䉬

Human Brain Mapping 20:22–28(2003) 䉬

Brain Activations During Conscious SelfMonitoring of Speech Production With Delayed
Auditory Feedback: An fMRI Study
Yasuki Hashimoto1 and Kuniyoshi L. Sakai1,2*
1

Department of Cognitive and Behavioral Science, Graduate School of Arts and Sciences,
The University of Tokyo, Komaba, Tokyo, Japan
2
SORST, Japan Science and Technology Corporation, Kawaguchi-shi, Japan䉬䉬

Abstract: When a speaker’s voice returns to one’s own ears with a 200-ms delay, the delay causes the
speaker to speak less ﬂuently. This phenomenon is called a delayed auditory feedback (DAF) effect. To
investigate neural mechanisms of speech processing through the DAF effect, we conducted a functional
magnetic resonance imaging (fMRI) experiment, in which we designed a paradigm to explore theconscious overt-speech processing and the automatic overt-speech processing separately, while reducing
articulatory motion artifacts. The subjects were instructed to (1) read aloud visually presented sentences
under real-time auditory feedback (NORMAL), (2) read aloud rapidly under real-time auditory feedback(FAST), (3) read aloud slowly under real-time auditory feedback (SLOW), and (4) read aloud under DAF(DELAY). In the contrasts of DELAY-NORMAL, DELAY-FAST, and DELAY-SLOW, the bilateral superior
temporal gyrus (STG), the supramarginal gyrus (SMG), and the middle temporal gyrus (MTG) showedsigniﬁcant activation. Moreover, we found that the STG activation was correlated with the degree of DAF
effect for all subjects. Because the temporo-parietal regions did not show signiﬁcant activation in thecomparisons among NORMAL, FAST, and SLOW conditions, we can exclude the possibility that its
activation is due to speech rates or enhanced attention to altered speech sounds. These results suggest thatthe temporo-parietal regions function as a conscious self-monitoring system to support an automatic
speech production system. Hum. Brain Mapping 20:22–28, 2003. © 2003 Wiley-Liss, Inc.
Key words: functional imaging; auditory system; speech production; self-monitoring; conscious processing; the superior temporal gyrus䉬䉬

INTRODUCTION

Contract grant sponsor: Japan Science and Technology Corporation(JST); Contract grant sponsor: Human Frontier Science Program(HFSP).
*Correspondence to: Dr. Kuniyoshi L. Sakai, Department of Cognitive and Behavioral Science, Graduate School of Arts and Sciences,
The University of Tokyo, Komaba, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan. E-mail: sakai@mind.c.u-tokyo.ac.jp
Received for publication 31 October 2002; Accepted 28 April 2003
DOI 10.1002/hbm.10119© 2003 Wiley-Liss, Inc.

Temporal asynchrony between speech production
and its feedback to the auditory system causes disruption of ﬂuent speech [Lee, 1950; Yates, 1963]. Thisphenomenon, known as the delayed auditory feedback (DAF) effect, is observed as articulatory changes,
such as slower speech rates, stuttering, intonation
changes, and phoneme exchanges [Chapin et al.,1981]. According to a recent behavioral measurement,
maximal disruptions occur at a delay of approximately 200 ms [Stuart et al., 2002]. While speech ﬂuency is maintained without any conscious effort underreal-time auditory feedback (RAF), conscious selfmonitoring for overt-speech processing is required䉬 fMRI Study of Delayed Auditory Feedback 䉬under DAF in accordance with the change of auditory
feedback. If the difference in the brain activations
between the DAF and RAF conditions can be clariﬁed,
this may shed light not only on the mechanism of the
DAF effect but on neural mechanisms of conscious
and automatic overt-speech processing in general.
In spite of the signiﬁcance of these issues, most of
the previous studies on the DAF effect have been
behavioral experiments, and there have been few
brain imaging studies. A positron emission tomography (PET) study has reported activation increases inthe bilateral superior temporal gyri (STG) for reading
aloud single words with modiﬁed feedback (by pitch
elevation or in someone else’s voice) [McGuire et al.,1996]. Another PET study has reported that activationin the bilateral STG was observed during the overtspeech processing under DAF compared to the resting
condition, but not during the overt-speech processing
under RAF compared to the resting condition [Hirano
et al., 1997]. These results suggest that the bilateral
STG is recruited under speech conditions with abnormal feedback, but this activation may be confoundedby either enhanced attention to altered speech sounds
or by slower speech rates under DAF. Moreover, adirect comparison between DAF and RAF conditions
should be conducted to identify critical regions for
eliciting the DAF effect.
In the present study, we conducted a functional
magnetic resonance imaging (fMRI) experiment toclarify the mechanism of the DAF effect and thereby
infer neural mechanisms that support overt-speechprocessing. We investigated the brain activations during four conditions: (1) normal speech production under RAF (NORMAL), (2) rapid speech production under RAF (FAST), (3) slow speech production underRAF (SLOW), and (4) speech production under DAF(DELAY). The common regions activated in the comparisons of SLOW-NORMAL and DELAY-NORMAL,
if any, would be primarily related to slower speechrates. In contrast, the comparisons among NORMAL,
FAST, and SLOW conditions may reveal general effects for effortful production of accelerated or prolonged speech, which are typically associated with the
DAF condition, as well as enhanced attention to thosealtered speech sounds. The fMRI technique has ahigher spatial resolution than does PET, but the articulatory movements of the jaw and mouth may produce magnetic susceptibility changes that affect MR
signals [Barch et al., 1999; Birn et al., 1999]. However,it has been suggested that the artifacts are reduced in
the comparison between two conditions that equally
include overt-speech processing [Barch et al., 1999]. In
the present study, we adopted this procedure for min-䉬

