Modulation of the Auditory Cortex during Speech:
An MEG StudyJohn F. Houde*,1, Srikantan S. Nagarajan* ,1, Kensuke Sekihara2,
and Michael M. Merzenich1

Abstract& Several behavioral and brain imaging studies have demonstrated a significant interaction between speech perception
and speech production. In this study, auditory corticalresponses to speech were examined during self-production
and feedback alteration. Magnetic field recordings wereobtained from both hemispheres in subjects who spoke whilehearing controlled acoustic versions of their speech feedback
via earphones. These responses were compared to recordings
made while subjects listened to a tape playback of theirproduction. The amplitude of tape playback was adjusted tomatch the amplitude of self-produced speech. Recordings of
evoked responses to both self-produced and tape-recorded

INTRODUCTION
Behavioral experiments have shown that different
aspects of speech production are sensitive to auditory
feedback. Adding noise to a speaker’s auditory feedback
results in an elevation of voice volume (Lane & Tranel,1971). Delaying a speaker’s auditory feedback by morethan about 50 msec produces noticeable disruptions inspeech (Yates, 1963; Lee, 1950). Shifting the spectrumof auditory feedback causes shifts in the spectrum of
produced speech (Gracco, Ross, Kalinowski, & Stuart,1994). Perturbing perceived pitch causes the subject toalter his pitch to compensate for those perturbations(Kawahara, 1993). Altering perceived formants inducescompensating changes in the production of vowels(Houde & Jordan, 1998). Prolonged listening to one
phoneme causes small but significant changes in the
production of that phoneme (Cooper, 1979).
A number of physiological studies have shown that
activity at different levels of the auditory system ismodulated by the act of vocal production. Early evidence came from animal studies in bats, birds, andmonkeys. In bats, responses in the lateral lemniscusof the midbrain are attenuated by about 15 dB during

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University of California, San Francisco, 2Tokyo Metropolitan
Institute of Technology, *These authors contributed equally tothis paper.
© 2002 Massachusetts Institute of Technology

speech were obtained free of movement-related artifacts.
Responses to self-produced speech were weaker than were
responses to tape-recorded speech. Responses to tones werealso weaker during speech production, when compared withresponses to tones recorded in the presence of speech fromtape playback. However, responses evoked by gated noise
stimuli did not differ for recordings made during selfproduced speech versus recordings made during taperecorded speech playback. These data suggest that during
speech production, the auditory cortex (1) attenuates its
sensitivity and (2) modulates its activity as a function of the
expected acoustic feedback. &vocalization (Suga & Schlegel, 1972; Suga & Shimozawa,1974). In birds, it is now well known that vocal production is disrupted by perturbation in the auditory
feedback, and that this feedback alteration affects the
responses in higher-order neurons of the auditory forebrain in birds (Leonardo & Konishi, 1999; Schmidt &Konishi, 1998; McCasland & Konishi, 1981). In monkeys, activity in the auditory cortex is found to be
inhibited due to either spontaneous vocalizations or
to vocalizations evoked by electrical stimulation of the
cingulate cortex (Muller-Preuss & Ploog, 1981). Similar,
albeit less consistent results, have been reported forrecordings from human temporal lobe (Creutzfeldt &Ojemann, 1989; Creutzfeldt, Ojemann, & Lettich, 1989a,
1989b). However, the exact characteristics of this attenuation have not been adequately investigated in animalstudies. Furthermore, the dynamics of this attenuationis poorly understood.
Recent functional brain imaging studies in humans
have investigated the activity of the auditory system
during speech perception. The analysis of phonologicaland semantic characteristics of externally generated
speech is found to engage the left inferior frontal, left
temporal, and posterior parietal cortices (Demonet
et al., 1992; Zatorre, Evans, Meyer, & Gjedde, 1992),
while processing of prosodic qualities engages analogous regions in the right hemisphere (Zatorre et al.,1992). It has been hypothesized that similar areas also
Journal of Cognitive Neuroscience 14:8, pp. 1125 –1138

