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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 VisualizationInformation Visualization

Maps, diagrams, and PERT charts Maps, diagrams, and PERT charts are examples of using visual are examples of using visual representations to see things. representations to see things. A A good picture is worth ten thousand good picture is worth ten thousand words. words.

一个好的图画所能表达的东西胜一个好的图画所能表达的东西胜过十万个字过十万个字

Today, computers help people to Today, computers help people to see and understand abstract data see and understand abstract data through pictures.through pictures.

Advances in science & Advances in science & technology have allowed technology have allowed people to people to see old things in new see old things in new ways.ways. Telescopes, microscopes Telescopes, microscopes and oscilloscopes are typical and oscilloscopes are typical instrument examples.instrument examples.

现代科学技术允许人们用新现代科学技术允许人们用新的方法来看旧的事物的方法来看旧的事物 . . 例如例如天文望远镜天文望远镜 , , 显微镜 …显微镜 … ..

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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

A

DC

B A

DC

B

r1 r1, r2, r3

r2 r3

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 couldn’t 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.

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

Example: Color Selection

Viewer can rapidly and accurately determinewhether the target (red circle) is present or absent.Difference detected in color.

Example: Shape Selection

Viewer can rapidly and accurately determinewhether the target (red circle) is present or absent.Difference detected in form

Pre-attentive Processing

• Identifying (or detection) tasks are performed with: – < 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.

Example: Conjunction of Features

Viewer cannot rapidly and accurately determinewhether the target (red circle) is present or absent when target has two or more features, each of which arepresent in the distractors. Viewer must search sequentially.

Example: Emergent Features

Target has a unique feature with respect to distractors (open sides) and so the groupcan be detected preattentively.

Example: Emergent Features

Target does not have a unique feature with respect to distractors and so the group cannot be detected preattentively.

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

Use Grouping of Well-Chosen Shapes for Displaying Multivariate Data

SUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP YLKCIUQ DEZIDIXOCERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEMSCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOCGOVERNS PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE YRUCREMCERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEMGOVERNS PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE YRUCREMSCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOCSUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP YLKCIUQ DEZIDIXOCERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEMSCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOC

SUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP YLKCIUQ DEZIDIXOCERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEMSCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOCGOVERNS PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE YRUCREMCERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEMGOVERNS PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE YRUCREMSCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOCSUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP YLKCIUQ DEZIDIXOCERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEMSCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOC

Text NOT PreattentiveText NOT Preattentive

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– Doesn’t 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

• Ware’s 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

28Images from lecture by Terrance Brooke

Small Color Patches More Difficult to Distinguish

29Slide adapted from Terrance Brooke

Ware’s 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

Which Properties & Forms are Appropriate for Which

Information Types?

Accuracy Ranking of Quantitative Perceptual TasksEstimated; only pairwise comparisons have been validated

(Mackinlay 88 from Cleveland & McGill)

Interpretations of Visual Properties

Some properties can be discriminated more accurately but don’t have intrinsic meaning(Senay & Ingatious 97, Kosslyn, others)

– Density (Greyscale)Darker -> More

– Size / Length / AreaLarger -> More

– PositionLeftmost -> first, Topmost -> 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

T yp e n am e h e reT yp e t it le h e re

T yp e n am e h e reT yp e t it le h e re

T yp e n am e h e reT yp e t it le h e re

T yp e n am e h e reT yp e t it le h e re

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

Types of Symbolic DisplaysGraphs

– 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

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

Common Chart Types

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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.