1. The lexical/phonetic interface: Evidence for gradient effects of within-category VOT on lexical access
Bob McMurray
Richard N. Aslin
Mickey K. TanenMouse
Bob McMurray
Richard N. Aslin
Michael K. Tanenhaus
With thanks to:
Dana Subik
University of Rochester
Michael J. Spivey
Cornell University

2. Prosody and consonants
We know prosodic domain has a large effect on vowels.
Recent evidence suggests it affects consonants, too.
Strong position in a prosodic domain characterized by:
Longer VOTs
Hyper-articulation (more extreme formant
transitions)
Burst amplitude

3. Prosody and consonants
Sensitivity to this information would:
help listeners recognize consonants in the face of contextual variation.
cue upcoming prosodic effects

4. Speech Perception Lexical Context Spectral vs. Temporal Cues
Speech perception shows probabilistic effects of many information sources:
Lexical Context Spectral vs. Temporal Cues
Visual Information Transition Statistics
Speech Rate Stimulus Naturalness
Sentential Context Compensatory Coarticulation
Embeddings Syllabic Stress
Lexical Stress Phrasal Stress
A system that was sensitive to fine-grained acoustic detail would be much more efficient than one that did not.

5. Discrimination poor within a phonetic category
Categorical Perception
CP suggests listeners are NOT sensitive to these differences. B
100
Discrimination
Discrimination poor within a phonetic category
Sharp identification of speech sounds on a continuum
ID (%/pa/)
% /p/ P B
VOT P

6. Revisiting Categorical Perception?
Some evidence against CP from
Discrimination Tasks (Pisoni and Tash, 1974)
Goodness Ratings (Miller, 1997)
Discrimination Training (Samuel, 1977)
Semantic Priming (Andruski, Blumstein & Burton, 1994)
Very little evidence from ID tasks…
Very little evidence for a gradient response…
Perhaps a more sensitive measure?

7. Experiment 1: Categorical Perception
9-step /ba/ - /pa/ VOT continuum (0-40ms)
Identification indicated by mouse click.
Eye movements monitored at 250 hz.
17 Subjects

8. Experiment 1: Categorical Perception2 B P Ba 3

9. Proportion of /p/ response
Experiment 1: Identification Results
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Category Boundary
17.5 +/- .83ms
Proportion of /p/ response 5 10 15 20 25 30 35 40 B P
VOT (ms)
Steep ID function characteristic of Categorical Perception. Stimuli are good.

10. Experiment 1: Data Analysis
Analyze “competitor” effects:
E.g. Given that
the subject heard /ba/
clicked on “ba”…
How often was the
Subject looking at
“pa”?
Target
(ba)
Fixation proportion
Competitor
(pa)
time

11. Proportion of /p/ response
Experiment 1: Data Analysis
Effective ID Function
Actual ID Function
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Proportion of /p/ response 5 10 15 20 25 30 35 40
VOT (ms)
Trials with low-frequency response excluded.
Effectively yields a “perfect” categorization function.

12. Experiment 1: Eye movement data
VOT=0
Response=B
VOT=40
Response=P
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9 B
Fixation proportion P
P (VOT=15)
B (VOT=20)
More fixations to competitor near the category boundary.
400
800
1200
1600
400
800
1200
1600
2000
Time (ms)
Fixations to competitor even on “endpoint” trials.

13. Experiment 1: Results and Conclusions
Steep slope for mouse response curves.
consistent with categorical perception
Small difference between stimuli near category boundary and others.
Consistent with previous research.

14. Experiment 1: However… We are really interested in lexical activation…
This sort of task purports to measure phoneme (not lexical) activation
2AFC tasks require metalinguistic judgments
What exactly are we measuring???
2AFC metalinguistic tasks may underestimate sensitivity to subphonemic acoustic information

15. Lexical sensitivity to subphonemic variation
Why would lexical sensitivity to subphonemic differences be a good idea?
Extract more information from the signal
Could be used to help resolve temporary phonetic/lexical ambiguities when subsequent information arrives (e.g. vowel length or sentential context)