participate in monitoring these aspects of speech thatare self-generated (Hickok & Poeppel, 2000; Levelt,
1983, 1989).
PET studies have shown that the response to primary
auditory cortex (A1) to self-produced speech is minimal(Hirano et al., 1996; Hirano, Naito, et al., 1997). However, this A1 response activity increased if the subject
heard an altered version of their normal speech (Hirano,
Kojima, et al., 1997). In other PET studies, varying therate of speech output while the speech-contingent
auditory input was masked by constant white noiseproduced significant modulation of the response fromthe secondary auditory cortex of the left hemisphere(McGuire, Silbersweig, & Frith, 1996). Furthermore,these studies have reported that the medial prefrontal
cortex and the left frontal insula/operculum were differentially activated during speech feedback alteration.
However, these studies have not examined the dynamics
of activity in these different auditory regions engaged
during speech production.
More recently, magnetoencephalography (MEG) studies have shown that the responses from the auditory
cortex to self-produced speech are attenuated in the
millisecond time scale, when compared with responses
from tape-recorded speech (Curio, Neuloh, Numminen,
Jousmaki, & Hari, 2000; Numminen & Curio, 1999;
Numminen, Salmelin, & Hari, 1999). These initial MEG
studies also reported a lack of a ‘‘change’’ response to
self-produced stimuli. The study by Curio et al. (2000)
examined 100 msec poststimulus (M100) response differences between speaking and tape playback conditions. The authors found significant differences in
M100 amplitude across listening conditions in the left
hemisphere, and significant differences in M100 latencies across listening conditions in both hemispheres.
Numminen et al. have investigated this phenomena in
two studies. In one study, they reported that M100
responses to 1-kHz tones were slightly delayed andsignificantly inhibited during overt speech as comparedwith silent speech (Numminen et al., 1999). In a related
study, they also reported that M100 responses to shortrecorded vowel sounds heard while subjects also heard
either self-produced or tape recorded vocalizations were
delayed and dampened relative to background-free presentations (Numminen & Curio, 1999).
In this study, we sought to further examine thisattenuation of responses to self-produced speech. In
particular, we were interested in discerning between
two possible mechanisms of the M100 response reduction. One possibility is that activity in the auditory
system is generally suppressed during speech. Such‘‘nonspecific’’ attenuation could be the indirect resultof middle-ear muscle activity during speaking (Papanicolaou, Raz, Loring, & Eisenberg, 1986). Alternatively,
the M100 response reduction during speaking could
result from a comparison between actual and predicted
auditory feedback—that is, an auditory version of Held’s
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Journal of Cognitive Neuroscience‘‘reafference hypothesis’’ (Hein & Held, 1962). Motorsystem activity during speaking could generate an internal representation of the expected auditory feedback,and a match between expected and actual feedback
could reduce the M100 response.
To test between the efference copy and nonspecificattenuation hypotheses, we performed a series of experiments in which MEG was used to examine M100
responses to speech in a variety of conditions. In theseexperiments, magnetic field recordings were obtained
from the auditory cortices in both hemispheres ofsubjects who spoke while hearing controlled versionsof their speech feedback via earphones. These responses were compared to MEG recordings made while
subjects listened to a tape recording of their production.
Three experiments were conducted. First, we repeat
the experiments of Numminen et al. and examined the
dynamics of attenuation of responses to self-produced
speech. Second, we tested the specificity of the recorded
signal attenuation by examining M100 responses to tone
pulses added to the speech heard by the subject. Third,
we tested the ‘‘reafference hypothesis’’ by examining
M100 responses to speech feedback altered to ‘‘mismatch’’ the expected speech feedback.

RESULTS
Experiment 1
Figure 1 shows the apparatus used in the experiments.
Magnetic fields were recorded from both hemispheresin a shielded room using two 37-channel biomagnetometers. A directional microphone was placed in thechamber at a distance where it did not distort the

Figure 1. Apparatus used in the experiments.

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recorded magnetic fields. Acoustic input was delivered
to the subject via air tube-based earphones. Triggeringof the data acquisition system and of stimuli delivered tothe ears was controlled and differed for each of threeexperiments in the study.
The schematic for our first experiment is shown in
Figure 2. This experiment consisted of two successive
conditions: speaking and tape playback. In the speaking
condition (Figure 2a), subjects were instructed to produce the short vowel sound / / by phonating with their
jaw and tongue relaxed and stationary. The subject’s
speech was picked up by a microphone and fed to a taperecorder, his earphones, and the trigger input for MEG
data acquisition. A total of 100 utterances were recorded
on tape. In the subsequent tape playback condition(Figure 2b), subjects were instructed to remain silent
while they heard a tape-recording of their utterancesfrom the speaking condition.
Results for one subject in the speaking condition are
shown in Figure 3, which shows evoked magnetic field
response recorded at each detector position, averaged
over the 100 utterance trials. In the figure, the traces
show average response at each detector position,aligned to the onset of vocalization (gray tick marks).
For each condition (speaking, tape playback), the RMS
responses at each detector were averaged together forthe left and right hemispheres, as shown in Figure 4.
The figure shows RMS responses averaged over detectors in the left (upper panel) and right (lower panel)
hemisphere detector arrays. In each panel, the thicktrace shows the average RMS response in the speaking

Figure 3. RMS responses across detector arrays recorded in the
speaking condition of Experiment 1, from a single representative
subject. The traces show average response at each detector position,aligned to the onset of vocalization (gray tick marks). (Gaps in thedetector arrays are due to offline detectors.)

Figure 2. Setup for the two conditions of Experiment 1.

condition and the thin trace shows the same for thetape playback condition. The vertical axis of each panel
indicates RMS field strength (fT) of the response, while
the horizontal axis indicates time (msec) relative to theonset of the vocalization, which occurs at 0 msec. Manydifferences between responses in the speaking andHoude et al.

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Figure 4. RMS responses recorded in Experiment 1, averaged over all
37 detectors in each hemisphere’s detector array, from a single
representative subject. In each plot, the thick traces are responses inthe speaking condition, the thin traces are responses in the tapeplayback condition. The onset of the audio stimulus is at 0 msec.

tape playback conditions can be seen in the figure. At
about 100 msec poststimulus onset, most of the traces
show a peak in RMS amplitude. This peak is referred toas the M100 response, and there are large differencesin its amplitude across conditions in both hemispheres
for this subject. In the left hemisphere, the M100
response in the speaking condition is greatly suppressed compared to that seen in the tape playback
condition. In the right hemisphere, this suppression iseven greater: In the tape playback condition, there isquite a large M100 response, but in the speaking,condition the M100 is essentially absent.
Figure 5 shows the same response waveforms of theprevious figure, averaged across all eight subjects. In thisfigure, in addition to the average response waveforms,
vertical bars indicating standard errors are also shown.
The asterisks in each panel indicate latencies at which
the speaking and tape playback responses differed significantly, relative to p < .001. These plots show that inboth hemispheres, mean M100 responses were significantly smaller in the speaking condition than in the tapeplayback condition. It appears that left hemisphereresponses are larger than right hemisphere responses
in the tape playback condition, but that both hemispheres are inhibited to about the same level in the
speaking condition. However, even in the speaking
condition, the left hemisphere M100 response appears
more focal in time than that of the right hemisphere.
Less consistent are the longer latency responses. For
example, the amplitude of the M200 response in the left
hemisphere was significantly larger in the tape playback
condition, while in the right hemisphere, it was significantly larger in the speaking condition.
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Journal of Cognitive Neuroscience

