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Title Slide. Stop-Consonant Perception in 7.5-month-olds: Evidence for gradient categories. Bob McMurray & Richard N. Aslin Department of Brain and Cognitive Sciences University of Rochester. With thanks to Julie Markant & Robbie Jacobs. Learning Language. Meaning. Lexicon. S. VP.

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title slide
Title Slide

Stop-Consonant Perception

in 7.5-month-olds:

Evidence for gradient categories

Bob McMurray & Richard N. Aslin

Department of Brain and Cognitive Sciences

University of Rochester

With thanks to Julie Markant & Robbie Jacobs

learning language
Learning Language

Meaning

Lexicon

S

VP

NP

Bob’s lab

produced

the

All labs

lab

Language Understanding

Understanding spoken language requires that children learn a complex mapping…

What is the form of this mapping?

How do the demands of learning affect this representation?

learning speech
Learning Speech

Syntax, semantics, pragmatics…

Speech Recognition

Speech perception and word recognition require mapping…

…continuous, variable perceptual input to

a something discrete, categorical.

What representations mediate acoustics and lexical or sublexical units?

How does learning affect this representation?

overview
Overview

Overview

  • Acoustic mappings: Categorical and gradient perception in adults and infants.
  • Infant speech categories are graded representations of continuous detail.
  • Statistical learning models and sparse representations.
  • Conclusions and future directions.
categorization categorical perception
Categorization & Categorical Perception

Representation of Speech Detail

What is the nature of the mapping between continuous perception and discrete categories?

How are these representations sensitive (or not) to within-category detail?

  • Empirical approach:
    • Use continuously variable stimuli.
    • Explore response using
      • Discrimination Identification (adults)
      • Habituation (infants)
categorical perception 1
Categorical Perception 1

100

100

B

Discrimination

% /p/

Discrimination

ID (%/pa/)

0

0

B

VOT

P

  • Sharp labeling of tokens on a continuum.

P

  • Discrimination poor within a phonetic category.

Categorical Perception

Subphonemic within-category variation in VOT is discarded in favor of a discretesymbol (phoneme).

categorical perception 2
Categorical Perception 2
  • Discrimination Task Variations
    • Pisoni and Tash (1974)
    • Pisoni & Lazarus (1974)
    • Carney, Widin & Viemeister (1977)

Goodness Ratings

Miller (1997)

Massaro & Cohen (1983)

  • Training
    • Samuel (1977)
    • Pisoni, Aslin, Perey & Hennessy (1982)

Many tasks have demonstrated within-category sensitivity in adults...

BUT…

And lexical activation shows systematic sensitivity to subphonemic detail (McMurray, Tanenhaus & Aslin, 2002).

infant categorical perception 1
Infant Categorical Perception 1

Categorical Perception in Infants

Infants have shown a different pattern.

For 30 years, virtually all attempts to address this question have yielded categorical discrimination.

  • Exception: Miller & Eimas (1996).
    • Only at extreme VOTs.
    • Only when habituated to non- prototypical token.

GWB

infant categorical perception 3
Infant Categorical Perception 3

Nonetheless, infants possess abilities that would require within-categorysensitivity.

  • Infants can use allophonic differences at word boundaries for segmentation (Jusczyk, Hohne & Bauman, 1999; Hohne, & Jusczyk, 1994)
  • Infants can learn phonetic categories from distributional statistics (Maye, Werker & Gerken, 2002).
distributional learning 2
Distributional Learning 2

Within a categories, VOT is distributed Gaussian.

Result: Bimodal distribution

0ms

40ms

VOT

Distributional Learning

Speech production causes clustering along contrastive phonetic dimensions.

E.g. Voicing / Voice Onset Time

B: VOT ~ 0

P: VOT ~ 40

distributional learning 1
Distributional Learning 1
  • track frequencies of tokens at each value along a stimulus dimension.
  • Extract categories from the distribution.

+voice

-voice

frequency

0ms

50ms

VOT

Distributional Learning

To statistically learn speech categories, infants must:

  • This requires ability to track specific VOTs.
question 1
Question 1

Prior examinations of speech-categories used:

?

