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The Journal of Neuroscience, April 15, 2003, 23(8):3423
Hierarchical Processing in Spoken Language Comprehension
Matthew H.
Davis and
Ingrid
S.
Johnsrude
Medical Research Council Cognition and Brain Sciences Unit,
Cambridge, United Kingdom CB2 2EF
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ABSTRACT |
Understanding spoken language requires a complex series of
processing stages to translate speech sounds into meaning. In this study, we use functional magnetic resonance imaging to explore the brain regions that are involved in spoken language comprehension, fractionating this system into sound-based and more abstract
higher-level processes. We distorted English sentences in three
acoustically different ways, applying each distortion to varying
degrees to produce a range of intelligibility (quantified as the number
of words that could be reported) and collected whole-brain
echo-planar imaging data from 12 listeners using sparse imaging. The
blood oxygenation level-dependent signal correlated with
intelligibility along the superior and middle temporal gyri in the left
hemisphere and in a less-extensive homologous area on the right, the
left inferior frontal gyrus (LIFG), and the left hippocampus. Regions surrounding auditory cortex, bilaterally, were sensitive to
intelligibility but also showed a differential response to the three
forms of distortion, consistent with sound-form-based processes. More
distant intelligibility-sensitive regions within the superior and
middle temporal gyri, hippocampus, and LIFG were insensitive to the
acoustic form of sentences, suggesting more abstract nonacoustic
processes. The hierarchical organization suggested by these results is
consistent with cognitive models and auditory processing in nonhuman
primates. Areas that were particularly active for distorted speech
conditions and, thus, might be involved in compensating for distortion,
were found exclusively in the left hemisphere and partially overlapped with areas sensitive to intelligibility, perhaps reflecting attentional modulation of auditory and linguistic processes.
Key words:
speech; language; auditory cortex; hierarchical
processing; primate; human; inferior frontal gyrus; temporal lobe; hippocampus; sentence processing; fMRI
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Introduction |
Understanding spoken language is a
rapid and seemingly automatic process. The translation of speech sounds
(in our native language) into meaning is generally achieved without
awareness of intervening processes, despite the background noise and
interspeaker variability that is characteristic of everyday speech.
This robustness reflects the multiple acoustic means by which stable
elements (such as phonetic features or syllables) are coded in clear
speech; this redundancy permits comprehension when some acoustic
information is lost. Robustness in speech comprehension may derive from
the operation of compensatory mechanisms that are recruited when speech becomes difficult to understand, such as listening to loudspeaker announcements at a busy train station or a radio with poor reception. In this study, we use functional magnetic resonance imaging (fMRI) to
explore the functional organization of brain regions involved in spoken
language comprehension, with a view to understanding the neural basis
for normal comprehension and processes that are recruited when speech
becomes more difficult to understand.
Several different levels of representation (e.g., phonetic
features, phonemes, morphemes, and words) have been proposed to mediate
between an incoming speech signal and the computation of its meaning
(McClelland and Elman, 1986 ; Gaskell and Marslen-Wilson, 1997). Models
of spoken language comprehension assume that processing is
hierarchically organized, with greater abstraction from the surface
(acoustic) properties of speech at higher processing levels. However,
the degree to which higher-level linguistic processes can be
distinguished from less-specialized auditory and sound-form-based processes remains unclear (Whalen and Liberman, 1987; Remez et al.,
1994; Scott et al., 2000).
This hierarchy of processing stages may map onto auditory anatomy. The
auditory cortex in primates comprises several cortical fields,
organized into core (primary), belt (secondary), and parabelt regions.
Anatomical and electrophysiological studies indicate that adjacent
regions are interconnected and information proceeds from core, to belt,
to parabelt, and to more distal areas as processing demands become more
complex (for review, see Rauschecker, 1998; Kaas and Hackett, 2000).
Neuroimaging studies have suggested a similar processing hierarchy in
humans, but, to date, such studies have been limited to nonlinguistic
stimuli (e.g., frequency-modulated tones or bandpassed noise)
(Wessinger et al., 2001; Hall et al., 2002).