16. Experiment 2: Lexical Identification
Six 9-step /ba/ - /pa/ VOT continuum (0-40ms)
Bear/Pear Beach/Peach
Butter/Putter Bale/Pale
Bump/Pump Bomb/Palm
12 L- and Sh- Filler items
Leaf Lamp Ladder Lock
Lip Leg Shark Ship
Shirt Shoe Shell Sheep
Identification indicated by mouse click on picture
Eye movements monitored at 250 hz
17 Subjects

17. Experiment 2: Lexical Identification
A moment to view the items

18. Experiment 2: Lexical Identification
500 ms later

19. Experiment 2: Lexical Identification
Bomb

20. Experiment 2: Identification Results
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Exp 2: Words
Word function not as steep.
Exp 1: BP
proportion /p/
BP: /- .83ms
Wordssubject: /-1.33ms
Wordsitem: /- 1.24ms
Boundaries
Category boundaries are the same. 5 10 15 20 25 30 35 40 B
VOT (ms) P

21. Experiment 2: Identification Results
0.1
0.2
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0.4
0.5
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ID Function after filtering
Yields a “perfect” categorization function.
Actual Exp2 Data
Again: Trials with low-frequency response excluded.
proportion /p/ 5 10 15 20 25 30 35 40 B
VOT (ms) P

22. Experiment 2: Eye Movement Results
VOT=0 Response=
VOT=40 Response=
0.1
0.2
0.3
0.4
0.5
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0.7
0.8
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Fixation proportion
400
800
1200
1600
400
800
1200
1600
2000
Time (ms)
More looks to competitor than unrelated items

23. Experiment 2: Data Analysis
Gradient “competitor” effects:
E.g. Given that
the subject heard bomb
clicked on “bomb”…
How often was the
Subject looking at
the “palm”?
Categorical Results
Gradient Effect
target
target
Fixation proportion
Fixation proportion
competitor
competitor
time
time

24. Experiment 2: Gradiency?
Looks to
Looks to
0.02
0.03
0.04
0.05
0.06
0.07
0.08
Andruski et al (schematic)
Gradient Sensitivity
“Categorical” Perception
Fixation proportion 5 10 15 20 25 30 35 40
VOT (ms)

25. Experiment 2: Eye Movement Results
Gradient effects of VOT?
Response=
Response=
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
VOT
VOT
0 ms
20 ms
5 ms
25 ms
10 ms
30 ms
15 ms
35 ms
40 ms
Fixation proportion
400
800
1200
1600
400
800
1200
1600
2000
Time since word onset (ms)
Smaller effect on the amplitude of activation—more effect on the duration:
Competitors stay active longer as VOT approaches the category boundary.

26. Experiment 2: Eye Movement Results
Response=
Response=
0.02
0.03
0.04
0.05
0.06
0.07
0.08
Looks to
Fixation proportion
Looks to
Category
Boundary 5 10 15 20 25 30 35 40
VOT (ms)
B: p=.017* P: p<.0001***
Clear effects of VOT
Linear Trend
B: p=.023* P: p=.002**

27. Experiment 2: Eye Movement Results
Response=
Response=
0.02
0.03
0.04
0.05
0.06
0.07
0.08
Looks to
Fixation proportion
Looks to
Category
Boundary 5 10 15 20 25 30 35 40
VOT (ms)
Unambiguous Stimuli Only
B: p=.017* P: p<.0001***
Clear effects of VOT
Linear Trend
B: p=.023* P: p=.002**

28. Experiment 2: Eye Movement Results
Response=
Response=
0.02
0.03
0.04
0.05
0.06
0.07
0.08
Looks to
Fixation proportion
Looks to
Category
Boundary 5 10 15 20 25 30 35 40
VOT (ms)
Replicates and extends Andruski et al (1994).
They compared stimuli near boundary to distant stimuli.
We demonstrate gradiency in between.

