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Correlating Consonant Confusability and Neural Responses:
An MEG Study
Valentine Hacquard1
Mary Ann Walter1,2
1Department of Linguistics and Philosophy, KIT-MIT MEG Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA, 2National
Science Foundation Fellowship
Nasal Confusability
• Numerous behavioral studies have investigated error rates in identification of phonemes masked with noise.
• These show that nasal consonants are more
confusable with each other than oral consonants; e.g. m/n vs b/d (Miller & Nicely 1955, Wang & Bilger 1973).
Consequences
• Such perceptibility asymmetries motivate much current work in phonology and (it’s claimed) phonological processes (Hume 1998, Steriade 1999).
• Unsurprisingly, given this stance, Mohanan (1993) finds that nasals are particularly susceptible to place assimilation.
An Acoustic Model
These confusability and assimilation facts, as well as studies on offline similarity judgments (Hura et al. 1992), support a model of similarity based on acoustic, perceptual factors.
context-dependent, but inventory-independent
Although nasality itself is highly salient, persevering nasality masks the F2 transition into the following vowel, which is the primary cue for place of articulation.
Another Proposal
Frisch et al. (1997) and Frisch (1996) propose a metric in which similarity is computed according to natural classes:
Similarity = shared natural classes shared + unshared natural classes
context-independent, but inventory-dependent
English: b/d (= .29) > m/n (= .28), where 1 is identity.So, nasals equally or less similar to each other than orals.
The Question
• Does a difference in similarity correlate with a difference in an auditory brain response?
• If so, brain data can be used to substantiate proposals for similarity metrics, as well as their internal organization.
MAGNETOENCEPHALOGRAPHY (MEG)
MEG measures the magnetic fields (B) generated by electrical activity in the brain: specifically, by potentials in the apical dendrites of pyramidal cells in cortex
To input Coil of SQUID
cortex
Scalp
10 -4
10 -5
10 -6
10 -7
10 -8
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10 -10
10 -11
10 -12
10 -13
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10 -15
Earth field
Urban noise
Contamination at lung
Heart ( QRS )
MuscleFetal heart
Spontaneous signal (-wave)
Signal from retina
Intrinsic noise of SQUID
Inte
nsity
of
mag
netic
si
gnal
(T)
Evoked signal
SCALE
The magnetic field of the brain, recorded with MEG, is 100 billion times weaker than the earth’s magnetic field!
MMF: mismatch field: automatic, auditory brain response evoked by a deviant stimulus following a sequence of standards, peaking ~180-250 ms post-stimulus onset.
MMF
M100
M100: automatic auditory evoked response that peaks
~100 ms post-stimulus onset
Origin of signal in auditory cortex
MMF LOCALIZATION
Note left-hemisphere concentration of the mismatch field with linguistic stimuli.
Properties of the MMFSharma & Dorman (1999) and Phillips et al. (2000) show that the same VOT span crossing a phonemic category boundary evokes a far greater MMF than one that doesn’t.
Näätänen et al. (1997) show that a small acoustic difference crossing a phonemic category boundary evokes a far greater MMF than a large one that doesn’t.
Phonological difference outweighs acoustic difference.
Do similarity distinctions matter when category is kept constant?
10 ms VOT spanwithin category
10 ms VOT spanacross category
MMF
A M P L I T U D E
50 HZ F2 spanwithin category
10 HZ F2 spanacross category
Procedure
Oddball paradigm: Conditions (8x30=240)
1) ba (400 ms) ba (400 ms) ba (400 ms) ba (400 ms) da deviant
2) da (400 ms) da (400 ms) da (400 ms) da (400 ms) da standard
3) da (400 ms) da (400 ms) da (400 ms) da (400 ms) ba deviant
4) ba (400 ms) ba (400 ms) ba (400 ms) ba (400 ms) ba standard
5) ma (400 ms) ma (400 ms) ma (400 ms) ma (400 ms) na deviant
6) na (400 ms) na (400 ms) na (400 ms) na (400 ms) na standard
7) na (400 ms) na (400 ms) na (400 ms) na (400 ms) ma deviant
8) ma (400 ms) ma (400 ms) ma (400 ms) ma (400 ms) ma standard
Subjects (n=16) made same-different button-press judgments.
Synthesized stimuli (4 tokens) were presented in six blocks of 40 trials, randomly ordered, with self-regulated breaks in between.
Predictions
• According to an acoustic-based similarity framework, the MMF-baseline gap should be larger for oral consonant pairs than for nasals.
• According to a natural-class-based one such as Frisch’s, it should be the opposite, or equivalent.
• If abstract phonological features are the only relevant factor in perceptibility at this stage, the gaps should be equivalent.
Behavioral Results – Error Rate
Error Rates
0
1
2
3
4
nasal oral
deviant
s tandard
• Deviants overall received significantly more errors than standards (p=.0009).
• No effect for manner was observed (p=.3538).
Cf. lack of masking noise or filtering, comparatively small number of trials.
Behavioral Results – Reaction Time Reaction Time
750
800
850
900
950
nasal oral
deviant
standard
Reaction times: Nasal vs. Orals
830840850860870880890
nasal oral
• RT to deviants overall was significantly faster than to standards (p<0001).
(Some subjects reported a waiting strategy in standard trials.)
• RT to nasals was significantly faster than to orals (p=.0197).
Orals: Deviants vs Standards
00.
010.
02
-50 0 50 100 150 200 250 300
oral dvts
oralstds
Nasals: Dev iants vs Standards
00.0
10.0
2
-50 0 50 100 150 200 250 300
nasaldvtsnasalstds
MMF AMPLITUDE
500
600
700
800
900
1000
1100
nasal oral
standard
deviant
Deviants have significantly greater amplitude in MMF window than standards (p<.0001).
Orals have significantly greater amplitude in MMF window than nasals (p<.0001).
But nasals have significantly greater M100 amplitude than orals (p<.0001).
MMF Results
selected sensors RMS waves
MMF Comparison-0
.01
00
.01
0.0
2
-50 0 50 100 150 200 250 300
nasdv-s t
oradv-s t
The MMF/baseline gap is significantly greater for oral consonant pairs than for nasals (p=.0399).
single subject
Conclusions• Oral mismatches elicit a stronger MMF than nasal
mismatches.• So oral consonants are perceived as more different
from each other than nasal ones at this stage.• Phonological categories are not the only relevant
factors in perception at this stage: acoustic similarity also plays a role.
• Finally, it is an acoustic-based similarity metric that appears to be operating at this time period, rather than a natural-class one, or feature-counting.
For Future Research
• Our results suggest that the MMF can be used to test proposals about degree of perceptual distance in acoustic-based similarity frameworks.
• Our next study will isolate Frisch-style similarity as a variable, testing the same phonological contrast with speakers of languages that have the contrast, but whose inventories differ in other ways.