Title: Perception, Cognition and the Visual
1Perception, Cognition and the Visual
- Seeing, thinking, knowing
2Perception cognition
3Inner Display?
4Perception 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)
5Preattentive Processes
- Grid stays while asterix disappears
6Preattentive Processes
- Cognitive operations prior to focusing attention
7Necker Cube 3 Ways
8Completing Broken images
9Visual Ambiguity
10How Many threes?
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11How many threes?
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12Response Time where is the horizonatal bar?
13Where is the letter L?
14Orientation
15Size/Scale
16Colour (hue)
17Hue where is the red circle? Not Usually
Pre-attentive
18Hue shape
19Region 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
20Area Estimation
- Blue rectangles? Sloped rectangles?
21Fill and Shape
22Brightnesss
23Shape
24Luminance/Contrast
25Color for Categories and Sequences
- Links to websites about color systems
- CIE, Munsell
26Using 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
27Perceptual Tasks
- Target detection ( Is something there?)
- Boundary detection (Can the elements be
grouped?) - Counting ( How many elements of a certain type
arepresent?)
28Perceptual 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
29Optical Illusions and Programmed Variations
30Pre-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
31Cognitive issues
- Visualization as a tool useful for
- aiding comprehension and understanding
- Seeing as thinking
- Visual Cognition Attention--Visual Cognition
Lab. U. Illinois
32Time Maps Framing/Containing Memory
origin
prehistory
history
33Time 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
34Processes 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)
35Triggers, 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)
36Time Maps the Social Shaping of Memory
- Questions of relevance
- Long and short term
- Eventful and uneventful periods
- Connections
- Discontinuities
37Analyzing 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
39Plotlines Narrative Forms
- Decline narratives
- Both imply single direction
40Zigzag Narratives
- Conversion
- Recovery
- Rise fall
41Evolutionary narratives
- Unilinear (deterministic)
- Multilinear
- (ex. Cladograms--branching)
42Circles (Cycles),
43Cycles (Rhymes)
44Density Variations --Mountains and valleys
- eventful vs. uneventful moments in the past
- Unevenly distributed
45Commemgram example
- Eventful times,
- Multiple pasts
46Historical Phrasing in Narratives
- Musical terms
- Legato (connected)
- Staccato (breaks)
472-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
48Mnemonic pasting
49Interconnectedness
- Genealogical Distance (consanguinity)
- Ancestral depth ( of generations)
50Time and Social Distance
- Not just people
- Can be practices, things, events
51Cousinhood Ancestral Depth
52Monogenist Polygenist Models of Human Descent
- Socio-mnemonic dimensions of ancestry
53Another look at Phylogeny
544-Discontinuities Mnemonic Cutting Shaping
Memory
- Conceptualizing Discontinuities (breaks)
55Assimilation Difference
- Periods, epochs as mnemonic transformation of
historical continuum
56History Prehistory--decapitation
57History Prehistory in Mnemonic Traditions
- Example Pre-contact and Post contact history of
N. America
58Lumping Splitting in Narratives
595-Beginnings and Claims based on the Past
60Visualizations of Home/House. Child Katrina
Survivors
61Recall House/Home (Katrina Victim)
62House/Home
63House?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.
64Graphical 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)
65Issues
- Cognitive Artifacts
- Matching Representation to Task
- Representations Aid Info Access and Computation
- Naturalness and Experiential Cognition
66Cognitive 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
67Process 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
68Information 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
69Navigation
70Crystallization
71Process
72Browsing 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
73If time
74User Tasks in Visualization Environments--Eleven
basic actions
- identify, locate, distinguish,
categorize,cluster, distribution, rank, compare
withinrelations, compare between
relations,associate, correlate
75Data Types and Tasks
76Terminology
- 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)
77Steps 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?
782. 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?
793. 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?
804. 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?
815. 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.
826. 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?
837. 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?
84Find 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?
859. 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?
8610. 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?
87Compound tasks
- Sort the cereal manufacturers by average
fatcontentCompute derived value Sort - Which actors have co-starred with
JuliaRoberts?Filter Retrieve value
88What 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?
89Concerns
- InfoVis tools may have influencedstudents
questions - Graduate students as group being studiedHow
about professional analysts? - Subjective Not an exact science
90Analytic gaps
- obstacles faced by visualizations in
facilitating higher-level analytic tasks, such as
decision making and learning.
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