200204 Joohee Kim

1. What Makes a Visualization Memorable?
327 cited/ Published in 2013/ IEEE Transactions on Visualization and Computer Graphics
Mishelle A. Borkin, Azalea A. Vo, Zoya Bylinskii, Phillip Isola, Shashank Sunkavalli, Aude Oliva, Hanspeter Pfister

2. 1
Motivation
• Memorability of images of natural scenes is consistent
• Memorability
 Engagement
 Effectiveness
• Provide a basis for understanding a number of cognitive aspects of visualization
• Build a new broad taxonomy of static visualizations
• Systematically analyzes memorizing intuition
• Not about memorability or comprehensibility
• Memorability of visualization as images

3. 2
Visualization taxonomy
Reference
R. L. Harris. Information graphics: A comprehensive illustrated reference. Oxford University Press, 1999.
Y. Englehardt. The language of graphics: A framework for the analysis of syntax and meaning in maps, charts and
diagrams. PhD thesis, Institute for Logic, Language and Computation, University of Amsterdam, 2002.
To aid characterization of
the visualizations
To gain insight,
Used in experiment

4. 3
Data Collection and Annotation
• Scrap 5,693 data visualizations
• Chosen based on
 A large number of static visualizations
 Able to scrap
• Infographic and scientific publications
have multiple vis to explain concept
 Fully responsible for whole story
• Exclude multiple
• Remain 2,070 single visualizations

5. 4
Analysis of Visualization types
• Scientific Publication
 Diagram
 Basic visual encodings
• Infographic
 Diagram
 Table
• News Media & Government
 Common forms of basic visual encodings
• Text visualization is not main stream
• Real-world examples

6. 5
Memorability Experiment: Hypotheses
H1) Participants will perform worse (i.e., overall have a harder time remembering visualizations) as
compared to natural images/photos.
H2) A visualization is more memorable if it includes a pictogram or cartoon of a recognizable image.
H3) A visualization is more memorable if there is more color.
H4) A visualization is more memorable if it has low visual density.
H5) A visualization is more memorable if it is more “minimalist” (i.e., “good” data-ink ratio).
H6) A visualization is more memorable if it includes a “familiar” visualization type (i.e., basic graph type
taught in school).
H7) A visualization is less memorable if it comes from a scientific publication venue.

7. 6
Memorability Experiment: Target images
• Select subset of 410 images
• Attributes rankings generated by 3 visualization researchers
 Rate individual
 If not same, make consensus
• Effect of chart junk
 Data-ink ratio  good : 145 | bad : 103 | medium 162
• Consider visualization categories as well

8. 7
Memorability Experiment: Participants and Set-up
• Target images are repeated twice
• Press a key at a second time
• Total 410 images of targets
• Over 17 levels
 Each with 120 images comprised of targets and filters
 Each takes 4.8 minute
• 1 sec for each image | 1.4 sec for gap
• 91-109 images apart
• Participants filtering
 Vigilance repeats for twice with spacing of 1-7 images
 False-alarm on > 50 % of last 30 non-repeat filter image
 To begin, should pass practice trial  miss rate < 50 % & false alarm rate < 30 %

9. 8
Memorability Experiment: Design & Analysis
• Based on signal detection theory
• Hit rate (HR) : 𝐻𝑅 =
𝐻𝐼𝑇𝑆
𝐻𝐼𝑇𝑆+𝑀𝐼𝑆𝑆𝐸𝑆
 Response on the second presentation
• False alarm rate (FAR) : 𝐹𝐴𝑅 =
𝐹𝐴
𝐹𝐴+𝐶𝑅
 CR: number of correct rejection
 Response on the first presentation
 To distinguish confusion by familiar image
• d-prime metric (sensitivity index) : 𝑑′ = 𝑍 𝐻𝑅 − 𝑍 𝐹𝐴𝑅
 Z : inverse cumulative Gaussian distribution
 Memorability score
 Confused for previous memory (low FAR) will result low score

10. 9
Memorability Experiment: Result & Discussion (H1)
• Visualization : HR of 55.36 % | FAR of 13.17%
• Scene : HR of 67.5 % | FAR of 10.7%
• Face : HR of 53.6 % | FAR of 14.5%
 Support H1
• Spearman’s rank correlations of 0.83 for HR, 0.78 for
FAR, and 0.81 for d-prime
 Participants split into two group
• Consistency of memorability score is verified
H1) Participants will perform worse (i.e., overall have a harder time
remembering visualizations) as compared to natural
images/photos.

11. 10
Memorability Experiment: Result & Discussion (H2)
• 145 contain either photographs, cartoons,
or human recognizable object = pictogram
• With pictogram: score (M) = 1.93
• Without pictogram: score (M) = 1.14
 Support H2
Overall Without pictogramH2) A visualization is more memorable if it includes a
pictogram or cartoon of a recognizable image.

12. 11
Memorability Experiment: Result & Discussion (H3 & H4)
H3) A visualization is more memorable if there is more color.
• Color ≥ 7 : score (M) = 1.71
• 2 ≤ Color ≤ 6 : score (M) = 1.48
• Color = 1 : score (M) = 1.18
 Support H3
• Visual density = High : score (M) = 1.83
• Visual density = Low : score (M) = 1.28
 Refute H4
H4) A visualization is more memorable if it has low visual density.

13. 12
Memorability Experiment: Result & Discussion (H5 & H7)
H5) A visualization is more memorable if it is more “minimalist” (i.e.,
“good” data-ink ratio
• Data-ink ratio
• Bad : score (M) = 1.81
• Good : score (M) = 1.23
 Refute H5
• Infographic : score (M) = 1.99
• Scientific Publication : score (M) = 1.48
• News Media : score (M) = 1.46
• Government : score (M) = 0.86
 Refute H7
H7) A visualization is less memorable if it comes from a scientific
publication venue.

14. 13
Memorability Experiment: Result & Discussion (H6)
• Grid/matrix, trees and networks and diagram have the highest memorability scores
• High FAR and low HR in bar vis
• Possible interpretation
 Low frequency contribute to uniqueness
 Image memorability correlate with recognizing natural things
 Refute H6
H6) A visualization is more memorable if it includes a “familiar” visualization type (i.e., basic graph type taught in school).

15. 14
Conclusion
• Visualizations are intrinsically memorable with consistency across people
 Natural scenes
 Colorful
 Inclusion of a human recognizable object enhance memorability
 Low data-to-ink ratios
 High visual densities (i.e., more chart junk and “clutter”)
 Natural looking visualization
• Future work to examine subtypes