The Curse of Knowledge in Data Visualizations.
Cindy Xiong, Lisanne van Weelden, Steven Franconeri
A viewer can extract many potential patterns from any set of visualized data values. But that means that 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.
A Design Space of Vision Science Methods for Visualization Research.
Madison A. Elliott, Christine Nothelfer, Cindy Xiong, Danielle Albers Szafir
We want to investigate how people see and make sense of visualizations. However, existing publications currently reflect a limited set of available methods to do so. We introduce a design space of experimental methods for empirically investigating the perceptual processes involved with viewing data visualizations to inform visualization design guidelines.
As organizing members of the VISxVISION initiative, 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.
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. We empirically show that differing aspect ratios of the bar marks contributes to these biases. 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.
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. and present three case studies showing that this framework expands on existing methods to comprehensively measure how a visualization design facilitates a viewer’s understanding of visualizations.
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.
Illusion 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.
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. For some data types, greater transparency in data visualization is also associated with an increase in the amount of information depicted. 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.
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.