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Perceptual Categories: Old and gradient, young and sparse. Bob McMurray University of Iowa Dept. of Psychology. Collaborators. Richard Aslin Michael Tanenhaus David Gow. Joe Toscano Cheyenne Munson Meghan Clayards Dana Subik Julie Markant Jennifer Williams.

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slide1

Perceptual Categories:

Old and gradient, young and sparse.

Bob McMurray

University of Iowa

Dept. of Psychology

slide2

Collaborators

Richard Aslin

Michael Tanenhaus

David Gow

Joe Toscano

Cheyenne Munson

Meghan Clayards

Dana Subik

Julie Markant

Jennifer Williams

The students of the MACLab

slide3

Categorization

Categorization occurs when:

1) discriminably different stimuli…

2) …are treated equivalently for some purposes…

3) …and stimuli in other categories are treated differently.

slide4

Categorization

Perceptual Categorization

  • Continuous input maps to discrete categories.
  • Semantic knowledge plays minor role.
  • Bottom-up learning processes important.
slide5

Categorization

Perceptual Categorization

  • Continuous inputs map to discrete categories.
  • Semantic knowledge plays less of a role.
  • Categories include:
  • Faces
  • Shapes
  • Words
  • Colors
  • Exemplars include:
  • A specific view of a specific faces
  • A variant of a shape.
  • A particular word in a particular utterance
  • Variation in hue, saturation, lightness
slide6

Categorization occurs when:

1) Discriminably different stimuli…

2) …are treated equivalently for some purposes…

3) …and stimuli in other categories are treated differently.

Premise

For Perceptual Categoriesthis definition largely falls short.

and

this may be a good thing.

Approach

Walk through work on speech and category development.

Assess this definition along the way.

slide7

Overview

  • Speech perception: Discriminably different and categorical perception.
  • Word recognition: exemplars of the same word are not treated equivalently.(+Benefits)

3) Speech Development: phonemes are not treated equivalently.

  • Speech Development (model): challenging other categories treated differently. (+Benefits)

5) Development of Visual Categories: challenging other categories treated differently.

slide8

Categorical Perception

B

100

100

Discrimination

% /p/

Discrimination

ID (%/pa/)

0

0

B

VOT

P

  • Sharp identification of tokens on a continuum.

P

  • Discrimination poor within a phonetic category.

Subphonemic variation in VOT is discarded in favor of adiscretesymbol (phoneme).

slide9

Categorical Perception

Categorical Perception: Demonstrated across wide swaths of perceptual categorization.

Line Orientation (Quinn, 2005)

Basic Level Objects (Newell & Bulthoff, 2002)

Facial Identity (Beale & Keil, 1995)

Musical Chords (Howard, Rosen & Broad, 1992)

Signs (Emmorey, McCollough & Brentari, 2003)

Color (Bornstein & Korda, 1984)

Vocal Emotion (Luakka, 2005)

Facial Emotion (Pollak & Kistlerl, 2002)

What’s going on?

slide10

Categorical Perception

  • Across a category boundary, CP:
    • enhances contrast.
  • Within a category, CP yields
    • a loss of sensitivity
    • a down-weighting of the importance of within-category variation.
    • discarding continuous detail.
slide11

Categorical Perception

  • Across a category boundary, CP:
    • enhances contrast.
  • Within a category, CP yields
    • a loss of sensitivity
    • a downweighting of the importance of within-category variation.
    • discarding continuous detail.

Categorization occurs when:

1) discriminably different stimuli…

2) …are treated equivalently for some purposes…

3) …and stimuli in other categories are treated differently

Stimuli are not discriminably different.

CP: Categorization affects perception.

Definition: Categorization independent of perception.

Need a more integrated view…

slide12

Perceptual Categorization

Categorization occurs when:

CP: perception not independent of categorization.

  • discriminably
  • different stimuli
  • 2) are treated
    • equivalently for
    • some purposes…
  • and stimuli in
  • other categories
  • are treated
  • differently.
slide13

Categorical Perception

  • Across a category boundary, CP:
    • enhances contrast.
  • Within a category, CP yields
    • a loss of sensitivity
    • a downweighting of the importance of within-category variation.
    • discarding continuous detail.

Is continuous detail really discarded?

slide14

Is continuous detail really discarded?

Evidence against the strong form of Categorical Perception from psychophysical-type tasks:

Sidebar

This has never been examined with non-speech stimuli…

    • Goodness Ratings
  • Miller (1994, 1997…)
  • Massaro & Cohen (1983)
  • Discrimination Tasks
  • Pisoni and Tash (1974)
  • Pisoni & Lazarus (1974)
  • Carney, Widin & Viemeister (1977)
  • Training
  • Samuel (1977)
  • Pisoni, Aslin, Perey & Hennessy (1982)
slide16

X

basic

bakery

bakery

X

ba…

kery

barrier

X

X

bait

barricade

X

baby

  • Online Word Recognition
  • Information arrives sequentially
  • At early points in time, signal is temporarily ambiguous.
  • Later arriving information disambiguates the word.
slide17

Input:

b... u… tt… e… r

time

beach

butter

bump

putter

dog

slide18

These processes have been well defined for a phonemic representation of the input.

But considerably less ambiguity if we consider within-category (subphonemic) information.

