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Chapter 3: Perception

Recognizing Patterns and Objects

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2Perception

Top-Down

Bottom-Up

Gestalt

Approaches

Ambig

Figures

Continuity

Proximity

Principles

Figure-

Ground

Pragnanz

Template

Feature

Exemplar

Emergence

Prototype

Scenes

Introduction

Similarity

Direct Perception

Closure

Fate

Context

Word SE

Illusions

Perceptual

Learning

Marr

Connectionist

Development

Individual

Differences Culture

Agnosias

Priming

Distal

Proximal

Top Down +

Bottom Up

Introduction• Processes by which people identify external objects

– Match physical information in Sensory Register (ch. 4) with mental representation in Semantic Memory

– Semantic Memory: Mental codes that represent knowledge of world– Galotti model (F3.1, +1)

Distal Stimulus � Proximal Stimulus � Percept(retinal image)

• Percept not equivalent to proximal stimulus– Same physical stimulus can produce different percepts (e.g., 13, B)– Different physical stimuli can produce same percept (e.g., letters, size

constancy)

• Pattern Recognition– Identification of particular object or event in environment– Combination of Bottom-up (data-driven) AND Top-down (theory- or

conceptually-driven) processesData � Perception Conceptual

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Gestalt Approaches • Fundamental mechanisms

by which brain “parses” external environment into units / wholes– Whole greater than sum of

parts– Doubted percepts could be

analyzed into parts without losing whole

• Some phenomena studied by Gestalt psychologists– Figure-Ground perception

(F3.2, top, F3.3 +1)

– Subjective or illusory contours (F3.4, bottom)

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F3.3 - Salvador Dali, The Slave Market With Disappearing Bust of Voltaire. The two nuns standing in the archway at left-center reverse to form a bust of Voltaire. The painting exploits the reversible figures phenomenon.

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7F3.5 (right)

Gestalt principles of perceptual organization

• (A) proximity• (B) similarity• (C) (D) good

continuation• (E) closure (Panda +1,

Dalmation in slide 1)• (F) common fate (see

text for explanation, and +1 …)

• Pragnanz: Simplicity

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The Principle of Common Fate• Elements that move together seen as common

object– Animals in nature blend into background, until they

move. Then suddenly visible. Become visible because “parts” move together in coherent way

• Watch next slide– Initially, simply see random dots, but when some of

dots move together, they are perceived as common object

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Click here for video demonstration

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Common Fate Example 9 Emergence• Gestalt Approaches:

whole greater than sum of parts– Properties of objects

not explained by individual elements or components

– F3.6 (�)• Odd-Quadrant

discrimination task• Configural

Superiority Effect (CSE)

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Bottom-up Processes• Perceiver starts with bits of external

information and builds percept– Perception formed from sensory input from the

distal stimulus

– Sensation � Perception

• Several Models– Template Matching

– Featural Analysis

– Prototype Matching

– Exemplar Theory (new)

11 Template Matching

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• Mental representation is literal copy of external object– Pure bottom-up processing?– Template Matching works in

very restricted domains, such as reading checks (F3.7 F3.8 top�)

– “4” matched successively or simultaneously with mental representations to identify best match

• Challenged by more realistic stimuli– e.g., handwriting (F3.9 �)

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Modified Template Theory

• Possible to adapt template theory to handle some of challenges, e.g., that stimuli vary in size, location, shape, orientation, ...– Preprocessing to cleanup pattern

and then match– Pattern normalized to some

standard form– Mental rotation: Jolicoeur and

effects of practice ( +1)– Store multiple examples (Exemplar

Theory, later)• Very high storage demands: many

templates to store– Is search rate fast enough to find

match• Search may involve Parallel rather than

Serial processing• Neisser: search for target letter(s)

suggests parallel (later slide)

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A14

Speed to identify Rotated letters in Familiar or Unfamiliar Fonts: Normalization or Pre-Processing?

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Feature Analysis

• Feature Theory– Internal patterns represented by lists

of features– Stimuli parsed into features, which

activate internal representation– Efficient way to store patterns

• Letter identification– Much work in this area– Restricted stimulus set: more tractable

than objects in general– Feature Representation for Letters (left)– Selfridge’s Pandemonium model (+1)

• Evidence– Neurons detect features– Letter Confusions– Search Task– Stabilized Images

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Depiction of Selfridge’s

(1959) Pandemonium model (early

version of Connectionist /

PDP Model)

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Sample Networks to Detect Line(s) 17

ON-Center / OFF-Surround Receptive Fields lined up

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Evidence for Features with Letters• Perceptual Confusions

– Rapid presentation of stimuli, or presented in noise– Errors often share common features

• Gibson (1969): GöC• Townsend (1971)

Error EöF EöO HöN HöKProportion .19 .00 .17 .00

– Same-Different RT: GW 458 ms PR 571 ms• Search studies

– Neisser (1963) (F3.13 +1)• Selecting / Searching for or against features influences

search time

• Stabilized images (+2)– Images do not degrade in haphazard way– Meaningful “chunks” (features?) disappear and appear

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Visual Search & Features

• More difficult (slower) to find letters embedded in other letters with similar features

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Stabilized Images (Pritchard, 1961) 20

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Features for Natural Objects• Letters a restricted domain: limited number of

units (26 or 52); possible patterns limited (although numerous)

• Features of speech sounds– Voiced (btk vs. pdg), Place of Articulation (bp vs. td

vs. kg)– Categorical perception of continuous speech

qualities• Objects have few, if any, constraints

– No good definition of features, arbitrary patterns– Geons (Biederman, 1987)