The acoustic signals reaching the ears via air conduction, the so-called ‘‘side-tone’’ sound amplitudes, were
adjusted to be the same for both the speaking and tapeplayback conditions; all our subjects reported that theintensity of mic and tape signals were perceptually
identical. However, in the speaking condition, because
the subject hears both the side tone and bone conduction of his voice, the speech heard by a subject is actually
about twice the amplitude of (i.e., 3 dB louder) the
speech heard in the tape playback condition (vonBekesy, 1949). Thus, a priori, we would expect thisamplitude difference to be a possible confound in theexperiment. The size of evoked magnetic field responsein the auditory cortex and, in particular, the amplitudeof the M100 response have been shown to be a monotonically increasing function of audio input level (Stufflebeam, Poeppel, Rowley, & Roberts, 1998). From this,
we would expect the M100 response to be larger in thevoiced condition than in the tape playback condition.
However, this response difference should be small: At
the audio levels used in this experiment (80 dBA at theearphones), the effect of audio amplitude on the M100
response has nearly saturated. Additionally, as we haveseen, the actual M100 response recorded in the speaking
condition was much less than that recorded in the tapeplayback condition—a result opposite of what would be
predicted based on audio amplitude differences.
Source locations were performed on the spatiotemporal magnetic field responses recorded 50 – 200
msec following stimulus onset. Across all our stimulus
conditions (mic vs. tape vs. tone response), we found nostatistically significant difference ( p > .5) in the locationof an equivalent dipole in each hemisphere that accounts

Figure 5. RMS response waveforms in Experiment 1, averaged
across all subjects. The vertical bars in each trace show standard errors,
while the asterisks show intervals where the response waveforms fromthe microphone condition (thick traces) and tape condition (thintraces) differ significantly, relative to p < .001 for both hemispheres.

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for these magnetic field responses. These localization
data suggest that the observed response suppression/
attenuation is arising from a restricted cortical region,
presumably A1 and its immediate environs.
Overall, all subjects’ M100 responses were localized tothe temporal cortex, in the area of the auditory cortex.
We infer this because lesion studies, intracranial recordings, and source modeling studies provide converging
evidence that the neuronal generators of the M100
response were located in the auditory regions of thetemporal lobes and include A1 and its immediate environs (Ahissar et al., 2001; Picton et al., 1999; LiegeoisChauvel, Musolino, Badier, Marquis, & Chauvel, 1994;
Reite et al., 1994; Richer, Alain, Achim, Bouvier, & SaintHilaire, 1989; Scherg & Von Cramon, 1985, 1986;
Woods, Knight, & Neville, 1984; Hari, Aittoniemi, Jarvinen, Katila, & Varpula, 1980).
Experiment 2
The results of Experiment 1 clearly confirmed that the
M100 response is suppressed in the speaking condition.
One possible mechanism that could cause this suppression is nonspecific attenuation. That is, neural signals
arising as a result of activity in the motor cortex might
directly and broadly inhibit auditory cortex activity.
If auditory cortex activity is nonspecifically suppressed
during speaking, then it should be less responsive to all
auditory signals. In Experiment 2, we tested this possibility by examining the M100 response to tones heard
during speech that was either self-produced or playedback from tape.
In this experiment, we measured the responses evoked
by tones pips under three successive conditions, as
shown in Figure 6. In the first condition (tones alone,
Figure 6a), subjects remained silent and heard 1.0-kHz
tone pips. In the second condition (tones and speaking,
Figure 6b), subjects again heard these tones pips, but thistime, the tones were presented while subjects produced
long utterances of the vowel / / (again, with no movement of their jaw or tongue). In the third condition(tones and tape playback, Figure 6c), subjects again
remained silent and heard both tone pips and tapeplayback of their speech produced in the previous condition. For the last two conditions in which the subject
heard tone pips and speech, the speech was attenuatedby 20 dB to reduce the masking of the tone pips.
The results of Experiment 2 are summarized in Figure
7. The figure shows a plot of the mean RMS response,
across detectors and subjects, for each condition and ineach hemisphere. Dashed traces show subjects’ responses to hearing the tones in silence (tones alone
condition). Thick traces show responses to hearing thetones while subjects produced / / (tones and speaking
condition). Thin traces show responses to hearing thetones along with the taped speech playback (tones andtape playback condition). As in Figure 5, the vertical bars

Figure 6. Setup for the three conditions of Experiment 2.

are standard errors. Asterisks indicate time intervals
during which responses to tones heard while producing speech (tones and speaking condition) differedsignificantly ( p < .001) from the responses to tones
heard with taped speech playback (tones and tapeplayback condition).
For most of the traces in Figure 7, the M100 and M200
responses to the tones across conditions stand out
clearly. For both hemispheres, these responses are very
prominent for tones heard in silence (dashed traces). In
the left hemisphere, there was a reduction in M100 and
M200 response to the tones in the speaking (thicktraces) and tape playback (thin traces) conditions. Interestingly, the response reduction in the speaking
condition appeared to be greater than the response
reductions in the tape playback condition. Indeed, thestatistical analysis confirmed that the M100 response in
Houde et al.

