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IEEE VIS 2022
How Do Viewers Synthesize Conflicting Information from Data Visualizations?

Prateek Mantri, Hariharan Subramonyam, Audrey L. Michal, and Cindy Xiong

Scientific knowledge develops through cumulative discoveries that build on, contradict, contextualize, or correct prior findings. Consequently, readers need to integrate diverse and contrasting evidence from multiple sources to form opinions or make decisions. However, the underlying mechanism for synthesizing information from multiple visualizations remains under-explored. We conducted a series of experiments to address this gap.

Comparison

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IEEE VIS 2022
Comparison Conundrum and the Chamber of Visualizations: An Exploration of How Language Influences Visual Design

Aimen Gaba, Vidya Setlur, Arjun Srinivasan, Jane Hoffswell, and Cindy Xiong

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 reason about comparisons in natural language, we explore a design space of utterances for comparing data entities. We identified different parameters of comparison utterances that indicate what is being compared (i.e., data variables and attributes) as well as how these parameters are specified (i.e., explicitly or implicitly).

Trust

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IEEE VIS BELIV 2022
How Do We Measure Trust in Visual Data Communication?

Hamza Elhamdadi, Aimen Gaba, Yea-Seul Kim, Cindy Xiong

Trust is fundamental to effective visual data communication between the visualization designer and the reader. Although personal experience and preference influence readers’ trust in visualizations, visualization designers can leverage design techniques to create visualizations that evoke a “calibrated trust,” at which readers arrive after critically evaluating the information presented. To systematically understand what drives readers to engage in “calibrated trust,” we must first equip ourselves with reliable and valid methods for measuring trust.

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IEEE VIS 2022
Dispersion vs Disparity: Hiding Variability Can Encourage Stereotyping When Visualizing Social Outcomes

Eli Holder and Cindy Xiong

Visualization research often focuses on perceptual accuracy or helping readers interpret key messages. But when designed poorly, data visualizations about social inequity can be misinterpreted in harmful ways and lead to stereotyping. Design choices can influence these biases: Hiding variability tends to increase stereotyping while emphasizing variability reduces it.

motivatedScatterplot

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IEEE VIS 2022
Seeing What You Believe or Believing What You See? Belief Biases Correlation Estimation

Cindy Xiong, Chase Stokes, Yea-Seul Kim, Steven Franconeri

When an analyst or scientist has a belief about how the world works, their thinking can be biased in favor of that belief. When viewers believed that two variables should have a strong relationship, they overestimated correlations between those variables by an r-value of about 0.1. When they believed that the variables should be unrelated, they underestimated the correlations by an r-value of about 0.1.

DecisionMakingIcon

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IEEE Computer Graphics and Applications 2022
VisHikers’ Guide to Evaluation: Competing Considerations in Study Design

Emily Wall, Cindy Xiong, Yea-Seul Kim

We describe the persistent tensions between various camps on the “right” way to conduct evaluations in visualization.

UncertaintyTheatre

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ACM CHI 2022
Investigating Perceptual Biases in Icon Arrays

Cindy Xiong, Ali Sarvghad, Çağatay Demiralp, Jake M. Hofman, Daniel G. Goldstein

Icon arrays are graphical displays in which a subset of identical shapes are filled to convey probabilities. They are widely used for communicating probabilities to the general public. We investigated the effect of different arrangements in icon arrays on probability perception. *Honorable Mention at CHI 2022*

Table

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IEEE TVCG 2022
Explaining with Examples: Lessons Learned from Crowdsourced Introductory Description of Information Visualizations

Leni Yang, Cindy Xiong, Jason K. Wong, Aoyu Wu, Huamin Qu

Data visualizations have been increasingly used in oral presentations to communicate data patterns to the general public. Clear verbal introductions of visualizations to explain how to interpret the visually encoded information are essential to convey the takeaways and avoid misunderstandings. We investigate how to effectively introduce visualizations to the audience with varying degrees of visualization literacy.

