Human Cognition Process

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Human Cognition Process Perception in
Visualization
August 12, 2005    
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Visualization

• Meant constructing a visual image in the mind
• Shorter Oxford English Dictionary
• Now it has come to mean something more like a
  graphical representation of data or concepts

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Information Visualization
Advances in science technology have allowed
people to see old things in new ways. Telescopes,
microscopes and oscilloscopes are typical
instrument examples. ?????????????????????.
???????, ??? .
Maps, diagrams, and PERT charts are examples of
using visual representations to see things. A
good picture is worth ten thousand words.
??????????????????? Today, computers help
people to see and understand abstract data
through pictures.
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Pictures as Sensory Languages

• Similarity between pictures and the things that
  they represent
• The evidence related to whether or not we must
  learn to see pictures
• The issue of how pictures, and especially line
  drawings, are able to unambiguously represent
  things
• Are we able to understand certain pictures
  without learning ?

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Presenting relational structures
r1
r1, r2, r3
A
B
A
B
r3
r2
D
C
D
C
Two different graphical methods for showing
relationships between entities.
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Presenting relational structures

• The lines that connect the various components
  are a notation that is easy to read, because the
  visual cortex of the brain contains mechanisms
  specifically designed to seek out continuous
  contours.
• Other possible graphical notations for showing
  connectivity would be far less effective.

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Visual Principles

• Sensory vs. Arbitrary Symbols
• Pre-attentive Properties
• Simple Visual forms Presentations

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Sensory vs. Arbitrary Symbols

• Sensory
• The word refers to symbols and aspects of
  visualization that derive their expressive power
  from their ability to use the perceptual
  processing power of the brain without learning
• Arbitrary
• The word refers to aspects of representation that
  must be learned, having no perceptual basis
• E.g. the written word dog bears no perceptual
  relationship to any actual animal

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Sensory vs. Arbitrary Symbols

• Sensory
• Sensory representations are effective because
  they are well matched to the early stage of
  neural processing. They tend to be stable across
  individuals and cultures and time.
• Arbitrary
• Arbitrary conventions derive their power from
  culture and are therefore dependent on the
  particular cultural milieu or an individual.

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Sensory vs. Arbitrary Symbols

• Sensory
• Understanding without training
• Resistance to instructional bias
• Sensory immediacy
• Hard-wired and fast
• Cross-cultural Validity
• Arbitrary
• Hard to learn
• Easy to forget
• Embedded in culture and applications

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American Sign Language

• Primarily arbitrary, but partly representational
• Signs sometimes based partly on similarity
• But you couldnt guess most of them
• Sublanguages in ASL are more representative
• Describing the layout of a room, there is a way
  to indicate by pointing on a plane where
  different items sit.

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Pre-attentive Processing

• A limited set of visual properties are processed
  pre-attentively
• (without need for focusing attention).
• This is important for design of visualizations
• what can be perceived immediately
• what properties are good discriminators
• what can mislead viewers

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Example Color Selection
Viewer can rapidly and accurately
determine whether the target (red circle) is
present or absent. Difference detected in color.
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Example Shape Selection
Viewer can rapidly and accurately
determine whether the target (red circle) is
present or absent. Difference detected in form
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Pre-attentive Processing

• Identifying (or detection) tasks are performed
  with
• lt 200 - 250ms qualifies as pre-attentive
• eye movements take at least 200ms
• yet certain processing can be done very quickly,
  implying low-level processing in parallel
• If a decision takes a fixed amount of time
  regardless of the number of distractors, it is
  considered to be pre-attentive.

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Example Conjunction of Features
Viewer cannot rapidly and accurately
determine whether the target (red circle) is
present or absent when target has two or more
features, each of which are present in the
distractors. Viewer must search sequentially.
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Example Emergent Features
Target has a unique feature with respect to
distractors (open sides) and so the group can be
detected preattentively.
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Example Emergent Features
Target does not have a unique feature with
respect to distractors and so the group cannot
be detected preattentively.
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Asymmetric and Graded Preattentive Properties

• Some properties are asymmetric
• a sloped line among vertical lines is
  preattentive
• a vertical line among sloped ones is not
• Some properties have a gradation
• some more easily discriminated among than others