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Figure 7. RMS waveforms for responses to 1.0-kHz tone pips,
averaged across all subjects for the three conditions of Experiment 2:
the tones alone condition (dashed traces), the tones and speaking
condition (thick traces), and the tones and tape playback condition(thin traces). The vertical bars in each trace show standard errors,
while the asterisks show intervals where the response waveforms fromthe tones and speaking condition (red) and the tones and tapeplayback condition (blue) differed significantly, relative to p < .001 forboth hemispheres.

the tones and speaking condition was significantly lessthan the M100 response in the tones and tape playback
condition. Nevertheless, the difference was small. In theright hemisphere, there was an approximately equal
reduction in M100 and M200 responses to the tones inthe speaking and tape playback conditions. Within the
M100 – M200 region, responses in these two conditions
were only significantly different at one latency, as shownby the asterisk.
It appears that, at the most, the act of speaking
creates only a limited degree of nonspecific attenuation
to the tones in the left hemisphere that is not seen in
the tape playback condition. To quantify how muchnonspecific attenuation contributes to the suppressionof the response to self-produced speech, we can estimate the signal reductions needed to produce theresponse reductions seen in these experiments. From
the results of Experiment 1, we have seen that, compared to speech from the tape playback, self-productionof speech created a 30% reduction in M100 amplitudein the left hemisphere and a 15% reduction in the righthemisphere, equivalent to 13 and 7 dB decreases in
effective input signal, respectively.1 In Experiment 2, inthe left hemisphere, there was possibly an extra 7%
reduction in M100 response to tones while subjects
spoke as compared to the M100 response to tones during
tape playback, which is equivalent to only a 3-dB reduction in effective input signal. Thus, it appears that nonspecific attenuation cannot account for all the response
suppression recorded for self-produced speech.
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Journal of Cognitive Neuroscience

It has also been argued that any attenuation in the
responses during vocalization could occur due to bone
conduction and activation of middle-ear muscles or due
to inhibition at the level of the brain stem (Papanicolaou
et al., 1986). Thus, to further test the hypothesis ofnonspecific attenuation, we measured brainstem evoked
responses to click trains in five subjects under three
conditions identical to those in Experiment 2—silence,
produced speech, and tape-recorded speech. In the latter
two conditions, we attenuated the speech background by
20 dB to obtain a clear evoked response to clicks. For
each of these conditions, we recorded the brainstemevoked response and measured the latency and amplitude of Waves I– V. The results from these experiments
indicated that there were no statistical differences ineither the amplitude or the latencies for all waves
between the three background conditions ( p > .2).
These results provide further support to the hypothesisthat our observed effects were cortical in origin.
Experiment 3
Another possible cause of the response reduction toself-produced speech is that activity in the auditory

Figure 8. Setup for the two conditions of Experiment 3.

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cortex is the result of a comparison between the incoming signal and an internally generated prediction of whatthat signal would be. If this were to be the case, thenaltering the incoming signal should create a mismatchwith that expectation that would reduce or abolish the
response suppression.
In Experiment 3, we tested this possibility by altering the auditory feedback to the subject during selfproduction of speech. The setup for Experiment 3 isshown in Figure 8. Like Experiment 1, this experimentconsisted of two conditions: speaking and tape playback. However, instead of hearing their speech (selfproduced or from tape playback), subjects heard a sumof their speech plus the output of a white noise generator that produced noise bursts gated to the durationof the speech utterance. Subjects reported that they
could not hear their own speech and that they only
heard noise gated to their vowel production.
The results of Experiment 3 are shown in Figure 9. For
both hemispheres, the thick traces show the average
evoked response of subjects hearing gated noise in the
speaking condition, while the thin traces show their
responses to gated noise in the tape playback condition.
Asterisks mark poststimulus latencies where the evoked
responses in the two conditions differ significantly,
relative to p < .001 for both hemispheres. The figure
shows subjects’ evoked responses in the two conditions
are significantly different at a number of latencies: In theleft hemisphere, the responses in the speaking and tapeplayback conditions differ significantly around the M100
responses and at a latency of 150 msec. In the righthemisphere, the speaking and tape playback responses

differ significantly at the 50- and 200-msec latencies.
Since we are limiting our analysis of these results toconsidering only M100 responses, the only response
differences of interest are the significant differences seenin the M100 region of the left hemisphere responses. It
is clear from the figure, however, that these response
differences arise principally from a latency difference
between M100 responses; the M100 response to gatednoise in the tape playback condition is about 20 msec
earlier that the M100 response to gated noise in the
speaking condition. The amplitudes of the two M100
responses appear nearly identical.
In sum, Figure 9 shows that the M100 amplitude
suppression seen in Experiment 1 is abolished; in bothhemispheres, the amplitude of M100 response to noise
generated from subjects’ speech via the microphone isas large as the M100 response to noise generated fromthe tape playback of their speech. Thus, when wealtered subjects’ speech feedback in Experiment 3, we
found that the response suppression to self-produced
speech disappeared, which is consistent with the suppression resulting from auditory input matching aninternally expected input.
DISCUSSION
The present study demonstrates that the human auditory cortex responds differently to self-produced speechthan to externally produced speech. In Experiment 1,
significant suppression in the amplitude of M100
responses was recorded in the speaking condition inboth hemispheres, confirming that self-production of
speech suppresses the response in the auditory cortex
to that speech. In Experiment 2, M100 responses totone pips were seen to be modestly more strongly
suppressed by self-produced than by externally produced speech. However, this relatively weak extra suppression was not sufficient to explain the suppression
seen in Experiment 1. On the other hand, in Experiment 3, we were able to abolish the suppression ofM100 responses to self-produced speech by altering the
feedback heard by subjects. This last result is consistent
with the hypothesis that the suppression of response in
the auditory cortex to self-produced speech resultsfrom a match with expected auditory feedback.
Comparison with Other Studies

Figure 9. RMS response waveforms in Experiment 3, averaged acrossall subjects. The vertical bars in each trace show standard errors, while
the asterisks show intervals where the response waveforms from the
speaking condition (thick traces) and tape playback condition (thintraces) differ significantly, relative to p < .001 for both hemispheres.