Example: subphonemic effects of motor processes.

slide19

Coarticulation

n n

ee

t c

k

Any action reflects future actions as it unfolds.

Example:Coarticulation

Articulation (lips, tongue…) reflectscurrent, futureandpastevents.

Subtle subphonemic variation in speech reflects temporal organization.

Sensitivity to theseperceptualdetails might yield earlier disambiguation.

slide20

Experiment 1

?

What does sensitivity to within-category detail do?

Does within-category acoustic detail systematically affect higher level language?

Is there a gradient effect of subphonemic detail on lexical activation?

slide21

Experiment 1

Gradient relationship: systematic effects of subphonemic information on lexical activation.

If this gradiency is used it must be preserved over time.

Need a design sensitive to bothsystematic acoustic detailand detailedtemporal dynamicsof lexical activation.

McMurray, Tanenhaus & Aslin (2002)

slide22

Acoustic Detail

Use a speech continuum—more steps yields a better picture acoustic mapping.

KlattWorks:generate synthetic continua from natural speech.

  • 9-step VOT continua (0-40 ms)
  • 6 pairs of words.
  • beach/peach bale/pale bear/pear
  • bump/pump bomb/palm butter/putter
  • 6 fillers.
  • lamp leg lock ladder lip leaf
  • shark shell shoe ship sheep shirt
slide24

Temporal Dynamics

How do we tap on-line recognition?

With an on-line task:Eye-movements

Subjects hear spoken language and manipulate objects in a visual world.

Visual world includes set of objects with interesting linguistic properties.

abeach, apeachand some unrelated items.

Eye-movements to each object are monitored throughout the task.

Tanenhaus, Spivey-Knowlton, Eberhart & Sedivy, 1995

slide25

Why use eye-movements and visual world paradigm?

  • Relatively naturaltask.
  • Eye-movements generated veryfast(within 200ms of first bit of information).
  • Eye movementstime-lockedto speech.
  • Subjectsaren’t awareof eye-movements.
  • Fixation probability maps ontolexical activation..
slide26

Task

A moment to view the items

slide28

Task

Bear

Repeat 1080 times

slide29

Identification Results

1

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

0

5

10

15

20

25

30

35

40

High agreement across subjects and items for category boundary.

proportion /p/

B

VOT (ms)

P

By subject:17.25 +/- 1.33ms

By item: 17.24 +/- 1.24ms

slide30

Task

200 ms

Trials

1

2

3

4

5

% fixations

Time

Target = Bear

Competitor = Pear

Unrelated = Lamp, Ship

slide31

Task

0.9

VOT=0 Response=

VOT=40 Response=

0.8

0.7

0.6

0.5

Fixation proportion

0.4

0.3

0.2

0.1

0

0

400

800

1200

1600

2000

0

400

800

1200

1600

Time (ms)

More looks to competitor than unrelated items.

slide32

Task

target

Fixation proportion

Fixation proportion

time

time

  • Given that
      • the subject heard bear
      • clicked on “bear”…

How often was the subject looking at the “pear”?

Categorical Results

Gradient Effect

target

target

competitor

competitor

competitor

competitor

slide33

Results

20 ms

25 ms

30 ms

10 ms

15 ms

35 ms

40 ms

0.16

0.14

0.12

0.1

0.08

0.06

0.04

0.02

0

0

400

800

1200

1600

0

400

800

1200

1600

2000

Response=

Response=

VOT

VOT

0 ms

5 ms

Competitor Fixations

Time since word onset (ms)

Long-lasting gradient effect: seen throughout the timecourse of processing.

slide34

0.08

0.07

0.06

0.05

0.04

0.03

0.02

0

5

10

15

20

25

30

35

40

Area under the curve:

Clear effects of VOT

B: p=.017* P: p<.001***

Linear Trend

B: p=.023* P: p=.002***

Response=

Response=

Looks to

Competitor Fixations

Looks to

Category

Boundary

VOT (ms)

slide35

0.08

0.07

0.06

0.05

0.04

0.03

0.02

0

5

10

15

20

25

30

35

40

Unambiguous Stimuli Only

Clear effects of VOT

B: p=.014* P: p=.001***

Linear Trend

B: p=.009** P: p=.007**

Response=

Response=

Looks to

Competitor Fixations

Looks to

Category

Boundary

VOT (ms)

slide36

Summary

Subphonemic acoustic differences in VOT have gradient effect on lexical activation.

  • Gradient effect of VOT on looks to the competitor.
  • Effect holds even for unambiguous stimuli.
  • Seems to be long-lasting.

Consistent with growing body of work using priming (Andruski, Blumstein & Burton, 1994; Utman, Blumstein & Burton, 2000; Gow, 2001, 2002).

Variants from the same category are not treated equivalently: Gradations in interpretation are related to gradations in stimulus.

slide37

Extensions

Word recognition is systematically sensitiveto subphonemic acoustic detail.

  • Voicing
  • Laterality, Manner, Place
  • Natural Speech
  • Vowel Quality
slide38

Extensions

B

Sh

L

P

Word recognition is systematically sensitiveto subphonemic acoustic detail.

