Visual Thinking and the Objects of Visual Attention Colin Ware University of New Hampshire.

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Presentation transcript:

Visual Thinking and the Objects of Visual Attention Colin Ware University of New Hampshire

Architecture for visual thinking Interaction Loop 2 billion neurons Parallel, automatic

Pattern finding Continuity, symmetry,connectedness Common fate, Field Hayes and Hess

Dual Coding Theory Verbal-Propositional Information Visual Structural Information Verbal Working Memory Baddeley Visual Working Memory Central Executive Visuo-Spatial Sketchpad Verbal input Visual input Words and symbols

Visual working memory is a very limited resource. Only 3 objects Change blindness (Vogel et al) GeoZui3D

Sequential comparison task

Change Blindness

Triesman serial processing of non-pre- attentive object (40 msec/item) Kahneman and Triesman “object files” Rensink - Fingers of attention reach into pro- object flux

Other components of working memory GistSemantic content

Searchlight Model of Attention

Eye movements Two or three a second Preserves Context The screen is a kind of buffer for visual ideas – we cannot see it all at once but we can sample it rapidly

Architecture for visual thinking Interaction Loop 2 billion neurons Parallel, automatic

Thinking visually Embedded processes Define problem and steps to solution Formulate parts of problem as visual questions/hypotheses Setup search for patterns Eye movement control loop IntraSaccadic Scanning Loop (form objects from proto-object flux

Problem Trip Port Bou- Calais (5 days 3 citise) Visual Problem Mayor Highways – Distance < 1.2 min = red smooth path Eye movements to identify major candidate pathways Pattern Identification: smooth, red, connected segments / reject non-red-wrong direction Part solutions into vwm – spatial markers Parts may be handed to verbal wm

Cost of Knowledge How do we navigate. Intra-saccade (0.04 sec) An eye movement (0.5 sec) A hypertext click (1.5 sec but loss of context) A pan or scroll (3 sec but we don’t get far) Walking (30 sec. we don’t get far) Flying (faster can be tuned) Zooming, fisheye, DragMag

Walking Flying (30 sec +) Naïve view that does not take perception or the cost of action into account.

How to navigate large 21/2D spaces? Zooming Vs Multiple Windows Key problem: How can we keep focus and maintain context. Focus is what we are attending to now. Context is what we may wish to attend to. 2 solutions: Zooming, multiple windows

When is zooming better than multiple windows (Matt Plumlee) Key insight: Visual working memory is a very limited resource. Only 3 objects Change blindness (Vogel et al) GeoZui3D

Task: searching for target patterns that match

Cognitive Model (grossly simplified) Time = setup cost + number of visits*time per visit Number of visits is a function of number of objects (& visual complexity) When there are too many multiple visits are needed

Predictions As targets (and visual working memory load) increases, multiple Windows become more attractive. Time =

Prediction Results

How to get more complex structure Use 3D? GraphVisualizer3D

Need low cost and low cognitive cost interactions Constellation: Hover queries (Munzer)

Lessons for design Low level- what stands out Medium level – pattern perception High level vwm and cognitive costs A large high resolution screen may be the best alternative.

Implications for design of information spaces Design for pattern perception, three chunk patterns Consider attention Implications for navigation Make all navigation devices visible. Do not ever make people walk Maintain context Minimize cognitive load of navigation Use multiple linked views for more complex pattern integration

Research topic What are easy visual queries Easy= single object comparison in vwm

Architecture for visual thinking Interaction Loop

Rensink

Software Engineering Example - with Graph Representation Segment Big Module into parts High Cohesion (semantics) Low Coupling Find highly connected subgraphs with minimal links Scan for candidate patterns Look for Low connectivity Look for Semantic similarity (symbols)

Searchlight Properties Size varies with data density Size varies with stress level Attention operators work within the searchlight beam Eye movements 3/sec – A series of saccades

How do we see No world in our heads Change blindness Knowledge on and need to know basis O’Regan, Rensink