Experiment 1 is quite similar to the study by Curio et al.
(2000), which also examined M100 response differences
between speaking and tape playback conditions. The
authors found M100 amplitude differences across listening conditions in both hemispheres, but, unlike thepresent study, these differences were not significant in
the right hemisphere. Further, in contradiction to ourstudies, Curio et al. described significant differencesin M100 latencies across listening conditions in bothHoude et al.

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hemispheres. Different results in M100 latencies could
be accounted for by the fact that we matched theacoustic waveform presented to each subject, while
Curio et al. behaviorally matched the perceptual intensity between the listening conditions. Because subjects
could set the tape playback volume to be different fromtheir speech feedback volume, differing audio levels in
the two experiment conditions could account for someof the M100 latency differences that they report. With
MEG data acquisition triggered by speech signal onset,
triggering occurs when the audio signal exceeds somethreshold voltage. As a result, triggering time is sensitive
to the amplification of the signal: The more a signal isamplified, the faster it will reach the trigger threshold.
Thus, if tape playback volume levels were less thanspeech feedback volume levels, triggering from tapeplayback could be delayed compared to the speaking
condition. If so, then for the same true M100 latency,
M100 peaks would appear to have shorter latencies inthe tape playback condition.
In Experiment 3, the amplitude difference in M100
responses between the speaking and tape playback
conditions was abolished by using gated noise to
distort the feedback heard by subjects. Interestingly,
however, this feedback manipulation created a latency
difference between the two conditions: In the left
hemisphere, the M100 response to gated noise triggered by tape playback was 15 msec earlier than thatfor gated noise triggered by the subject’s own speech,
while in the right hemisphere, such a latency difference
is not seen. Curio et al. (2000) also found that the
reduced latency of M100 responses to tape playback
versus self-produced speech was significantly more
pronounced in the left hemisphere. Like Curio et al.,we attribute this left hemisphere latency difference totwo factors. First, this latency difference may represent
differential processing of externally produced versus
self-produced auditory feedback. Although the gatednoise altered the spectral characteristics of their speech
feedback, such that it was likely to mismatch any
internally generated expectations of how their feedback
would sound, the gated noise did not alter the temporal characteristics of their speech feedback: Subjects
still heard auditory feedback begin at the same time as
they commenced phonation. This temporal predictability could potentially have allowed subjects to process
differently gated noise arising from their own speech,
whose onset they could predict, from gated noise
arising from tape playback, whose onset they could
not predict. Second, the fact that this latency difference
was seen primarily in the left hemisphere is consistent
with the left hemisphere’s dominance in the production of speech in right-handed speakers. Other aspectsof our results may also reflect this left hemisphere
dominance: In Experiment 1, the M100 response totape playback speech was larger in the left hemispherethan in the right, and the M100 response to self1132