  • Voicing
  • Laterality, Manner, Place
  • Natural Speech
  • Vowel Quality

 Metalinguistic Tasks

slide39

Extensions

0.1

Response=P

Looks to B

0.08

0.06

Competitor Fixations

Response=B

Looks to B

0.04

Category

Boundary

0.02

0

0

5

10

15

20

25

30

35

40

VOT (ms)

Word recognition is systematically sensitiveto subphonemic acoustic detail.

  • Voicing
  • Laterality, Manner, Place
  • Natural Speech
  • Vowel Quality

 Metalinguistic Tasks

slide40

Extensions

0.1

0.08

0.06

0.04

0.02

0

Word recognition is systematically sensitiveto subphonemic acoustic detail.

  • Voicing
  • Laterality, Manner, Place
  • Natural Speech
  • Vowel Quality

 Metalinguistic Tasks

Response=P

Looks to B

Competitor Fixations

Response=B

Looks to B

Category

Boundary

0

5

10

15

20

25

30

35

40

VOT (ms)

slide41

Categorical Perception

Within-category detail surviving to lexical level.

Abnormally sharp categories may be due to meta-linguistic tasks.

There is a middle ground: warping of perceptual space (e.g. Goldstone, 2002)

Retain: non-independence of perception and categorization.

slide42

Perceptual Categorization

Categorization occurs when:

CP: perception not independent of categorization.

  • discriminably
  • different stimuli

Exp 1: Lexical variants not treated equivalently (gradiency)

  • 2) are treated
    • equivalently for
    • some purposes…
  • and stimuli in
  • other categories
  • are treated
  • differently.
slide43

Perceptual Categorization

Categorization occurs when:

CP: perception not independent of categorization.

  • discriminably
  • different stimuli

Exp 1: Lexical variants not treated equivalently (gradiency)

  • 2) are treated
    • equivalently for
    • some purposes…

WHY?

  • and stimuli in
  • other categories
  • are treated
  • differently.
slide44

Progressive Expectation Formation

Any action reflects future actions as it unfolds.

  • Can within-category detail be used to predict future acoustic/phonetic events?
  • Yes: Phonological regularities create systematic within-category variation.
    • Predicts future events.
slide45

Input:

m… a… r… oo… ng… g… oo… s…

time

maroon

goose

goat

duck

Experiment 3: Anticipation

Word-final coronal consonants (n, t, d) assimilate the place of the following segment.

Maroong Goose

Maroon Duck

Place assimilation -> ambiguous segments

—anticipate upcoming material.

slide46

We should see faster eye-movements to “goose” after assimilated consonants.

Subject hears

“select the maroonduck”

“select the maroon goose”

“select the maroong goose”

“select the maroong duck” *

slide47

Onset of “goose” + oculomotor delay

0.9

0.8

0.7

0.6

Fixation Proportion

0.5

0.4

Assimilated

0.3

Non Assimilated

0.2

0.1

0

0

200

400

600

Time (ms)

Looks to “goose“ as a function of time

Results

Anticipatory effect on looks to non-coronal.

slide48

Onset of “goose” + oculomotor delay

0.3

Assimilated

0.25

Non Assimilated

Fixation Proportion

0.2

0.15

0.1

0.05

0

0

200

400

600

Time (ms)

Looks to “duck” as a function of time

Inhibitory effect on looks to coronal(duck, p=.024)

slide49

Experiment 3: Extensions

Possible

lexical

locus

Green/m Boat

Eight/Ape Babies

Assimilation creates competition

slide50

Sensitivity to subphonemic detail:

    • Increase priors on likely upcoming events.
    • Decrease priors on unlikely upcoming events.
    • Active Temporal Integration Process.
  • Possible lexical mechanism…

NOT treating stimuli equivalently allows within-category detail to be used for temporal integration.

slide51

Adult Summary

  • Lexical activation is exquisitely sensitive to within-category detail: Gradiency.
  • This sensitivity is usefulto integrate material over time.
    • Progressive Facilitation
    • Regressive Ambiguity resolution (ask me about this)
slide52

Perceptual Categorization

Categorization occurs when:

CP: perception not independent of categorization.

  • discriminably
  • different stimuli

Exp 1: Lexical variants not treated equivalently (gradiency)

  • 2) are treated
    • equivalently for
    • some purposes…

Exp 2: non equivalence enables temporal integration.

  • and stimuli in
  • other categories
  • are treated
  • differently.
slide53

Development

Historically, work in speech perception has been linked to development.

Sensitivityto subphonemic detail must revise our view of development.

Use:Infants face additional temporal integration problems

No lexicon available to clean up noisy input: rely on acoustic regularities.

Extracting a phonology from the series of utterances.

slide54

Sensitivityto subphonemic detail:

For 30 years, virtually all attempts to address this question have yieldedcategorical discrimination(e.g. Eimas, Siqueland, Jusczyk & Vigorito, 1971).

  • Exception: Miller & Eimas (1996).
    • Only at extreme VOTs.
    • Only when habituated tonon- prototypicaltoken.
slide55

Use?

Nonetheless, infants possess abilities that would requirewithin-category sensitivity.

  • Infants can useallophonic differencesat word boundaries forsegmentation(Jusczyk, Hohne & Bauman, 1999; Hohne, & Jusczyk, 1994)
  • Infants can learn phonetic categories fromdistributional statistics(Maye, Werker & Gerken, 2002; Maye & Weiss, 2004).
slide56

Statistical Category Learning

Within a category, VOT forms Gaussian distribution.

