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Research Article
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Language-Related Brain Potential Maps for Semantic Perception of Cluster Consonants in Consonant-Vowel Syllables
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Wichian Sittiprapaporn
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ABSTRACT
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Mismatch Negativity (MMN) was used to investigate the processing
of cluster and noncluster initial consonants in consonant-vowel syllables in
the human brain. The MMN was elicited by either syllable with cluster or noncluster
initial consonant, phonetic contrasts being identical in both syllables. Compared
to the noncluster consonant, the cluster consonant elicited a more prominent
MMN. The strong MMN peaks at ~128 msec after change onset in cluster-to-noncluster
initial consonants changes and at ~212 msec in noncluster-to-cluster initial
consonants changes. The significantly different neuronal populations were thus
active between 128-212 msec when syllables with cluster and noncluster initial
consonants were present. Microstate segmentation analyses showed that the phonological
perception for cluster consonant was at 212 msec whereas 128 msec for non-cluster
consonant. After approximately 220 msec, semantic perception started in order
to perceive the meaning of the words.
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Received:
September 13, 2012; Accepted: September 18, 2012;
Published: December 27, 2012 |
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INTRODUCTION
Mismatch Negativity (MMN) is an ERP component elicited by rare deviant stimuli
within a sequence of repetitive auditory stimuli. The MMN component appears
as a frontocentrally negative wave usually peaking between 100 and 300 msec
after the onset of stimulus deviation (Naatanen et al.,
1978). The MMN/MMF component, reflective neuronal correlates of change detection
and sound discrimination (Naatanen, 2001), is enhanced
by acoustic deviances of duration, frequency, or intensity in speech and non-speech
(Naatanen et al., 1978). Previous studies have
shown that the MMN amplitude is enhanced when the acoustic discrepancy between
the stimuli is increased (Naatanen et al., 1978;
Jaramillo et al., 2000). Parallel behavioral
and MMN studies have shown that MMN amplitude correlates with the accuracy of
perceptual discrimination (Lang et al., 1990;
Naatanen et al., 1993) thus, MMN provides an
objective method for measuring the accuracy of auditory processing (Nenonen
et al., 2003).
It is well-known that auditory signals can be differentiated by a variety of
factors including temporal information. It is also important to recognize that
languages differ in the way they exploit temporal cues (Gandour
et al., 2002). Standard Thai, the official language of Thailand,
exhibits a phonological contrast in consonant. Perceptually, duration has been
shown to be the primary cue in signaling the contrast between cluster and noncluster
consonant phonemes (Sittiprapaporn et al., 2006).
The phonemic consonant is sometime not predictable from context but can change
the meaning of a word (e.g., /kaang/ spread or make wider vs. /klaang/
middle).
Thai consonants are classified into three classes-namely, high, middle and
low consonants-which can affect the syllable tone when functioning as initial
sound. The Thai sound system is best described in relationship to the syllable,
the tone-bearing unit. A Thai syllable has the maximum shape of C(C)V(V)(C)+Tone.
There are twenty consonants in syllable-initial position. Among these, the initial
cluster consonants include the labials -pr, pl, phr and phl; the alveolars -tr,
thr and the velars - kr, kl, khr, khl, khw. Cluster simplification (kl>k,
for example) is often a fixed feature in spoken communication. In the present
study, preattentive brain processes during the discrimination of cluster and
noncluster initial consonants in consonant-vowel syllables was compared. A single
pair of consonant-vowel syllables with cluster and noncluster initial consonants
were selected to represent ideal exemplars. In spoken communication, the consonant-vowel
syllable with cluster initial consonant is usually pronounced as a simplification
of the cluster initial consonant. This study chose to record and compared the
MMN elicited by the consonant-vowel syllables with cluster and noncluster initial
consonants, hoping to find evidence for specific brain signatures of cluster
and noncluster initial consonant processing. Additionally, the ERP Microstate
Segmentation Analysis technique was used to locate the pint where semantic perception
started in order to perceive the meaning of the words.
