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Perceptual ProcessesPerceptual Processes

IntroductionIntroduction Pattern RecognitionPattern Recognition Top-down Processing & Pattern RecognitionTop-down Processing & Pattern Recognition Face PerceptionFace Perception

AttentionAttention Divided attentionDivided attention Selective attentionSelective attention Theories of attentionTheories of attention

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PerceptionPerception

Process that uses our previous Process that uses our previous knowledge to gather and interpret the knowledge to gather and interpret the stimuli that our senses registerstimuli that our senses register

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Pattern RecognitionPattern Recognition

The identification of a complex The identification of a complex arrangementarrangement of sensory stimuliof sensory stimuli

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PatternsPatterns

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66

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Theories of Pattern Theories of Pattern RecognitionRecognition

Template Matching TheoryTemplate Matching Theory

Prototype ModelsPrototype Models

Distinctive Features ModelDistinctive Features Model

Recognition by Components ModelRecognition by Components Model

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Template Matching TheoryTemplate Matching Theory

Compare a new stimulus (e.g. ‘T’ or ‘5’) to Compare a new stimulus (e.g. ‘T’ or ‘5’) to a set of specific patterns stored in memorya set of specific patterns stored in memory

Stored pattern most closely matching Stored pattern most closely matching stimulus identifies it.stimulus identifies it.

To work – must be single matchTo work – must be single match

Used in machine recognitionUsed in machine recognition

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Examples of Template Matching Examples of Template Matching AttemptsAttempts

1010

Used in machine recognitionUsed in machine recognition

Continue here tuesday

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Problems for Template Problems for Template MatchingMatching

Inefficient - large # of stored patterns Inefficient - large # of stored patterns

requiredrequired

Extremely inflexibleExtremely inflexible

Works only for isolated letters and simple Works only for isolated letters and simple

objectsobjects

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Prototype TheoriesPrototype Theories

Store Store abstractabstract, idealized patterns (or , idealized patterns (or

prototypes) in memoryprototypes) in memory

Summary - some aspects of stimulus Summary - some aspects of stimulus

stored but not othersstored but not others

Matches need not be exactMatches need not be exact

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Examine the faces below, which belong to two different categories.

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PrototypesPrototypes

Family resemblances (e.g. birds, faces, Family resemblances (e.g. birds, faces,

etc.)etc.)

Evidence supporting prototypesEvidence supporting prototypes

Problems - Vague; not a well-specified Problems - Vague; not a well-specified theory of pattern recognitiontheory of pattern recognition

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Distinctive Features ModelsDistinctive Features Models

Comparison of stimulus Comparison of stimulus featuresfeatures to a to a storedstored list of features list of features

DistinctiveDistinctive features differentiate one features differentiate one pattern from anotherpattern from another

Can discriminate stimuli on the basis of a Can discriminate stimuli on the basis of a small # of characteristics – featuressmall # of characteristics – features

Assumption: feature identification possibleAssumption: feature identification possible

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Distinctive Features Models: Distinctive Features Models: EvidenceEvidence

Consistent with physiological researchConsistent with physiological research

Psychological EvidencePsychological Evidence Gibson 1969Gibson 1969

Neisser 1964Neisser 1964

Waltz 1975Waltz 1975

Pritchard 1961Pritchard 1961

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1919

2020

First, scan for the letter ‘Z’ in the first column of letter strings.

Next, scan for the letter ‘Z’ in the second column of letter strings.

Which is easier? Why?

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Letter Detection TaskLetter Detection Task

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T

ZA

How a Distinctive Features Model Might Work:

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Distinctive FeaturesDistinctive Features

Theory must specify how the features are Theory must specify how the features are combined/joinedcombined/joined

These models deal most easily with fairly These models deal most easily with fairly simple stimuli -- e.g. letterssimple stimuli -- e.g. letters

Shapes in nature more complex -- e.g. Shapes in nature more complex -- e.g. dog, human, car, telephone, etcdog, human, car, telephone, etc

What would the features here be?What would the features here be?

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Recognition by Components Recognition by Components ModelModel

Irving Biederman (1987, 1990)Irving Biederman (1987, 1990) Given view of object can be represented Given view of object can be represented

as arrangement of basic 3-D shapes as arrangement of basic 3-D shapes ((geonsgeons))

Geons = derived features or higher level Geons = derived features or higher level featuresfeatures

In general 3 geons usually sufficient to In general 3 geons usually sufficient to identify an objectidentify an object

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Examples of GeonsExamples of Geons

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Status of Recognition by Status of Recognition by Components TheoryComponents Theory

Distinctive features theory for 3-D object Distinctive features theory for 3-D object

recognitionrecognition

Some research consistent with the model; Some research consistent with the model;

some notsome not

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Recognition by ComponentsRecognition by Components Pro – Biederman found that obscuring vertices impairs objects Pro – Biederman found that obscuring vertices impairs objects

recognition while obscuring other parts of objects has a lesser effect.recognition while obscuring other parts of objects has a lesser effect.

Which is easiest to recognize as a cup? The left or right?Which is easiest to recognize as a cup? The left or right?

Con – Biederman – Not all natural objects can bedecomposed into Con – Biederman – Not all natural objects can bedecomposed into geons. What about a shoe?geons. What about a shoe?