Result: Bimodal distribution

0ms

40ms

VOT

Speech production causesclusteringalongcontrastivephonetic dimensions.

E.g. Voicing / Voice Onset Time

B: VOT ~ 0

P: VOT ~ 40-50

slide57

Record frequencies of tokens at each valuealong a stimulus dimension.

  • Extract categories from the distribution.

+voice

-voice

frequency

0ms

50ms

VOT

Tostatistically learnspeech categories, infants must:

  • This requires ability to track specific VOTs.
slide58

Experiment 4

Why no demonstrations of sensitivity?

  • Habituation
    • Discrimination not ID.
    • Possible selective adaptation.
    • Possible attenuation of sensitivity.
  • Synthetic speech
    • Not ideal for infants.
  • Single exemplar/continuum
    • Not necessarily a category representation

Experiment 3: Reassess issue with improved methods.

slide59

HTPP

  • Head-Turn Preference Procedure
  • (Jusczyk & Aslin, 1995)
  • Infants exposed to a chunk of language:
      • Words in running speech.
      • Stream of continuous speech (ala statistical learning paradigm).
      • Word list.
  • Memory for exposed items (or abstractions) assessed:
      • Compare listening time between consistent and inconsistent items.
slide64

Beach… Beach… Beach…

When infant looks at side-light…

…he hears a word

slide66

Methods

Bomb

Palm

Bear

Pear

Bail

Pail

Beach

Peach

Measure listening time on…

Original words

Bear

Pear

Competitors

Pear

Bear

VOT closer to boundary

Bear*

Pear*

7.5 month old infants exposed to either 4 b-, or 4 p-words.

80 repetitions total.

Form a category of the exposed

class of words.

slide67

B: M= 3.6 ms VOT

P: M= 40.7 ms VOT

B*: M=11.9 ms VOT

P*: M=30.2 ms VOT

B* and P* were judged /b/ or /p/ at least90% consistentlyby adult listeners.

B*: 97%

P*: 96%

Stimuli constructed by cross-splicingnaturally producedtokens of each end point.

slide68

Novelty or Familiarity?

Novelty

Familiarity

Within each group will we see evidence forgradiency?

B

36

16

P

21

12

Novelty/Familiarity preferencevariesacross infants and experiments.

We’re only interested in the middle stimuli (b*, p*).

Infants were classified asnoveltyorfamiliaritypreferring by performance on the endpoints.

slide69

Categorical

Gradient

After being exposed to

bear… beach… bail… bomb…

Infants who show a novelty effect…

…will look longer for pear than bear.

What about in between?

Listening Time

Bear

Bear*

Pear

slide70

Experiment 3: Results

Noveltyinfants (B: 36 P: 21)

10000

9000

8000

Listening Time (ms)

7000

Exposed to:

6000

B

P

5000

4000

Target

Target*

Competitor

Target vs. Target*:

Competitor vs. Target*:

p<.001

p=.017

slide71

10000

Exposed to:

9000

B

P

8000

Listening Time (ms)

7000

6000

5000

4000

Target

Target*

Competitor

Familiarityinfants (B: 16 P: 12)

Target vs. Target*:

Competitor vs. Target*:

P=.003

p=.012

slide72

.009**

.009**

10000

.024*

.024*

9000

8000

Listening Time (ms)

7000

6000

.028*

.028*

9000

5000

4000

8000

.018*

.018*

P

P

P*

P*

B

B

7000

Listening Time (ms)

Familiarity

N=12

6000

5000

4000

P

P*

B

Infants exposed to /p/

Novelty

N=21

slide73

>.1

>.2

10000

<.001**

<.001**

Novelty

N=36

9000

8000

Listening Time (ms)

7000

6000

.06

10000

5000

.15

9000

4000

B

B*

P

8000

Listening Time (ms)

7000

Familiarity

N=16

6000

5000

4000

B

B*

P

Infants exposed to /b/

slide74

Experiment 3 Conclusions

Contrary to all previous work:

  • 7.5 month old infants show gradient sensitivity to subphonemic detail.
    • Clear effect for /p/
    • Effect attenuated for /b/.
slide75

Null Effect?

Listening Time

Bear

Bear*

Pear

Listening Time

Expected Result?

Bear

Bear*

Pear

Reduced effect for /b/… But:

slide76

Actual result.

Listening Time

Bear

Bear*

Pear

  • Bear*  Pear
  • Category boundary lies between Bear & Bear*
    • - Between (3ms and 11 ms) [??]
  • Within-category sensitivity in a different range?
slide77

Experiment 4

Same design as experiment 3.

VOTs shifted away from hypothesized boundary

Train

Test:

Bomb Bear

Beach Bale

-9.7 ms.

Bomb* Bear*

Beach* Bale*

3.6 ms.

Palm Pear

Peach Pail

40.7 ms.

slide78

Familiarityinfants (34 Infants)

=.01**

9000

=.05*

8000

7000

Listening Time (ms)

6000

5000

4000

B-

B

P

slide79

Noveltyinfants (25 Infants)

=.002**

9000

=.02*

8000

7000

Listening Time (ms)

6000

5000

4000

B-

B

P

slide80

Experiment 4 Conclusions

  • Within-category sensitivity in /b/ as well as /p/.

