Group Title: BMC Neuroscience
Title: Thai lexical tone perception in native speakers of Thai, English and Mandarin Chinese: An event-related potentials training study
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Title: Thai lexical tone perception in native speakers of Thai, English and Mandarin Chinese: An event-related potentials training study
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Language: English
Creator: Kaan, Edith
Barkley, Christopher
Bao, Mingzhen
Wayland, Ratree
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Publication Date: 2008
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Abstract: BACKGROUND:Tone languages such as Thai and Mandarin Chinese use differences in fundamental frequency (F0, pitch) to distinguish lexical meaning. Previous behavioral studies have shown that native speakers of a non-tone language have difficulty discriminating among tone contrasts and are sensitive to different F0 dimensions than speakers of a tone language. The aim of the present ERP study was to investigate the effect of language background and training on the non-attentive processing of lexical tones. EEG was recorded from 12 adult native speakers of Mandarin Chinese, 12 native speakers of American English, and 11 Thai speakers while they were watching a movie and were presented with multiple tokens of low-falling, mid-level and high-rising Thai lexical tones. High-rising or low-falling tokens were presented as deviants among mid-level standard tokens, and vice versa. EEG data and data from a behavioral discrimination task were collected before and after a two-day perceptual categorization training task.RESULTS:Behavioral discrimination improved after training in both the Chinese and the English groups. Low-falling tone deviants versus standards elicited a mismatch negativity (MMN) in all language groups. Before, but not after training, the English speakers showed a larger MMN compared to the Chinese, even though English speakers performed worst in the behavioral tasks. The MMN was followed by a late negativity, which became smaller with improved discrimination. The High-rising deviants versus standards elicited a late negativity, which was left-lateralized only in the English and Chinese groups.CONCLUSION:Results showed that native speakers of English, Chinese and Thai recruited largely similar mechanisms when non-attentively processing Thai lexical tones. However, native Thai speakers differed from the Chinese and English speakers with respect to the processing of late F0 contour differences (high-rising versus mid-level tones). In addition, native speakers of a non-tone language (English) were initially more sensitive to F0 onset differences (low-falling versus mid-level contrast), which was suppressed as a result of training. This result converges with results from previous behavioral studies and supports the view that attentive as well as non-attentive processing of F0 contrasts is affected by language background, but is malleable even in adult learners.
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Research article

Thai lexical tone perception in native speakers of Thai, English and
Mandarin Chinese: An event-related potentials training study
Edith Kaan* 1, Christopher M Barkley2, Mingzhen Bao1 and Ratree Wayland'


Address: 'Linguistics, University of Florida, Box 115454, Gainesville, FL 32611, USA and 2Department of Linguistics, University of California at
San Diego, 9500 Gillman Drive #108, La Jolla, CA 92093, USA
Email: Edith Kaan* kaan@ufl.edu; Christopher M Barkley cbarkley@ling.ucsd.edu; Mingzhen Bao joanneb@ufl.edu;
Ratree Wayland ratree@ufl.edu
* Corresponding author



Published: 23 June 2008 Received: 6 November 2007
BMC Neuroscience 2008, 9:53 doi: 10.1 186/1471-2202-9-53 Accepted: 23 June 2008
This article is available from: http://www.biomedcentral.com/1471-2202/9/53
2008 Kaan et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.



Abstract
Background: Tone languages such as Thai and Mandarin Chinese use differences in fundamental
frequency (Fo, pitch) to distinguish lexical meaning. Previous behavioral studies have shown that
native speakers of a non-tone language have difficulty discriminating among tone contrasts and are
sensitive to different F0 dimensions than speakers of a tone language. The aim of the present ERP
study was to investigate the effect of language background and training on the non-attentive
processing of lexical tones. EEG was recorded from 12 adult native speakers of Mandarin Chinese,
12 native speakers of American English, and I I Thai speakers while they were watching a movie
and were presented with multiple tokens of low-falling, mid-level and high-rising Thai lexical tones.
High-rising or low-falling tokens were presented as deviants among mid-level standard tokens, and
vice versa. EEG data and data from a behavioral discrimination task were collected before and after
a two-day perceptual categorization training task.
Results: Behavioral discrimination improved after training in both the Chinese and the English
groups. Low-falling tone deviants versus standards elicited a mismatch negativity (MMN) in all
language groups. Before, but not after training, the English speakers showed a larger MMN
compared to the Chinese, even though English speakers performed worst in the behavioral tasks.
The MMN was followed by a late negativity, which became smaller with improved discrimination.
The High-rising deviants versus standards elicited a late negativity, which was left-lateralized only
in the English and Chinese groups.
Conclusion: Results showed that native speakers of English, Chinese and Thai recruited largely
similar mechanisms when non-attentively processing Thai lexical tones. However, native Thai
speakers differed from the Chinese and English speakers with respect to the processing of late F0
contour differences (high-rising versus mid-level tones). In addition, native speakers of a non-tone
language (English) were initially more sensitive to F0 onset differences (low-falling versus mid-level
contrast), which was suppressed as a result of training. This result converges with results from
previous behavioral studies and supports the view that attentive as well as non-attentive processing
of F0 contrasts is affected by language background, but is malleable even in adult learners.





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Background
Variation invoice pitch, an auditory impression of the rate
of vocal fold vibration (F0), plays a different linguistic
function in tone and non-tone languages. Tone languages,
such as Thai and Mandarin Chinese, use differences in
either average F0 or F0 contours (or slopes) over strings of
otherwise identical phonemes to distinguish between dif-
ferent words in the lexicon from one another. For
instance, the Thai syllable [kha:] means something com-
pletely different when pronounced with a tone that is low-
falling ("galangal root"), low-falling and then rising
("leg"), high-falling ("I, servant"), high-rising ("to do
business in") or mid-level ("to be lodged in"). In non-
tone languages such as English, on the other hand, pitch
variation is not used to differentiate word meaning. How-
ever, even though F0 is not used to distinguish meaning
between words in English, it can make one syllable more
perceptually prominent or more salient than neighboring
syllables in multi-syllabic words. For example, the first
syllable of the word 'cookie' is stressed, and perceptually
more salient than the second syllable. The F0 or pitch (as
well as intensity or loudness and vowel duration) of the
stressed syllable is typically higher than its neighboring
unstressed syllable. In addition, lexical stress can also be
used to distinguish a compound word 'a hotdog' from a
noun phrase 'a hot dog'. Variation in the linguistic func-
tions of F0 may account for perceptual difficulty typically
experienced among adult native speakers of a non-tone
language when consciously perceiving and distinguishing
among lexical tones differing in pitch level or pitch con-
tours. The aim of the present ERP study was to investigate
whether the processing of lexical tones is affected by the
listener's native language (tone or non-tone) even when
the participants are not paying conscious attention to the
stimuli, and whether such non-attentive perception can
be altered by laboratory training, even in adults.

