Perception, Cognition and the Visual - PowerPoint PPT Presentation

1 / 91
About This Presentation
Title:

Perception, Cognition and the Visual

Description:

Musical terms. Legato (connected) Staccato (breaks) 2-Creating Continuity by bridging gaps ... What comedies have won awards? - Which funds underperformed the SP-500? ... – PowerPoint PPT presentation

Number of Views:82
Avg rating:3.0/5.0
Slides: 92
Provided by: janmar
Category:

less

Transcript and Presenter's Notes

Title: Perception, Cognition and the Visual


1
Perception, Cognition and the Visual
  • Seeing, thinking, knowing

2
Perception cognition
3
Inner Display?
4
Perception Pre-attentive Perceptual/Cognitive
issues
  • How do human visual systems analyzeimages?
  • preattentively,without the need for focused
    attention
  • Generally less than 200-250 msecs (eyemovements
    take 200 msecs)

5
Preattentive Processes
  • Grid stays while asterix disappears

6
Preattentive Processes
  • Cognitive operations prior to focusing attention

7
Necker Cube 3 Ways
8
Completing Broken images
9
Visual Ambiguity
10
How Many threes?
  • 12817687561389765469845069856049828267629809858458
    22450985645894509845098094358590910302099059595957
    72564675050678904567884578980982167765487636490856
    0912949686

11
How many threes?
  • 12817687561389765469845069856049828267629809858458
    22450985645894509845098094358590910302099059595957
    72564675050678904567884578980982167765487636490856
    0912949686

12
Response Time where is the horizonatal bar?
13
Where is the letter L?
14
Orientation
15
Size/Scale
16
Colour (hue)
17
Hue where is the red circle? Not Usually
Pre-attentive
18
Hue shape
19
Region Search
Form boundary NOT identified pre-attentively Hue
variations interfere with form boundary identifica
tion
  • Hue boundary identified
  • pre-attentively
  • Form variations do NOT
  • interfere with hue
  • boundary identification

20
Area Estimation
  • Blue rectangles? Sloped rectangles?

21
Fill and Shape
22
Brightnesss
23
Shape
24
Luminance/Contrast
25
Color for Categories and Sequences
  • Links to websites about color systems
  • CIE, Munsell

26
Using Color--Varied Opnions, More later in the
course
  • blue in large regions, not thin lines
  • red and green in the center of thefield of view
    (edges of retina not sensitiveto these)
  • black, white, yellow in periphery
  • Color Brewer
  • Pantone

27
Perceptual Tasks
  • Target detection ( Is something there?)
  • Boundary detection (Can the elements be
    grouped?)
  • Counting ( How many elements of a certain type
    arepresent?)

28
Perceptual Properties
  • Luminance (measured) Brightness (perceived)
  • Color
  • sensory response to electromagnetic radiation--
    wavelengths 0.4-0.7 micrometers)
  • Hue, value, saturation
  • Texture
  • Shape
  • Sources Colin Ware Morgan Kaufmann Information
    Visualization--Perception for Design

29
Optical Illusions and Programmed Variations
  • Link
  • Chernoff Faces link

30
Pre-attentive Perception, cognition
visualization
  • Understanding of what is processed
    pre-attentively
  • is probably the most important contribution that
    vision science can make to data visualization.
  • BUT
  • Humans do not perceive much unless we have
  • at least some expectation and need to see it.
  • - Colin Ware

31
Cognitive issues
  • Visualization as a tool useful for
  • aiding comprehension and understanding
  • Seeing as thinking
  • Visual Cognition Attention--Visual Cognition
    Lab. U. Illinois

32
Time Maps Framing/Containing Memory
origin
prehistory
history
33
Time Frames in Collective Memory Studies
  • Assumptions about mnemonic traces
  • Cognitive vs. unconscious processes
  • History vs. representations of the past
  • mental structures

Salvador Dali, The Persistence of Memory, 1931
34
Processes Forms for Framing Memory in time
  • Sociomental topography of how communities
    remember the past
  • Unconventional approach to links between
    conventional ideas of history
    public/collecctive memory
  • mnemonic traditions
  • recalling the past together synchronizing
    attention on particular moments
  • social norms of remembering
  • Mnemonic transitivity (allows memory to pass from
    one person to another even when there is no
    directe contact)