In this study, we alter (distort) the specific surface properties of
speech in three different ways (see Fig. 1) and use a correlation
design to relate brain activity to intelligibility. We operationalize
"intelligibility" as the amount of a sentence that is understood,
an aggregate measure of the multiple hierarchically organized processes
involved in comprehension. Within areas that are sensitive to
intelligibility, we can differentiate regions that are also sensitive
to the type of distortion used (form-dependent) and, thus, probably
involved in acoustic analysis, and those that are insensitive to
distortion type (form-independent); these areas may be involved in
higher-level linguistic processes.
We can also identify the neural correlates of effortful understanding
by contrasting the neural response to distorted (yet still
intelligible) sentences for which comprehension is difficult with
conditions that involve less effort (cf. Poldrack et al., 2001).
Activation in this contrast may reflect the action of compensatory mechanisms that modulate activity at an acoustic level or at higher levels of processing.
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Materials and Methods |
Stimulus preparation
There were 190 declarative English sentences ranging
in topics, comprising 5-17 words (1.7-4.3 sec in duration), and
digitized at a sampling rate of 22.1 kHz taken from the
test and filler sentences used in a previous behavioral study (Fig.
1a) (Davis et al., 2002).
Three forms of distortion were applied to these sentences using Praat
software (Institute of Phonetic Sciences, University of
Amsterdam, Amsterdam, The Netherlands) (available at
www.praat.org). All three forms of distortion preserved the duration,
amplitude, and average spectral composition of the original sentences,
although the acoustic form of sentences processed with the three types
of distortion were markedly different.
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Figure 1.
Spectrograms of sample experimental stimuli.
a, The sentence "The poster was advertising a concert
to be held next week" in undistorted normal form. b,
Segmented with 500 msec noise bursts alternating with 200 msec of
undistorted speech. c, Vocoded speech shown with four
frequency bands. d, Speech in noise with a background of
speech-spectrum noise at a signal-to-noise ratio of  1 dB.
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Segmented. Segmented speech was created by dividing the
speech waveform into short chunks at fixed intervals and replacing even-numbered chunks of speech with a signal-correlated noise (SCN) version of the original speech (Bashford et al., 1996). Signal-correlated noise is a waveform with the same spectral profile and amplitude envelope as the original speech but consisting entirely of noise. Although it retains some physical properties of the speech
that it replaces (e.g., a speech-like rhythmical structure), these
periods of signal-correlated noise do not contain any intelligible speech sounds (Schroeder, 1968). The duration of clear speech was fixed
at 200 msec and 500, 200, or 100 msec sections of speech were replaced
by signal-correlated noise (Fig. 1b).
Vocoded. Noise-vocoded speech (Shannon et al., 1995)
was created by dividing the speech signal between 50 and 8000 Hz into 4, 7, or 15 bandpass-filtered frequency bands. Sentences were resynthesized by replacing information in each frequency band with
amplitude-modulated bandpass noise (Fig. 1c). Frequency
bands were approximately lineally spaced (i.e., the width of each band was proportional to the center frequency of that band). Noise vocoded
speech sounds like a harsh robotic whisper.
Noise. Speech in noise was generated by adding a
continuous speech-spectrum noise background to sentences at three
signal-to-noise ratios (1, 4, or 6 dB) (Fig. 1d). The
overall amplitude of each speech-in-noise stimulus was reduced to match
the amplitude of the original sentence.
In addition to these three forms of distortion, a signal-correlated
noise baseline was generated using the same algorithm as that for
segmented speech but without periods of clear speech. Sentences
processed in this way sound like a rhythmic sequence of noise bursts,
carry no linguistic information, and are entirely unintelligible
(Schroeder, 1968).
Pilot study
A pilot behavioral study was conducted to ensure that a
continuum of intelligibility was obtained for each form of distortion. Eighteen native English speakers heard single-stimulus sentences over
closed-ear headphones (model DT770; Beyerdynamic, West Sussex, UK)
played from the soundcard of a Dell laptop PC (Dell Computer Company,
Round Rock, TX). Participants were required to either type as many
words as they could understand or to rate intelligibility (on a
nine-point scale) immediately after each item. Sentences were
pseudorandomly assigned to a type and level of distortion (three
versions of the test were created with the same sentences assigned to
different conditions). Each subject was tested on one version of this
behavioral study and therefore heard each sentence only once.
