Cognition and Perception

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Cognition and Perception
This is not a pipe. Just try stuffing tobacco in
it! Rene Magritte, 1930

• This is not a rose.

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The myth of vision as a faithful record
       Concentric circles or continuous
spiral?        The pattern of light is of
concentric circles        Human vision sees a
continuous spiral
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Gestalt

• The whole is greater than the sum of its parts
• Law of Pragnanz (good figure) We perceive
  things in the way that is simplest to organize
  them into cohesive and constant objects.

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Gestalt Laws

• Laws of Figure-Ground Segregation
• 1. Convex region becomes figure
• 2. Smaller region becomes figure
• 3. Moving region becomes figure
• 4. Symmetric ("good") region becomes figure
• 5. Nearer region becomes figure (multiple depth
  cues apply)

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Gestalt Laws

• Laws of Grouping
• 1. Proximity
• 2. Similarity
• 3. Common fate
• 4. Good continuation
• 5. Closure/ convexity
• 6. Common region
• 7. Connectedness
• 8. Parse regions at deep concavities

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• Common Fate
• http//dragon.uml.edu/psych/commfate.html

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Figure 1. A Kanizsa figure. B Tses volumetric
worm. C Idesawas spiky sphere. D Tses sea
monster
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Gestalt Laws

• Laws of Grouping
• Closure/ convexity

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The Myth of vision as a passive process

• The Grand illusion of complete perception
• (1) Vision is not rich in detail
• the size of a thumbnail at arms length is all
  that gets processed
• (2) Attention is limited the law of ONEs
• vision sees one object, one event, one location
• These two factors are illustrated by
• Impossible triangle
• Escher drawings
• Bistable images

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Brains construct a well-behaved 3-D world so we
cannot experience a world that is not. Here we
see an ordinary triangle and building with normal
corners and angles instead of the shocking
reality. Why?
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       A perceptually ambiguous wire
cube        How many different interpretations
can you see?
Go to
http//mindbluff.com/necker.htm
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Figure 1.5. Subjective perceptions are not
necessarily arbitrary perceptions
Brains see two instead of all of these
interpretations? Why not? Humans bring shared
assumptions to the vision project, (1) that
objects are generally convex, (2) that straight
lines in a picture represent straight edges in an
object, and (3) that three-edge junctions are
generally right-angled corners.
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Bi-stable Images
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Bi-stable Images
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Law of One in Audition

• Shepard Tone
• http//www.youtube.com/watch?vDfJa3IC1txI
• Each square in the figure indicates a tone, any
  set of squares in vertical alignment together
  making one Shepard tone. The color of each square
  indicates the loudness of the note, with purple
  being the quietest and green the loudest.
  Overlapping notes that play at the same time are
  exactly one octave apart, and each scale fades in
  and fades out so that hearing the beginning or
  end of any given scale is impossible.

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Demos

• Charlie Chaplin mask demo
• http//www.youtube.com/watch?vQbKw0_v2clofeature
  related
• Visual Illusions
• http//www.michaelbach.de/ot/
• Moving random dot stereogram
• http//dragon.uml.edu/psych/commfate.html
• Spinning silhouette
• http//www.youtube.com/watch?vuBTvKboX84E
• Gestalt Illusions
• http//www.opprints.co.uk/gallery.php

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Object Recognition

• Mike the blind guy given sight
• http//www.youtube.com/watch?vVVgfC_FV2hIfeature
  PlayListp32BC95C9D7E5959Cindex1

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Object Recognition(Called Pattern Recognition in
Book)

• How do you solve problem of Object Constancy?
• How does the brain know the objects are the same
  despite change in perspective?

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What letter are these, and how do you know?

• A

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Object Recognition
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Receptive Fields of cortical neuronsPrimary
Visual cortex

• 1. Simple Cells
• --respond to points of light or bars of
  light in a particular orientation
• 2. Complex cells
• --respond to bars of light in a particular
  orientation moving in a specific direction.
• 3. Hypercomplex Cells
• respond to bars of light in a particular
  orientation, moving in a specific direction, of
  a specific line length.

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What is the organization of the visual cortex?