Infants do NOT treat stimuli from the same category equivalently: Gradient.

slide81

Perceptual Categorization

Categorization occurs when:

CP: perception not independent of categorization.

  • discriminably
  • different stimuli

Exp 1: Lexical variants not treated equivalently (gradiency)

  • 2) are treated
    • equivalently for
    • some purposes…

Exp 2: non equivalence enables temporal integration.

Exp 3/4: Infants do not treat category members equivalently

  • and stimuli in
  • other categories
  • are treated
  • differently.
slide82

Experiment 4 Conclusions

  • Within-category sensitivity in /b/ as well as /p/.

Infants do NOT treat stimuli from the same category equivalently: Gradient.

  • Remaining questions:
  • Why the strange category boundary?
  • Where does this gradiency come from?
slide83

Experiment 4 Conclusions

Remaining questions:

2) Where does this gradiency come from?

Listening Time

B-

B

B*

P*

P

VOT

slide84

Remaining questions:

2) Where does this gradiency come from?

Results resemble

half a Gaussian…

B-

B

B*

P*

P

VOT

slide85

Remaining questions:

2) Where does this gradiency come from?

Results resemble

half a Gaussian…

And the distribution of VOTs is Gaussian

Lisker & Abramson (1964)

Statistical Learning Mechanisms?

slide86

/b/

/p/

Category Mapping

Strength

VOT

Remaining questions:

1) Why the strange category boundary?

/b/ results consistent with (at least) two mappings.

1)Shifted boundary

  • Inconsistent with prior literature.
slide87

Adult boundary

2) Sparse Categories

unmapped

space

/b/

/p/

Category Mapping

Strength

VOT

HTPP is a one-alternative task.

Asks: B or not-B not: B or P

Hypothesis: Sparse categories: by-product of efficient learning.

slide88

Remaining questions:

  • Why the strange category boundary?
  • Where does this gradiency come from?

?

Are both a by-product of statistical learning?

Can a computational approach contribute?

slide89

Computational Model

3) Each Gaussian has three

parameters:

VOT

Mixture of Gaussian model of speech categories

  • Models distribution of tokens as
    • a mixture of Gaussian distributions
    • over phonetic dimension (e.g. VOT) .

2) Each Gaussian represents a category. Posterior probability of VOT ~ activation.

slide90

VOT

VOT

Statistical Category Learning

1) Start with a set of randomly selected Gaussians.

  • After each input, adjust each parameter to find best description of the input.
  • Start with more Gaussians than necessary--model doesn’t innately know how many categories.
  •  -> 0 for unneeded categories.
slide92

Overgeneralization

    • large 
    • costly: lose phonetic distinctions…
slide93

Undergeneralization

    • small 
    • not as costly: maintain distinctiveness.
slide94

1

0.9

0.8

39,900

Models

Run

0.7

0.6

2 Category Model

P(Success)

0.5

3 Category Model

0.4

0.3

0.2

0.1

0

0

10

20

30

40

50

60

Starting 

  • To increase likelihood of successful learning:
    • err on the side of caution.
    • start with small 
slide95

Small 

Unmapped

space

Starting 

0.4

.5-1

0.35

0.3

0.25

Avg Sparseness Coefficient

0.2

0.15

0.1

0.05

0

0

2000

4000

6000

8000

10000

12000

Training Epochs

Sparseness coefficient: % of space not strongly mapped

to any category.

VOT

slide96

.5-1

20-40

Start with large σ

VOT

Starting 

0.4

0.35

0.3

0.25

Avg Sparsity Coefficient

0.2

0.15

0.1

0.05

0

0

2000

4000

6000

8000

10000

12000

Training Epochs

slide97

.5-1

12-17

20-40

3-11

Intermediate starting σ

VOT

Starting 

0.4

0.35

0.3

0.25

Avg Sparsity Coefficient

0.2

0.15

0.1

0.05

0

0

2000

4000

6000

8000

10000

12000

Training Epochs

slide98

Model Conclusions

Continuous sensitivity required for statistical learning.

Statistical learning enhances gradient category structure.

To avoid overgeneralization…

…better to start with small estimates for 

Small or even mediumstarting  => sparse category structure during infancy—much of phonetic space is unmapped.

Tokens that are treated differently may not be in different categories.

slide99

Perceptual Categorization

Categorization occurs when:

  • discriminably
  • different stimuli

CP: perception not independent of categorization.

Exp 1: Lexical variants not treated equivalently (gradiency)

Exp 2: non equivalence enables temporal integration.

  • 2) are treated
    • equivalently for
    • some purposes…

Exp 3/4: Infants do not treat category members equivalently

Model: Gradiency arises from statistical learning.

  • and stimuli in
  • other categories
  • are treated
  • differently.

Model: Tokens treated differently are not in different categories (sparseness).

Model: Sparseness by product of optimal learning.

slide100

AEM Paradigm

Treating stimuli equivalently

Treating stimuli differently

Identification, not discrimination.

Existing infant methods:

Habituation

Head-Turn Preference

Preferential Looking

Mostly test

discrimination

Examination of sparseness/completenessof categories needs a two alternative task.

