#datavizinsixweeks, week 2 - perception & cognition
TRANSCRIPT
#DataVizInSixWeeksCopyright Anne Stevens
Perception & cognition
#DataVizInSixWeeksCopyright Anne Stevens
Week One
What is data visualization? Historical context
Week Four
Design issues & best practices
Week Two
Visualization typesWeek Five
Big data, data management
Week Three
Perception and cognitionWeek Six
Synthesis
Data Viz In Six WeeksAn Introduction to Visual Analytics course taught at OCAD University, Toronto
By Anne Stevens
#DataVizInSixWeeksCopyright Anne Stevens
c/o Fanny Chevalier
#DataVizInSixWeeksCopyright Anne Stevens
Why perception & cognition
Work with laws of perception & cognition, not against them
Gives the designer objective tools to assess own work
#DataVizInSixWeeksCopyright Anne Stevens
Distributed cognition
#DataVizInSixWeeksCopyright Anne Stevens
Cognition
Myth
Continuous
Seamless
People are passive receivers of external info
Reality
Fragmented
That’s why we can drive and talk at the same time
People construct their reality
Not all thought is created equal
#DataVizInSixWeeksCopyright Anne Stevens
Not all thought is created equal
System 1 (primitive)
Reactive
Automatic
Fast
Can multitask
Consumes few calories
System 2 (rational)
Goal driven
Conscious, deliberate
Not automatic
Slow
Single tasks
Consumes lots of brain calories
“Working Memory”
#DataVizInSixWeeksCopyright Anne Stevens
#DataVizInSixWeeksCopyright Anne Stevens
Perception
Myth
Continuous
That we see everything at once …
… and equally well
We are passive receptors
Reality
Details on demand
Just in time delivery
Most efficient use of brain resources
Bottom-up and top-down processes
Preattentive & Attentive processes
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Preattentive perception
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Find the red circle
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Preattentive Attentive
Equiv. to System 1 thinking
Fast, automatic
Bottom-up
Instinctive
Efficient
Multitasks
Equiv. to System 2 thinking
Slow, deliberate, focussed
Top-down
Consumes more ‘Visual Working Memory’
VWM can hold very few objects at a time
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Preattentive features
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Not all preattentive features are equal
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Good design avoids interference
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Brain – pattern recognition machine
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Gestalt psychology - reification
en.wikipedia.org/wiki/File:Reification.jpg
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Multistability
en.wikipedia.org/wiki/File:Reification.jpg
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Gestalt laws
en.wikipedia.org/wiki/File:reification.jpg
Closure Similarity
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Gestalt laws
en.wikipedia.org/wiki/File:reification.jpg
Proximity
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Gestalt laws
en.wikipedia.org/wiki/File:reification.jpg
Symmetry Continuity
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#DataVizInSixWeeksCopyright Anne Stevens
Gestalt laws
en.wikipedia.org/wiki/File:reification.jpg
Common Fate Figure / ground
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Example of Gestalt principles at work
Paul Butler. Visualizing Friendship
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Jacques Bertin – patron saint
Semiology of Graphics, 1967
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Visual variables - quantitaties
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Visual variables - categories
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Don’t use colour for quantity
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Bertin Archives
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Bertin Archives
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Reconstruction of a Bertin matrix
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Visual variables: line and size
University of Munstercartography.uni-muenster.de/en/symbols/visual-variables
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Texture and brightness
University of Munstercartography.uni-muenster.de/en/symbols/visual-variables
#DataVizInSixWeeksCopyright Anne Stevens
University of Munstercartography.uni-muenster.de/en/symbols/visual-variables
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Dan Berlinslideshare.net/Banderlin/visual-principles-of-experience-design-blending-art-and-science
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Visual variable: colour value
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Visual variable: colour hue
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Examples
en.wikipedia.org/wiki/Demographics_of_Chicago (left)chicagostraightdope.com (right)
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Map of the Marketmarketwatch.com/tools/stockresearch/marketmap
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Data mapping
One dimensional data: easy
Multi-dimensional data: more tricky
Map each data dimension to a different visual variable
Chose visual variables that suit data type
Avoid interference with preattentive perception
Map primary data to primary visual feature; secondary to secondary, and so on
#DataVizInSixWeeksCopyright Anne Stevens
Week One
What is data visualization? Historical context
Week Four
Design issues & best practices
Week Two
Visualization typesWeek Five
Big data, data management
Week Three
Perception and cognitionWeek Six
Synthesis
Data Viz In Six WeeksAn Introduction to Visual Analytics course taught at OCAD University, Toronto
By Anne Stevens
stevensanne.com
stevensanne.com/blog/
@3_ring_binder