Previous behavioral studies have shown that native speak-
ers of a non-tone language (e.g. English) poorly discrimi-
nate among lexical tones as compared with native
speakers of a tone language (e.g. Mandarin Chinese), even
when the latter are unfamiliar with the tones being tested
[1-5]. This perceptual difficulty for speakers of non-tone
languages is due in part to differences in the way lexical
tones are processed among native and nonnative listeners
of tone languages. Native speakers of a non-tone language
have been shown to focus more on the average F0, and F0
offset or onset values, whereas speakers of a tone language
focus more on F0 contour [6-8]. Interestingly, previous
behavioral studies have also shown that adult native
speakers of a non-tone language may improve in their per-
ception of lexical tones after exposure to the tones either
in a natural or classroom setting, or during laboratory
training [3,4,9,10]. Training also affects the brain areas
involved in lexical tone processing. fMRI studies compar-


ing brain activation during lexical tone perception after
versus before training showed an increase in activation in
the left posterior superior gyrus [ 11,12]. In addition, right
hemisphere activation was observed [12], especially in
poor learners [11]. This suggests that the perceptual and
neural systems involved in processing differences in pitch
and pitch contours are still malleable, even in adulthood.

The discrimination or identification tasks used in the
behavioral and fMRI studies on lexical tone perception
involve conscious comparison or categorization. Perform-
ance in these experiments may therefore have been
affected by factors such as working memory load or atten-
tion. In the present study we therefore studied the non-
attentive discrimination of lexical tones and the effect of
language background and training by using Event-Related
brain Potentials (ERPs). ERPs can be recorded while the
participant is presented with auditory stimuli, but
engaged in an unrelated task such as watching a movie.
The mismatch negativity (MMN) is a frontal negative ERP
component occurring about 100-300 ms after stimulus
onset. It is elicited by infrequent stimuli that deviate from
frequently presented (standard) stimuli in pitch, dura-
tion, voice onset time, or other acoustic or phonetic prop-
erties [13]. Since this component is elicited even while
people are asleep or in a coma, this component is
regarded as an index of automatic processing of auditory
differences, that is, processing that does not require volun-
tary attention. The MMN has been shown to increase in
amplitude and, in some cases, to have a shorter peak
latency as behavioral discrimination performance
improves. In addition, changes in the MMN have been
attested before changes in behavioral discrimination per-
formance [14]. The MMN is therefore a useful tool to
study the processing and acquisition of non-native lan-
guage contrasts [14-20]. Since this technique taps into a
different level of processing, and does not require overt
attention and active comparison by the participant, this
method may help us further tease apart the aspects of the
stimuli that different language groups are differentially
sensitive to at a non-attentive level of processing.

Only a few studies have employed the MMN to investigate
the processing of lexical tones. Chandrasekaran et al. [21]
investigated the effect of language background on lexical
tone perception. Both Mandarin Chinese and untrained
English speakers showed a MMN to tone contrasts in
Mandarin Chinese. However, only the Chinese partici-
pants showed a larger MMN to a distinction that was
acoustically more salient, suggesting that language back-
ground affects non-attentive processing of lexical tones to
some extent. To investigate the effect of both training and
language background, Kaan et al. [22] recorded ERPs from
native speakers of English, Mandarin Chinese and Thai
while they were presented with three Thai tones in an


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oddball paradigm. ERPs showed no differences between
the groups before training. After a two-day perceptual
training on the mid-level and low-falling tone, the English
showed an increase in MMN amplitude to untrained high-
rising deviants, whereas the Chinese showed a decrease in
a later negativity in that condition. This suggested that
native speakers of tone and non-tone languages were sen-
sitive to different aspects of the stimuli as a result of train-
ing. However, no effect of training was observed on the
(trained) low-falling tone deviant, to which all groups
showed a large MMN before and after training. In addi-
tion, behavioral performance at the start of training was
close to ceiling for all three subject groups. The differences
found in the ERPs may therefore have not been indicative
of improved perception of the tones. The ceiling perform-
ance may have been due to the use of only one token per
tone condition, which did not encourage abstraction of
contour categories. In the present experiment we therefore
used multiple tokens of three Thai tones, all generated
from one naturally produced token (see Methods and Fig-
ure 1).

Three subject groups (Thai, Mandarin Chinese and Eng-
lish speakers) were tested in an ERP oddball task in which
they were presented with the stimuli while watching a
silent movie. Although this task does not prevent partici-
pant from occasionally paying attention to the stimuli,


the auditory stimuli are not task-relevant and do not
require voluntary attention, in contrast to overt behavio-
ral tasks. High-rising or low-falling tokens were presented
as deviants among mid-level standard tokens, and vice
versa. In addition a behavioral same/different discrimina-
tion task was conducted on the same stimuli. Both the
behavioral discrimination and the ERP oddball task were
conducted before and after a two-day perceptual categori-
zation training task. We were particularly interested in see-
ing how the MMN and the later negativity for deviant
versus standard stimuli would be affected by language
background, training and the degree of behavioral
improvement as a result of training. As one can see in Fig-
ure 1, the three tone categories differed from each other
with respect to their F0 onset values, the steep F0 slope
right after the F0 onset, as well as with respect to a later,
more gradually developing F0 slope. Given that speakers
of a non-tone language (English) have been shown to be
sensitive to F0 onset and offset differences, whereas native
speakers of a tone language are more sensitive to the later
F0 contour, we expected the native English speakers to ini-
tially show a larger MMN than the native Chinese and
Thai speakers. The Chinese and Thai speakers, on the
other hand, were expected to show a more pronounced
later negative effect, which may be related to the later con-
tour differences [22]. As the native English speakers
become more sensitive to the contour differences, we


240

220

200

180
N
I 160

140

120

100


210


260


310


360


410


time (msec)


Figure I
Pitch tracks. Pitch tracks of the three high-rising (red lines), mid-level (black lines) and low-falling (blue lines) tokens.