35
Triggers, memory retrieval (types of Mnemonic
devices)
  • Words, facts, skills, events
  • Ideals, goals, intentions, promises
  • Feelings, states-of-mind, earlier selves etc
  • Things, odours, ex. Madeleine (Proust,
    Remembrance of things past, triggered by smell
    and taste of Madeleines, a style of French
    cupcake)

36
Time Maps the Social Shaping of Memory
  • Questions of relevance
  • Long and short term
  • Eventful and uneventful periods
  • Connections
  • Discontinuities

37
Analyzing the Structures of Socio-Mental memory
traditions
  • conventional ways of stringing memories together
    into culturally-meaningful narratives
  • strategies to create the illusion of historical
    continuity (bridges)
  • genealogical structures of ancestry descent
  • watersheds that separate one period from the
    next inflating mental divides
  • The social construction of beginnings (origin
    myths and the legitimation of claims about the
    past)

38
(1)Plotlines Narrative Forms
  • Establish connections in narratives,
  • scenarios, plotlines
  • Mental historical outlooks,
  • Selective use of history,
  • Often anticipate future
  • Progress narratives

39
Plotlines Narrative Forms
  • Decline narratives
  • Both imply single direction

40
Zigzag Narratives
  • Conversion
  • Recovery
  • Rise fall

41
Evolutionary narratives
  • Unilinear (deterministic)
  • Multilinear
  • (ex. Cladograms--branching)

42
Circles (Cycles),
  • recurrence

43
Cycles (Rhymes)
44
Density Variations --Mountains and valleys
  • eventful vs. uneventful moments in the past
  • Unevenly distributed

45
Commemgram example
  • Eventful times,
  • Multiple pasts

46
Historical Phrasing in Narratives
  • Musical terms
  • Legato (connected)
  • Staccato (breaks)

47
2-Creating Continuity by bridging gaps
  • Linking noncontiguous points in time or place to
    establish continuity
  • Same place
  • Same things (relics memorabilia)
  • Imitation of the past (ex. Courtroom etiquette
    religious ritual)
  • same time (commemorative holidays,
    reenactments, seasonal identity

48
Mnemonic pasting
49
Interconnectedness
  • Genealogical Distance (consanguinity)
  • Ancestral depth ( of generations)

50
Time and Social Distance
  • Not just people
  • Can be practices, things, events

51
Cousinhood Ancestral Depth
52
Monogenist Polygenist Models of Human Descent
  • Socio-mnemonic dimensions of ancestry

53
Another look at Phylogeny
54
4-Discontinuities Mnemonic Cutting Shaping
Memory
  • Conceptualizing Discontinuities (breaks)

55
Assimilation Difference
  • Periods, epochs as mnemonic transformation of
    historical continuum

56
History Prehistory--decapitation
57
History Prehistory in Mnemonic Traditions
  • Example Pre-contact and Post contact history of
    N. America

58
Lumping Splitting in Narratives
59
5-Beginnings and Claims based on the Past
60
Visualizations of Home/House. Child Katrina
Survivors
61
Recall House/Home (Katrina Victim)
62
House/Home
  • House as roof

63
House?Home
  • Sources Slide show of Katrina victims drawings
    of house/home, Dewann, S. Using Crayons to
    Exorcise Katrina, New York Times, Monday
    September 17, 2007, Arts Section, B1,5.

64
Graphical visualization to support more efficient
task performance
  • Allowing substitution of rapid perceptual
  • influences for difficult logical inferences
  • Reducing search for information required for task
    completion
  • (Sometimes text is better, however)

65
Issues
  • Cognitive Artifacts
  • Matching Representation to Task
  • Representations Aid Info Access and Computation
  • Naturalness and Experiential Cognition

66
Cognitive Maps
  • You have some existing internal model of the
  • system, stops, how to get there
  • glance at SFU map for help
  • Refine your internal model, clarifying items and
    extending it
  • Note differences between your map the official
    one

67
Process Models Navigation in Visual systems
  • -process by which a person looks at a graphic and
    makes some use of it
  • substeps
  • Can you describe process?
  • Navigation in visual systems - Creation and
    interpretation of an internal mental model

68
Information foraging
  • Search for schema (representation)
  • Problem solve to trade off features
  • Search for a new schema that reducesproblem
  • Package the patterns found in someoutput product