Word-report performance (calculated as the proportion of words per
sentence that were reported correctly) and rated intelligibility were
averaged over five items per condition per subject. A total of six
levels of intelligibility were tested for each form of distortion. For
the 19 conditions tested (six levels of three types of distortion and
clear speech), word-report scores and rated intelligibility were
reliably correlated (r = 0.99; p < 0.001).
We selected three levels of each form of distortion described above: a
low-intelligibility condition (~20% of words reported correctly), a
medium-intelligibility condition (~65% of words reported correctly),
and a high-intelligibility condition (~90% of words reported
correctly) (Fig. 2). ANOVA comparing
intelligibility ratings showed no significant difference between the
three types of distortion at each level of intelligibility (low
intelligibility, F(2,34) = 1.58, p > 0.1; medium and high intelligibility, both F values < 1). However, for word-report scores, some
differences between types of distortions were reliable at each level of
intelligibility. For low intelligibililty
(F(2,34) = 8.75; p < 0.001) pairwise comparisons indicated significantly reduced
intelligibility for vocoded speech, medium
intelligibility(F(2,34) = 4.35;
p < 0.05), with increased intelligibility for
segmented speech, and for high intelligibility stimuli
(F(2,34) = 3.76; p < 0.05) marginally reduced intelligibility for speech in noise.
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Figure 2.
Word-report scores from the pilot study and
subject ratings during scanning for the 11 different kinds of stimuli.
Each point represents the average (and SE) for 18 subjects in the pilot study (percentage of words reported correctly) or
eight subjects in the fMRI study (fMRI ratings). Correlation between
word report and ratings was r = 0.98;
p < 0.001.
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Scanning procedure
Twelve right-handed volunteers (five females) between 18 and 42 years of age were scanned. All subjects were native speakers of
English, without any history of neurological illness, head injury, or
hearing impairment. This study was approved by the Addenbrooke's Local
Research Ethics Committee (Cambridge, UK), and written informed
consent was obtained from all subjects. Volunteers were told that they
would be listening to sentences that were distorted with different
amounts and types of noise and were asked to rate the intelligibility
of each item using a four-alternative button press with their right
hand. The alternatives ranged from understanding most or all of the
sentence (index finger; button 4) to none or not very much of the
sentence (little finger; button 1). Volunteers were given a short
period of practice in the scanner with a different set of sentences
that were processed in the same way as the experimental items.
We acquired imaging data using a Medspec (Bruker,
Ettlingen, Germany) 3 tesla MRI system with a head gradient set.
Echo-planar imaging (EPI) volumes (228 in total) were acquired over two
17 min sessions. Each volume consisted of 21 × 4 mm thick slices with an interslice gap of 1 mm; field of view, 25 × 25 cm;
matrix size, 128 × 128; echo time, 27 msec; acquisition time,
3.02 sec; and actual repetition time, 9 sec. We used a sparse
imaging technique in which stimuli are presented in the silent period
between successive scans, minimizing acoustic interference (Edmister et
al., 1999; Hall et al., 1999). Acquisition was transverse oblique,
angled away from the eyes, and covered all of the brain except in a few cases (the very top of the superior parietal lobule, the anterior inferior temporal cortex, and the inferior aspect of the cerebellum).
Two scanning sessions of 114 trials were performed. Each trial
comprised a stimulus item followed by a tone pip and a single EPI
volume (Fig. 3). Stimulus items in other
trials were pseudorandomly drawn from the 11 experimental conditions
(low-, medium-, and high- intelligibility conditions for each of three
forms of distortion, plus signal-correlated noise and clear speech).