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expected them to pattern more with the Thai and Chinese
after training. Moreover, since the stimuli were meaning-
ful words to Thai speakers, but not to Chinese and English
speakers, we expected some differences related to the lin-
guistic status of the stimuli. Linguistically perceived stim-
uli have been shown to involve the left hemisphere more
than the right [3,18,23-26], but see [27,28]. The Thai were
therefore expected to differ from the English and the Chi-
nese participants in terms of the lateralization of the
MMN and late negativity, at least, to the extent that the lat-
eralization of scalp-recorded ERPs reflects hemispheric
differences in the neural processes involved.

Results
Behavioral discrimination task and categorization training
Performance on the behavioral discrimination task (see
Table 1) improved after training [F(1,32) = 20.64, p <
0.001]. This was more so for Chinese and English than for
Thai participants, although the LANGUAGE GROUP by
TEST TIME interaction did not reach significance [interac-
tion: F(2, 32) = 2.87, p = 0.071; Post versus pre-training:
English: t(11) = 3.46, p = 0.005; Chinese: t(11) = 4.13, p
= 0.002; Thai: t(10) = 0.66, N.S.]. Before training, the
three language groups differed from each other, with the
English performing worse than the Chinese and Thai
[LANGUAGE GROUP (pre-training): F(2,32) = 7.39, p <
0.001; English versus Chinese: p = 0.007; English versus
Thai: p = 0.001]. The Chinese and Thai groups did not dif-
fer in their performance [p = 0.42]. After training, the
groups did not differ in their ability to discriminate
among the tones [LANGUAGE GROUP: F
Performance in the categorization training (see Table 2)
improved between the first and the last training sessions
in all three language groups [F(1,31) = 33.92, p < 0.001].
The English showed the largest improvement, the Thai
group the smallest [LANGUAGE GROUP by TEST TIME
F(2,31) = 4.98, p = 0.013]. Overall, the English performed
the worst [LANGUAGE GROUP: F(2,31) = 9.93, p <
0.001; English versus Chinese: p = 0.004; English versus
Thai: p = 0.001; Thai versus Chinese: p = 0.26]. After the
first training session, the language groups performed sig-
nificantly different from each other [F(2,31) = 10.18, p <
0.001], with the English performing worse that the Chi-
nese [p = 0.005] and the Thai [p < 0.001]. The Chinese
and Thai did not differ significantly from each other [p =

Table I: Results for the behavioral discrimination task


English


Chinese


Pre-training 1.19 (0.66) 1.86 (0.39) 2.06 (0.65)
Post-training 2.06 (1.28) 2.57 (0.84) 2.21 (0.68)

The mean d' scores pre- and post-training per language group.
Standard deviation in parentheses.


Table 2: Results for the categorization training


English


Chinese


First session 24.28 (14.28) 11.67 (7.39) 5.94 (5.59)
Last session 10.67 (7.36) 4.50 (3.65) 2.81 (3.86)

Mean percentage of errors after the first and last 30-minute training
session, per language group. Standard deviation in parentheses.

0.19]. A similar statistical pattern was obtained after the
last training session [LANGUAGE GROUP: F(2,32) =
7.18, p = 0.003; English versus Chinese: p = 0.007; English
versus Thai: p = 0.001; Chinese versus Thai: p = 0.451].

Pre-and post training performance in the behavioral dis-
crimination task correlated strongly with accuracy in the
first and last categorization training, respectively [Pre-
training: Pearson's p = -0.67, p < 0.001; Post-training: p =
-0.63, p < 0.001]: the fewer errors made in the categoriza-
tion training, the higher the d' scores in the discrimination
task. This indicates that the behavioral discrimination task
is a good measure of a participant's pre- and post-training
perception ability.

ERP experiment: movie comprehension questions
Mean comprehension accuracy on the movie-related
questions in the ERP experiment was 84% (SD 7%),
before as well as after training. Before training, the English
group scored 87% correct (SD 5%), the Chinese 83% (SD
6%) and the Thai 81% (SD 9%). After training, the accu-
racy was 85% (SD 6%) for the English group, 85% (SD
9%) for the Chinese, and 84% (SD 7%) for the Thai
groups. There were no significant differences in accuracy
between pre- and post training sessions and/or among the
language groups [ps > 0.2].

ERPs to low-falling tones
MMN
The low-falling deviants (minus low-falling standards)
showed a MMN at the F3 and F4 electrodes. ERPs for the
F3 electrode are displayed in Figure 2. Figure 3 shows the
isovoltage maps for the MMN. T-tests of the MMN ampli-
tude at F3 and F4 versus a hypothetical zero showed that
the MMN was significant before and after training in the
English and Thai speakers [ps <0.004]. In the Chinese, the
MMN was weakly present before training [p = 0.067] and
significantly after [p < 0.001].

An ANOVA on the MMN amplitude (deviant minus
standard) at the F3 and F4 electrodes showed an interac-
tion of TEST TIME by HEMISPHERE [F(1,32) = 4.33, p =
0.046]: Before training, the MMN was numerically larger
at the left hemisphere electrode [F3: -1.64 gV; F4: -1.49 iV
; t(34) = 0.78, N.S.]; after training, the MMN was numeri-
cally larger at the right electrode, with the difference


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F3 English


pV -4


Pre-test -,


I I I I


Thai


900ms


....... Low-falling deviant


Post-test


_ Low-falling standard


Figure 2
ERPs to Low-falling deviants and standards. ERPs at the left frontal electrode (F3) for the low-falling deviants (dotted
line) versus standards (solid line).


almost reaching significance [F3: -1.54 pV; F4: -1.91 piV;
t(34) = 1.97, p = 0.057]. Training also weakly affected the
differences in the MMN between the groups [TEST TIME
by LANGUAGE GROUP: F(2,32) = 2.69, p = 0.084]: The
groups weakly differed from each other before training
[F(1,32) = 2.89, p = 0.07], with the MMN being larger for
the English compared to the Chinese [LSD post hoc com-
parison, p = 0.025], but not compared to the Thai [p =
0.12]. After training, the groups did not differ [ps > 0.68]
(see Appendix).

