Beyond Memorability: Visualization Recognition and Recall Borkin, - - PowerPoint PPT Presentation

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Beyond Memorability: Visualization Recognition and Recall Borkin, - - PowerPoint PPT Presentation

Feb 15, 2024 524 likes •1.02k views

Beyond Memorability: Visualization Recognition and Recall Borkin, M., Bylinskii, Z., Kim, N.W., Bainbridge C.M., Yeh, C.S., Borkin, D., Pfister, H., & Oliva, A. IEEE Transactions on Visualization and Computer Graphics, 2015 Presented by

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Beyond Memorability: Visualization Recognition and Recall

Borkin, M., Bylinskii, Z., Kim, N.W., Bainbridge C.M., Yeh, C.S., Borkin, D., Pfister, H., & Oliva, A. IEEE Transactions on Visualization and Computer Graphics, 2015 Presented by Julieta Martinez

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What makes an image memorable? What makes a vis memorable? What makes a face memorable?

vs. vs. vs.

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Perception Memory Aesthetics Engagement Comprehension Impact

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Perception Memory

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What makes a visualization memorable?

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Let’s play a game

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If you see a repeated visualization, clap Seriously, get ready to clap

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That’s it. Thanks!

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attention check memory check likely to be a false positive

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Data

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Government Infographic News Science

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“These particular web sites were chosen because each contained a large number of static visualizations that could be automatically scraped without requiring a large manual clean-up effort.”

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“The annotations were done by ten Harvard University undergraduates who had completed the Harvard introductory visualization course”

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data-ink ratio? bad bad medium = bad

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Results

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A visualization is memorable if…

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… it has a pictogram (M=1.93 vs. M=1.14)

Thus, all results are presented with and without pictograms

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… it is colorful

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… it has low data-ink ratio

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… it is visually dense

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Some questions remain

  • What visual elements do

people use to

○ store into memory? ○ retrieve from memory?

  • Does giving more time

make a difference?

  • What do people

remember?

  • Same data
  • More labels
  • Less participants (33)
  • More time
  • + Eye tracking
  • + Word descriptions

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Step 1: Encode

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Step 2: Recognize

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Step 3: recall

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Does giving more time make a difference?

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What do people look at?

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Most recognizable Encoding Least recognizable Recognition

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“Percent of people born on each day of the year. X-axis is month Y-axis is day. Most popular birthdays are in late summer and early fall.”

  • Quality was rated from 0 to 3

○ 0 → incorrect or incoherent ○ 3 → visualization topic, what data or information is presented in the visualization, the main message of the visualization, and one additional specific detail about the visualization

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Evaluating recall

“this was a chart of most common birthdays. the darker the color the more common the birthday. september was the darkest month”

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Titles help!

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So does redundancy!

Description quality

Titles improve recall quality

Description quality

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Strengths and weaknesses

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Strengths

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  • First dataset of its kind
  • High quality, transparent
  • research. All the data is

available online.

  • Props for collecting verbal

descriptions of visualizations → machine learning (:

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  • Data is very skewed
  • Who would think tables

are memorable?

  • Infographics != infovis
  • Maybe scientific

visualizations are inherently harder to understand

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Weaknesses

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Beyond Memorability: Visualization Recognition and Recall

Borkin, M., Bylinskii, Z., Kim, N.W., Bainbridge C.M., Yeh, C.S., Borkin, D., Pfister, H., & Oliva, A. IEEE Transactions on Visualization and Computer Graphics, 2015 Presented by Julieta Martinez

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