• Proposed set of geons or standard features (F3.10 F3.11 F3.12 +1) to construct real or artificial objects

• Recognition easier with vertices (intersections) (+2)

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Some Biederman Geons & Objects 22

F3.10

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Biederman’s Degraded Stimuli 23

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Prototype Matching

• New stimuli matched to prototype in memory– Prototype idealized or typical

abstraction of pattern– Exact match not necessary– Letter R (F3.14), chair, pants (right)

• Learning artificial prototypes• Posner & Keele (1968)

(F3.15, +1)• Faces: false recognition of

prototype– Artificial faces: Solso & McCarthy

(1981) (+2)– Photographs: Cabeza et al (1999)

(F3.16)– Strategy reports: most subjects

describe prototype-like strategy58% Prototype: formed abstract image28% Feature: compared each feature10% Neighbour: compared to all faces4% Average distance

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Old 87% correctNew 67%

Proto 85% Never Seen!

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Never Seen!

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Exemplar Theories• Store specific

representations of objects– not abstract information

(e.g., features) or prototypes

• Concept identified on basis of number of exemplars activated (matched) by stimulus

• See Semantic Memory– Theories discussed here

in context of perception overlap with how mind/brain stores general knowledge of world

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Comparing Models

Features?

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Top-down Processes

• Expectations guide selection and combination of parts to form whole

• Theory-driven or conceptually driven processes (+1)

• Evidence– Ambiguous Figures

• Permit two or more interpretations (right)

– Context Effects• Scenes: David Marr’s

theory• Word superiority effect• Priming effects (new)• Illusions (new)

– Perceptual learning

29 30Phantom Limb• After amputation of limb,

people often sense that limb still present

• Not due to stimulation of nerve endings, but rather to activation of central representations for missing body parts and associated nodes

• Gradually weakens• Pure Top-Down

processing?

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Context: Scenes

• Object Recognition– Faster to recognize

object (e.g., toaster) in context (bottom) than alone (top)

– Marr’s Theory• Combines Bottom-Up &

Top-Down processes• Primal sketch: B-U• 2 ½-D sketch: B-U• 3-D sketch: T-D

processes incorporated

31 Context: Word Superiority Effect 32

• Easier to recognize letter in context of word (WORD vs. WORK) than when letter presented alone (D vs. K) (top right)

• Connectionist Model (+1)• Other word context

effects (e.g., bottom right)

Connectionist Models

• Network integrates bottom-up and top-down processes

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Context: Priming Effects• Preceding events affect

object / word identification• Preceding text: Tulving &

Gold (1963)– Combination of facilitation &

interference (right)

• Priming object identification (+1)

• Action Priming (+2)

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35• Priming object identification– Priming ambiguous image by series of animals or faces

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Action Priming

Faster

Slower

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• Priming shared action facilitates identification of subsequent object

• e.g., think of scissors & make cutting action

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Context: Illusions• Demonstrates top-down processes

– Object perceived differently depending on context– Important context effect involves illusions, stimuli that

“deceive” system into perceiving world “incorrectly”

• Illusions– Various Size illusions: Muller-Lyer (below), next few

slides …

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Center circles same size (see bottom left)

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Size and PonzoIllusion

39 Explanation for Muller-Lyer Illusion• Inward arrows

appear on edges extended towards us

• Outward arrows on receding edges

• 2 lines, same image on retina; one (>−−<) that appears further away than other (�) must be longer. Seen as so!

• Similar explanation for other illusions?

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Hollow Face Illusion• Remarkable illusion

showing role of top-down processes in “forcing” a sensible interpretation of unusual stimulus input (i.e., bottom-up information)

• See video or example to right

41 Perceptual Learning• Practice effects & Top-

Down processes– Experienced wine tasters

learn to recognize elements of scent and flavor that novices miss

– See also concept learning– Gibson & Gibson study (right)

• Learn to distinguish center stimulus from similar patterns

• Errors based on similarities• Learn what aspects of stimulus

to attend to

– Hull (1920) (+1)

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• Hull (1920): Perceptual (Concept) Learning– Chinese characters (below)

– Learned names for lists 1 to 6

– Tested on lists 7 to 12: 67% correct vs 17% by chance (100*1/6)

– Subjects abstracted during learning

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43 Direct Perception• Gibson: Stimuli have

intrinsic properties (affordances) that are elicited automatically– Perceiver does little work to

interpret world– Light on retina already

organized information– Information exists not merely

in environment, but in animal-environment ecosystem

– Vs. Constructivist views• Examples

– Human motion (top)– Optic Flow (F3.23 bottom)

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Video

Video

Visual Agnosias:Disrupted Perception

• Impaired ability to interpret visual stimuli, even though nothing wrong with eyes– e.g., can copy (F3.24 left) but

not identify or name object– Difficulty with incomplete or

unusual perspectives (F3.25 +1)

– Not memory or language problem, perceptual problem

– Person may still recognize objects by touch or smell

– Apperceptive, Associative, Prosopagnosia (faces)

– Different brain regions (+1)

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Apperceptive

Associative

Left Right

F3.25

Culture & Perception (pp 378-383)

• Cross-cultural studies– Depth perception in

pictures (F14.1)

– Visual illusions: M-L & carpentered world

– Analytical vs. Holistic perception (F14.3)

– Education

47 Development & Perception• Certain

perceptual abilities seen early– Depth

perception: Visual Cliff (video)

• Some lost if not maintained– Hindi t sounds

(left, video)

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