imizing the motion artifacts, together with scanning
while not speaking.
SUBJECTS AND METHODS
Subjects
Eighteen native Japanese speakers (ages 21–37; 14
men and 4 women) participated in the present study.
All subjects showed right-handedness. Twelve subjects participated in both experiment I (NORMAL,
FAST, and DELAY) and II (NORMAL, SLOW, andDELAY), while six subjects were tested in either experiment (15 subjects in each experiment). The participant’s head was immobilized with padding inside theradio-frequency coil, and the subject was instructed
not to speak during inter-trial intervals. Informed consent was obtained from each participant after the nature and possible consequences of the studies had
been explained. Approval for these experiments was
obtained from the institutional review board of the
University of Tokyo, Komaba.
Stimuli
We prepared 27 Japanese sentences, each of which
consisted of seven hiragana letters: for example, “reiseini kike” (Listen calmly). We used a ﬁxed number ofhiragana letters to ensure constant reading time among
all sentences, as one hiragana letter basically corresponds to one syllable. There are ﬁve vowels in Japanese: [a], [i], [u], [e], and [o]; they are classiﬁed into
two groups according to articulatory positions of the
tongue: front vowels ([i] and [e]) and back vowels ([a],[u], and [o]). When the back vowels are vocalized, theposterior part of the tongue is articulated, the front of
the mouth cavity becomes narrower, and the back of
the mouth cavity broadens out. On the other hand,when the front vowels are vocalized, the oppositechanges occur. In our pilot experiments, we observed
strong artifacts near the cerebral ventricles when thesubjects pronounced the back vowels but not when
they pronounced the front vowels. It is possible thatthe volume changes in the back of the mouth cavity
lead to the artifacts. We, therefore, prepared stimuli
that contained noise-free vowels of [i] and [e] alone.
We used an eyeglass-like MRI compatible display(resolution: 800 ⫻ 600) and a sound delivery system(VisuaStim XGA, Resonance Technology, Inc.,
Northridge, CA). The sentence stimuli were visuallypresented against a dark background at the center ofthe display. When a subject read the sentences into theheadset microphone, his or her own voice was heard

23 䉬䉬 Hashimoto and Sakai 䉬through the headphone via the sound effecter (RFX2000; ZOOM Corp., Tokyo, Japan). By using the soundeffecter, we controlled the sound delivery from themicrophone to the headphone with or without delay.
In addition, the sound delivery was interrupted byusing an intermittent timer during scanning periods,
so that subjects did not hear the scanning noise
through the headphone.

ﬁned by (1 ⫺ MDELAY/MNORMAL) ⫻ 100. Its positive
value indicates that the subjects showed less ﬂuent
speech under the DELAY condition. We also assessed
speech ﬂuency during the FAST or SLOW condition
by using a ﬂuency index (1 ⫺ MX/MNORMAL) ⫻ 100,
where X is FAST or SLOW.