The difference in MMN latency and amplitude after versus
before training correlated weakly with the degree of learn-
ing: the greater the improvement in the behavioral dis-
crimination task (d' scores post minus pre-training), the
earlier the MMN peak and the smaller (i.e. less negative)
the MMN amplitude was after compared to before train-
ing [Latency: Pearson's p = -0.32, p = 0.063; Amplitude: p
= 0.32, p = 0.063].

Late negativity
A late negativity was seen for the deviant versus standard
in the 350-500 ms window [Midline: F(1,32) = 7.29, p =
0.011; Lateral electrodes: F(1,32) = 6.75, p = 0.014] (see
Figure 4.) The negativity was larger over the left hemi-
sphere [F(1,32) = 9.18, p = 0.005], especially before train-


ing [CONDITION by TEST TIME by HEMISPHERE:
F(1,32) = 6.95, p = 0.013; Pre-training: CONDITION by
HEMISPHERE: F(1,32) = 13.94, p = 0.001; CONDITION:
Left hemisphere: F(1,32) = 7.76, p = 0.009; Right hemi-
sphere: F(1,32) = 1.32, p = 0.26; Post-training: no effects].
The degree of learning affected the change in the late neg-
ativity in the 350-500 ms interval: the larger the increase
in d' scores from pre- to post-training, the smaller the late
negativity in the 350-500 ms interval post- versus pre-
training [Pearson's p = 3.68, p = 0.029].

The negativity persisted in the 500-700 ms interval, see
Figure 5 [Midline: F(1,32) = 10.11, p = 0.003; Lateral:
F(1,32) = 12.70, p = 0.001], and remained larger over the
left than the right hemisphere [CONDITION by HEMI-
SPHERE: F(1,32) = 5.203, p = 0.029; Effect of CONDI-
TION: Left hemisphere: F(1,32) = 4.201, p = 0.049; Right:
F(1,32) = 2.37, p = 0.13]. Effects involving the factor TEST
TIME were not significant in this time window. The later
negativity was not affected by language background in
either the 350-500 ms or the 500-700 ms time window.

ERPs to high-rising tones
Results for the high-rising deviants versus high-rising
standards are displayed in Figures 6 to 9.



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Chinese


-4 '


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MMN low-falling


English


Chinese


Thai


Pre


-3.0 pV +3.0


-3.0 pV +3.0


-3.0 pV +3.0


-3.0 pV +3.0


-3.0 pV +3.0


-3.0 pV +3.0


Figure 3
Isovoltage maps to Low-falling deviants minus standards: MMN. Isovoltage maps for the 100 ms window surrounding
the most negative peak between 100-350 ms, for the low-falling deviants minus standards, defined separately for the language
groups and test time.


MMN
Separate T-tests on the MMN amplitude (high-rising devi-
ant minus high-rising standard) at F3 and F4 versus a
hypothetical zero showed no significant differences in any
of the groups before training [ps >0.18]. After training, the
MMN was most robust in the native Thai speakers [Thai:
p = 0.014; Chinese and English: ps >0.062]. ANOVAs
showed no effects of the experimental manipulations on
the MMN (amplitude or latency).

Late negativity
Between 350 and 500 ms, a negativity was elicited by the
high-rising deviants versus standards, particularly over the
left hemisphere [CONDITION by HEMISPHERE F(1,32)
= 10.95, p = 0.002], see Figure 8. This left-lateralized neg-
ativity was only seen in the English and in the Chinese,
but not in the Thai, leading to a weak interaction of CON-


EDITION by HEMISPHERE by LANGUAGE GROUP [3-
way interaction: F(2, 32) = 2.93, p = 0.068; CONDITION
by HEMISPHERE, English: [F(1,11) = 6.37, p = 0.028.
Chinese: F(1,11) = 9.04, p = 0.012;Thai: F(1,10)< 1, N.S.].
Training had an effect on the anterior-posterior distribu-
tion of the negativity [TEST TIME by CONDITION by
ANTERIORITY F(4, 128) = 5.18, p = 0.018]: Pre-training,
the negativity was numerically largest at frontal sites
[CONDITION by ANTERIORITY: F(4, 128) = 3.96, p =
0.038], after training the negativity became broader in dis-
tribution and the two-way interaction between CONDI-
TION and ANTERIORITY was no longer significant [F(4,
128) = 1.37, p = 0.26, N.S.]. Figure 8 suggests that this
effect was mainly driven by the English group, however
the interaction with LANGUAGE GROUP was not signifi-
cant.


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350-500ms low-falling


English


-3.0 pV +3.0


-3.0 pV +3.0


-3.0 pV +3.0


-3.0 pV +3.0


-3.0 pV +3.0












-3.0 pV +3.0


Figure 4
Isovoltage maps to Low-falling deviants minus standards: 350-500 ms. Isovoltage maps for the 350-500 ms window
for the low-falling deviants minus standards.


The negativity for the high-rising deviants persisted in the
500-700 ms interval (see Figure 9) [Midline: F(1,32) =
7.25, p = 0.011; Lateral: F(1,32) = 3.24, p = 0.066], with
the negativity being larger over the left hemisphere [CON-
DITION by HEMISPHERE: F(1,32) = 13.619, p = 0.001;
Effect of CONDITION: Left hemisphere: F(1,32) = 4.20, p
= 0.049; Right hemisphere: F(1,32) = 2.73, p = 0.133]. The
negativity was greater left than right at all except parietal
regions [CONDITION by HEMISPHERE by ANTERIOR-
ITY: F(4,128) = 3.14, p = 0.032; CONDITION by HEMI-
SPHERE was significant in all regions (ps ranging from
0.001 to 0.016) but for the parietal region (p = 0.15)]. The
left lateralization was especially seen in the Chinese and
English participants compared with the Thai [CONDI-
TION by HEMISPHERE by LANGUAGE GROUP: F(2,64)
= 3.43, p = 0.045; CONDITION by HEMISPHERE: Chi-
nese: F(1, 11) = 8.88, p = 0.013; English: F(1, 11) = 9.172
p = 0.011; Thai: F <1, N.S. ]. As in the previous interval, the


distribution was frontal before training and became
broader after training [TEST TIME by CONDITION by
ANTERIORITY: Midline: F(4,128) = 5.34, p = 0.014; Lat-
eral: F(4,128) = 9.02, p = 0.002; CONDITION by ANTE-
RIORITY, Pre-training: Lateral: F(4,128) = 4.66, p = 0.024;
Post training: Lateral: F(4,128) = 2.44, p = 0.120. Effect of
CONDITION Pre-training: Lateral frontal sites F(1,32) =
3.86, p = 0.058; Lateral: fronto-central sites: F(1,32) =
3.06, p = 0.09; Remaining regions: ps>0.18].