Journal of Cognitive Neuroscience

produced speech was temporally sharper in the left
hemisphere than in the right. Additionally, in Experiment 2, in the left hemisphere, the M100 response totones was significantly more suppressed by self-produced speech than by tape playback speech—a difference not seen in the right hemisphere.
Results from Experiment 2 are mostly consistent withthe recent experiments by Numminen and Curio (1999).
They reported that neuromagnetic responses to shortrecorded vowel sounds were delayed and dampened
relative to background-free presentations. They did notreport how much they externally attenuated the produced speech signal in order to obtain a clear responseto the probe-vowel sound. In our experiments, we usedprobe tones instead of vowel sounds, and we attenuatedthe produced speech signal to the ear by 20 dB.
Surprisingly, we did not observe any latency differencesin the M100 responses for tones between the three
conditions. We believe that the lack of a latency effectcould be accounted for by the fact that we used tones
instead of vowel sounds (Diesch & Luce, 1997; Poeppel
et al., 1996; Kuriki & Murase, 1989). Furthermore, by anargument analogous to that discussed above for the
Curio et al. (2000) latency results, it is also reasonable
that any amplitude mismatches in the masking sounds
could account for discrepancies in the latency effect(Hari & Makela, 1988). However, while Numminen andCurio identified auditory interference as the main causeof these modifications, their results also provide evidence against the nonspecific attenuation hypothesis,
although they do not specifically discuss their results inthis form. For instance, they found an additional 6 – 9%
decrease in amplitude in response to probe-vowel
sounds during produced speech background whencompared with tape-recorded speech background.
These amplitude differences are consistent with ourExperiment 2 results.
Gunji et al. have found that vocalization-related cortical magnetic fields activated six sources that were foundto temporally overlap in the period 0 – 100 msec after
vocalization onset (Gunji, Hoshiyama, & Kakigi, 2000;
Gunji, Kakigi, & Hoshiyama, 2000). Sources 1 and 2 wereactivated approximately 150 msec before the vocalization
onset and were located in laryngeal motor areas of theleft and right hemispheres respectively. Kuriki, Mori, andHirata (1999) have also found a motor planning centerfor speech articulation that is activated 120 – 320 msec
before the onset of vocalization and located in a region
around the superior end of the left insula. Gunji et al.
Sources 5 and 6 were located in the truncal motor area ineach hemisphere and were similar to Sources 1 and 2. In
contrast, their Sources 3 and 4 were located in theauditory cortices of the left and right hemisphere,
respectively, and were activated after vocalization onset.
However, these experiments did not explore either themodulation of the responses in the auditory or motor
cortex due to vocalization or feedback alteration. In our
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experiments, because we were unable to simultaneously
measure activity from motor and supplementary motor
areas using a 37-channel sensor array over each hemisphere, we focus here only on the activity from auditory
cortices of both hemispheres.
More relevant to our results are the studies of Hirano
et al. (Hirano et al., 1996; Hirano, Kojima, et al., 1997;
Hirano, Naito, et al., 1997), who used PET to look at
cortical activation during speaking. In their first study,
Hirano et al. found a lack of activation in the auditory
cortex while subjects vocalized. In their second study,
Hirano et al. altered how subjects heard their own
vocalizations, and this time recorded significant activation of the auditory cortex. These results are consistent
with our findings in Experiments 1 and 3 of our study.
Why is the Response to Self-Produced Speech
Suppressed?
The present study, along with recent MEG studies by
Curio et al. and other brain imaging studies that haveused PET or fMRI demonstrate that during speech
production, the auditory cortex suppresses its response
to expected acoustic signals (Hirano et al., 1996; Hirano,
Kojima, et al., 1997; Hirano, Naito, et al., 1997). Does
that suppression serve any function? We speculate about
two possibilities.
Auditory Perception
One possibility is that the suppression results from theprocess of distinguishing self-produced from externallyproduced sources. It would be useful for sensory systems
to make this distinction since sensory information fromthese two types of sources is commonly used for different purposes. Sensory information from self-produced
motor actions could potentially be used for feedback
control. On the other hand, sensory information fromexternal sources is primarily used for recognition.
But how could the sensory systems distinguish selfproduced from externally produced sensory information? It has been proposed by previous researchers that
this is done by having motor actions produce expectations of their sensory consequences ( Jeannerod, 1988).
These sensory outcome predictions are then comparedwith the actual sensory input; whatever matches theoutcome predictions is inhibited. In that way, the sensory consequences of self-produced actions could be
filtered from incoming sensory data, thus highlighting
any unpredicted and therefore external (or novel) stimulus that should potentially be attended to.
For a number of reasons, the results of our experiments are consistent with the above account. First,
although we found significant suppression of the responses of the auditory cortex to self-produced speech,
we found little evidence that the act of producing
speech suppresses responses to other sounds (in ourcase, the 1-kHz tone). This finding is consistent with the
hypothesis that only responses to self-produced sounds
are being filtered from the auditory cortex. In addition,
we found that altering the auditory feedback with gatednoise abolished the suppression in the auditory cortex
induced by speaking, consistent with the hypothesis thatthe filtering out of auditory information arising from selfproduced speech is done by comparison with predicted
auditory feedback.
Recently, Blakemore et al. have recorded the sametypes of response suppression phenomena in the somatosensory cortex, suggesting that the need to distinguishself-produced from externally generated sensory input
may be a general property of sensory systems. In theirseries of experiments, Blakemore et al. used bothbehavioral and physiological procedures to look at responses to both self-produced and externally produced
somatosensory stimulation. In one experiment theyexamined the well-known phenomenon that people
cannot tickle themselves ( Weiskrantz, Elliott, & Darlington, 1971). This phenomenon could be explained by
supposing that somatosensory responses are inhibitedwhen the incoming sensory (touch) information matchesa prediction of that input generated by the person’s own
motor system. To test this hypothesis, these investigators
had subjects tickle their palm via an apparatus that could
introduce a time delay between their movement andthe touch stimulation of their palm (Blakemore, Frith, &Wolpert, 1999). When this was done, subjects reported
that with increasing delay, the stimulation of theirpalm was increasingly stimulating (ticklish). That theperceived stimulation increased with delay could be
explained by supposing that the delay made the incoming sensory information mismatch, in time, the predictedsensory input. This experiment was followed up in an
fMRI imaging study looking at activation of the somatosensory cortex and cerebellum to both self-produced
and externally generated palm stimulation (Blakemore,
Wolpert, & Frith, 1999). It was found that activation of
the somatosensory cortex to self-produced palm stimulation was suppressed, compared to externally produced
palm stimulation—a direct analog of the results recordedin the auditory cortex in our experiments.
Speech Motor Control
The suppression could also serve to regulate howmuch auditory feedback is used in the ongoing controlof speech.
A problem with using auditory feedback to control
speech is the delay incurred by the processing of theauditory input. To control speech, auditory feedback
must convey information about the positions of the
vocal tract articulators. From speech acoustics, we knowthat formant frequencies and pitch are the acoustic
parameters that convey this information (Stevens,
1999). Extracting these parameters from the type of
Houde et al.

1133

signal provided by the cochlea requires significant signal
processing, which, in turn, suggests the possibility ofsignificant delay if a succession of auditory brain areas
are needed for this processing (Jürgens, 2002; Burnett,
Freedland, Larson, & Hain, 1998; Perkell, 1997). In
addition, there will be delays associated with the generation of a response in the motor cortex. There will also
be intrinsic delays in transmitting the neural signals tothe appropriate muscles, as well as delays in muscle
responses to these signals ( Jürgens, 2002). Together,
these factors could contribute to a significant feedback
loop delay between auditory input and motor output.
Unfortunately, we know from control theory that, ingeneral, using feedback to control a system with a large
feedback loop delay will not work because the system
becomes unstable (Franklin, Powell, & Emami-Naeini,1991). Intuitively, this instability arises because the delay
makes the feedback uncorrelated with the state of thesystem; sensory feedback from some time in the past
does not entirely reflect the current state of the system.
Yet, auditory feedback does appear to be involved in the
online control of some aspects of speech production. As
noted in the Introduction, if the pitch of a speaker’s
feedback is perturbed, they will typically compensate
within 100 – 200 msec (Burnett et al., 1998; Kawahara,
1993; Elman, 1981). Thus, in spite of a large (100 –200 msec) feedback loop delay, auditory feedback does
stably regulate a speaker’s pitch. How is this possible?
Again, from control theory, we know that when feedback
is delayed, corrupted by noise, or otherwise madeuncorrelated with current motor output, a successful
approach to using feedback control is to attenuate thesensory feedback to the degree that it is uncorrelated.
Specifically, in feedback control based on Kalman filtering, the Kalman gain on sensory feedback is set, in part,to be inversely proportional to the degree to which
sensory feedback is correlated with the current system
state ( Jacobs, 1993). In an analogous way, it may be thatthe suppression of the auditory cortex during speaking
functions to attenuate the auditory feedback to a levelcommensurate with its delay, such that it can be usedproperly in the control of speech.
Finally, it is possible that both of the above accounts
are true. It may be that the suppression of self-produced
speech acts as a filter for incoming sensory data, allowing attention to be paid to externally produced sensations, while at the same time properly attenuating theauditory feedback for use in the online control ofspeech. Future experiments must be designed to distinguish between the above hypothesized possibilities.