29. Experiment 2: Effect of Time?
How long does the gradient sensitivity to VOT remain?
Need to examine:
the effect of time on competitor fixations
interaction with VOT

30. Experiment 2: Effect of Time?
Analysis:
early
late
Trial 1
Trial 2
Trial 3
Trial 4
Trial 5
Trial 6
Trial 7
Trial 8
Randomly sorted trials into two groups (early and late).
Early
Late
For each group, fixations from
only 1 time-bin were used
Early: ms
Late: ms
Ensures independence of data in each time-bin (since each trial only contributes to one)

31. Experiment 2: Eye Movement Results
Response=
Response=
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
0.11
Early
( ms)
Late
( ms)
Fixation proportion
Looks to
Looks to
Category
Boundary 5 10 15 20 25 30 35 40
VOT (ms)
Main effect of time /b/: p=.001*** /p/: p=.0001****
Main effect of VOT /b/: p=.015* /p/: p=.001***
Linear Trend for VOT /b/: p=.022* /p/: p=.009**
No Interaction p>.1

32. Experiment 2: Eye Movement Results
Response=
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
0.11
Response=
Early
( ms)
Late
( ms)
Fixation proportion
Looks to
Looks to
Category
Boundary 5 10 15 20 25 30 35 40
VOT (ms)
Main effect of time /b/: p=.001*** /p/: p=.0001****
Main effect of VOT /b/: p=.006** /p/: p=.013*
Linear Trend for VOT /b/: p=.0012** /p/: p=.02**
No Interaction p>.1

33. g k Experiment 2: Temporal ambiguity resolution uny
The lexical/phonetic identity of a segment can be determined by acoustic features that arrive after the segment in question. g k
uny
The ambiguous first consonant of
is clearly a /k/ after hearing ”uny”
Thus, like in higher level language comprehension, temporal ambiguity resolution is an important issue.

34. Experiment 2: Temporal ambiguity resolution
Lexical/Phonetic Temporal Ambiguity can be caused by
Vowel length (cue to speaking rate and stress)
Lexical/Statistical effects
Embedded words
Subphonemic sensitivity can minimize or eliminate the effects of temporary phonetic ambiguity by
Storing how ambiguous a segment is
Keeping competitors active until resolution occurs.

35. Results and Conclusions
Slope of identification curve varies as a function of task—classic 2AFC phoneme ID judgments underestimate subphonemic sensitivity.
Subphonemic acoustic differences in VOT affect lexical activation.
Gradient effect of VOT on looks to the competitor
Effect holds even for unambiguous stimuli.
Effect is long-lasting.
VOT affects duration of activation, not amplitude.
Much smaller effect in non-lexical tasks (BP)

36. Results and Conclusions
Subphonemic effects on lexical activation seem consistent with a probabilistic parallel processing mechanism.
Gradient sensitivity to VOT could be used for
Early detection of prosodic domain
Resolving temporal phonetic ambiguities

37. Subphonemic variation in VOT is
Results and Conclusions
Lexical activation exhibits gradient effects of subphonemic (VOT) variation.
Subphonemic variation in VOT is
not discarded
It is not but signal.

38. The lexical/phonetic interface: Evidence for gradient effects of within-category VOT on lexical access
Bob McMurray
Michael K. Tanenhaus
Richard N. Aslin
University of Rochester
With thanks to:
Dana Subik
Michael J. Spivey
Cornell University

39. Experiment 1: Eye movement data
Experiment 1 does show hints of gradiency
400
800
1200
1600
0.05
0.1
0.15
0.2
0.25
0.3
400
800
1200
1600
2000
VOT
VOT
0 ms
20 ms
5 ms
25 ms
10 ms
30 ms
Fixation proportion
15 ms
35 ms
40 ms
Time (ms)
Very small
Difference between stimuli near boundary and endpoints.
Maybe something gradient for /pa/.

40. Experiment 1: A BaPa Reprise
Hints of gradiency:
/b/: p =.044 *
/p/: p<.001 ***
Could be driven by big differences near category boundary.
(Consistent with Andruski et al)
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
Response=B
Looks to P
Response=B
Looks to P
Fixation proportion
Category
Boundary 5 10 15 20 25 30 35 40
VOT (ms)

41. Experiment 1: Eye movement data
Remove items near boundary from analysis
0.01
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0.03
0.04
0.05
0.06
0.07
0.08
/b/: p =.884
/p/: p<.003
No effect for /ba/
Small effect for /pa/.
Response=B
Looks to P
Response=B
Looks to P
Fixation proportion
Category
Boundary 5 10 15 20 25 30 35 40
VOT (ms)