• Hubel Wiesel found that the visual cortex is
  organized into columns.
• Location specific For each place on the retina
  there is a column of cells in cortex.

• Two columns next to one another in the cortex
  respond to stimulation of two adjacent points on
  the retina.

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Spatial Frequency

• These grids are low to high spatial frequencies.
• Many light bars / square High S.F.
• Few light bars / square Low S.F.
• Part of visions organization

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Spatial Frequency

• By playing with spatial frequency, you can induce
  a the intense luminance perception of a bright
  sun.

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Spatial Frequencies Work Together

• Low S.F. give you outlines, High give you
  details.
• Broad spectrum give you Local and Global features

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Bottom-Up Processing

• Perception comes from the stimuli in the
  environment
• Parts are identified, put together, and then
  recognition occurs
• Context does not matter

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Gibsons Direct Perception (Bottom-Up)

• All the information needed to form a perception
  is available in the environment
• Perception is immediate and spontaneous
• Affordances and attunements
• Perception and action cannot be separated
• Action defines the meaningful parameters of
  perception and provides new ways of perceiving

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Top-down Processing
0

• Perception is not automatic from raw stimuli
• Context is needed to build perception
• Meaning is constructed by making inferences,
  guessing from experience, and basing one
  perception on another

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Template TheoryPerception as a Cookie Cutter
0

• Basics of template theory
• Multiple templates are held in memory
• Compare stimuli to templates in memory for one
  with greatest overlap until a match is found

Search memory for a match
See stimuli
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Template Theory
0

• Weakness of theory
• Problem of imperfect matches
• Cannot account for the flexibility of pattern
  recognition system
• More problems

Search for match in memory
See stimuli
No perfect match in memory
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Template Theory
0

• More Weaknesses of theory
• Comparison requires identical orientation, size,
  position of template to stimuli
• Does not explain how two patterns differ
• e.g., theres something wrong with it this, but I
  cant put my finger on it AHA! I see!

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Feature Theories
0

• Recognize objects on the basis of a small number
  of characteristics (features)
• Detect specific elements and assemble them into
  more complex forms
• Brain cells that respond to specific features,
  such as lines and angles are referred to as
  feature detectors

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Two Feature Theories of Object Recognition

• Recognition By Components (Biederman Marr)
• vs.
• View-Based Recognition (Tarr Bülthoff)

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Superquadratics (Pentland, 1986)
Geons (Biederman, 1987)
Generalized Cylinders (Binford, 1971 Marr, 1982)
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• Recognition By Components (Biederman)
• Basic set of geometrical shape
• Geons (geometric ions)
• Distinguishable from almost any viewing angle
• Recognizable even with occlusion
• Grammatical relationship b/w parts
• Part-whole hierarchies

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Evidence of Geons
0

• Beiderman (1987)
• Can you identify these objects?

These objects have been rendered unidentifiable
because their geons are nonrecoverable
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Evidence of Geons
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• Beiderman (1987)
• Can you identify these objects?

These objects have had the same amount of the
object taken out but because the geons can still
be recreated, one can recover the objects
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Testing Biederman

• Objects are decomposed
• Omitting Vertices
• Retaining Vertices
• In accordance with theory, easier to identify
  object with vertices

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Object Recognition

• Pros
• Explains why it can be hard to recognize familiar
  objects from highly unusual perspectives
• Cons
• Absence of physiological evidence
• Does not explain expert discriminations or quirks
  of facial recognition

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Marrs Computational Approach

• Primal Sketch 2-D description includes changes
  in light intensity, edges, contours, blobs
• 2 ½ -D Sketch Includes information about depth,
  motion, shading. Representation is
  observer-centered
• 3-D Representation A representation of objects
  and their relationships, observer-independent.