Summary
All groups showed a MMN before and after training to the
low-falling deviants. The MMN was larger over the right
hemisphere after training. The English group tended to
show a larger MMN before training than the Chinese, even
though they performed worse in the behavioral tasks.
Both MMN amplitude and latency decreased after training
the more the participant improved in the behavioral dis-


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Chinese


Thai


Pre












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500-700ms low-falling


English


Pre


-3.0 pV +3.0


-3.0 pV +3.0


-3.0 pV +3.0


-3.0 pV +3.0


-3.0 pV +3.0


-3.0 pV +3.0


Figure 5
Isovoltage maps to Low-falling deviants minus standards: 500-700 ms. Isovoltage maps for the 500-700 ms window
for the low-falling deviants minus standards.


crimination task. The MMN was followed by a slow nega-
tivity, which was slightly larger over the left than the right
hemisphere, and reduced in amplitude as a function of
learning. The high-rising deviants elicited no or only a
small MMN. The late negativity in this condition was left-
lateralized for the English and the Chinese groups. The
later negativity was frontal before training, but became
broader after training.

Discussion
The aim of the present ERP study was to investigate the
processing of lexical tones when participants are not
forced to pay attention to the stimuli, as opposed to pre-
vious studies using behavioral techniques only, and to see
to what extent such non-attentive processing is affected by
training and by native language background. In contrast
to previous ERP studies [21,22], we used multiple tokens


per stimulus type to encourage the formation of abstract
contour categories and to avoid pre-training ceiling
effects. Results from the behavioral discrimination task
suggest that this manipulation was successful: perform-
ance significantly increased after training in the English
and the Chinese groups, who were initially unfamiliar
with the Thai stimuli used. Furthermore, behavioral dis-
crimination scores correlated significantly with perform-
ance in the categorization training task.

Based on previous experiments showing that native speak-
ers of a non-tone language are more sensitive to F0 onset
and offset when discriminating lexical tones [6-8,22], we
predicted that the English group would show a larger
MMN to the deviant categories; the Chinese and Thai on
the other hand, previously shown to be more sensitive to
F0 contours, were expected to show a more robust later


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F3

IV


Pre-test


English


Chinese


Thai


900ms


....... High-rising deviant


Post-test


_ High-rising standard


Figure 6
ERPs to High-rising deviants and standards. ERPs at the left frontal electrode (F3) for the high-rising deviants (dotted
line) versus standards (solid line).


effect. In addition, given that the stimuli were meaningful
words in Thai, we predicted a lateralization difference
between Thai on the one hand, and English and Chinese
on the other.

Our predictions were only partly borne out. We will dis-
cuss our findings in turn for the MMN and the late nega-
tivity.

The MMN
All groups showed a MMN to the low-falling tone devi-
ants, before as well as after training; whereas no, or only a
smaller MMN was elicited by the high-rising tone devi-
ants. Note that two of the three low-falling tones have an
onset frequency falling below the range of the mid-level
tones (see Figure 1). The onset frequency of the high-ris-
ing tones, on the other hand, falls within the range of that
of the mid-level tokens. It is therefore likely that the large
MMN found for the low-falling tones reflects differences
in F0 onset between the deviant and standard stimuli pre-
sented in the same block. These differences were much
smaller in the high-rising tones [29,30].

The MMN was weakly affected by native language back-
ground: the English showed a larger MMN to the low-fall-
ing tones than the Chinese before training. This supports


previous findings [6-8,22] that speakers of a non-tone lan-
guage are more sensitive to differences in onset F0. Our
English speaking participants may have been more sensi-
tive to the early F0 differences in the Low-falling condi-
tions, eliciting a larger MMN compared to the Chinese
and Thai groups. Note that although the English language
group showed the largest MMN before training, they per-
formed worse than the Thai and Chinese in the behavioral
discrimination and training. This can also be accounted
for by the different sensitivity of tone versus non-tone lan-
guage speakers. The behavioral tasks probed participant's
sensitivity to differences in F0 slope and direction rather
than F0 onset, and was therefore harder for non-tone lan-
guage speakers. The categorization training with multiple
tokens per type caused the English speaking participants
to become more sensitive to the direction of the pitch con-
tour. This may have induced a modulation of their non-
attentive perception, hence a reduction of the MMN
amplitude in the English language group after training to
the level of the speakers of tone languages.

The MMN became smaller and earlier with behavioral
improvement. Typically, the MMN has been found to
become larger after training [15,16,18]. The decrease in
MMN amplitude therefore suggests that the participants,
and especially the learners, non-attentively perceived the


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MMN high-rising


English


-3.0 pV +3.0


-3.0 pV +3.0


-3.0 pV +3.0


-3.0 pV +3.0


-3.0 pV +3.0


-3.0 pV +3.0


Figure 7
Isovoltage maps to High-rising deviants minus standards:MMN. Isovoltage maps for the 100 ms window surrounding
the most negative peak between 100-350 ms, for the high-rising deviants minus standards, defined separately for the language
groups and test time.


stimuli in a different way and became less sensitive to the
F0 onset differences after training, or at least, as a result of
repeated exposure.