There were 19 trials of each stimulus type and an additional 19 silent
trials. Stimulus onset and offset were jittered relative to scan onset by temporally aligning the midpoint of the stimulus item (0.8-2.1 sec
after sentence onset) with the midpoint of the gap between scans (6 sec), thus ensuring that scans were obtained 3-6 sec after
stimulation. This coincided with the peak of the hemodynamic response
evoked by the stimulus (Edmister et al., 1999; Hall et al., 1999). The
tone pip occurred 1 sec after stimulus offset (or at a matched position
in silent trials) and cued the subject to rate the intelligibility of
the item just presented [or a self-determined (random) button press
for silent scans]. Items from the 190-sentence corpus were
pseudorandomly assigned to different distortion conditions using three
different forms of randomization. Sentences presented as SCN were
chosen from the other 12 conditions; no other items were presented more
than once in the experiment. Stimuli were presented diotically, using a
high-fidelity auditory stimulus-delivery system incorporating
flat-response electrostatic headphones inserted into sound-attenuating
ear defenders (Palmer et al., 1998). To further attenuate scanner
noise, participants wore insert earplugs (E.A.R. Supersoft; Aearo
Company, Indianapolis, IN) rated to attenuate by ~30 dB. When
wearing earplugs and ear defenders, participants reported that the
scanner noise was unobtrusive and that sentences were presented at a
comfortable listening volume and at equal levels in both ears. Custom
software (Palmer et al., 1998) was used to present the stimulus items,
and DMDX (Forster and Forster, 2003) was used to record
button-press responses.
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Figure 3.
Details of the scanning procedure. A sparse
imaging technique was used (see Materials and Methods) in which a
single stimulus item was presented in the silent periods between scans.
A tone pip after each sentence cued the subject's intelligibility
judgment (button press). Timings of sentence onset and offset and tone
cue relative to scan onset were jittered across trials.
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Analysis of fMRI data
Data processing and analysis was accomplished using Statistical
Parametric Mapping (SPM99; Wellcome Department of Cognitive Neurology,
London, UK). Preprocessing steps included within-subject realignment,
spatial normalization of the functional images to a standard EPI
template (masking regions of susceptibility artifact to reduce tissue
distortion) (Brett et al., 2001), and spatial smoothing using a
Gaussian kernel of 12 mm, suitable for random-effects analysis (Xiong
et al., 2000).
We were interested, first of all, in identifying areas in which
activation correlated with intelligibility (see Fig. 4a). Within these intelligibility-sensitive areas, we then wanted to differentiate between areas of form dependence (activation that was
sensitive to the acoustic form of the stimulus, as shown in Fig.
4b) and areas of form independence (areas that responded equivalently to the different forms of distortion). This distinction might plausibly separate areas involved in lower-level acoustic processes from higher-order linguistic levels of processing. In addition, we thought it would be informative to establish the overlap,
if any, between intelligibility-sensitive form-dependent areas and
primary-like cortical auditory areas. Such cortical auditory areas were
identified as those exhibiting elevated response to signal-correlated
noise over silence. Some spatial segregation of the two response types
might indicate a hierarchy of processing within auditory cortices as
stimulus characteristics become more complex, such as has been observed
in the macaque (Rauschecker et al., 1995; Rauschecker, 1998).
We also wanted to identify brain areas exhibiting increased response to
degraded speech stimuli, over and above any correlation with
intelligibility. We hypothesized that, when speech is difficult to
understand (i.e., when speech is distorted yet still potentially intelligible), listeners will make additional effort to extract as much
meaning as possible. This might be reflected in an increased brain
response to distorted speech compared with clear speech (which, with
sparse imaging, a high-fidelity sound delivery system, and comfortable
listening volume, was presented under near-ideal conditions for
effortless comprehension). Because this elevated response for more
distorted speech could also arise from processes that are recruited as
the auditory stimulus becomes less comprehensible and therefore less
engaging (cf. Stark and Squire, 2001), we also compared activation for
distorted speech with signal-correlated noise, which, as is immediately
evident to the subjects, is not speech (see Fig. 4c). An
elevated response to the distorted conditions, over normal speech and
SCN, would be consistent with mechanisms acting to enhance
comprehension for potentially intelligible input. Such mechanisms may
be domain general (e.g., attentional modulation of auditory processing)
or more specific to speech processing. Overlap with other kinds of
response would be informative in this regard; a distortion-elevated
response that overlapped with sensitivity to distortion type might
indicate compensation acting on an acoustic level (consistent with
attentional facilitation). In contrast, areas exhibiting an elevated
response to distorted input and sensitivity to intelligibility,
independent of distortion type, might indicate that distortion places
additional "load" on higher-level linguistic processes (these
alternatives are presented in more detail in Discussion).