METHODS
Subjects
Eight male volunteers (ages 25 – 40) participated inExperiments 1 and 3 of this study. Of these volun1134

Journal of Cognitive Neuroscienceteers, six also participated in Experiment 2. All subjects
gave their informed consent. All studies were performed with the approval of the UCSF Committee for
Human Research.
Experimental Design
Magnetic fields were recorded from both hemispheresin a shielded room using two 37-channel biomagnetometers with SQUID-based first-order gradiometer sensors(Magnes II, 4-D Neuroimaging, San Diego, CA). A schematic of the experimental setup is shown in Figure 1.
A directional microphone was placed in the chamber at a
distance where it did not distort the recorded magnetic
fields. Triggering of the data acquisition system and of
stimuli delivered to the ears was controlled, and it
differed for each of three experiments in the study.
Fiduciary points were marked on the skin for later
coregistration with structural magnetic resonanceimages and the head shape was digitized to constrain
subsequent source modeling. The sensor was initially
positioned over the estimated location of the auditory
cortices in both hemispheres, such that a dipolar M100
response was evoked by single 400-msec duration tone
pips (1 kHz, 5 msec rise/fall ramps, 90 dB SPL). Dataacquisition epochs were 600 msec in total duration witha 100-msec prestimulus period referenced to the onsetof the first speech stimulus. Data were acquired at asampling rate of 1041 Hz. The position of the sensor
was then refined so that a single dipole localization
model of the averaged evoked magnetic field responses
to 100 tones resulted in a correlation and goodness-of-fit
greater than .95.
With satisfactory sensor positioning over the auditory
cortices in both hemispheres, but before starting theexperiments, subjects practiced producing the neutral
vowel sound / / by phonating while relaxing their upper
vocal tract, generating no movement of their jaw or
tongue. Preliminary experiments revealed that this formof speech production produced no movement artifacts
in these magnetic field recordings.
During the speaking condition of Experiments 1 and 3,
the audio onset of each utterance picked up the microphone triggered the MEG data acquisition system, which
recorded 1 sec of data (300 msec pretrigger, 700 msec
posttrigger) at a sampling rate of 1041 Hz. Subjects
were instructed to produce short vowel sounds / / byphonating their vocal chords without any movement of
their jaw or tongue. Responses to 100 such utteranceswere recorded, and these magnetic field recordings werethen averaged to obtain the evoked magnetic field
responses to self-produced speech for each MEG detector channel. In the subsequent tape playback condition,
the audio signal from the tape recorder was fed to thesubjects earphones and the trigger input for MEG dataacquisition. Again, magnetic field recordings wereaveraged to obtain the evoked magnetic field responses.
Volume 14, Number 8