For all three language groups, the MMN to the low-falling
deviants became more prominent over the right hemi-
sphere after training. This is in contrast to several previous
ERP studies that reported an increase in MMN over the left
hemisphere after training on linguistic contrasts [18]. To
the extent that the lateralization of scalp-recorded ERPs
reflects hemispheric differences in the neural processes
involved, our findings suggest that even native speakers of
Thai employ the right hemisphere more than the left in
processing the low-falling versus mid-level tone contrast.
This is in spite of the fact that the stimuli are meaningful
words for the Thai. A previous study on Mandarin Chinese
speakers reports a similar right hemisphere distribution


for meaningful lexical tone contrasts [28]. Under an alter-
native account of hemispheric specialization of speech,
the left hemisphere is involved in processing rapid form-
ant transitions, whereas the right hemisphere deals with
slower differences in pitch [31]. It may therefore be the
case that our participants became more sensitive to the
gradual change in F0 contour, focused less on the differ-
ences in F0onset values and the abrupt change in F0 at the
beginning of the stimuli, and thus involved the right hem-
isphere more as a result of training.

The later negativity
Second, we were interested in the later negativity. In con-
trast to our prediction, no difference was seen between
English and Chinese speakers. All groups displayed a neg-
ativity to both the low-falling and high-rising deviants
versus standards. Late negativities reported in the litera-


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Chinese


Thai


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350-500ms high-rising


English


Chinese


Thai


-3.0 pV +3.0


-3.0 pV +3.0


-3.0 pV +3.0


-3.0 pV +3.0


-3.0 pV +3.0


-3.0 pV +3.0


Figure 8
Isovoltage maps to High-rising deviants minus standards: 350-500 ms. Isovoltage maps for the 350-500 ms window
for the high-rising deviants minus standards.


ture have been associated with cognitive, possibly non-
attentive processing of sound change [32], or processing
at a higher level of abstraction [33,34] including har-
monic integration in music contexts [35,36]. Alterna-
tively, the late negativity may reflect reorienting of
attention after involuntary attention to deviant stimuli
[37,38]. A smaller late negativity may then indicate a
more efficient neural processing, or less attentional reori-
enting. For the low-falling deviants, the late negativity
became less left-lateralized after training and smaller in
amplitude the more the participant improved on the
behavioral task. For the high-rising deviants, the Chinese
and English speaking groups showed a left-lateralization
of this negativity for the high-rising deviants, regardless of
training.

Note that the low-falling stimuli continue to differ from
the mid-level stimuli in terms of a falling pitch slope right


after the initial sharp fall in F0 (see Figure 1). The high-ris-
ing tones, on the other hand, only show a gradual increase
in F0 compared to the mid-level tones, starting at around
290 ms after onset. Two of the three high-rising tokens
start to exceed the Fo range of mid-level tones even later.
The contour deviance is therefore more subtle in the high-
rising than low-falling conditions in the current study.
Since training focused on contour differences, the process-
ing of the low-falling contour may therefore have required
less effort after training in the learners, hence the reduc-
tion of the late negativity in this condition, but not in the
high-rising condition in this experiment. The left-laterali-
zation of the late negativity in the high-rising condition in
the Chinese and English groups suggests that the non-
native language groups process the Thai high-rising con-
tour in a manner that is different from native Thai speak-
ers. Comparable to the MMN, and the late negativity in
the low-falling condition, this waveform may shift from


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500-700ms high-rising


English


-3.0 pV +3.0












-3.0 pV +3.0


-3.0 pV +3.0


-3.0 pV +3.0


-3.0 pV +3.0


-3.0 pV +3.0


Figure 9
Isovoltage maps to High-rising deviants minus standards: 500-700 ms. Isovoltage maps for the 500-700 ms window
for the high-rising deviants minus standards.


the left to the right hemisphere when listeners become
more proficient in detecting the contours. Apparently, the
categorization training was not sufficient to give rise to
these effects in the non-native speakers. It remains to be
seen if a longer period of training will lead to a shift in
hemispheric lateralization to be observed.

The relation between ERPs and behavioral data
Behavioral studies on the perception and acquisition of
foreign language contrasts are potentially confounded by
the attentional and memory load that is imposed by most
discrimination or categorization tasks. Using ERPs over-
comes this problem because passive listening tasks can be
used which do not require any explicit attention or overt
behavioral response from the participant. On the other
hand, behavioral and ERP studies may tap into different
aspects of processing. ERPs may be more sensitive to dif-
ferences in physical properties of the stimuli than behav-


ioral tasks. In addition, behavioral studies may encourage
participants to actively form abstract perceptual catego-
ries, whereas passive listening oddball tasks, as used in the
current ERP study, may do so to a lesser extent. It is there-
fore not surprising that we observed some discrepancies
between our behavioral and ERP data. ERPs are therefore
a good complementary method to behavioral studies, and
are a good tool to help uncover what aspects of the stimuli
different language groups are differently sensitive to.

We have already discussed the larger MMN to low-falling
deviants seen in the English group pre-training in spite of
this group's poor performance on the behavioral tasks.
This can be accounted for by the MMN being a reflection
of a participant's sensitivity to early differences between
the stimuli, whereas the behavioral tasks tapped more
into the participant's ability to actively form categories on
the basis of the later pitch contour. In contrast to the


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Thai


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MMN, the late negativity did not correspond to the behav-
ioral differences observed between the groups before
training. However, the amplitude of the late negativity in
the low-falling conditions did correlate with behavioral
improvement: the late negativity amplitude became
smaller the more the participant improved in the discrim-
ination task. In the high-rising condition, the late negativ-
ity became more broadly distributed after training. Finally
we would like to point out that in spite of differences in
language background, all participant groups elicited
largely similar ERP components and that, with the excep-
tion of the MMN, the effect of training was largely the
same among the groups. This suggests that the neural
mechanisms involved in non-attentively perceiving tone
stimuli and the effects of training thereon may have been
largely unaffected by language background.

Conclusion
In sum, native speakers of English, Chinese and Thai
recruited largely similar neural mechanisms when non-
attentively processing Thai lexical tones. Training induced
comparable changes in the language groups. However,
and converging with results from behavioral methods
using different stimuli and techniques, we found that
native speakers of English were initially more sensitive to
early F0 differences before training. After training, this lan-
guage group became more similar to native tone-language
speakers. In addition, native speakers of English and of
Mandarin Chinese processed the late shallow contour in
the high-rising Thai tone differently from native Thai
speakers. Future experiments will determine whether this
can be affected by a more extended period of training.