To AEM

slide101

AEM Paradigm

i

a a a a…

  • Infant hears constant stream of distractor stimuli.
  • Conditioned to turn head in response to a target stimulus using visual reinforcer.

Exception: Conditioned Head Turn (Kuhl, 1979)

  • After training generalization can be assessed.
  • Approximates Go/No-Go task.
slide102

AEM Paradigm

  • When detection occurs this could be because
    • Stimulus is perceptually equivalent to target.
    • Stimulus is perceptually different but member of same category as target.
  • When no detection, this could be because
    • Stimuli are perceptually different.
    • Stimuli are in different categories.

A solution: the multiple exemplar approach

slide103

AEM Paradigm

  • Multiple exemplarmethods (Kuhl, 1979; 1983)
    • Training: single distinction i/a.
    • Irrelevant variation gradually added (speaker & pitch).
    • Good generalization.
  • This exposure may masknatural biases:
    • Infants trained on irrelevant dimension(s).
    • Infants exposed to expected variation along irrelevant dimension.

Infants trained on a single exemplar did not generalize.

slide104

AEM Paradigm

Is a member of

’s category?

HTPP, Habituation and Conditioned Head-Turn methods all rely on a single response: criterion effects.

  • Yes:
    • Both dogs
    • Both mammals
    • Both 4-legged animals
  • No:
    • Different breeds
    • Different physical properties

How does experimenter establish the decision criterion?

slide105

AEM Paradigm

Multiple responses:

Is a member of

or ?

Pug vs. poodle: Decision criteria will be based

on species-specific properties (hair-type, body-shape).

  • Two-alternative tasks specify criteriawithout explicitly teaching:
    • What the irrelevant cues are
    • Their statistical properties (expected variance).
slide106

AEM Paradigm

  • Conditioned-Head-Turn provides right sort of response, but cannot be adapted to two-alternatives (Aslin & Pisoni, 1980).
    • Large metabolic cost in making head-movement.
    • Requires 180º shift in attention.
  • Could we use a different behavioral response in a similar conditioning paradigm?
slide107

AEM Paradigm

Eye movements may provide ideal response.

  • Smaller angular displacements detectable with computer- based eye-tracking.
  • Metabolically cheap—quick and easy to generate.

How can we train infants to make eye movements target locations?

slide108

AEM Paradigm

Visual Expectation Paradigm

(Haith, Wentworth & Canfield,

1990; Canfield, Smith, Breznyak

& Snow, 1997)

Movement under an occluder

(Johnson, Amso & Slemmer, 2003)

Infants readily make anticipatory eye movements to regularly occurring visual events:

slide109

AEM Paradigm

Anticipatory Eye-Movements (AEM):

Train infants to use anticipatory eye movements as a behavioral label for category identity.

  • Two alternative response (left-right)
  • Arbitrary, identification response.
  • Response to a single stimulus.
  • Many repeated measures.
slide110

AEM Paradigm

Each category is associated with the left or right side of the screen.

Categorization stimuli followed by visual reinforcer.

slide111

AEM Paradigm

STIMULUS

Delay between stimulus and reward gradually increases throughout experiment.

trial 1

REINFORCER

STIMULUS

trial 30

REINFORCER

time

Delay provides opportunity for infants to make anticipatory eye-movements to expected location.

slide114

AEM Paradigm

After training on original stimuli, infants are tested on a mixture of:

  • new, generalization stimuli (unreinforced)
  • Examine category structure/similarity relative to trained stimuli.
  • original, trained stimuli (reinforced)
    • Maintain interest in experiment.
    • Provide objective criterion for inclusion
slide115

AEM Paradigm

MHT Receiver

MHT Receiver

Remote

Remote

MHT Transmitter

MHT Transmitter

Eye

Eye

-

-

TV

tracker

tracker

TV

TV

Baby

Baby

Infrared

Infrared

Video

Video

Camera

Camera

Eye

Eye

-

-

tracker Control Unit

tracker Control Unit

MHT Control Unit

MHT Control Unit

To Eye tracking Computer

To Eye tracking Computer

Gaze position assessed with automated, remote eye-tracker.

Gaze position recorded on standard video for analysis.

slide116

Experiment 5

Multidimensional visual categories

Can infants learn to make anticipatory eye movements in response to visual category identity?

?

  • What is the relationship between basic visual features in forming perceptual categories?
    • Shape
    • Color
    • Orientation
slide117

Experiment 5

Train: Shape (yellow square and yellow cross)

Test: Variation in color and orientation.

Yellow 0º (training values)

Orange 10º

Red 20º

If infants ignore irrelevant variation in color or orientation, performance should be good for generalization stimuli.

If infants’ shape categories are sensitive to this variation, performance will degrade.

slide118

Experiment 5: Results

80

70

No effect of color (p>.2)

60

50

40

Angle (p<.05)

Color (n.s.)

30

Significant performance deficit due to orientation (p=.002)

20

Yellow, 0°

10

0

Yellow

Orange

10°

Red

20°

9/10 scored better than chance on original stimuli.

M = 68.7% Correct

Percent Correct

Training

Stimuli

slide119

Some stimuli are uncategorized (despite very reasonable responses): sparseness.

Sparse

region of

input

spaces

slide120

Perceptual Categorization

Categorization occurs when:

  • discriminably
  • different stimuli

CP: perception not independent of categorization.