MATERIALS AND METHODS
Subjects: Ten healthy right-handed (Handedness assessed according to
Oldfield (1971) native speakers of Thai (7 females;
aged 18-35 years) with normal hearing sensitivity gave their written informed
consent before participation in the study. The mean (±SD) age was 24.35
(±4.95) years. All subjects were adults with normal hearing and no known
neurological disorders. The approval of the institutional committee on human
research and written consent from each subject were obtained.
Stimuli: Stimuli consisted of two monosyllabic Thai words. All stimuli
were spoken by native female Thai speaker and digitally generated and edited
to have equal peak energy level in decibels SPL with the remaining data within
each of the stimuli scaled accordingly using the Cool Edit Pro v. 2.0 (Syntrillium
Software Corporation) with 500 msec duration. The sounds were presented binaurally
via headphones (Telephonic TDH-39-P) at 85 dB (determined using a Brüel
and Kjaer 2230 sound level meter). Five Native speakers listened to the synthesized
words and evaluated them all as natural sounding. Two different stimuli were
synthetically generated as follows:
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Stimulus 1: Monosyllabic with cluster initial consonant
/kl-/as/klaang/ middle -cluster consonant, level tone |
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Stimulus 2: Monosyllabic with noncluster initial consonant /k-/as
in: /kaang/ spread or make wider-noncluster consonant, level
tones |
The standard (S)/deviant (D) pairs for each experiment which was randomized
across subjects, were shown:
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Experiment 1: Standard (1)-Deviant (2): (Stimulus 1:
/klang/-cluster consonant, level tone)-(Stimulus 2: /kang/-noncluster consonant,
level tone) |
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Experiment 2: Standard (2), Deviant (1): (Stimulus 2: /kang/-noncluster
consonant, level tone)-(Stimulus 1: /klang/-cluster consonant, level tone) |
All The standard (S)/deviant (D) pairs for each condition were:
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Condition 1: Cluster-to-noncluster change: S-(2), D-(1) |
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Condition 2: Noncluster-to-cluster change: S-(1), D-(2) |
Thus, in both conditions pairs were designed to contrast noncluster and cluster
initial consonants. Deviant stimuli appeared randomly among the standards at
10% probability. The stimuli were binaurally delivered using SuperLab software
(Cedrus Corporation, San Pedro, USA) via headphones (Telephonic TDH-39-P) at
85 dB. The inter-stimulus interval (ISI) was 1.25 sec (offset-onset). EEG signal
recording was time-locked to the onset of a word. Subjects were instructed not
to pay attention to the stimuli presented via headphones but rather to concentrate
on a self-selected silent, subtitled movie. Afterwards, they reported the impression
of the movie. The experiment lasted 1-2 h, including breaks.
Procedures: Subjects were seated in an electrically and acoustically
shielded chamber, instructed to focus their attention on reading books of their
own choices and to ignore any auditory signals. During the auditory stimulation,
electric activity of the subjects
brain was continuously recorded with 21 active electrodes positioned according
to the International 10/20 System of Electro-cap and referred to linked mastoids.
Electroencephalographic (EEG) recording: The Electroencephalogram (EEG)
was recorded in a sound-attenuated and electrically shielded room with a Biologic
Brain Atlas III system and amplifier using a sampling rate of 128 Hz. During
the auditory stimulation, electric activity of each subjects
brain was continuously recorded with 21 active electrodes (Fp1/2, F3/4, C3/4,
O1/2, F7/8, T3/4, T5/6, P3/4, Fpz, Fz, Cz, Pz and Oz) positioned according to
the International 10/20 System of Electro-cap and referred to linked mastoids.
