Continuous detail is used in language comprehension and language learning:

Temporal Integration at two time scales

Bob McMurrayUniversity of Iowa

Dept. of Psychology

Collaborators

Richard AslinMichael TanenhausDavid Gow

Joe ToscanoCheyenne MunsonMeghan ClayardsDana SubikJulie Markant

The students of the MACLab

Temporal Integration

Temporal integration: a critical problem for cognition. - information never arrives synchronously.

• Vision: integration across head-movements, saccades and attention-shifts.

• Music perception: long-term dependencies and short term expectancies.

In language, information arrives sequentially.

• Partial syntactic and semantic representations are formed as words arrive.

The Hawkeyes beat the Illini

• Words are identified over sequential phonemes.

(once)

Spoken Word Recognition is an ideal arena in which to study these issues because:

• Speech production gives us a lot of rich temporal information to use in this way.

• We have a clear understanding of the input (from phonetics).

• The output is easy to measure online (visual world paradigm).

Scales of temporal integration in word recognition

• A Word: ordered series of articulations.- Build abstract representations.- Form expectations about future events.- Fast (online) processing.

• A phonology: - Abstract across utterances.- Expectations about possible future events.- Slow (developmental) processing

Mechanisms of Temporal Integration

Stimuli do not change arbitrarily.

Perceptual cues reveal something about the change itself.

Active integration:• Anticipating future events• Retain partial present representations.• Resolve prior ambiguity.

Overview

2) Lexical activation is sensitive to fine-grained detail in speech.

1) Speech perception and Spoken Word Recognition.

3) Fast temporal integration: taking advantage of regularity in the signal for temporal integration.

4) Slow temporal integration: Developmental consequences

bakeryba…

basic

barrier

barricade bait

baby

Xkery

bakery

X

XXX

Online Word Recognition

• Information arrives sequentially• At early points in time, signal is temporarily ambiguous.

• Later arriving information disambiguates the word.

Current models of spoken word recognition

• Immediacy: Hypotheses formed from the earliest moments of input.

• Activation Based: Lexical candidates (words) receive activation to the degree they match the input.

• Parallel Processing: Multiple items are active in parallel.

• Competition: Items compete with each other for recognition.

time

Input: b... u… tt… e… r

beach

bump putter

dog

butter

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

But considerably less ambiguity if we consider subphonemic information.

Example: subphonemic effects of motor processes.

Coarticulation

Sensitivity to these perceptual details might yield earlier disambiguation.

Example: CoarticulationArticulation (lips, tongue…) reflects current, future and past events.

Subtle subphonemic variation in speech reflects temporal organization.

n ne et c

k

Any action reflects future actions as it unfolds.

These processes have largely been ignored because of a history of evidence that perceptual variability gets discarded.

Example: Categorical Perception

Categorical Perception

B

P

Subphonemic variation in VOT is discarded in favor of a discrete symbol (phoneme).

• Sharp identification of tokens on a continuum.

VOT

0

100

PB

% /p

/

ID (%/pa/)0

100Discrim

ination

Discrimination

• Discrimination poor within a phonetic category.

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

Discrimination Tasks Pisoni and Tash (1974) Pisoni & Lazarus (1974)Carney, Widin & Viemeister (1977)

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

Goodness Ratings Miller (1997)Massaro & Cohen (1983)

?Does within-category acoustic detail

systematically affect higher level language?

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

Experiment 1

A gradient relationship would yield systematic effects of subphonemic information on lexical activation.

If this gradiency is useful for temporal integration, it must be preserved over time.

Need a design sensitive to both acoustic detail and detailed temporal dynamics of lexical activation.

McMurray, Aslin & Tanenhaus (2002)

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

KlattWorks: generate synthetic continua from natural speech.

Acoustic Detail

9-step VOT continua (0-40 ms)

6 pairs of words.beach/peach bale/pale bear/pearbump/pump bomb/palm butter/putter

6 fillers.lamp leg lock ladder lip leafshark shell shoe ship sheep shirt

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.

a beach, a peach and some unrelated items.

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

Temporal Dynamics

Tanenhaus, Spivey-Knowlton, Eberhart & Sedivy, 1995

• Relatively natural task.

• Eye-movements generated very fast (within 200ms of first bit of information).

• Eye movements time-locked to speech.