Exp 1: Lexical variants not treated equivalently (gradiency)

Exp 2: non equivalence enables temporal integration.

  • 2) are treated
    • equivalently for
    • some purposes…

Exp 3/4: Infants do not treat category members equivalently

Model: Gradiency arises from statistical learning.

  • and stimuli in
  • other categories
  • are treated
  • differently.

Model: Tokens treated differently are not in different categories (sparseness).

Model: Sparseness by product of optimal learning.

Exp 5: Shape categories show similar sparse structure.

slide121

Occlusion-Based AEM

Infants do make eye-movements to anticipate objects’ trajectories under an occluder. (Johnson, Amso & Slemmer, 2003)

  • AEM is based on an arbitrary mapping.
    • Unnatural mechanism drives anticipation.
    • Requires slowly changing duration of delay-period.

Can infants associate anticipated trajectories (under the occluder) with target identity?

slide124

Yellow Square

To faces

To end

slide125

Experiment 6

Can AEM assess auditory categorization?

Can infants “normalize” for variations in pitch and duration?

or…

Are infants’ sensitive to acoustic-detail during a lexical identification task?

slide126

“teak!”

“lamb!”

Training:

“Teak” -> rightward trajectory.

“Lamb” -> leftward trajectory.

Test:

Lamb & Teak with changes in:

Duration: 33% and 66% longer.

Pitch: 20% and 40% higher

If infants ignore irrelevant variation in pitch or duration, performance should be good for generalization stimuli.

If infants’ lexical representations are sensitive to this variation, performance will degrade.

experiment 6 results 2
Experiment 6 Results 2

Experiment 6: Results

0.9

Pitch

p>.1

0.8

0.7

0.6

0.5

Duration

p=.002

0.4

0.3

0.2

0.1

0

20 Training trials.

11 of 29 infants performed better than chance.

Proportion Correct Trials

Duration

Pitch

Training

Stimuli

D1 / P1

D2 / P2

Stimulus

slide129

Variation in pitch is tolerated for word-categories.

Variation in duration is not.

- Takes a gradient form.

Again, some stimuli are uncategorized (despite very reasonable responses): sparseness.

slide130

Perceptual Categorization

Categorization occurs when:

  • discriminably
  • different stimuli

CP: perception not independent of categorization.

Exp 1: Lexical variants not treated equivalently (gradiency)

Exp 2: non equivalence enables temporal integration.

  • 2) are treated
    • equivalently for
    • some purposes…

Exp 3/4: Infants do not treat category members equivalently

Model: Gradiency arises from statistical learning.

Exp 6: Gradiency in infant response to duration.

  • and stimuli in
  • other categories
  • are treated
  • differently.

Model: Tokens treated differently are not in different categories (sparseness).

Model: Sparseness by product of optimal learning.

Exp 5,6: Shape, Word categories show similar sparse structure.

slide131

Exp 7: Face Categorization

Can AEM help understand face categorization?

Are facial variants treated equivalently?

Train: two arbitrary faces

Test: same faces at

0°, 45°, 90°, 180°

Facial inversion effect.

slide133

Experiment 7: Results

Vertical

45º

90º

180º

22/33 successfully categorized vertical faces.

1

0.8

0.6

Percent Correct

0.4

0.2

0

  • 90º vs. Vertical: p<.001
  • 90º vs. 45º & 180º : p<.001.
  • 45º, 180º: chance (p>.2).
  • 90º: p=.111
slide134

Experiment 7

AEM useful with faces.

Facial Inversion effect replicated.

Generalization not simple similarity–90º vs. 45º –Infants’ own category knowledge is reflected.

Resembles VOT (b/p) results: within a dimension, some portions are categorized, others are not.

Again, some stimuli are uncategorized (despite very reasonable responses): sparseness.

slide135

Perceptual Categorization

Categorization occurs when:

  • discriminably
  • different stimuli

CP: perception not independent of categorization.

Exp 1: Lexical variants not treated equivalently (gradiency)

Exp 2: non equivalence enables temporal integration.

  • 2) are treated
    • equivalently for
    • some purposes…

Exp 3/4: Infants do not treat category members equivalently

Model: Gradiency arises from statistical learning.

Exp 6: Gradiency in infant response to duration.

  • and stimuli in
  • other categories
  • are treated
  • differently.

Model: Tokens treated differently are not in different categories (sparseness).

Model: Sparseness by product of optimal learning.

Exp 5,6,7: Shape, Word, Face categories show similar sparse structure.

slide136

Again, some stimuli are uncategorized (despite very reasonable responses): sparseness.

Evidence for complex, but sparse categories: some dimensions (or regions of a dimension) are included in the category, others are not.

slide137

Infant Summary

  • Infants show graded sensitivity to continuous speech cues.
  • /b/-results: regions ofunmapped phonetic space.
  • Statistical approach provides support for sparseness.
    • - Given current learning theories, sparseness results from optimal starting parameters.
  • Empirical test will require a two-alternative task: AEM
  • Test of AEM paradigm also shows evidence for sparseness in shapes, words, and faces.
slide138

Audience Specific Conclusions

For speech people

Gradiency: continuous information in the signal is not discarded and is useful during recognition.