Two separate design matrices were constructed for each listener to
optimize sensitivity to our effects of interest. The primary analysis
included two columns indicating both the presentation of a sentence
before each scan (as opposed to a silent period) and the mean
proportion of words reported correctly for that type and level of
distortion in the behavioral pilot. [We used word-report scores as a
covariate for two reasons: (1) to compensate for the small but
significant differences in the intelligibility of the three types of
distortion identified in the pilot study and (2) report scores provide
a more objective measure than the ratings obtained during scanning, and
the two measures are highly correlated (see Fig. 2).] Three additional
columns were included in the design matrix that coded which of the
three types of distortion was applied to each sentence (scans
following signal-correlated noise and clear-speech stimuli were modeled
only in the first two columns). Realignment parameters and a dummy
variable coding the two scanning sessions were included as covariates
of no interest, and a correction for global signal magnitude was made.
The second design matrix was used to evaluate activation for
signal-correlated noise sentences compared with silence and to obtain
signal change estimates for each condition (see Figs. 5f,
7d); it included a single column for each of
the twelve conditions in the experiment and covariates of no interest
as before.
The parameter estimates for each subject, derived from the
least-mean-squares fit of these models, were entered into second-level group analyses in which t values were calculated for each
voxel, treating intersubject variation as a random effect. For main
effects of intelligibility and compensation for distortion, we report activation foci that survive a whole-brain false discovery rate (FDR)
(Genovese et al., 2002) correction at p < 0.05. This
procedure controls the expected proportion of false positives among
suprathreshold voxels to the specified rate (0.05). Where the null
hypothesis is true (i.e., there are no activated voxels), the FDR
procedure produces identical results to a Bonferroni correction,
providing stringent control of familywise-error rate (Benjamini
and Hochberg, 1995). Contrasts that were used to evaluate sensitivity
to acoustic form (i.e., detecting differences between the three forms
of distortion) were applied over the whole brain (with appropriate
correction) and within regions of interest defined by the areas
revealed as active by the intelligibility and
compensation-for-distortion contrasts.
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Results |
Behavioral data were not available from four subjects in the fMRI
study. Mean four-point ratings from the remaining eight subjects
correlated highly (r = 0.98; p < 0.001) with report scores from the 18 subjects in the pilot study (Fig.
2). (Report scores of zero were assumed for signal-correlated
noise in the pilot study.) Given the greater accuracy and consistency
of the report scores compared with the ratings obtained during
scanning, we used these as regressors in the analysis of the fMRI data.
Intelligibility-sensitive areas were identified as those voxels in
which blood oxygenation level-dependent (BOLD) signal was correlated
with word-report scores (Fig.
4a).
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Figure 4.
a-c, Predicted BOLD signal for
three contrasts: linear correlation between BOLD signal and
intelligibility (a), a differential response to
the three forms of distortion (b), and an
elevated response to all forms of distorted speech
(c), compared with clear speech and
signal-correlated noise. Open triangles,
Signal-correlated noise; circles, segmented speech;
x symbols, vocoded speech; squares,
speech in noise; filled triangles, normal speech.
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The comparison of SCN versus silence across subjects yielded activation
bilaterally, in Heschl's gyrus and surrounding areas, consistent with
recruitment of core and belt auditory cortex, as predicted (Fig.
5a,b,
pink-blue intensity scale).
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Figure 5.
Areas showing a significant linear response to
increasing intelligibility. Activations are shown superimposed on the
mean EPI image across subjects and thresholded at p < 0.001, uncorrected for multiple comparisons. a,
Sagittal sections depicting areas in which activation was observed for
signal-correlated noise relative to rest (pink-blue
scale) and areas in which activation correlates with report
score (intelligibility response; yellow-red scale).
b, Axial sections through Heschl's gyrus. Activation to
sound is predominant on Heschl's gyrus, whereas correlation with
intelligibility is observed posteriorly, inferiorly, and laterally to
Heschl's gyrus. c, The intelligibility response pattern
shown in a but with a mask to show voxels that are
sensitive to the acoustic properties of the stimulus (dependent on
distortion type). Because the identification of form-independent
regions depends on the absence of any reliable difference between
distortions, we cannot precisely localize the transition between
form-dependent and form-independent regions and so present the mask in
graded color scale. The 95% contour of the mask (form
dependence; p < 0.05) corresponds to the boundary
between blue and green; the 99.9%
contour (form dependence at p < 0.001) corresponds
to the boundary between orange and red.
