1. Visual Queries:
The foundation of visual thinking
Designing with
cyborgs in mind
Colin Ware
Data Visualization Research Lab
University of New Hampshire

2. Change Blindness
Simons and Levin

3. Vogel Woodman and Luck

Capacity of visual working memory 3
simple shapes

4. Sequential comparison task

11. Central Problem: How do we perceive
the world in all its rich detail?


Only detail in fovea
Only a small amount of Information in
visual working memory.

12. Solution

“The world is its own memory” O’Regan
Task-related active vision
“What you see is what you need”
Treish et al. (2003)

Seeing is a process that helps us solve problems




13. Visualizations are much better databases than what we have in our heads

15. Architecture for visual thinking

16. Stage 2 Pattern perception
Visual queries are executed by finding
patterns in displays
Attentional Demands
Tune the pattern finding processes
Top down meets bottom up

17. Visual search
U fu V al
se l isu
F ldo V
ie f iew
V a
isu l
Sa o
e rch r
M n rin
o ito g
S te y
tra g
Ee
y
Mv m t
o e en
Cn l
o tro

18. Eye movements

Two or three a second
Preserves Context

We seek patterns


19. ME Graph
Constellation

21. Why visualize?





Human Memory: 100 meg (Landauer)
= 108 (not unique)
World information: 1 exabyte/year
= 1018 (unique)
= 108 bytes new information per person per year
Conclusion: we are cognitive cyborgs – our
memories are not in our heads.

22. Why do we care about
perception?



It is about what makes information display
effective.
Can there be a science of visualization?
Evaluation

23. Visualizations

Maps




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
Multi-dimensional Discrete
Multi-dimensional continuous
Graphs



Social Networks
Flow
Narrative – explaining data


Route
Flow
Thematic (geology, vegetation, etc)
Animations, assembly diagrams
Other thinking tools

Calendars, Planners, search engines, News pages, Design tools

27. Understanding surface shape
Victoria Interrante

28. GeoZui4D
Linked
Windows
Tide
Aware
Show
GeoNav

29. Flow visualization

How do we optimally display vector fields?

35. 




Length - 420 ft
Beam – 82 ft
Draft – 29 ft
16,000 Tons
30,000 HP
Diesel Elec AC/AC
Fuel – 1,165,000 gal Top Speed – 17kts
Ice Breaking – 4.5 ft @ 3 kts

37. CAVE





Head tracking – stereo
Resolution problems
Light scattering problems
Vergence focus problem for near object
Occlusion problems for near objects

38. Immersion VR


HMD + head tracking
Data glove

41. Capacity of visual working
memory (Vogal, Woodman, Luck,
2001)



Task – change detection
Can see 3.3 objects
Each object can be complex
1 second

42. Just enough, just in time

43. Dual Processing
Display
Features
Proto-objects and
Patterns
Verbal
Working
Memory
OBJECT
FILES
“Nexus”
Visual
Working
Memory
Dog
GIST
Visual
Query
Egocentric object and
Pattern map

44. Attention and Patterns