Tasks

The fMRI scans were conducted using a 1.5-T MRI
scanner (STRATIS II, Premium; HITACHI, Tokyo, Japan). We scanned over 15 horizontal slices, each 7 mm
thick, covering from z ⫽ ⫺49 to 56 mm, with a gradient echo echo-planner imaging sequence (repetition
time ⫽ 5 sec; echo time ⫽ 50.5 ms; acquisition time,
2,200 msec; ﬂip angle, 90°; ﬁeld of view, 192 ⫻ 192
mm2; resolution, 3 ⫻ 3 mm2). To eliminate articulatory
motion artifacts during scanning, the scanning sounds
were conﬁned within the inter-stimulus interval byusing a clustered volume acquisition sequence [Edmister et al., 1999].
For analyses of functional data, we used statisticalparametric mapping software (SPM99, Wellcome Department of Cognitive Neurology, London, UK). We
removed sessions that included data with a translationof more than 2 mm or a rotation of more than 1.2° in
one of the three directions. The data were realigned,
spatially normalized to the standard brain space, resampled every 3 mm using sinc interpolation, andsmoothed with an isotropic Gaussian kernel of 14 mm
full width at half maximum. Low-frequency noise andglobal changes in activity were further removed. Taskspeciﬁc effects were estimated with a general linear
model using a boxcar waveform convolved with thecanonical hemodynamic response function. For random effects analyses, a contrast image between tasks
was generated for each participant and used for intersubject comparisons. A statistical threshold was set at
P ⬍ 0.05 for the voxel level, corrected for multiple
comparisons. For the anatomical identiﬁcation of activated regions, we used the Anatomical Automatic
Labeling method [Tzourio-Mazoyer et al., 2002].

Using a block design paradigm, we tested three
types of overt-speech processing: NORMAL, FAST,
and DELAY for experiment I, and NORMAL, SLOW,and DELAY for experiment II. Under the NORMAL
and DELAY conditions, in which letters were presented in green, the subjects were asked to read thepresented sentences overtly at normal speed. Under
the FAST condition, in which letters were presented inpurple, they were asked to read them at rapid speed;
under the SLOW condition, in which letters were presented in purple, they were asked to read them at slow
speed. The subject’s voice was returned to the headphone without delay under the RAF conditions,whereas it was returned with 200 msec delay under
the DAF condition.
Each sentence was presented for 2,800 msec, and thevoice was fed back for 2,100 msec within this stimulus
period. During the inter-stimulus interval of 2,200
msec that exactly coincides with the scanning period,
the subjects were instructed to stop speaking until thenext stimulus was presented, even if the subjects did
not ﬁnish reading aloud the stimulus presented in thepreceding trial. A red cross for ﬁxation was always
shown at the center of the display. We presented six
consecutive stimuli in a single block of one session,
which was in a sequence of either N-D-N-X-N-X-ND-N or N-X-N-D-N-D-N-X-N (N, NORMAL; X, FAST
or SLOW; D, DELAY). Each sentence was presented
twice in one session and arranged not to appear twicein D or X. We tested 16 sessions (eight sessions foreach sequence) for each subject, and the order of tasks
was counterbalanced within and across subjects.

fMRI Data Acquisition and Analyses

RESULTS
Behavioral Data Analyses
To assess the degree of the DAF effect on the subjects’ speech performance, we measured the change inspeech ﬂuency as follows. First, we counted the number of morae pronounced correctly for each sentence
and measured its reading time. Next, the mean spoken
morae per second (M) under each condition were
calculated. Finally, we calculated a DELAY index de-䉬All subjects showed the DAF effect, and the DELAY
index for each subject was always positive (Table I).
As instructed, subjects spoke more rapidly under the
FAST condition than NORMAL and spoke more
slowly under the SLOW condition than NORMAL, asshown by the negative and positive ﬂuency indices,
respectively. Generally, speech under DAF is less ﬂuent until speakers begin to disregard the auditory24 䉬䉬 fMRI Study of Delayed Auditory Feedback 䉬

TABLE I. Speech rates in all conditions*
Experiment

DELAY

FAST

SLOW

I
II

23.8 ⫾ 13.8 (6.8⬃53.9)
18.4 ⫾ 12.5 (4.0⬃41.1)⫺62.0 ⫾ 14.0 (⫺82.2⬃⫺24.0)——
36.4 ⫾ 10.4 (16.9⬃49.7)* DELAY indices and ﬂuency indices (see Materials and Methods) are shown in mean ⫾ SD (range; n ⫽ 15).

