Perception, Cognition and the Visual

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Perception, Cognition and the Visual

• Seeing, thinking, knowing

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Perception cognition
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Inner Display?
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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)

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Preattentive Processes

• Grid stays while asterix disappears

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Preattentive Processes

• Cognitive operations prior to focusing attention

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Necker Cube 3 Ways
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Completing Broken images
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Visual Ambiguity
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How Many threes?

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How many threes?

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  72564675050678904567884578980982167765487636490856
  0912949686

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Response Time where is the horizonatal bar?
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Where is the letter L?
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Orientation
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Size/Scale
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Colour (hue)
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Hue where is the red circle? Not Usually
Pre-attentive
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Hue shape
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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

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Area Estimation

• Blue rectangles? Sloped rectangles?

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Fill and Shape
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Brightnesss
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Shape
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Luminance/Contrast
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Color for Categories and Sequences

• Links to websites about color systems
• CIE, Munsell

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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

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Perceptual Tasks

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

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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

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Optical Illusions and Programmed Variations

• Link
• Chernoff Faces link

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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

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Cognitive issues

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

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Time Maps Framing/Containing Memory
origin
prehistory
history
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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
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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)

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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)

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Time Maps the Social Shaping of Memory

• Questions of relevance
• Long and short term
• Eventful and uneventful periods
• Connections
• Discontinuities

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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)

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(1)Plotlines Narrative Forms

• Establish connections in narratives,
• scenarios, plotlines
• Mental historical outlooks,
• Selective use of history,
• Often anticipate future
• Progress narratives

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Plotlines Narrative Forms

• Decline narratives
• Both imply single direction

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Zigzag Narratives

• Conversion
• Recovery
• Rise fall

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Evolutionary narratives

• Unilinear (deterministic)
• Multilinear
• (ex. Cladograms--branching)

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Circles (Cycles),

• recurrence

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Cycles (Rhymes)
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Density Variations --Mountains and valleys

• eventful vs. uneventful moments in the past
• Unevenly distributed

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Commemgram example

• Eventful times,
• Multiple pasts

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Historical Phrasing in Narratives

• Musical terms
• Legato (connected)
• Staccato (breaks)

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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

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Mnemonic pasting
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Interconnectedness

• Genealogical Distance (consanguinity)
• Ancestral depth ( of generations)

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Time and Social Distance

• Not just people
• Can be practices, things, events

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Cousinhood Ancestral Depth
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Monogenist Polygenist Models of Human Descent

• Socio-mnemonic dimensions of ancestry

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Another look at Phylogeny
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4-Discontinuities Mnemonic Cutting Shaping
Memory

• Conceptualizing Discontinuities (breaks)

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Assimilation Difference

• Periods, epochs as mnemonic transformation of
  historical continuum

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History Prehistory--decapitation
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History Prehistory in Mnemonic Traditions

• Example Pre-contact and Post contact history of
  N. America

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Lumping Splitting in Narratives
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5-Beginnings and Claims based on the Past
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Visualizations of Home/House. Child Katrina
Survivors
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Recall House/Home (Katrina Victim)
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House/Home

• House as roof

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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.

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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)

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Issues

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

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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

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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

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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

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Navigation
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Crystallization
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Process
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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

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If time
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User Tasks in Visualization Environments--Eleven
basic actions

• identify, locate, distinguish,
  categorize,cluster, distribution, rank, compare
  withinrelations, compare between
  relations,associate, correlate

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Data Types and Tasks
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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)

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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?

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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?

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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?

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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?

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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.

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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?

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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?

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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?

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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?

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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?

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Compound tasks

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

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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?

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Concerns

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

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Analytic gaps

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

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