Jurassic Mark: Inattentional Blindness for a Datasaurus Reveals that Visualizations are Explored, not Seen

Boger, Tal; Most, Steven B.; Franconeri, Steven

doi:10.1109/vis49827.2021.9623273

Cited by 11 publications

(12 citation statements)

References 33 publications

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“…As a second example, when viewers are challenged to complete a tough comparison task with the blue dots of the scatterplot shown in Figure 7 (right), they can fail to process the positions of the green dots, such that 93% fail to notice the presence of a dinosaur shape among them (Boger et al, 2021).…”

Section: Avoid a Visual Processing Limit: Making Comparisonsmentioning

confidence: 99%

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The Science of Visual Data Communication: What Works

Franconeri¹,

Padilla

Shah

et al. 2021

Psychol Sci Public Interest

Self Cite

139

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Effectively designed data visualizations allow viewers to use their powerful visual systems to understand patterns in data across science, education, health, and public policy. But ineffectively designed visualizations can cause confusion, misunderstanding, or even distrust—especially among viewers with low graphical literacy. We review research-backed guidelines for creating effective and intuitive visualizations oriented toward communicating data to students, coworkers, and the general public. We describe how the visual system can quickly extract broad statistics from a display, whereas poorly designed displays can lead to misperceptions and illusions. Extracting global statistics is fast, but comparing between subsets of values is slow. Effective graphics avoid taxing working memory, guide attention, and respect familiar conventions. Data visualizations can play a critical role in teaching and communication, provided that designers tailor those visualizations to their audience.

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Section: Avoid a Visual Processing Limit: Making Comparisonsmentioning

confidence: 99%

“…On the right, viewers tasked with processing the blue set of marks failed to notice the dinosaur shape created by the green set of marks. The image on the right is reprinted from Boger et al (2021).…”

Section: How To Design a Perceptually Efficient Visualizationmentioning

confidence: 99%

The Science of Visual Data Communication: What Works

Franconeri¹,

Padilla

Shah

et al. 2021

Psychol Sci Public Interest

Self Cite

139

View full text Add to dashboard Cite

show abstract

“…For instance, Hajas likened the navigation our prototypes afforded to “shifting eye gaze, shifting focus of perceptual attention. When I navigate a visualization, naturally I would say ‘I'm looking at this figure’ and not that ‘I'm interacting with this figure’.” Analogously, recent results in graphical perception find that sighted readers do not simply “see” visualizations in a single glance but rather perform active visual filtering operations [BMF21]. However, when using the binary tree prototype (Fig.…”

Section: Discussion and Future Workmentioning

confidence: 73%

Rich Screen Reader Experiences for Accessible Data Visualization

Zong

Lee

Lundgard

et al. 2022

Computer Graphics Forum

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Current web accessibility guidelines ask visualization designers to support screen readers via basic non‐visual alternatives like textual descriptions and access to raw data tables. But charts do more than summarize data or reproduce tables; they afford interactive data exploration at varying levels of granularity—from fine‐grained datum‐by‐datum reading to skimming and surfacing high‐level trends. In response to the lack of comparable non‐visual affordances, we present a set of rich screen reader experiences for accessible data visualization and exploration. Through an iterative co‐design process, we identify three key design dimensions for expressive screen reader accessibility: structure, or how chart entities should be organized for a screen reader to traverse; navigation, or the structural, spatial, and targeted operations a user might perform to step through the structure; and, description, or the semantic content, composition, and verbosity of the screen reader's narration. We operationalize these dimensions to prototype screen‐reader‐accessible visualizations that cover a diverse range of chart types and combinations of our design dimensions. We evaluate a subset of these prototypes in a mixed‐methods study with 13 blind and visually impaired readers. Our findings demonstrate that these designs help users conceptualize data spatially, selectively attend to data of interest at different levels of granularity, and experience control and agency over their data analysis process. An accessible HTML version of this paper is available at: http://vis.csail.mit.edu/pubs/rich-screen-reader-vis-experiences.

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“…Rocketshipness: We took inspiration from Alberto Cairo's Datasaurus [9], in which he extends Anscombe's argument [2,10,6] that data points with the same summary statistics can have many different shapes, thus it's important to plot them properly to understand the distribution. Alberto Cairo reinforces this argument by drawing a dinosaur with the same summary statistics.…”

Section: Future Work: More Metrics To Optimizementioning

confidence: 99%

The worst graph layout algorithm ever

Bartolomeo¹,

Lang²,

Dunne³

2022

Preprint

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Graph layout algorithms strive to improve the utility of node-link visualizations or graph drawings by optimizing for readability criteria. One such criteria that has been widely used is to count edge crossings. Prior work has focused solely on minimizing the number of edge crossings, including provably-optimal layout algorithms for layered graphs. The research community has completely ignored the other side of the coin --- can we optimally maximize edge crossings? This paper answers this question in the affirmative. Our WORSTisfimal layout algorithm produces the most unreadable layered graph drawing. It does so by using linear programming to produce a provably-optimally-awful solution. We hope that this groundbreaking result opens up an entirely new field of inquiry for graph drawing researchers --- optimally-worst layout algorithms.

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Jurassic Mark: Inattentional Blindness for a Datasaurus Reveals that Visualizations are Explored, not Seen

Cited by 11 publications

References 33 publications

The Science of Visual Data Communication: What Works

The Science of Visual Data Communication: What Works

Rich Screen Reader Experiences for Accessible Data Visualization

The worst graph layout algorithm ever

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