feedback of their own speech. However, under thepresent condition of sufﬁcient feedback of speech, thesame individuals did not show signiﬁcant reduction of
the DAF effect, i.e., the change of the DELAY indices,
between the experiments I and II (paired t-test, P⬎ 0.4).
We ﬁrst identiﬁed the activated regions under the
DELAY condition. In DELAY–NORMAL of the two
experiments, signiﬁcant activations were observed
mostly in the bilateral superior temporal gyrus (STG)
and the supramarginal gyrus (SMG) that extended
into the middle temporal gyrus (MTG) (Fig. 1A, Table
II). In DELAY–FAST, a similar pattern of activation
was evident (Fig. 1B), and the right temporo-parietal
regions showed signiﬁcant activation in DELAY-SLOW (Table II). These results demonstrated that thebilateral temporo-parietal regions were more preferentially activated under the DAF condition than theother RAF conditions.
Next, we tested whether the activation of the temporo-parietal regions were predictive of how mucheach individual subject showed the DAF effect. We
found that the signal changes of the left STG in DELAY–NORMAL were signiﬁcantly correlated with the
DELAY index among the subjects (Fig. 2; r ⫽ 0.73, P⬍ 0.01). Moreover, the right STG also showed correlation with the degree of DAF effect (r ⫽ 0.59, P⬍ 0.05). Therefore, we conclude that activation in thebilateral temporo-parietal regions is a good indicatorof the degree of the DAF effect.

Figure 1.
Activations under speech conditions with delayed auditory feed- activation patterns were observed in the bilateral temporo-pariback. A: Regions identiﬁed by DELAY–NORMAL. Activated re- etal regions. For display purposes, the threshold is set at uncorgions are rendered on the surface of a standard brain. Left to right: rected P ⬍ 0.0005 with an extent threshold of 22 voxels, but thethe left lateral view, the posterior view, and the right lateral view. local maxima of t-values (yellow dots) reached a threshold of P
Note that the bilateral temporo-parietal regions showed signiﬁ- ⬍ 0.05, corrected for multiple comparisons.
cant activation. B: Regions identiﬁed by DELAY–FAST. Similar

䉬

25 䉬

䉬 Hashimoto and Sakai 䉬

TABLE II. Brain regions selectively associated with the
DAF effect*
Brain region
DELAY ⬎ NORMAL
Superior temporal g
Supramarginal g
Postcentral g
Superior temporal g
Supramarginal gSupramarginal g
DELAY ⬎ FAST
Superior temporal g
Middle temporal g
DELAY ⬎ SLOW
Middle temporal g

Side

x

y

z

Z

L
L
L
R
R
R

⫺60
⫺57
⫺63
60
60
66

⫺30
⫺33
⫺18
⫺18
⫺30
⫺21

21
33
24
3
27
18

5.7
5.5
5.3
5.2
5.5
5.4

L
R

⫺60
60

⫺12
⫺12

3
⫺12

5.2
5.2

R

66

⫺27

⫺6

5.0

* Stereotactic coordinates (x, y, z) in Montreal Neurological Institute(MNI) space are shown for each voxel with a local maximum of Z
values in the contrasts indicated (P ⬍ 0.05, corrected for the voxellevel; shown as yellow dots in Fig. 1). g, gyrus.

Because the same RAF condition was employed
under the NORMAL, FAST, and SLOW conditions,the comparisons among these conditions would reveal
activations that reﬂect changes in speech rates when
speaking ﬂuently. FAST–NORMAL resulted in significant activation of the left insula [(⫺30, 18, 6), Z ⫽ 4.5]
and the caudate [(⫺9, 0, 9), Z ⫽ 4.5], which may berelated to the motor control of accelerated speech production, together with the left cuneus [(⫺21, 45, 24), Z⫽ 4.6]. In contrast, we observed no signiﬁcant activation in NORMAL–FAST and SLOW–NORMAL,
which are the contrasts for slower speech production.
Furthermore, FAST–DELAY and SLOW–DELAY did
not elicit signiﬁcant activation. Finally, NORMAL–
DELAY (experiment I) resulted in signiﬁcant activation of the superior prefrontal cortex [(⫺9, 54, 0), Z ⫽5.1; (⫺12, 66, 6), Z ⫽ 4.5], the middle frontal cortex[(⫺3, 45, ⫺6), Z ⫽ 5.4; (6, 57, ⫺3), Z ⫽ 4.9], and thebilateral fusiform gyrus [(⫺45, ⫺51, ⫺21), Z ⫽ 4.5; (36,⫺60, ⫺15), Z ⫽ 4.6], whereas NORMAL–SLOW resulted in activation of the left middle occipital gyrus[(⫺42, ⫺72, 33), Z ⫽ 4.8].