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Support for BiedermanSupport for Biederman

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SummarySummary

Distinctive Features approach currently Distinctive Features approach currently strongest theorystrongest theory

Perhaps all 3 approaches (distinctive Perhaps all 3 approaches (distinctive features, prototypes, recognition by features, prototypes, recognition by components) are correctcomponents) are correct

Regardless, pattern recognition is too Regardless, pattern recognition is too rapid and efficient to be completely rapid and efficient to be completely explained by these modelsexplained by these models

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Two types of ProcessingTwo types of Processing

Bottom-up or data-driven processingBottom-up or data-driven processing

Top-down or conceptually driven Top-down or conceptually driven

processingprocessing

Theme 5 -- most tasks involve bottom-up Theme 5 -- most tasks involve bottom-up

and top-down processingand top-down processing

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Thought ExperimentThought Experiment

Assume each letter 5 feature detections involvedAssume each letter 5 feature detections involved

Page of text approximately 250-300 words of 5 Page of text approximately 250-300 words of 5

letters per word on average letters per word on average

Each page: 5 x 5 x 250-300 = 6250 - 7500 Each page: 5 x 5 x 250-300 = 6250 - 7500

feature detectionsfeature detections

Typical reader 250 words/min readingTypical reader 250 words/min reading

6250/60 secs =100 feature detections per 6250/60 secs =100 feature detections per

secondsecond

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3333

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3535

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Word Superiority EffectWord Superiority Effect

We can identify a single letter more rapidly We can identify a single letter more rapidly and more accurately when it appears in a and more accurately when it appears in a word than when it appears in a non-word.word than when it appears in a non-word.

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Word Superiority- Non-word Word Superiority- Non-word TrialTrial

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Word Superiority: Word TrialWord Superiority: Word Trial

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Single Letter ‘K’ vs ‘K’ in a Single Letter ‘K’ vs ‘K’ in a wordword

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Word Superiority: Single Letter Word Superiority: Single Letter TrialTrial

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Word Superiority: Word TrialWord Superiority: Word Trial

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Altered Sentences in Warren and Warren Altered Sentences in Warren and Warren (1970)(1970)

Sentence that was presented Word Heard

It was found that the *eel was on the axle

It was found that the *eel was on the shoe

It was found that the *eel was on the orange

It was found that the *eel was on the table

wheel

heel

peel

meal

*Denotes the replaced sound

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The Effect of Varying Sentence Frame Context on Interpreting an Ambiguous Stimulus

The __________ raised (________) to supplement his income.

lion tamer

zoo keeper

botanist

dairy farmer

botanist

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The Influence of Stimulus Features & Sentence Context on Word Identification

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AttentionAttention

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Definitions of AttentionDefinitions of Attention

Concentration of mental resourcesConcentration of mental resources

Allocation of mental resourcesAllocation of mental resources

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Divided AttentionDivided Attention

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Reinitz & Colleagues (1974)

Divided Attention Condition

Subjects count the dots

Full Attention Condition

No instruction about dots

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Proportion of Responses that were “old” for Each of Two Study Conditions and Two Test Conditions

(Reinitz & Colleagues, 1994).

Study Condition

TestCondition

Full Attention Divided Attention

Old Face

ConjunctionFaces

.81

.48

.48

.42

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Divided Attention & PracticeDivided Attention & Practice

Hirst, et. al. 1980Hirst, et. al. 1980

Spelke, 1976Spelke, 1976

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UpsetUpset

HotelHotel

JudgeJudge

EmploymentEmployment

MapMap

IndulgeIndulge

PencilPencil

ProblemProblem

KeyKey

TerribleTerrible

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Selective Selective AttentionAttention

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Selective Attention Selective Attention (Dichotic Listening Task)(Dichotic Listening Task)

ShadowingShadowing

Irrelevant ChannelIrrelevant Channel

Cocktail Party Effect - Morray (1959)Cocktail Party Effect - Morray (1959)

Wood and Cowan (1995)Wood and Cowan (1995)

Treisman (1960)Treisman (1960)

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Dichotic Listening Task

T, 5, H

LEFT

T

5

H

RIGHT

S

3

G

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Cocktail EffectCocktail Effect

5656

Treisman’s Shadowing Treisman’s Shadowing StudyStudy

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Stroop EffectStroop Effect

5858

Filter Models of AttentionFilter Models of Attention

5959

Capacity Model of AttentionCapacity Model of Attention

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Diagnostic Criteria for Automatic and Controlled ProcessesAutomatic Controlled

1. The process occurs withoutintention , wit hout a consciousdecision.

2. the mental process is not open toconscious awareness orintrospection.

3. The process consumes few i f anyconscious resources; that is, itconsumes littl e if any consciousattention.

4. (Informal) The process operatesvery rapid ly, usually w ithin onesecond.

1. The process occurs only withintention , wit h a deli beratedecision.

2. The process is open to awarenessand introspection .

3. The process uses consciousresources; that is, it d rains the poolof conscious attentional capacity.

4. (Informal) The process is relativelyslow, taking more than a second ortw o for completion.

Part ial Autonomy/ AutomaticityA process is said to be partially autonomous if it can begin automatically butrequi res a more conscious set of operations for completion (see Zbrodoff &Logan, 1986).

Diagnostic Criteria for Automatic Processes

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Cerebral Cortex & AttentionCerebral Cortex & Attention