All 21 recording channels used for Microstate segmentation. A biologic Brain
Atlas system amplified (bandpass 0.01-100 Hz), analog-digital converted (128
samples/s/channel) and stored the data. Epochs of-100-924 msec from stimulus
onset were averaged and digitally filtered (bandpass 1-30 Hz). Epochs contaminated
by artifacts exceeding±100 μV at any electrode as well as 10 standards
after each deviant were rejected.
EEG data processing: The recordings were filtered and carefully inspected
for eye movement and muscle artifacts. Event-Related Potentials (ERPs) were
obtained by averaging epoch which started 100 msec before the stimulus onset
and ended 900 msec thereafter; the-100-0 msec interval was used as a baseline.
Epochs with voltage variation exceeding ±100 μV at any EEG channel
were rejected from further analysis. Grand-averaged difference waveforms were
calculated by subtracting the S from the D wavefoms. For each condition, presence
of a prominent MMN was identified by measuring the integrated power amplitudes
over the 40-msec time window centered on the MMN peak in the difference waveform.
An MMN component was judged prominent if the amplitude difference between S
and D within predefined the window was statistically significant. For each subject,
the averaged MMN responses contained 125 accepted deviants.
Spatial analysis: The average MMN latency was defined as a moment of
the Global Field Power (GFP) with an epoch of 40 msec time window related stable
scalp-potential topography (Lehmann, 1987). The individual
momentary potential measures from 21 electrodes at the MMN latency were analyzed
with Microstate Segmentation technique to determine the MMN generator. In addition,
the comparison between the microstate segmentation of cluster-and-noncluster
consonants were analyzed using ERP Microstate Segmentation Analysis techniques
(Koenig and Lehmann, 1996; Koenig
et al., 1999, 2002).
Statistical analysis: During the auditory stimulation, electric activity
of the subjects brain was continuously
recorded. The MMN was obtained by subtracting the response to the standard from
that to the deviant stimulus. The statistical significance of MMN was tested
with one sample t-test. An across-experiment ANOVA was carried out so as to
make cross-linguistic comparisons. The statistical significance of MMN was tested
with paired-sample t-tests between the MMN amplitude of consonant-vowel syllables
with noncluster and cluster initial consonants. This was done by comparing the
mean MMN amplitude against a hypothetical zero at the frontal (Fz) electrode
site, where the MMN is most prominent. The MMN latency values were also compared.
RESULTS
The results of the grand-mean difference waveform analysis demonstrated that
significantly different neuronal populations were active between 128-212 msec
when syllables with cluster and noncluster initial consonants were present.
Both cluster and noncluster initial consonants elicited MMN with reference to
the standard-stimulus ERPs. The MMN mean amplitude was statistically significant
( t-test) for both cluster-and noncluster initial consonants changes.
Table 1: |
MMN mean amplitude, standard deviations and t-values for the
different deviant stimuli used |
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The paired-sample t-test (Table 1) revealed a significant
difference between conditions (t (10) = 73.00; p<0.0001) showing that both
cluster and noncluster initial consonants changes in consonant-vowel syllables
equally elicited a MMN.
The results of the grand-mean difference waveform analysis demonstrated that
the MMN latency for the cluster and noncluster initial consonant differences
was significantly longer in the syllable with noncluster-to-cluster initial
consonants changes than in the cluster-to-noncluster initial consonants changes.
The strong MMN peaks at ~128 msec after change onset in cluster-to-noncluster
initial consonants changes and at ~212 msec in noncluster-to-cluster initial
consonants changes. The significantly different neuronal populations were thus
active between 128-212 msec when syllables with cluster and noncluster initial
consonants were present. The comparison between the microstate segmentation
of cluster-and-noncluster consonants were analyzed using ERP Microstate Segmentation
Analysis techniques (Koenig and Lehmann, 1996; Koenig
et al., 1999, 2002). Microstate segmentation
analyses showed that the phonological perception for cluster consonant was at
212 msec whereas 128 msec for non-cluster consonant. After approximately 220
msec, semantic perception started in order to perceive the meaning of the words
(Fig. 1).