present study, we successfully eliminated the possible
confounding factors of the slower speech rates occurring under DAF, as well as enhanced attention to
those altered speech sounds. Another possible reason
for the enhanced activation in the temporo-parietal
regions is that the subjects heard their own voice for alonger time under DELAY than NORMAL and FAST,
because they spoke less ﬂuently under the DELAY
condition. However, this possibility can be excluded
by noting that signiﬁcant activation was not observedin the temporo-parietal regions in NORMAL–FAST
and SLOW–NORMAL, which should have shown thesame effect. Here we propose that an additional system of the bilateral temporo-parietal regions is recruited when conscious self-monitoring is required
under the DAF condition, in addition to the automatic
speech production system that is used under the RAF
conditions.
It is interesting to note that the auditory and association cortices of the bilateral temporo-parietal regions show selective activation modulation for the
DAF effect, but motor and premotor cortices do not.
Recent fMRI studies have shown that the bilateral STG
is related to auditory attention and conscious awareness of auditory stimuli [Hashimoto et al., 2000; Pughet al., 1996], which is consistent with the fact that
feedback control is conscious processing. However,
the absence of signiﬁcant activation in SLOW–NOR-DISCUSSION
In the present study, we observed signiﬁcant activations in the temporo-parietal regions, when the
DAF condition (DELAY) was compared with the RAF
conditions (NORMAL, FAST, and SLOW). Furthermore, we found that the bilateral STG activation wascorrelated with the degree of the DAF effect. By introducing both FAST and SLOW conditions in the䉬Figure 2.
A correlation between the degree of the DAF effect and the signal
change in the left STG [the most ventral local maximum at (⫺60,⫺30, 21) in Fig. 1A]. Among the subjects, the DELAY index [(1⫺ MDELAY/MNORMAL) ⫻ 100], where M is the mean spoken morae
per second, showed signiﬁcant correlation with the signal change
in the left STG for the comparison DELAY-NORMAL, as ﬁtted
with a straight line.