69
Navigation
70
Crystallization
71
Process
72
Browsing useful when
  • Good underlying structure so that items close to
    oneanother can be inferred to be similar
  • Users are unfamiliar with collection contents
  • Users have limited understanding of how system
    isorganized and prefer less cognitively loaded
    methodof exploration
  • Users have difficulty verbalizing
    underlyinginformation need
  • Information is easier to recognize than describe

73
If time
74
User Tasks in Visualization Environments--Eleven
basic actions
  • identify, locate, distinguish,
    categorize,cluster, distribution, rank, compare
    withinrelations, compare between
    relations,associate, correlate

75
Data Types and Tasks
76
Terminology
  • Data case An entity in the data set
  • Attribute A value measured for all datacases
  • Aggregation function A function thatcreates a
    numeric representation for a setof data cases
    (eg, average, count, sum)

77
Steps in Creating Visualization
  • 1. Retrieve ValueGeneral DescriptionGiven a
    set of specific cases, find attributes ofthose
    cases.Examples- What is the mileage per gallon
    of the Audi TT?- How long is the movie Gone with
    the Wind?

78
2. Filter
  • General DescriptionGiven some concrete
    conditions on attribute values,find data cases
    satisfying those conditions.Examples- What
    Kelloggns cereals have high fiber?- What
    comedies have won awards?- Which funds
    underperformed the SP-500?

79
3. Compute Derived Value
  • General DescriptionGiven a set of data cases,
    compute an aggregatenumeric representation of
    those data cases.Examples- What is the gross
    income of all stores combined?- How many
    manufacturers of cars are there?- What is the
    average calorie content of Post cereals?

80
4. Find Extremum
  • General Description
  • Find data cases possessing an extreme value of
    anattribute over its range within the data
    set.Examples- What is the car with the highest
    MPG?- What director/film has won the most
    awards?- What Robin Williams film has the most
    recentrelease date?

81
5. Sort
  • General DescriptionGiven a set of data cases,
    rank them according tosome ordinal
    metric.Examples- Order the cars by weight.-
    Rank the cereals by calories.

82
6. Determine Range
  • General DescriptionGiven a set of data cases
    and an attribute of interest,find the span of
    values within the set.Examples- What is the
    range of film lengths?- What is the range of car
    horsepowers?- What actresses are in the data set?

83
7. Characterize Distribution
  • General DescriptionGiven a set of data cases
    and a quantitative attribute ofinterest,
    characterize the distribution of that
    attributesvalues over the set.Examples- What
    is the distribution of carbohydrates in
    cereals?- What is the age distribution of
    shoppers?

84
Find Anomalies
  • General DescriptionIdentify any anomalies
    within a given set of data caseswith respect to
    a given relationship or expectation,e.g.
    statistical outliers.Examples- Are there any
    outliers in protein?- Are there exceptions to
    the relationship betweenhorsepower and
    acceleration?

85
9. Cluster
  • General DescriptionGiven a set of data cases,
    find clusters of similarattribute
    values.Examples- Are there groups of cereals
    w/ similar fat/calories/sugar?- Is there a
    cluster of typical film lengths?

86
10. Correlate
  • General DescriptionGiven a set of data cases
    and two attributes, determineuseful
    relationships between the values of those
    attributes.Examples- Is there a correlation
    between carbohydrates and fat?- Is there a
    correlation between country of origin and MPG?-
    Do different genders have a preferred payment
    method?- Is there a trend of increasing film
    length over the years?

87
Compound tasks
  • Sort the cereal manufacturers by average
    fatcontentCompute derived value Sort
  • Which actors have co-starred with
    JuliaRoberts?Filter Retrieve value

88
What questions were left out?
  • Basic mathWhich cereal has more sugar, Cheerios
    or Special K?Compare the average MPG of
    American and Japanese cars.
  • Uncertain criteriaDoes cereal (q, Y, Zj) sound
    tasty?What are the characteristics of the most
    valued customers?
  • Higher-level tasksHow do mutual funds get
    rated?Are there car aspects that Toyota has
    concentrated on?
  • More qualitative comparisonHow does the Toyota
    RAV4 compare to the Honda CRV?What other
    cereals are most similar to Trix?

89
Concerns
  • InfoVis tools may have influencedstudents
    questions
  • Graduate students as group being studiedHow
    about professional analysts?
  • Subjective Not an exact science

90
Analytic gaps
  • obstacles faced by visualizations in
    facilitating higher-level analytic tasks, such as
    decision making and learning.

91
(No Transcript)
Write a Comment
User Comments (0)
About PowerShow.com