DISCUSSION
The main finding of the present study indicates that the prominent response
to consonant-vowel syllables with cluster and noncluster initial consonant changes
elicited MMN peaking at 128-212 msec from stimulus onset. The magnitude of the
acoustic difference between the stimulus pairs was reflected by the MMN amplitude,
showing larger MMN amplitudes in consonant-vowel syllable deviants with cluster
initial consonants compared to the noncluster consonant. Microstate segmentation
analyses showed that the phonological perception for cluster consonant was at
212 msec whereas 128 msec for non-cluster consonant. After approximately 220
msec, semantic perception started in order to perceive the meaning of the words.
The difference in MMN latencies to /kaang/ and /klaang/ may reflect differential
processing of syllables with physical differences in their initial consonants.
The delay in the MMN to the cluster initial consonant of deviant stimulus i.e.,
/kl-/ as in /klaang/, may reflect additional time required to process the syllable.
This processing apparently involves activation of a memory trace, or cell assembly
which possibly represents and the processes the initial consonant in the syllable
(Sittiprapaporn et al., 2006).
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Fig. 1(a-c): |
(a) The comparison between the microstate segmentation of
cluster-and-noncluster consonants. Phonological perception for (b) cluster
consonant is at 212 msec and for (c) non-cluster consonant is at 128 msec,
After approximately 220 msec, semantic perception started in order to perceive
the meaning of the words (arrow) |
The tuned processing of initial consonant may be caused by the different roles
of consonant phonemes in the subjects native languages. This implies that
even if one has two almost closely related phonemes, i.e., cluster and noncluster
consonant, fine tuning in the processing of syllable may be inhibited at the
pre-attentive level (Sittiprapaporn et al., 2006).
As it is well established that the MMN amplitude indexes the accuracy of change
detection (Naatanen et al., 1978), the larger
MMN amplitude to the speech sound change in the present study suggests more
accurate sound change detection in syllables with cluster rather than with noncluster
initial consonants. The electric MMN responses differed significantly between
syllables with either cluster or noncluster initial consonant. Importantly,
there was significant difference between exemplar syllables with cluster and
noncluster initial consonants, implying that the basic ability to detect speech
sound changes in general is on average different in the two initial consonant
phonemes.
The present study found an earlier MMN for the short noncluster consonant stimulus
and a delayed MMN for the cluster consonant, as well as differential topography
of the two responses (Fig. 1). The difference in MMN latencies
to the two stimuli may reflect differential processing of the syllables. Thus,
the delay in the MMN to syllable with cluster initial consonant deviant stimulus
may reflect additional time required to process the syllable. Because the MMN
is known to depend primarily on the magnitude of stimulus contrast (rather than
on its direction) (Shtyrov and Pulvermuller, 2002).
The acoustic difference per se between syllables with the cluster and
noncluster initial consonants is therefore, unlikely to have confounded the
present results. The present results parallel the findings in previous studies
(Inouchi et al., 2002, 2003;
Sittiprapaporn et al., 2005) demonstrating that
the detection of speech sound changes is most likely acoustically driven rather
than semantically driven, such that the stimuli were processed without any access
to semantic information. The acoustic aspect in the absence of phonetic or higher-order
properties may account for why syllable with cluster consonant had similar neuronal
responses to noncluster one. The present finding is, thus, in accord with a
previous experiment that reported a clear MMN elicited by both increments and
decrements of speech sound duration (Naatanen et al.,
1989) but a larger MMN elicited by increments than decrements (Jaramillo
et al., 1990).
CONCLUSION
The MMN component is more sensitive to consonant-vowel syllables with cluster
initial consonant rather than noncluster consonant. After approximately 220
msec, semantic perception might start in order to perceive the meaning of the
words. Automatic detection of changes in cluster initial consonant-vowel syllable
may be a useful index of auditory memory traces of word.
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