26 䉬䉬 fMRI Study of Delayed Auditory Feedback 䉬

MAL suggests that enhanced attention to prolonged
speech sounds in SLOW is not comparable to the DAF
effect. Moreover, the correlation between the bilateral
STG activation and the DAF effect indicates that themore STG is activated, the more it interferes with
controlling one’s own overt speech. This possibility isin agreement with the perceptual theory of self-monitoring, such that speakers have little or no access totheir speech production process, and that self-monitoring is probably based on parsing one’s own inner orovert speech [Levelt, 1983]. Furthermore, both behavioral and theoretical studies have shown that articulatory errors tend to be more perseveratory (e.g., “beefneedle soup”) than anticipatory (e.g., “cuff of coffee”)
when the error rate is higher [Dell et al., 1997]. Consistent with this serial-order model, the higher error
rate accompanied by the DAF effect may be due toperseveratory errors. For example, when “beef” isheard with DAF while trying to speak “noodle,” thespeech output may result in erroneous “needle.” This
explains why the mechanism of the DAF effect mainly
involves the auditory cortex and it leads to disruptionsof self-monitoring.
In NORMAL–DELAY and NORMAL–SLOW, weobserved activation in the medial prefrontal cortex
and occipital regions, which suggests that these regions were signiﬁcantly deactivated during DELAY
and SLOW than NORMAL. Recent imaging studies
have demonstrated that these regions show task-induced deactivation [Gusnard et al., 2001; Raichle et al.,
2001; Stark and Squire, 2001]. It has been proposedthat these areas are involved in default brain activity,
such as day-dreaming, self-reﬂection, and problem
solving. This default activity decreased during a highload task condition in comparison with a low-loadtask condition, since subjects must pay more attention
to the former condition. It follows from this discussionthat the subjects probably paid more attention to the
DELAY condition than other conditions, which is consistent with our proposal that conscious self-monitoring is involved in overt-speech processing under DAF.
Speech production experiments have been hampered in fMRI studies, because articulatory movements can potentially produce motion artifacts. In previous fMRI studies of overt-speech processing, theevent-related paradigm was preferred to the blockdesign paradigm [Huang et al., 2001; Palmer et al.,2001], because the event-related paradigm can minimize motion-artifacts by taking advantage of the different temporal characteristics of the hemodynamic
response and motion-related signal changes [Birn etal., 1999]. However, a recent fMRI study demonstrated
that the block-design paradigm could obtain artifact-䉬free images when one compared two conditions thatboth used overt-speech processing, because motionrelated artifacts were subtracted out [Barch et al.,1999]. Therefore, we used task conditions that all contained overt-speech processing. In addition, we wereable to reduce possible motion artifacts in our blockdesign fMRI experiments by selecting vowels, together with a clustered volume acquisition sequence.
The present study thus extends the limit of fMRI experiments for understanding cortical mechanisms ofaudition and speech production.
ACKNOWLEDGMENTS
We thank Dr. Ryuichiro Hashimoto, Dr. Fumitaka
Homae, Mr. Kei Suzuki, and Mr. Yasuki Noguchi for
their technical assistance; and Ms. Hiromi Matsuda forher administrative assistance. This work was supported in part by a SORST grant from JST (K.L.S.) andby a Young Investigators’ Grant from HFSP (K.L.S.).
REFERENCES
Barch DM, Sabb FW, Carter CS, Braver TS, Noll DC, Cohen JD(1999): Overt verbal responding during fMRI scanning: Empirical investigations of problems and potential solutions. Neuroimage 10:642– 657.
Birn RM, Bandettini PA, Cox RW, Shaker R (1999): Event-related
fMRI of tasks involving brief motion. Hum Brain Mapp 7:106 –114.
Chapin C, Blumstein SE, Meissner B, Boller F (1981): Speech production mechanisms in aphasia: a delayed auditory feedback
study. Brain Lang 14:106 –113.
Dell GS, Burger LK, Svec WR (1997): Language production andserial order: a functional analysis and a model. Psychol Rev
104:123–147.
Edmister WB, Talavage TM, Ledden PJ, Weisskoff RM (1999): Improved auditory cortex imaging using clustered volume acquisitions. Hum Brain Mapp 7:89 –97.
Gusnard DA, Akbudak E, Shulman GL, Raichle ME (2001): Medial
prefrontal cortex and self-referential mental activity: relation to adefault mode of brain function. Proc Natl Acad Sci U S A
98:4259 – 4264.
Hashimoto R, Homae F, Nakajima K, Miyashita Y, Sakai KL (2000):
Functional differentiation in the human auditory and language
areas revealed by a dichotic listening task. Neuroimage 12:147–
158.
Hirano S, Kojima H, Naito Y, Honjo I, Kamoto Y, Okazawa H, Ishizu
K, Yonekura Y, Nagahama Y, Fukuyama H, Konishi J (1997):
Cortical processing mechanism for vocalization with auditory
verbal feedback. Neuroreport 8:2379 –2382.
Huang J, Carr TH, Cao Y (2001): Comparing cortical activations forsilent and overt speech using event-related fMRI. Hum Brain
Mapp 15:39 –53.
Lee BS (1950): Some effects of side-tone delay. J Acoust Soc Am
22:639 – 640.
Levelt WJM (1983): Monitoring and self-repair in speech. Cognition
14:41–104.

27 䉬䉬 Hashimoto and Sakai 䉬

McGuire PK, Silbersweig DA, Frith CD (1996): Functional neuroanatomy of verbal self-monitoring. Brain 119:907–917.
Palmer ED, Rosen HJ, Ojemann JG, Buckner RL, Kelley WM, Petersen SE (2001): An event-related fMRI study of overt andcovert word stem completion. Neuroimage 14:182–193.
Pugh KR, Shaywitz BA, Shaywitz SE, Fulbright RK, Byrd D, Skudlarski P, Shankweiler DP, Katz L, Constable RT, Fletcher J,
Lacadie C, Marchione K, Gore JC (1996): Auditory selective
attention: an fMRI investigation. Neuroimage 4:159 –173.
Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA,
Shulman GL (2001): A default mode of brain function. Proc Natl
Acad Sci U S A 98:676 – 682.

䉬Stark CEL, Squire LR (2001): When zero is not zero: the problem ofambiguous baseline conditions in fMRI. Proc Natl Acad Sci
U S A 98:12760 –12766.
Stuart A, Kalinowski J, Rastatter MP, Lynch K (2002): Effect ofdelayed auditory feedback on normal speakers at two speechrates. J Acoust Soc Am 111:2237–2241.
Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F,
Etard O, Delcroix N, Mazoyer B, Joliot M (2002): Automatedanatomical labeling of activations in SPM using a macroscopic
anatomical parcellation of the MNI MRI single-subject brain.
Neuroimage 15:273–289.
Yates AJ (1963): Delayed auditory feedback. Psychol Bull 60:213–232.

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