• Subjects aren’t aware of eye-movements.

• Fixation probability maps onto lexical activation..

Why use eye-movements and visual world paradigm?

A moment to view the items

Task

Task

Bear

Repeat 1080 times

By subject: 17.25 +/- 1.33ms By item: 17.24 +/- 1.24ms

High agreement across subjects and items for category boundary.

0 5 10 15 20 25 30 35 400

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

VOT (ms)

prop

ortio

n /p

/

B P

Identification Results

Task

Target = Bear

Competitor = Pear

Unrelated = Lamp, Ship

200 ms

1

2

3

4

5

Trials

Time

% fi

xatio

ns

Task

00.10.20.30.40.50.60.70.80.9

0 400 800 1200 1600 0 400 800 1200 1600 2000

Time (ms)

More looks to competitor than unrelated items.

VOT=0 Response= VOT=40 Response=Fi

xatio

n p

ropo

rtio

n

Task

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

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

Categorical Results Gradient Effect

target

competitortime

Fixa

tion

prop

ortio

n target

competitor competitorcompetitortime

Fixa

tion

prop

ortio

n target

Results

0 400 800 1200 16000

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0 ms5 ms10 ms15 ms

VOT

0 400 800 1200 1600 2000

20 ms25 ms30 ms35 ms40 ms

VOT

Com

petit

or F

ixat

ions

Time since word onset (ms)

Response= Response=

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

0 5 10 15 20 25 30 35 400.02

0.03

0.04

0.05

0.06

0.07

0.08

VOT (ms)

CategoryBoundary

Response= Response=

Looks to

Looks to C

ompe

titor

Fix

atio

ns

B: p=.017* P: p<.001***Clear effects of VOTLinear Trend B: p=.023* P: p=.002***

Area under the curve:

0 5 10 15 20 25 30 35 400.02

0.03

0.04

0.05

0.06

0.07

0.08

VOT (ms)

Response= Response=

Looks to

Looks to

B: p=.014* P: p=.001***Clear effects of VOTLinear Trend B: p=.009** P: p=.007**

Unambiguous Stimuli Only

CategoryBoundaryC

ompe

titor

Fix

atio

ns

Summary

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

• Gradient effect of VOT on looks to the competitor.

• Seems to be long-lasting.• Effect holds even for unambiguous stimuli.

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

1) Word recognition is systematically sensitive to subphonemic acoustic detail.

2) Acoustic detail is represented as gradations in activation across the lexicon.

3) This sensitivity enables the system to take advantage of subphonemic regularities for temporal integration.

4) This has fundamental consequences for development: learning phonological organization.

Sensitivity & Use

Lexical Sensitivity

1) Word recognition is systematically sensitive to subphonemic acoustic detail.

Voicing Laterality, Manner, Place Natural Speech Vowel Quality

Lexical Sensitivity

1) Word recognition is systematically sensitive to subphonemic acoustic detail.

Voicing Laterality, Manner, Place Natural Speech Vowel Quality

? Non minimal pairs? Duration of effect

(experiment 1)

2) Acoustic detail is represented as gradations in activation across the lexicon (a lexical basis vector for speech).

time

Input: b... u… m… p…

bun

bumper

pump

dump

bump

bomb

3) This sensitivity enables the system to take advantage of subphonemic regularities for temporal integration.

Regressive ambiguity resolution (exp 1):• Ambiguity retained until more information arrives.

Progressive expectation building (exp 2):• Phonetic distinctions are spread over time• Anticipate upcoming material.

Temporal Integration

4) Consequences for development: learning phonological organization.

Learning a language: • Integrating input across many utterances to build

long-term representation.

Sensitivity to subphonemic detail (exp 4 & 5).• Allows statistical learning of categories (model).

Development

To assimilation

?Experiment 2

?

How long are gradient effects of within-category detail maintained?

Can subphonemic variation play a role in ambiguity resolution?

How is information at multiple levels integrated?

Competitor still active - easy to activate it rest of the way.

Competitor completely inactive- system will “garden-path”.

P ( misperception ) distance from boundary.

Gradient activation allows the system to hedge its bets.

What if initial portion of a stimulus was misperceived?

