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

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Presentation transcript:

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

Change Blindness Simons and Levin

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

Sequential comparison task

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.

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

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

Architecture for visual thinking

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

Visual search

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

ME Graph Constellation

Why visualize? Human Memory: 100 meg (Landauer) = 10 8 (not unique) World information: 1 exabyte/year = (unique) = 10 8 bytes new information per person per year Conclusion: we are cognitive cyborgs – our memories are not in our heads.

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

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

Understanding surface shape Victoria Interrante

Linked Windows Tide Aware Show GeoNav GeoZui4D

Flow visualization How do we optimally display vector fields?

Length ft16,000 Tons Beam – 82 ft30,000 HP Draft – 29 ftDiesel Elec AC/AC Fuel – 1,165,000 galTop Speed – 17kts Ice Breaking – kts

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

Immersion VR HMD + head tracking Data glove

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

Just enough, just in time

Dual Processing OBJECT FILES “Nexus” Dog

Attention and Patterns