  • 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 1: Reassess this issue with improved methods.

htpp 1
HTPP 1

Head-Turn Preference Procedure

  • 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.

After exposure, memory for exposed items (or abstractions) is assessed by comparing listening time to consistent items with inconsistent items.

Misperception 3

htpp 2
HTPP 2

Test trials start with all lights off.

Misperception 3

htpp 21
HTPP 2

Center Light blinks.

Misperception 3

htpp 3
HTPP 3

Brings infant’s attention to center.

Misperception 3

htpp 31
HTPP 3

One of the side-lights blinks.

Misperception 3

htpp 4
HTPP 4

Beach… Beach… Beach…

When infant looks at side-light…

…he hears a word

Misperception 3

htpp 5
HTPP 5

…as long as he keeps looking.

Misperception 3

experiment 1 methods
Experiment 1 Methods

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

80 repetitions total.

Form a categoryof the exposed

class of words.

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*

Experiment 1

Misperception 3

experiment 1 stimuli
Experiment 1 Stimuli

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 least 90% consistently by adult listeners.

B*: 97%

P*: 96%

Stimuli constructed by cross-splicing naturally producedtokens of each end point.

Misperception 3

experiment 1 familiarity vs novelty
Experiment 1 Familiarity vs. Novelty

Novelty

Familiarity

Within each group will we see evidence for gradiency?

B

36

16

P

21

12

Familiarity vs. Novelty

Novelty/Familiarity preference variesacross infants and experiments.

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

Infants were classified as noveltyor familiaritypreferring by performance on the endpoints.

Misperception 3

experiment 1 fam vs nov 2
Experiment 1 Fam. vs. Nov. 2

Categorical

Listening Time

Gradient

Bear

Bear*

Pear

Gradiency

After being exposed to

bear… beach… bail… bomb…

Infants who show a noveltyeffect…

…will look longer for pear than bear.

What about in between?

Misperception 3

experiment 1 results nov
Experiment 1 Results Nov

Exposed to:

B

P

Experiment 1 Results

Novelty infants (B: 36 P: 21)

10000

9000

8000

Listening Time (ms)

7000

6000

5000

4000

Target

Target*

Competitor

Target vs. Target*:

Competitor vs. Target*:

p<.001

p=.017

experiment 1 results fam
Experiment 1 Results Fam

10000

Exposed to:

9000

B

P

8000

Listening Time (ms)

7000

6000

5000

4000

Target

Target*

Competitor

Familiarity infants (B: 16 P: 12)

Target vs. Target*:

Competitor vs. Target*:

P=.003

p=.012

experiment 1 results planned p
Experiment 1 Results Planned P

.009**

.009**

10000

Novelty

N=21

.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

Planned Comparisons

Infants exposed to /p/

Misperception 3

experiment 1 results planned b
Experiment 1 Results Planned B

>.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/

Misperception 3

experiment 1 conclusions
Experiment 1 Conclusions

Experiment 1 Conclusions

Contrary to all previous work:

  • 7.5 month old infantsshow gradient sensitivityto subphonemic detail.
    • Clear effect for /p/
    • Effect attenuated for /b/.

Misperception 3

experiment 1 conclusions 2
Experiment 1 Conclusions 2

Null Effect?

Listening Time

Bear

Bear*

Pear

Expected Result?

Listening Time

Bear

Bear*

Pear

Reduced effect for /b/… But:

Misperception 3

experiment 1 conclusions 3
Experiment 1 Conclusions 3

Actual result.

Listening Time

Bear

Bear*

Pear

  • Bear*  Pear
  • Category boundary lies between Bear & Bear*
    • Between (3ms and 11 ms).
  • Will we see evidence for within-category sensitivity with a different range?

Misperception 3

experiment 2
Experiment 2

Test:

Bomb Bear

Beach Bale

-9.7 ms.

Bomb* Bear*

Beach* Bale*

3.6 ms.