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View-Based Recognition

• Tarr Bulthoff
• Multiple stored views of objects
• Viewer-centered frame of reference
• Specific views correspond to specific patterns of
  neural activation (possibly involves place
  neurons)
• Match b/w current and stored pattern of
  activation
• Interpolating (educated guessing or impletion)
  b/w seen and stored views

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The End
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Opponent Process in a Movement Illusion
Waterfall Effect

• http//video.google.com/videoplay?docid6294268981
  850523944eir5PRSNGPD6fcqAPS48y6Agqspiralvisua
  lillusionvtlfhlen
• http//video.google.com/videoplay?docid-292742279
  6086500362vtlfhlen

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Cognition and Perception

• The finished files are the result of years of
  scientific study combined with the experience of
  many years.

• The finished files are the result of years of
  scientific study combined with the experience of
  many years.

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Two Visual SystemsWhat your hands see differs
from what the eyes see

• Ventral What system
• Dorsal Where/ How system
• Brain lesions
• Ventral lesions patients cannot name telephone
  but mime using it
• Dorsal lesions can name it, but reach in wrong
  direction for it
• Roelofs Effect

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X
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X
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Top-Down Bottom-Up
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Orientation Ocular Dominance columns in Primary
Visual Cortex
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Simple Cells
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Complex Cells
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What is a receptive field of retinal ganglion
cells?

• The receptive field for these cells is the region
  of the retina that, when stimulated excites or
  inhibits the cells firing pattern.

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The Visual cortex has a retinotopic map

• Visual cortex has a map of the retinas surface.
• More cortical neurons are devoted to fovea of
  retina.
• As fovea only has cones, they are widely mapped
  on cortexs surface.
• The reason cones allow us to see detail color.

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Spatial Frequency in Action

• http//www.metacafe.com/watch/1749277/animated_opt
  ical_illusions/

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Top-down Processing Evidence
0

• Context effects

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Theories

• Template Matching
• Prototype
• Feature Matching
• Object-Based
• Viewer-Based

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Change Blindness

• Counter experiment http//www.youtube.com/watch?v
  mAnKvo-fPs0
• Campus Door Demo
• http//viscog.beckman.uiuc.edu/flashmovie/12.php
• Construction door http//viscog.beckman.uiuc.edu/f
  lashmovie/10.php
• Gradual Change http//viscog.beckman.uiuc.edu/fla
  shmovie/1.php

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Prototype Theories
0

• Modification of template matching (flexible
  templates)
• Possesses the average of each individual
  characteristic
• No match is perfect a criterion for matching is
  needed

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Prototype Evidence
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• Franks Bransford (1971)
• Presented objects based on prototypes
• Prototype not shown
• Yet participants are confident they had seen
  prototype
• Suggests existence of prototypes

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Prototype Evidence
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• Solso McCarthy (1981)
• Participants were shown a series of faces
• Later, a recognition test was given with some old
  faces, a prototype face, and some new faces that
  differed in degree from prototype

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Solso McCarthy (1981) Results
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• The red arrow notes that participants were more
  confident they had seen the prototype than actual
  items they had seen

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Research on Prototypes
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• Researchers have found that prototypical faces
  are found to be more attractive to participants
• Halberstadt Rhodes (2000)
• Examined the impact of prototypes of dogs,
  wristwatches, and birds on attractiveness of the
  stimuli
• Results indicate a strong relationship between
  averageness and attractiveness of the dogs,
  birds, and wristwatches

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Feature Evidence
0

• Hubel Wiesel (1979) using single cell technique
• Simple cells detect bars or edges of particular
  orientation in particular location
• Complex cells detect bars or edges of particular
  orientation, exact location abstracted
• Hypercomplex cells detect particular colors
  (simple and complex cells), bars, or edges of
  particular length or moving in a particular
  direction

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• Selfridges (1959) Pandemonium Model of visual
  word perception where R is the target letter.

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• Feature net model by Rumelhart and McClelland
  (1987), this is an Interactive Activation Model,
  which means lower and higher layers can both
  inhibit and excite each other, providing a
  mechanism for both top-down and bottom-up effects.

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• Biederman Stage 1, extract appropriate geon from
  image, and stage 2, match to similar
  representation stored in long-term memory.
• Biederman proposed that certain properties of 2-D
  images are non-accidental, representing real
  properties in the world.

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Viewer Based Recognition

• Physiological evidence
• Explains behavioral evidence
• Does not explain how novel objects are learnt