Gradiency: Infant speech categories are also gradient, a result of statistical learning.

For infant people

Methodology: AEM is a useful technique for measuring categorization in infants (bonus: works with undergrads too).

Sparseness: Through the lens of a 2AFC task, (or interactions of categories) categories look more complex.

slide139

Perceptual Categorization

1) discriminably different stimuli…

CP: discrimination not distinct from categorization.

Continuous feedback relationship between perception and categorization

2) …are treated equivalently for some purposes…

Gradiency: Infants and adults do not treat stimuli equivalently. This property arises from learning processes as well as the demands of the task.

3) and stimuli in other categories are treated differently

Sparseness: Infants’ categories do not fully encompass the input. Many tokens are not categorized at all…

slide140

Conclusions

Categorization is an approximationof an underlyingly continuous system.

Clumps of similarity in stimulus-space.

Reflect underlying learning processes and demands of online processing.

During development, categorization is not common (across the complete perceptual space)—small, specific clusters may grow to larger representations.

This is useful: avoid overgeneralization.

slide141

Take Home Message

Early, sparse, regions of graded similarity space

grow, gain structure

but retain their fundamental gradiency.

slide142

Perceptual Categories:

Old and gradient, young and sparse.

Bob McMurray

University of Iowa

Dept. of Psychology

slide143

IR Head-Tracker

Emitters

Head-Tracker Cam

Monitor

Head

2 Eye cameras

Computers connected

via Ethernet

Subject

Computer

Eyetracker

Computer

slide145

10 Pairs of b/p items.

    • 0 – 35 ms VOT continua.

20 Filler items (lemonade, restaurant, saxophone…)

Option to click “X” (Mispronounced).

26 Subjects

1240 Trials over two days.

slide146

Identification Results

1.00

0.90

0.80

0.70

0.60

Voiced

Response Rate

0.50

Voiceless

0.40

NW

0.30

0.20

0.10

0.00

0

5

10

15

20

25

30

35

Barakeet

Parakeet

1.00

0.90

0.80

0.70

Significant target responses even at extreme.

Graded effects of VOT on correct response rate.

Voiced

0.60

Response Rate

0.50

Voiceless

0.40

NW

0.30

0.20

0.10

0.00

0

5

10

15

20

25

30

35

Barricade

Parricade

slide147

Phonetic “Garden-Path”

VOT = 0 (/b/)

VOT = 35 (/p/)

1

0.8

Barricade

0.6

Parakeet

Fixations to Target

0.4

0.2

0

0

500

1000

0

500

1000

1500

Time (ms)

Time (ms)

“Garden-path” effect:

Difference between looks to each target (b vs. p) at same VOT.

slide148

0.15

0.1

0.05

Garden-Path Effect

( Barricade - Parakeet )

0

-0.05

-0.1

0

5

10

15

20

25

30

35

VOT (ms)

0.06

0.04

0.02

0

Garden-Path Effect

-0.02

( Barricade - Parakeet )

-0.04

-0.06

-0.08

-0.1

0

5

10

15

20

25

30

35

VOT (ms)

Target

GP Effect:

Gradient effect of VOT.

Target: p<.0001

Competitor: p<.0001

Competitor

slide150

When /p/ is heard, the bilabial feature can be assumed to come from assimilation (not an underlying /m/).

When /t/ is heard,the bilabial feature is likely to be from an underlying /m/.

runm picks

runm takes ***

slide151

Exp 3 & 4: Conclusions

  • Within-category detail used in recovering from assimilation: temporal integration.
    • Anticipate upcoming material
    • Bias activations based on context
      • - Like Exp 2: within-category detail retained to resolve ambiguity..
  • Phonological variation is a source of information.
slide152

Subject hears

“select the mud drinker”

“select the mudg gear”

“select the mudg drinker

Critical Pair

slide153

Initial Coronal:Mud Gear

Initial Non-Coronal:Mug Gear

Onset of “gear”

Avg. offset of “gear” (402 ms)

0.45

0.4

0.35

0.3

Fixation Proportion

0.25

0.2

0.15

0.1

0.05

0

0

200

400

600

800

1000

1200

1400

1600

1800

2000

Time (ms)

Mudg Gear is initially ambiguous with a late bias towards “Mud”.

slide154

Onset of “drinker”

Avg. offset of “drinker (408 ms)

0.6

0.5

0.4

Fixation Proportion

0.3

0.2

Initial Coronal: Mud Drinker

0.1

Initial Non-Coronal: Mug Drinker

0

0

200

400

600

800

1000

1200

1400

1600

1800

2000

Time (ms)

Mudg Drinker is also ambiguous with a late bias towards “Mug” (the /g/ has to come from somewhere).

slide155

Onset of “gear”

0.8

0.7

0.6

0.5

Fixation Proportion

0.4

Assimilated

0.3

Non Assimilated

0.2

0.1

0

0

200

400

600

Time (ms)

Looks to non-coronal (gear) following assimilated or non-assimilated consonant.

In the same stimuli/experiment there is also a progressiveeffect!

slide156

Non-parametric approach?

Categories

  • Competitive Hebbian Learning (Rumelhart & Zipser, 1986).

VOT

  • Not constrained by a particular equation—can fill space better.
  • Similar properties in terms of starting  and sparseness.
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