Methods
Participants
Twelve native speakers of American English (8 men), 12
native speakers of Mandarin Chinese (People's Republic
of China) (6 men), and 11 native speakers of Thai (5
men) were recruited from the University of Florida com-
munity. Informed consent was obtained from each partic-
ipant according to the procedures of the University of
Florida Institutional Review Board. All participants were
healthy young adults, aged 19-35, right handed as
assessed by the Edinburgh handedness inventory [39],
and with no history of neurological disease or language
disorders as indicated by a self-report. All had a minimal
bilateral hearing range of 500 to 8,000 Hz measured at 25
dB HL. The American English speakers did not have any
experience with a tone language; the native Chinese
speakers did not have experience with any other tone lan-
guage, except one who spoke a Chinese dialect in addition
to Mandarin Chinese. Participants were paid for participa-
tion. Ten additional participants were run, but were omit-
ted from analysis because of incomplete data sets (due to
technical difficulties or failure to return for all sessions).


Stimuli
Nine stimuli were synthesized on the basis of one natu-
rally generated instance of the Thai mid-level tone syllable
[kha:] produced by a female native speaker of Thai and
digitized at 22050 Hz sampling rate with a 16-bit ampli-
tude resolution. Using the Praat speech analysis software,
the original mid-level tone was shortened from 610 ms to
450 ms. The pitch contour of this mid-level tone was then
manually changed to approximate the pitch contours of
the natural tokens of the Thai low-falling and high-rising
tones. The entire F0 contour of each of the three resulting
stimuli was then shifted down -15 Hz and -30 Hz to sim-
ulate three different talkers, thus yielding three tokens for
each of the three tone types, see Figure 1. All stimuli were
normalized for RMS amplitude (98% of the scale). All 3
tokens of each tone were then presented to two native
Thai speakers (one male and one female) and were judged
to be acceptable exemplars of each of the three tone cate-
gories. Sound files and spectrograms of each token are
provided as supplementary materials (Additional files 1,
2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18).

Procedure
Participants were tested on these stimuli on four consecu-
tive days. Stimuli were presented binaurally, one at a time
over head phones at a comfortable hearing level (65 dB).
An ERP oddball task was conducted on Days 1 and 4; two
categorization training sessions each were conducted on
Days 2 and 3, with a behavioral discrimination task either
preceding (Day2) or following (Day 3) the training.

Behavioral discrimination task
In the behavioral discrimination task (Days 2 and 3) the
participant heard a sequence of three different stimuli A B
C, separated by 575 ms. A and B were always from the
same tone category (either low-falling, high-rising or mid-
level). The last stimulus, C, was either of the same or of a
different contour category, and the participant was asked
to indicate whether the contour was same or different by
clicking a mouse button (113 trials total: 108 experimen-
tal trials and 5 warm-up trials that were not analyzed). The
response side for the 'same' and 'different' responses was
counterbalanced among participants. If no response was
given after 3 seconds, the next trial started. Responses
longer than 3 seconds (2.2-3.5% per session and lan-
guage group) were treated as no-response errors. D' scores
were calculated on the percentage of hits (correct 'differ-
ent' response in case tone C was of a different type than A
and B) and false alarms (incorrect 'different' response
when A, B and C were of the same category). Null
responses were not included in d'score calculation.

Categorization training
In the categorization training sessions (Days 2 and 3), par-
ticipants heard one stimulus per trial. They were asked to


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classify a token as being of tone type A, B or C by clicking
a box on the screen [4,5,22]. During the introduction
phase of the training, they heard the three tokens of tone
type A (low-falling), followed by the three tokens of tone
type B (mid-level), followed by the three tokens of tone
type C (high-rising). After this was repeated three times,
the tokens were presented in random order for a total of
81 trials (each token presented 9 times) and accuracy was
recorded. Participants were allowed to replay the sound. If
an incorrect response was given, the frame around the box
with the correct answer would blink. The inter-trial inter-
val was 3 seconds. Responses longer than 3 seconds
(including replays) were omitted from analysis. This
amounted to 0.6-3.2% of the data per session and lan-
guage group. One session lasted 30 minutes and was
repeated on the same day after a short break. Data from
one Chinese participant for the first training session on
Day 2 were missing due to technical failure. Hence, this
participant is omitted in all analyses involving this first
session.

ERP oddball experiment
In the ERP oddball task (Days 1 and 4), the stimuli were
presented in a continuous stream. Four stimulus blocks
were presented, the order counterbalanced across partici-
pants: (1) mid-level presented as standard, high-rising as
deviant; (2) high-rising as standard, mid-level as deviant;
(3) mid-level as standard, low-falling as deviant; (4) low-
falling as standard, mid-level as deviant. A total of 1200
stimuli were presented per block: 1080 of the standard
category and 40 of each of the three deviant tokens (i.e.,
10% deviants). The inter-stimulus (offset-to-onset) inter-
val was randomized between 500-650 ms to prevent
interference from regular biological rhythms on the wave-
forms. The order of the stimuli was pseudo randomized
such that two deviants were separated by at least two
standards. The length of each block was 17 minutes.
While the auditory stimuli were presented over head-
phones, participants watched a silent movie (Charlie
Chaplin's 'The Gold Rush' or Buster Keaton's 'The Gen-
eral'). They were told that they would receive questions
about the movie after each of blocks, and were instructed
to ignore the sounds. A different movie fragment was
played during each session. Each ERP session lasted about
2 hours, including set up and debriefing.