Palm Pear

Peach Pail

40.7 ms.

Same design as experiment 1.

VOTs shifted away from hypothesized boundary (7 ms).

Train

Misperception 3

experiment 2 results fam
Experiment 2 Results Fam

Experiment 2 Results

Familiarity infants (34 Infants)

=.01**

9000

=.05*

8000

7000

Listening Time (ms)

6000

5000

4000

B-

B

P

Misperception 3

experiment 2 results nov
Experiment 2 Results Nov

Experiment 2 Results

Noveltyinfants (25 Infants)

=.002**

9000

=.02*

8000

7000

Listening Time (ms)

6000

5000

4000

B-

B

P

Misperception 3

experiment 2 conclusions
Experiment 2 Conclusions

Adult boundary

/b/

/p/

Adult

Categories

Category Mapping

Strength

VOT

Experiment 2 Conclusions

  • Within-category sensitivity in /b/ as well as /p/.
  • Shifted category boundary in /b/: not consistent with adult boundary (or prior infant work). Why?

Misperception 3

experiment 2 conclusions 2
Experiment 2 Conclusions 2

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

Adult boundary

/b/

/p/

1) Shifted boundary

Category Mapping

Strength

VOT

  • Inconsistent with prior literature.
  • Why would infants have this boundary?

Misperception 3

experiment 2 conclusions 3
Experiment 2 Conclusions 3

Adult boundary

unmapped

space

/b/

/p/

Category Mapping

Strength

2) Sparse Categories

VOT

HTPP is a one-alternative task.

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

Sparse categories may in fact by a by-product of efficient statistical learning.

Misperception 3

model intro
Model Intro

3) Each Gaussian has three

parameters:

VOT

Computational Model

Distributional learning model

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

2) After receiving an input, the Gaussian with the highest posterior probability is the “category”.

Misperception 3

model intro 2
Model Intro 2

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.
  •  -> for unneeded categories.

Misperception 3

model intro 3
Model Intro 3

Misperception 3

model overgen
Model Overgen
  • Overgeneralization
    • large 
    • costly: lose phonetic distinctions…

Misperception 3

model undergen
Model Undergen
  • Undergeneralization
    • small 
    • not as costly: maintain distinctiveness.

Misperception 3

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

1

0.9

0.8

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 

model sparseness
Model Sparseness

Small 

Unmapped

space

VOT

Starting 

0.4

.5-1

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

Sparseness coefficient: % of space not mapped to any category.

model sparseness 2
Model Sparseness 2

.5-1

20-40

Sparseness coefficient: % of space not mapped to any category.

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

model sparseness 3
Model Sparseness 3

.5-1

12-17

20-40

3-11

Sparseness coefficient: % of space not mapped to any category.

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

model conclusions
Model Conclusions

Model Conclusions

To avoid overgeneralization…

…better to start with small estimates for 

  • Small starting ’s lead to sparse category structure during infancy—much of phonetic space is unmapped.
  • Occasionally model leaves sparse regions at the end of learning.
    • 1) Competition/Choice framework:
      • Additional competition or selection mechanisms during processing allows categorization on the basis of incomplete information.
model conclusions 2
Model Conclusions 2

Categories

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

VOT

  • 2) Non-parametric models
  • Not constrained by a particular equation—can fill space better.
  • Similar properties in terms of starting  and the resulting sparseness.
conclusions 3
Conclusions 3

Final Conclusions

Infants show graded response to within-category detail.

  • /b/-results suggest regions of unmapped 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: train infants to make eye-movements in response to stimulus identity.
future work
Future Work

Future Work

  • Infants make anticipatory eye-movements along predicted trajectory, in response to stimulus identity.
  • Two alternatives allows us to distinguish between category boundary and unmapped space.
last word
Last Word

-60

-40

-20

0

20

40

60

80

VOT

The last word

  • Early speech categories emerge from an interplay of
  • Exquisite sensitivity to graded detail in the signal.
  • Long-term sensitivity to statistics of the signal.
  • Early biases to optimize the learning problem.
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