Misperception

time

Input: p/b eI r ə k i t…

parakeetbarricade

Categorical Lexicon

barricade vs. parakeet

parakeet

barricade

Gradient Sensitivity

/ beIrəkeId / vs. / peIrəkit /

10 Pairs of b/p items.

Voiced Voiceless OverlapBumpercar Pumpernickel 6Barricade Parakeet 5Bassinet Passenger 5Blanket   Plankton 5Beachball Peachpit 4Billboard Pillbox 4Drain Pipes Train Tracks 4Dreadlocks Treadmill    4Delaware Telephone   4Delicatessen Television   4

Methods

X

05101520253035

0

0.2

0.4

0.6

0.8

1

300 600 900

Time (ms)

Fixa

tions

to T

arge

t

VOT

Barricade -> Parricade

Eye Movement Results

Faster activation of target as VOTs near lexical endpoint.

--Even within the non-word range.

05101520253035

0

0.2

0.4

0.6

0.8

1

300 600 900

Time (ms)

Fixa

tions

to T

arge

t

VOT

Barricade -> Parricade

Eye Movement Results

Parakeet -> Barakeet

300 600 900 1200

Time (ms)

Faster activation of target as VOTs near lexical endpoint.

--Even within the non-word range.

XXXb/parricade

Is the latency to switch to the target related to VOT?

• Accelerated latency = more (residual) target activation

Sparse databut: B: p=.002

P: p=.00725

75

125

175

225

275

0 10 20 30 40

Distance from Prototype (VOT, ms)

Tim

e to

Tar

get

VoicedVoiceless

Experiment 2: Garden Path Analyses

1) Replication: longer continua (0-45 ms).

Experiment 2 Extensions

00.10.20.30.40.50.60.70.80.9

300 500 700 900 1100 1300Time (ms)

Fixa

tions

to T

arge

t

00.10.20.30.40.50.60.70.80.9

300 500 700 900 1100 1300Time (ms)

Fixa

tions

to T

arge

t

2) Effect holds with non-displayed competitors.

XXX0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

300 500 700 900 1100 1300

Time (ms)

Fixa

tions

to T

arge

t

0

5

10

15

20

25

30

35

40

450

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

300 500 700 900 1100 1300

Time (ms)

Fixa

tions

to T

arge

t

0

5

10

15

20

25

30

35

40

45

Gradient effect of within-category variation without minimal-pairs.

Experiment 2 Conclusions

Gradient effect long-lasting: mean POD = 240 ms.

Regressive ambiguity resolution:

• Subphonemic gradations maintained until more information arrives.

• Subphonemic gradation can improve (or hinder) recovery from garden path.

(to developmental work)

Progressive Expectation Formation

Can within-category detail be used to predict future acoustic/phonetic events?

Yes: Phonological regularities create systematic within-category variation.

• Predicts future events.

time

Input: m… a… rr… oo… ng… g… oo… s…

maroongoose

goatduck

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

Place assimilation -> ambiguous segments —anticipate upcoming material.

Experiment 3: Anticipation

Maroong Goose Maroon Duck

Subject hears “select the maroon duck”“select the maroon goose”“select the maroong goose”“select the maroong duck” *

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

Results

Looks to “goose“ as a function of time

00.10.20.30.40.50.60.70.80.9

0 200 400 600Time (ms)

Fixa

tion

Prop

ortio

n

AssimilatedNon Assimilated

Onset of “goose” + oculomotor delay

Anticipatory effect on looks to non-coronal.

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

0

0.05

0.1

0.15

0.2

0.25

0.3

0 200 400 600Time (ms)

Fixa

tion

Prop

ortio

n

AssimilatedNon Assimilated

Looks to “duck” as a function of time

Onset of “goose” + oculomotor delay

Experiment 3: Extensions

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

200 300 400 500 600 700 800

Time (ms)

Loo

ks to

Lab

ial

Assim-LabialsLabials

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

200 300 400 500 600 700 800

Time (ms)

Loo

ks to

Lab

ial

AssimilatedNeutral

Possible lexical locus

Green/m Boat

Eight/Ape Babies

Assimilation creates

competition

Sensitivity to subphonemic detail:• Increase priors on likely upcoming events.• Decrease priors on unlikely upcoming events.• Active Temporal Integration Process.

