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Cognition and Perception. This is not a rose. This is not a pipe. “Just try stuffing tobacco in it!” – Rene Magritte, 1930. The myth of vision as a faithful record. ·       Concentric circles or continuous spiral? ·       The pattern of light is of concentric circles

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Cognition and Perception

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Cognition and Perception

  • This is not a rose.

This is not a pipe.

“Just try stuffing tobacco in it!”

– Rene Magritte, 1930

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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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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 arm’s 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:

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Bi-stable Images

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Bi-stable Images

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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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Movement Illusion



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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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  • 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 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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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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Figure 1. A: Kanizsa figure. B: Tse’s volumetric worm. C: Idesawa’s spiky sphere. D: Tse’s sea monster

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  • Common Fate


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Gestalt Laws

Laws of Grouping

Closure/ convexity

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Optic Flow


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  • Charlie Chaplin mask demo


  • Visual Illusions


  • Moving random dot stereogram


    • Spinning silhouette


  • Gestalt Illusions


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Which one of the following statements is not true about Gestalt psychology.

  • It deals with figure-ground segregation

  • It deals with object recognition

  • It explains receptive fields

  • Some of the gestalt principles include: good continuation, common fate and similarity

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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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Object Recognition

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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?





















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Top-Down & Bottom-Up

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Bottom-Up Processing

  • Direct Perception theories

    • Perception comes from the stimuli in the environment

    • Parts are identified, put together, and then recognition occurs

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Gibson’s Direct Perception (Ecological model)

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

  • Constructive Perception theories

    • People actively construct perceptions using information based on expectations

    • Top down processing

  • Perception is not automatic from raw stimuli

  • Processing is needed to build perception

  • Top down processing occurs quickly and involves making inferences, guessing from experience, and basing one perception on another

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Top-down Processing Evidence

  • Context effects

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  • Template Matching

  • Prototype

  • Feature Matching

  • Object-Based

  • Viewer-Based

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What is relationship between activity in the retina & the brain?

  • An electrode is inserted into various parts of the visual system (retina, cortex) of an animal.

  • The cell’s activity in response to the presentation of visual stimuli (lights, bars, complex images) to the animal’s retina is recorded.

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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 cell’s firing pattern.

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Are receptive fields of cortical cells like those of retinal ganglion cells?

  • No!!

  • Our visual cortex cells respond to more complex stimuli (e.g., bars of light).

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Hubel & Wiesel (1950s & 60s)

  • Recorded cortical cells in the visual cortex of cats in response to visual images they presented to the cat’s retina.

  • They found three types of cells with different receptive fields (bar detectors).

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Receptive Fields of cortical neurons—Primary 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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Simple Cells

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Complex Cells

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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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Orientation & Ocular Dominance columns in Primary Visual Cortex

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The Visual cortex has a retinotopic map

  • Visual cortex has a map of the retina’s surface.

  • More cortical neurons are devoted to fovea of retina.

  • As fovea only has cones, they are widely mapped on cortex’s surface.

  • The reason: cones allow us to see detail & color.

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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 vision’s 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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Template Theory:Perception as a Cookie Cutter

  • 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

  • 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

  • More Weaknesses of theory

    • Comparison requires identical orientation, size, position of template to stimuli

    • Does not explain how two patterns differ

      • e.g., there’s something wrong with it this, but I can’t put my finger on it – AHA! I see!

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Prototype Theories

  • 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

  • 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

  • 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

  • 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

  • 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 Theories

  • 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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Feature Evidence

  • 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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  • Selfridge’s (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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Object Recognition

  • Recognition By Components (Biederman; Marr)


  • 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

  • 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

  • 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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  • 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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Marr’s 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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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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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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Viewer Based Recognition

  • Physiological evidence

  • Explains behavioral evidence

  • Does not explain how novel objects are learnt

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The End

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