Picture1

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IEEE VIS 2021
Visual Arrangements of Bar Charts Influence Comparisons in Viewer Takeaways

Cindy Xiong, Vidya Setlur, Benjamin Bach, Kylie Lin, Eunyee Koh, and Steven Franconeri

To help a viewer intuitively compare values to quickly generate key takeaways, visualization designers can manipulate how data values are arranged in a chart to afford particular comparisons.

declutter

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IEEE TVCG 2021
Declutter and Focus: Empirically Evaluating Design Guidelines for Effective Data Communication

Kiran Ajani, Elsie Lee, Cindy Xiong, Cole Nussbaumer Knaflic, William Kemper, and Steven Franconeri

To more clearly communicate data, consider including a clear headline that describes the relevant data pattern, highlighting a subset of relevant data values with a unique color, and connecting those values to written annotations that contextualize them in a broader argument.

IEEE VIS 2020

A Design Space of Vision Science Methods for Visualization Research.

Madison A. Elliott, Christine Nothelfer, Cindy Xiong, Danielle Albers Szafir

We introduce a design space of experimental methods for empirically investigating the perceptual processes involved with viewing data visualizations to inform visualization design guidelines. We advocate for a deeper relationship between human perception and visualization research to extend the methodological design space for understanding visualization and human vision. Join us here.

Blooms_Preview

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IEEE VIS BELIV 2020

How to Evaluate Data Visualizations across Different Levels of Understanding.

Alyxander Burns. Cindy Xiong, Steven Franconeri, Alberto Cairo, Narges Mahyar

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 and other potential contexts, and draw or evaluate conclusions from the data. We diagnose levels of understanding of visualized data by adapting a common framework from the education literature.

ToS

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IEEE VIS 2020

Truth or Square: Aspect Ratio Biases Recall of Position Encodings.

Cristina Ceja, Caitlyn McColeman, Cindy Xiong, Steven Franconeri

Bar charts are among the most frequently used visualizations, yet people's recall of bar marks' position can be biased. Viewers are biased to remember a bar mark as being more similar to a prototypical square, leading to an overestimation of bars with a wide aspect ratio, and an underestimation of bars with a tall aspect ratio.

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IEEE TVCG 2019
The Curse of Knowledge in Data Visualizations.

Cindy Xiong, Lisanne van Weelden, Steven Franconeri

Two people can see different patterns in the same visualization, potentially leading to miscommunication. We show that when people are primed to see one pattern in the data as visually salient, they believe that naïve viewers will experience the same visual salience.

Correlation

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IEEE VIS 2019

llusion of Causality in Visualized Data

Cindy Xiong, Joel Shapiro, Jessica Hullman, Steven Franconeri

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 would lead to higher grades. This is a reasoning error because correlation does not necessarily indicate causation – X and Y can be correlated without one directly causing the other. While this error is pervasive, its prevalence might be amplified or mitigated by the way that the data is presented to a viewer.

Anchoring

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IEEE VIS 2019

Biased Average Position Estimates in Line and Bar Graphs: Underestimation, Overestimation, and Perceptual Pull

Cindy Xiong, Cristina R. Ceja, Casimir J.H. Ludwig, Steven Franconeri

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. We show that reports of average position across a short delay can be biased such that line positions are underestimated and bar positions overestimated.

trust1

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EuroVis TrustVIS 2019

Examining the Components of Trust in Map-Based Visualizations

Cindy Xiong, Lace Padilla, Kent Grayson, Steven Franconeri

Perceived transparency is often associated with perceived trust, and greater transparency in data visualization can be associated with an increase in the amount of information. We showed that perceived clarity, amount of disclosure. and thoroughness significantly predicted individuals’ selection of a Google Maps-like application with either less information or more information.

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International Music Perception and Cognition Conference 2016

Perceptual learning of abstract musical patterns: Recognizing composer style

Carolyn A. Bufford, Khanh-Phuong Thai, Joselyn Ho, Cindy Xiong, Carly A. Hines, Philip J. Kellman

How can we improve abstract pattern recognition in music? Can principles enhancing visual learning be extended to auditory stimuli, such as music? Perceptual learning, improvements in the pickup of information from experience, is well-established in both vision and audition. We showed that perceptual learning training can improve participants’ recognition of composers’ styles, demonstrating that composer style can be learned, and perceptual-learning-based interventions are effective in complex auditory domains.