Occasionally assimilation creates ambiguity• Resolves prior ambiguity: mudg drinker• Similar to experiment 2…

Possible lexical mechanism…

Lexical activation is exquisitely sensitive to within-category detail.

This sensitivity is useful to integrate material over time.

• Regressive Ambiguity resolution. • Progressive Facilitation

Taking advantage of phonological and lexical regularities.

Adult Summary

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

Sensitivity to subphonemic detail must revise our view of development.

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.

Sensitivity to subphonemic detail:

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

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

Nonetheless, infants possess abilities that would require within-category sensitivity.

• 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; Maye & Weiss, 2004).

Use?

Speech production causes clustering along contrastive phonetic dimensions.

E.g. Voicing / Voice Onset TimeB: VOT ~ 0P: VOT ~ 40

Result: Bimodal distribution

Within a category, VOT forms Gaussian distribution.

VOT0ms 40ms

Statistical Category Learning

• Extract categories from the distribution.

+voice -voice

• Record frequencies of tokens at each value along a stimulus dimension.

VOT

freq

uenc

y

0ms 50ms

To statistically learn speech categories, infants must:

• This requires ability to track specific VOTs.

Why no demonstrations of sensitivity?

• HabituationDiscrimination not ID.Possible selective adaptation.Possible attenuation of sensitivity.

• Synthetic speechNot ideal for infants.

• Single exemplar/continuumNot necessarily a category representation

Experiment 4: Reassess issue with improved methods.

Experiment 4

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.

HTPP

Test trials start with all lights off.

Center Light blinks.

Brings infant’s attention to center.

One of the side-lights blinks.

When infant looks at side-light……he hears a word

Beach… Beach… Beach…

…as long as he keeps looking.

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.

PeachBeachPailBailPearBearPalmBomb

Measure listening time on…

VOT closer to boundaryCompetitors

Original words

Pear*Bear*BearPearPearBear

Methods

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 produced tokens of each end point.

B: M= 3.6 ms VOTP: M= 40.7 ms VOT

B*: M=11.9 ms VOTP*: M=30.2 ms VOT

Novelty/Familiarity preference varies across infants and experiments.

1221P

1636B

FamiliarityNoveltyWithin each group will we see evidence for gradiency?

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

Infants were classified as novelty or familiarity preferring by performance on the endpoints.

Novelty or Familiarity?

əə

Categorical

What about in between?

After being exposed to bear… beach… bail… bomb…

Infants who show a novelty effect……will look longer for pear than bear.

Gradient

Bear*Bear Pear

List

enin

g Ti

me

4000

5000

6000

7000

8000

9000

10000

Target Target* Competitor

Lis

teni

ng T

ime

(ms)

BP

Exposed to:

Novelty infants (B: 36 P: 21)

Target vs. Target*:Competitor vs. Target*:

p<.001p=.017

Results

əə

Familiarity infants (B: 16 P: 12)

Target vs. Target*:Competitor vs. Target*:

P=.003p=.012

4000

5000

6000

7000

8000

9000

10000

Target Target* Competitor

Lis

teni

ng T

ime

(ms) B

P

Exposed to:

NoveltyN=21

P P* B

.024*

.009**

P P* B

.024*

.009**

4000

5000

6000

7000

8000

9000

10000

Lis

teni

ng T

ime

(ms)

Infants exposed to /p/

P* B4000

5000

6000

7000

8000

9000

.018*

.028*

.018*

P

Lis

teni

ng T

ime

(ms)

.028*

FamiliarityN=12

NoveltyN=36

<.001**>.1

<.001**>.2

4000

5000

6000

7000

8000

9000

10000

B B* P

Lis

teni

ng T

ime

(ms)

Infants exposed to /b/

FamiliarityN=16

4000

5000

6000

7000

8000

9000

10000

B B* P

Lis

teni

ng T

ime

(ms)

.06.15

7.5 month old infants show gradient sensitivity to subphonemic detail.

• Clear effect for /p/• Effect attenuated for /b/.

Contrary to all previous work:

Experiment 4 Conclusions

Reduced effect for /b/… But:

Bear Pear

List

enin

g Ti

me

Bear*

Null Effect?

Bear Pear

List

enin

g Ti

me

Bear*

Expected Result?

• Bear* Pear

Bear Pear

List

enin

g Ti

me

Bear*

Actual result.

