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Visual Queries: The Foundation of Visual Thinking in Data Visualization Research

Explore the capacity of visual memory in perceiving rich details & how visual inquiries inform problem-solving in design with cyborgs in mind. Dive into the cognitive processes behind effective information display & the science of visualization evaluation through patterns and attentional demands.

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Visual Queries: The Foundation of Visual Thinking in Data Visualization Research

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

  2. Change Blindness Simons and Levin

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

  4. Sequential comparison task

  5. Central Problem: How do we perceivethe world in all its rich detail? • Only detail in fovea • Only a small amount of Information in visual working memory.

  6. 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

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

  8. Architecture for visual thinking

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

  10. Visual search

  11. Eye movements • Two or three a second • Preserves Context • We seek patterns

  12. ME Graph Constellation

  13. 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.

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

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

  16. Understanding surface shape Victoria Interrante

  17. GeoZui4D Linked Windows Tide Aware Show GeoNav

  18. Flow visualization • How do we optimally display vector fields?

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

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

  21. Immersion VR • HMD + head tracking • Data glove

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

  23. Just enough, just in time

  24. Dual Processing OBJECT FILES “Nexus” Dog

  25. Attention and Patterns

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