level process. A reader must extract and extrapolate from numeric facts, understand how those facts apply to both the context of the data a…
Why Shouldn't All Charts Be Scatter Plots? Beyond Precision-Driven Visualizations
Enrico Bertini, Michael Correll, Steven Franconeri
A central concept in information visualization research and practice is the notion of visual variable effectiveness, or the perceptual precision at which values are decoded given v…
Illusion of Causality in Visualized Data
Cindy Xiong, Joel Shapiro, Jessica Hullman +1
Students who eat breakfast more frequently tend to have a higher grade point average. From this data, many people might confidently state that a before-school breakfast program wou…
Biased Average Position Estimates in Line and Bar Graphs: Underestimation, Overestimation, and Perceptual Pull
Cindy Xiong, Cristina R. Ceja, Casimir J. H. Ludwig +1
In visual depictions of data, position (i.e., the vertical height of a line or a bar) is believed to be the most precise way to encode information compared to other encodings (e.g.…
Truncating the Y-Axis: Threat or Menace?
Michael Correll, Enrico Bertini, Steven Franconeri
Bar charts with y-axes that don't begin at zero can visually exaggerate effect sizes. However, advice for whether or not to truncate the y-axis can be equivocal for other visualiza…