EEG was recorded from 39 Ag/AgCl scalp electrodes
mounted in an elastic cap with active shielding (Easy-Cap,
Falk Minow, Herrsching-Breitbrunn, Germany) combined
with an ANT amplifier (ANT Software b.v., Enschede, The
Netherlands). Electrode positions used were: Midline: Fz,
FCz, Cz, CPz, Pz; Lateral left/right hemisphere: FP1/2, F7/
8, F5/6, F3/4, FT7/8, FC5/6, FC3/4, T7/8, C5/6, C3/4,
TP7/8, CP5/6, CP3/4, P7/8, P5/6, P3/4, 01/2. Horizontal
and vertical EOG were recorded from the outer canthi,


and below and above the right eye, respectively. Addi-
tional electrodes were placed on the right and left mas-
toids. The signal was acquired using the left mastoid as
reference, but was arithmetically re-referenced off-line to
the mean of the left and right mastoids. Electrode imped-
ance was kept below 5 KOhm. The signal was sampled at
a rate of 512 Hz, and was filtered off-line between 0.3 and
30 Hz. We only analyzed low-falling and high-rising stim-
uli. These were always presented with mid-level stimuli in
the presentation blocks. Any differences between the ERPs
to the low-falling and high-rising tones can therefore not
be due to different alternate stimuli in the presentation
blocks. Epochs were defined spanning -100 to 900 ms
from the stimulus onset. EEG to low-falling and high-ris-
ing tone deviants were averaged separately. We also sepa-
rately averaged the EEG to 120 low-falling and high-rising
tones when these were used as standards. To avoid any
potentially confounding effects from preceding deviant
tones, we selected 120 standard stimuli that were pre-
ceded and followed by a standard stimulus. Trials with eye
movements and other artifacts were rejected. The percent-
age of rejection was on average 28% per condition (SD
15%) in the Chinese group; 20% in the English group (SD
9%), and 26% (SD 13%) per condition in the Thai group.

The mismatch negativity was analyzed using the F3 and F4
electrodes. These were electrodes where the MMN was
largest on the lateral sites. First, difference waves (deviant
minus standard) were calculated for the high-rising devi-
ants minus standards, and low-falling deviants minus
standards. Next, the most negative peak was found
between 100 and 350 ms, and the mean amplitude for the
windows spanning 100 ms centered around this peak was
calculated for every channel, participant, tone type and
session. Analyses were conducted on the mean difference
in amplitude thus calculated and on the peak latency.

A later negative component was observed as well. Since we
had no clear prediction as to the scalp distribution of this
component and since the wave did not have a clear peak,
analyses were conducted on the mean amplitudes to both
the deviant and standard tones, and included a large
number of electrodes. Statistical analyses were conducted
on the mean amplitudes between 350-500 ms and 500-
700 ms, based on visual inspection, using lateral (F3/4,
F5/6, F7/8, FC3/4, FC5/6, FT7/8, C3/4, C5/6, T7/8, CP3/
4, CP5/6, TP7/8, P3/4, P5/6, P7/8), as well as midline (Fz,
FCz, Cz, CPz, Pz) electrodes.

ERP data were analyzed separately for low-falling and
high-rising tones, using an (SPSS) General Linear Model
multivariate repeated measures procedure with the
within-participant factors: TEST TIME (pre/post training),
and, when applicable, CONDITION (standard, deviant),
HEMISPHERE (2 levels) and/or ANTERIORITY (5 levels).


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LANGUAGE GROUP was included as a between-partici-
pants factor (3 levels). When a two or three-way interac-
tion was significant, separate analyses were conducted to
determine the source of the interaction. For the late nega-
tivity only effects involving the factor Condition are
reported below. When interactions involving factors with
more than two levels were significant, F- and p-values
were reported after the Greenhouse-Geisser correction to
control for violations of sphericity [40].

Abbreviations
EEG: electroencephalogram; ERP: event-related potential;
F0: fundamental frequency; LSD: least significant differ-
ence; MMN: mismatch negativity; N.S.: not significant;
SD: standard deviation.

Authors' contributions
EK designed and coordinated the ERP study, analyzed the
data and drafted the manuscript. CMB and MB carried out
the experiment. RW conceived of the study, constructed
the stimuli and designed the discrimination and training
tasks. All authors read and approved the final manuscript.

Appendix
Tests involving 34 lateral electrodes did not show any sig-
nificant interaction of location with Language group
except for a weak interaction ofTESTTIME BY ANTERIOR-
ITY BY LANGUAGE for the low-falling deviants minus
standards [F(8, 128) = 2.50, p = 0.0761. Numerically, the
English group showed a larger frontal negativity than the
Thai and Chinese before, but not after training. This effect
is the same as the weak LANGUAGE BY TESTTIME inter-
action found for the F3 and F4 electrodes discussed in the
main text.

Additional material


Additional File 1
Sound file of the .... i.. it m, token.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2202-9-53-S1 wav]

Additional File 2
Sound file of the second '..,i im.. token.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2202-9-53-S2.wav]

Additional File 3
Sound file of the I,,,.l i.... ii,, token.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2202-9-53-S3.wav]


Additional File 4
Sound file of the first mid-level token.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471
2202-9-53-S4.wav]

Additional File 5
Sound file of the second mid-level token.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471
2202-9-53-S5.wav]

Additional File 6
Sound file of the third mid-level token.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471
2202-9-53-S6.wav]

Additional File 7
Sound file of the first high-rising token.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471
2202-9-53-S7.wav]

Additional File 8
Sound file of the second high-rising token.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471
2202-9-53-S8.wav]

Additional File 9
Sound file of the third high-rising token.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471
2202-9-53-S9.wav]

Additional File 10
Spectrogram of ',,., i..,, i ,I,,, token.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471
2202-9-53-S10.bmp]

Additional File 11
Spectrogram of second i'.. ,I ,I, token.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471
2202-9-53-S11.bmp]

Additional File 12
Spectrogram of third '.., i f".. token.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471
2202-9-53-S12.bmp]

Additional File 13
Spectrogram of first mid-level token.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471
2202-9-53-S13.bmp]


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Additional File 14
Spectrogram of second mid-level token.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2202-9-53-S14.bmp]

Additional File 15
Spectrogram of third mid-level token.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471 -
2202-9-53-S15.bmp]

Additional File 16
Spectrogram of first high-rising token.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471 -
2202-9-53-S16.bmp]

Additional File 17
Spectrogram of second high-rising token.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471 -
2202-9-53-S17.bmp]

Additional File 18
Spectrogram of third high-rising token.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471 -
2202-9-53-S18.bmp]




Acknowledgements
The authors would like to thank Pedro Alcocer for his help with running
participants. EK is currently supported by NIDCD #5R03DC006160-02

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