• Category boundary lies between Bear & Bear*- Between (3ms and 11 ms) [??]

• Within-category sensitivity in a different range?

Same design as experiment 3.

VOTs shifted away from hypothesized boundary

Train

40.7 ms.Palm Pear Peach Pail

3.6 ms.Bomb* Bear* Beach* Bale*

-9.7 ms.Bomb Bear Beach Bale

Test:

Bomb Bear Beach Bale -9.7 ms.

Experiment 5

Familiarity infants (34 Infants)

4000

5000

6000

7000

8000

9000

B- B P

Lis

teni

ng T

ime

(ms) =.05*

=.01**

Novelty infants (25 Infants)

=.02*

=.002**

4000

5000

6000

7000

8000

9000

B- B P

Lis

teni

ng T

ime

(ms)

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

• Shifted category boundary in /b/: not consistent with adult boundary (or prior infant work). Why?

Experiment 5 Conclusions

əə

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

ateg

ory

Map

ping

Stre

ngth 1) Shifted boundary

• Inconsistent with prior literature.

• Why would infants have this boundary?

VOT

/b/ /p/

/p/

VOT

Adult boundary

/b/

Cat

egor

y M

appi

ngSt

reng

th

HTPP is a one-alternative task. Asks: B or not-B not: B or P

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

2) Sparse Categoriesunmappedspace

Distributional learning model

1) Model distribution of tokens asa 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”.

VOT

3) Each Gaussian has threeparameters:

/b/

VOT

Adult boundary

/p/

Categ

ory M

appi

ngStr

engt

h

unmappedspace/b/

VOT

Adult boundary

/p/

Categ

ory M

appi

ngStr

engt

h

unmappedspace

Computational Model

Statistical Category Learning

1) Start with a set of randomly selected Gaussians.

2) After each input, adjust each parameter to find best description of the input.

3) Start with more Gaussians than necessary--model doesn’t innately know how many categories.

-> 0 for unneeded categories.

VOT VOT

Overgeneralization • large • costly: lose phonetic distinctions…

Undergeneralization• small • not as costly: maintain distinctiveness.

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

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 10 20 30 40 50 60

Starting

P(Su

cces

s)

2 Category Model39,900ModelsRun

3 Category Model

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

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0 2000 4000 6000 8000 10000 12000

Training Epochs

Avg

Spa

rsen

ess C

oeff

icie

nt Starting

VOT

Small

.5-1

Unmapped space

Start with large σ

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0 2000 4000 6000 8000 10000 12000

Training Epochs

Avg

Spa

rsity

Coe

ffic

ient

20-40

Starting

VOT

.5-1

əə

Intermediate starting σ

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0 2000 4000 6000 8000 10000 12000

Training Epochs

Avg

Spa

rsity

Coe

ffic

ient

12-173-11

Starting

VOT

.5-1

20-40

Small or even medium starting ’s lead to sparse category structure during infancy—much of phonetic space is unmapped.

To avoid overgeneralization……better to start with small estimates for

Sparse categories:Similar to exp 2: Retain ambiguity (and partial

representations) until more input is available.

Model Conclusions

to conclusions

Need to integrate fast (dynamical systems) online processing to account for adult data.

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

Anticipatory Eye Movements(McMurray & Aslin, 2005)

Infants are trained to make anticipatory eye movements in response to auditory or visual stimulus.

Post-training, generalization can be assessed with respect to both targets.

bear

pail

AEM Paradigm

QuickTime Demo

Also useful with• Color• Shape• Spatial Frequency• Orientation• Faces

Anticipatory Eye Movements

Train: Bear0: LeftPail35: Right

Test: Bear0 Pear40

Bear5 Pear35

Bear10 Pear30

Bear15 Pear25

Same naturally-produced tokens from Exps 4 & 5.

palm

beach

Experiment 6

Expected results

VOT

Adult boundary

unmapped

space

VOTVOT

Pail

Perf

orm

ance

Bear

Sparse categories

% Correct: 67%9 / 16 Better than chance.Training Tokens {

0

0.25

0.5

0.75

1

0 10 20 30 40

VOT

% C

orre

ct

Beach

Palm

Results

Infants show graded sensitivity to subphonemic detail.

/b/-results: 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.

Infant Summary

Conclusions

Infant and adults sensitive to subphonemic detail.

