25 citations · 76 across the 15 of their papers we have counts for
16 papers
Mitigating Confirmation Bias through Hand-Drawing Videos
Chenyu Lin, Cindy Xiong, Icy Zhang
Understanding data visualizations is essential for informed decision-making, yet interpretation is often shaped and even distorted by prior beliefs. We investigate whether an embod…
Using Processing Fluency as a Metric of Trust in Scatterplot Visualizations
Hamza Elhamdadi, Lace Padilla, Cindy Xiong
Establishing trust with readers is an important first step in visual data communication. But what makes a visualization trustworthy? Psychology and behavioral economics research ha…
How Do We Measure Trust in Visual Data Communication?
Hamza Elhamdadi, Aimen Gaba, Yea-Seul Kim +1
Trust is fundamental to effective visual data communication between the visualization designer and the reader. Although personal experience and preference influence readers' trust…
Dispersion vs Disparity: Hiding Variability Can Encourage Stereotyping When Visualizing Social Outcomes
Eli Holder, Cindy Xiong
Visualization research often focuses on perceptual accuracy or helping readers interpret key messages. However, we know very little about how chart designs might influence readers'…
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…
How Do Viewers Synthesize Conflicting Information from Data Visualizations?
Prateek Mantri, Hariharan Subramonyam, Audrey L. Michal +1
Scientific knowledge develops through cumulative discoveries that build on, contradict, contextualize, or correct prior findings. Scientists and journalists often communicate these…
Comparison Conundrum and the Chamber of Visualizations: An Exploration of How Language Influences Visual Design
Aimen Gaba, Vidya Setlur, Arjun Srinivasan +2
The language for expressing comparisons is often complex and nuanced, making supporting natural language-based visual comparison a non-trivial task. To better understand how people…
Explaining with Examples: Lessons Learned from Crowdsourced Introductory Description of Information Visualizations
Leni Yang, Cindy Xiong, Jason K. Wong +2
Data visualizations have been increasingly used in oral presentations to communicate data patterns to the general public. Clear verbal introductions of visualizations to explain ho…
Making the Invisible Visible: Risks and Benefits of Disclosing Metadata in Visualization
Alyxander Burns, Thai On, Christiana Lee +3
Accompanying a data visualization with metadata may benefit readers by facilitating content understanding, strengthening trust, and providing accountability. However, providing thi…
Show or Tell? Visual and Verbal Representations Bias Position Recall
Cristina R. Ceja, Cindy Xiong
When we view visualizations, we not only have a visual representation of the data, but also a verbal one. Recent work has shown that these visual representations of data can be bia…
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…
A Design Space of Vision Science Methods for Visualization Research
Madison Elliott, Christine Nothelfer, Cindy Xiong +1
A growing number of efforts aim to understand what people see when using a visualization. These efforts provide scientific grounding to complement design intuitions, leading to mor…
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…
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.…