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Use Grouping of Well-Chosen Shapes for
Displaying Multivariate Data
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SUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP
YLKCIUQ DEZIDIXO CERTAIN QUICKLY PUNCHED METHODS
NIATREC YLKCIUQ DEHCNUP SDOHTEM SCIENCE ENGLISH
RECORDS COLUMNS ECNEICS HSILGNE SDROCER
SNMULOC GOVERNS PRECISE EXAMPLE MERCURY SNREVOG
ESICERP ELPMAXE YRUCREM CERTAIN QUICKLY PUNCHED
METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEM GOVERNS
PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE
YRUCREM SCIENCE ENGLISH RECORDS COLUMNS ECNEICS
HSILGNE SDROCER SNMULOC SUBJECT PUNCHED QUICKLY
OXIDIZED TCEJBUS DEHCNUP YLKCIUQ
DEZIDIXO CERTAIN QUICKLY PUNCHED METHODS NIATREC
YLKCIUQ DEHCNUP SDOHTEM SCIENCE ENGLISH RECORDS
COLUMNS ECNEICS HSILGNE SDROCER SNMULOC
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Text NOT Preattentive
SUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP
YLKCIUQ DEZIDIXO CERTAIN QUICKLY PUNCHED METHODS
NIATREC YLKCIUQ DEHCNUP SDOHTEM SCIENCE ENGLISH
RECORDS COLUMNS ECNEICS HSILGNE SDROCER
SNMULOC GOVERNS PRECISE EXAMPLE MERCURY SNREVOG
ESICERP ELPMAXE YRUCREM CERTAIN QUICKLY PUNCHED
METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEM GOVERNS
PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE
YRUCREM SCIENCE ENGLISH RECORDS COLUMNS ECNEICS
HSILGNE SDROCER SNMULOC SUBJECT PUNCHED QUICKLY
OXIDIZED TCEJBUS DEHCNUP YLKCIUQ
DEZIDIXO CERTAIN QUICKLY PUNCHED METHODS NIATREC
YLKCIUQ DEHCNUP SDOHTEM SCIENCE ENGLISH RECORDS
COLUMNS ECNEICS HSILGNE SDROCER SNMULOC
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Preattentive Visual Properties(Healey 97)

• length Triesman
  Gormican 1988
• width Julesz
  1985
• size Triesman
  Gelade 1980
• curvature Triesman
  Gormican 1988
• number Julesz
  1985 Trick Pylyshyn 1994
• terminators Julesz
  Bergen 1983
• intersection Julesz
  Bergen 1983
• closure Enns
  1986 Triesman Souther 1985
• colour (hue) Nagy
  Sanchez 1990, 1992 D'Zmura 1991
  Kawai et al.
  1995 Bauer et al. 1996
• direction of motion Nakayama
  Silverman 1986 Driver McLeod 1992
• stereoscopic depth Nakayama
  Silverman 1986
• 3-D depth cues Enns 1990
• lighting direction Enns 1990

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Color
Most of this segment taken from Colin Ware, Ch. 4
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Color Issues

• Complexity of color space
• 3-dimensional
• Computer vs. Print display
• There are many models and standards
• Color not critical for many visual tasks
• Doesnt help with determination of
• Layout of objects in space
• Motion of objects
• Shape of objects
• Color-blind people often go for years without
  knowing about their condition
• Color is essential for
• breaking camouflage
• Recognizing distinctions
• Picking berries out from leaves
• Spoiled meat vs. good
• Aesthetics

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Color Palettes for Computer Tools

• From Powerpoint

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Colors for Labeling

• Wares recommends to take into account
• Distinctness
• Unique hues
• Component process model
• Contrast with background
• Color blindness
• Number
• Only a small number of codes can be rapidly
  perceived
• Field Size
• Small changes in color are difficult to perceive
• Conventions

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Small Color Patches More Difficult to Distinguish
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Wares Recommended Colors for Labeling
Red, Green, Yellow, Blue, Black, White, Pink,
Cyan, Gray, Orange, Brown, Purple. The top six
colors are chosen because they are the unique
colors that mark the ends of the opponent color
axes. The entire set corresponds to the eleven
color names found to be the most common in a
cross-cultural study, plus cyan (Berlin and Kay)
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Attributed Visualization of Collaborative
Workspace
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Which Properties Forms are Appropriate for
Which Information Types?
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Accuracy Ranking of Quantitative Perceptual
TasksEstimated only pairwise comparisons have
been validated(Mackinlay 88 from Cleveland
McGill)
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Interpretations of Visual Properties

• Some properties can be discriminated more
  accurately but dont have intrinsic meaning
• (Senay Ingatious 97, Kosslyn, others)
• Density (Greyscale)
• Darker -gt More
• Size / Length / Area
• Larger -gt More
• Position
• Leftmost -gt first, Topmost -gt first
• Hue
• ??? no intrinsic meaning
• Slope
• ??? no intrinsic meaning

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A Chart is

• A visual display that illustrates one or more
  relationships among entities
• A shorthand way to present information
• Allows a trend, pattern, or comparison to be
  easily apprehended

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Types of Symbolic Displays

• Charts
• Graphs
• Maps
• Diagrams

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Types of Symbolic Displays

• Charts
• at least two scales required
• values associated by a symmetric paired with
  relation
• Examples scatter-plot, bar-chart, layer-graph

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Types of Symbolic Displays

• Graphs
• discrete relations among discrete entities
• structure relates entities to one another
• lines and relative position serve as links

Examples family tree flow chart network
diagram
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Types of Symbolic Displays

• Maps
• internal relations determined (in part) by the
  spatial relations of what is pictured
• labels paired with locations

Examples map of census data topographic
maps From www.thehighsierra.com
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Common Chart Types
of accesses
of accesses
length of access
URL
url 1 url 2 url 3 url 4 url 5 url 6 url 7
45
40
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of accesses
30
length of access
25
20
15
10
5
0
long
very
long
short
of accesses
medium
days
length of page
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Classical Graph Types
hierarchical
orthogonal
symmetric
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References

• Chapters 1, 3, 4 of the book Information
  Visualization Perception for Design, Colin
  Ware, Morgan Kaufmann Publisher, 1999.
• Perception in Visualization, Christopher G.
  Healey.