Sensitivity is important to adult and developing word recognition systems.

1) Short term cue integration.2) Long term phonology learning.

In both cases…Partially ambiguous material is retained until more data arrives.

Partially active representations anticipate likelihood of future material

Conclusions

Spoken language is defined by change.

But the information to cope with it is in the signal—if we look online.

Within-category acoustic variation is signal, not noise.

Within-Category Variation is Used in Spoken Word Recognition

Temporal Integration at Two Time Scales

Bob McMurrayUniversity of Iowa

Dept. of Psychology

Head-Tracker Cam Monitor

IR Head-Tracker Emitters

EyetrackerComputer

SubjectComputer

Computers connected via Ethernet

Head

2 Eye cameras

Lexical Sensitivity

1) Word recognition is systematically sensitive to subphonemic acoustic detail.

0 5 10 15 20 25 30 35 40

VOT (ms)

CategoryBoundary

0

0.02

0.04

0.06

0.08

0.1

Response=BLooks to B

Response=PLooks to B

Com

petit

or F

ixat

ions

Voicing Laterality, Manner, Place Natural Speech Vowel Quality

Metalinguistic Tasks

Lexical Sensitivity

1) Word recognition is systematically sensitive to subphonemic acoustic detail.

0 5 10 15 20 25 30 35 40

VOT (ms)

CategoryBoundary

0

0.02

0.04

0.06

0.08

0.1

Response=BLooks to B

Response=PLooks to B

Com

petit

or F

ixat

ions

Voicing Laterality, Manner, Place Natural Speech Vowel Quality

Metalinguistic Tasks

Misperception: Additional Results

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.

0.000.100.200.300.400.500.600.700.800.901.00

0 5 10 15 20 25 30 35

Barricade

Res

pons

e R

ate

VoicedVoicelessNW

Identification Results

Parricade

0.000.100.200.300.400.500.600.700.800.901.00

0 5 10 15 20 25 30 35

VoicedVoicelessNW

Barakeet Parakeet

Res

pons

e R

ate

Significant target responses even at extreme.

Graded effects of VOT on correct response rate.

“Garden-path” effect:Difference between looks to each target (b

vs. p) at same VOT.

VOT = 0 (/b/)

0

0.2

0.4

0.6

0.8

1

0 500 1000

Time (ms)

Fixa

tions

to T

arge

t

BarricadeParakeet

VOT = 35 (/p/)

0 500 1000 1500

Time (ms)

Phonetic “Garden-Path”

-0.1

-0.05

0

0.05

0.1

0.15

0 5 10 15 20 25 30 35

VOT (ms)

Gar

den-

Path

Eff

ect

( Bar

rica

de -

Para

keet

)

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0 5 10 15 20 25 30 35

VOT (ms)

Gar

den-

Path

Eff

ect

( Bar

rica

de -

Para

keet

)

Target

Competitor

GP Effect:Gradient effect of VOT.

Target: p<.0001Competitor: p<.0001

Assimilation: Additional Results

runm picks

runm takes ***

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/.

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.

Exp 3 & 4: Conclusions

Subject hears“select the mud drinker”“select the mudg gear” “select the mudg drinker

Critical Pair

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0 200 400 600 800 1000 1200 1400 1600 1800 2000Time (ms)

Fixa

tion

Prop

ortio

n

Initial Coronal:Mud Gear

Initial Non-Coronal:Mug Gear

Onset of “gear” Avg. offset of “gear” (402 ms)

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

0

0.1

0.2

0.3

0.4

0.5

0.6

0 200 400 600 800 1000 1200 1400 1600 1800 2000

Time (ms)

Fixa

tion

Prop

ortio

n

Initial Coronal: Mud Drinker

Initial Non-Coronal: Mug Drinker

Onset of “drinker” Avg. offset of “drinker (408 ms)

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

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 200 400 600Time (ms)

Fixa

tion

Prop

ortio

n

AssimilatedNon Assimilated

Onset of “gear”

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

In the same stimuli/experiment there is also a progressive effect!

• Similar properties in terms of starting and sparseness.

VOT

Categories• Competitive Hebbian Learning

(Rumelhart & Zipser, 1986).• Not constrained by a particular

equation—can fill space better.

Non-parametric approach?