During the tape playback condition of Experiments 1
and 3, the audio level of the signal fed to the earphones
and trigger was adjusted to be the same as that of the
speaking condition. Because of this, the trigger of MEG
data acquisition (which was based on an audio voltage
threshold) occurred at the same time relative to utterance onset in both conditions. The equal audio levels
ensured that triggering latency differences could notaccount for any differences between the evoked responses to self-produced and tape-recorded speech. In
Experiment 1, the subject heard these equal audio levelsin the earphones, which meant that his side tone amplitude was the same in both the speaking and tape playback conditions. In Experiment 3, the audio signal foreach utterance was used to trigger both the MEG dataacquisition system and a white noise generator (20 Hz–
20 kHz bandwidth, 85 dB SPL). In this case, a sum of theiraudio signal and noise was presented to the subject.
In Experiment 2, onset of the tone pips triggered MEG
data acquisition in each of the three conditions (tones
alone, tones and speaking, and tones and tape playback).
These 1-kHz tones each had a 400-msec duration, 5-msec
onset and offset ramps, and a 0.8 – 1.2-sec ISI between
tones. The subject heard the tones at 90 dB SPL. As with
Experiment 1, data acquisition epochs were 1.0 sec in
total duration with a 300-msec prestimulus period. Data
were acquired at a sampling rate of 1041 Hz. In eachcondition, the evoked magnetic field responses to thetone pips were obtained by averaging 100 epochs.
Epochs 1500 msec long with a 500-msec prestimulus
period were collected at a sampling rate of 1041 Hz. The
raw epoch magnetic field data from these 100 utteranceswere then averaged to obtain the evoked magnetic field
responses to gated noise during self-production.
The voltage level of the audio signal fed to earphone
and trigger signal in this condition was adjusted to be
identical to the voltage levels recorded during the speaking condition, ensuring that triggering differences could
not account for any differences between the evoked
responses to noise in the self-produced and tape playback blocks.
Data Analysis
Data from the two sensor arrays positioned over eachhemisphere were separately analyzed. From the evokedmagnetic field response for each condition in eachchannel, the RMS value of evoked magnetic field strength
averaged across channels was computed. The RMS method of averaging (square root of the mean of squaredchannel field strengths) was used to avoid the cancellation that occurs when the magnetic fields from detectors
on opposite sides of the current dipole (whose fields areroughly equal in magnitude but opposite in sign) areaveraged together. This process resulted in one RMS
time waveform for each hemisphere, for each condition,for each subject. For statistical comparisons across con-ditions, a one-way analysis of variance was performed
with condition as factor and time as a repeated measure.
The RMS across channels is a convenient measure ofactivity that is independent of the position of individual
sensors. Following optimal placement of the sensor for asignificant M100 dipolar response, RMS has been shownto correlate well with the ‘‘equivalent current-dipole
strength’’ (also called the ‘‘Q value’’) of a dipole positioned in and around the auditory cortex. The RMS
measure is also correlated with the data obtained from
(1) the channel with the biggest signal-to-noise ratio,and (2) the first two principal components of the array
response (Ahissar et al., 2001; Mahncke, 1998).
In sensor arrays comprising of planar gradiometers(like the Neuromag 122 or 306), the channel with largest
signal-to-noise ratio is often closest to the source (asdictated by the physics). However, in the case of sensor
arrays comprising of axial gradiometers (as in our
Magnes II system), the channel with the largest signalto-noise ratio reflects the peak of the magnetic field
which is not the closest channel to a source. Moreover,the ‘‘best’’ channel could reflect either a peak ‘‘inward’’or ‘‘outward’’ field, requiring signed inversion while
averaging such data across subjects. Finally, since thesensor array position can varying considerably acrosssubjects, it is difficult to use procedures typical for EEG
and whole-head studies wherein single channel data isoften picked for analysis across subjects. These considerations have led us to use RMS instead of picking
specific channels for our analysis.
Single Dipole Model Fit
A single equivalent current-dipole model was calculated
separately for each hemisphere, for each poststimulus
time point, using standard 4-D Neuroimaging software
operating on data that had been filtered between 1 and
20 Hz. The localization algorithm used an iterative leastsquares minimization to compute the strength andlocation of a single dipole in a spherical volume ofuniform conductivity that can account for the sensor
data. Dipole fits were accepted based on a local correlation maximum criteria of .95 and goodness-of-fit values
greater than .95.
The MEG anatomical reference frame was establishedusing a digital device called the ‘‘sensor position indicator.’’ A set of three table-mounted and one arraymounted receivers triangulates the signal from a stylustransmitter positioned at fiducial reference points on thesubject’s head surface (typically nasion, left, right preauricular points, Cz, and inion). The same stylus transmitter arrangement served to define the curvature of thehead by tracing the surface from which a ‘‘local sphere’’model of the head was generated. To coregister the
dipole locations on the MRI image, the fiduciary points
serve as the basis for a common coordinate system. By
superimposing these fiducial landmarks on MR images
Houde et al.

1135

of the subject, it was possible to define the position of
the computed point sources with an accuracy of 5 mm.
Multiple Dipole Localization Analysis
Multiple dipole localization analyses of spatio-temporal
evoked magnetic fields was also performed using Multiple Signal Classification (MUSIC) (Sekihara, Poeppel,
Marantz, Koizumi, & Miyashita, 1997; Mosher, Lewis,& Leahy, 1992) and beamformer algorithms (Mosher
et al., 1992; Sekihara, Nagarajan, Poeppel, Marantz, &Miyashita, 2001; Robinson & Vrba, 1999). MUSIC methods are based on estimation of a signal ‘‘subspace’’
from entire spatio-temporal MEG data using singularvalue decomposition (SVD). A version of the MUSIC
algorithm, referred to as the ‘‘conventional’’ MUSIC
algorithm, was implemented in MATLAB under theassumption that the sources contributing to the MEG
data arose from multiple dipoles (<37 in number)which were stationary and were located within a spherical volume of uniform conductivity (Sekihara et al.,1997). The locations of dipoles are typically determinedby conducting a search over a three-dimensional grid ofinterest within the head. Given the sensor positions
and the coordinates of the origin of a ‘‘local sphere’’approximation of the head shape for each subject, alead-field matrix was computed for each point in this3-D grid. At each point in this grid, from the lead-field
matrix and the covariance matrix of the spatio-temporal
MEG data, the value of a MUSIC localizer function canthen be computed (Sekihara et al., 1997, Equation (4)).
Maxima of this localizer function then corresponded to
the location of dipolar sources. We used the MUSIC
algorithm to compute the localizer functions over athree-dimensional grid in the hemisphere contralateral
to the stimulus delivery. The localizer was computed
using the evoked magnetic field responses over a time
window of 1000 msec following the onset of thesentences. For each subject, at each point in a 3-D grid(¡4 < x < 6, 0 < y < 8, 3 < z < 11) in the left
hemisphere, the localizer function was computed using
the averaged evoked auditory magnetic field responses.
Acknowledgments
This work was funded in part by Scientific Learning Co. and a
grant from the Whitaker Foundation.
Reprint requests should be sent to John F. Houde, W. M. KeckCenter for Integrative Neuroscience, University of California,
San Francisco, 513 Parnassus Avenue, S-762, San Francisco, CA
94143, USA, or via e-mail: houde@phy.ucsf.edu.

Note
1. In a recent study of M100 responses to tones, it was foundthat, near detection thresholds, a 15-dB change in SPL caused a
34% change in M100 RMS amplitude (Stufflebeam et al., 1998).
1136

Journal of Cognitive Neuroscience

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