Activation foci related to intelligibility and common to all forms of
distortion (form independent) are shown in blue and
listed in Table 1 (top). Arrow indicates the approximate
location of the form-independent left anterior temporal lobe voxel from
which data are plotted in f. d, Axial
sections through Heschl's gyrus. Extending posteriorly and laterally
from primary auditory cortex into belt and parabelt cortices,
form-dependent (graded color) intelligibility responses
give way to form-independent (blue) responses.
e, Coronal section (y = 20)
depicting a form-independent intelligibility response in left
hippocampus. f, The graph shows the size
of the response (percentage of signal change from the mean) for a
form-independent voxel ( ) in the left anterior temporal lobe (58,
2, 24) against word-report score for the different listening
conditions. Open triangle, Signal-correlated noise;
circles, segmented speech; x symbols,
vocoded speech; squares, speech in noise; filled
triangle, normal speech. Error bars indicate SEM
across subjects.
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Correlation with intelligibility
The BOLD signal was positively correlated with word-report score
in voxels along the length of the superior and middle temporal gyri in
the left hemisphere, extending outward from auditory cortex toward the
temporal pole and the temporoparietal junction (Fig. 5a,b, red and yellow
scale). Similar less-extensive activation was observed in
the right superior and middle temporal gyri. A portion of left inferior
frontal gyrus also showed a positive correlation with intelligibility,
as did the body of the left hippocampal complex (Fig. 5d).
Within the superior temporal gyri, a correlation with intelligibility
was observed in areas adjacent to those activated in the SCN-silence
contrast (Fig. 5a,b).
To test for brain areas sensitive to differences between the three
forms of distortion, the intelligibility-responsive region was masked
by all six possible contrasts between pairs of the three distortion
types (Fig. 4b). Setting the threshold for each of these six
contrasts to p = 0.00851 results in a combined  level of p < 0.05 [because, by binomial expansion,
0.95 = (1 0.00851)6].
Intelligibility-responsive areas in which none of these contrasts reach
significance at p < 0.00851 can therefore be
considered to be form independent (i.e., insensitive to differences
between types of distortion) at an uncorrected p > 0.05 (Fig. 5c,d,e, blue;
Table 1, top). A form-independent
correlation with intelligibility was observed in the anterior middle
temporal gyrus bilaterally and in posterior superior temporal
sulcus, inferior frontal gyrus, hippocampus, and precuneus in the left
hemisphere (for a typical response profile, see Fig.
5f).
By increasing the statistical threshold for the form-dependent
contrasts to a more stringent significance level
(p < 0.001 uncorrected, corresponding to
p < 1.67 × 10 4 in
each of the six contrasts, or to an FDR corrected p < 0.00851 within the region of interest), we identified two areas
in which activation not only reflects a response to intelligibility but also shows form dependence at a corrected level of significance. This
contrast identifies areas that are engaged in spoken language comprehension (as shown by the significant correlation between activation and intelligibility) but also shows differential activation depending on the acoustic form(s) of distorted speech presented. Such a
response was observed bilaterally in the superior temporal gyrus (Fig.
5c,d,e, red).
To explore the nature of the sensitivity to acoustic form observed in
the form-dependent regions, an estimate of the effect of different
distortion types was calculated for peak voxels in the left and right
superior temporal gyrus (Fig.
6a,b; see figure legend for coordinates). In both hemispheres, this difference arises
through an elevated response to segmented speech compared with the
other two forms of distortion, particularly to speech in noise.
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Figure 6.
BOLD signal in peak voxels showing a
form-dependent response. Graphs show mean percentage
signal change for each distortion type compared with the mean response
to signal-correlated noise and normal speech. Error bars indicate SEM
after between-subject variability has been removed, which is
appropriate for repeated-measures comparisons (cf. Loftus and Masson,
1994). Braces show significance of paired
t tests comparing the three distortion types
(*p < 0.05; **p < 0.01;
***p < 0.001). a, Form-dependent
response in left superior temporal gyrus (52, 28, 6).
b, Right superior temporal gyrus (66, 16, 0).
c, Left inferior frontal gyrus (42, 20, 6).
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Compensation for distortion
We reasoned that mechanisms involved in compensating for
distortion are not required in either the clear-speech condition or the
signal-correlated-noise condition, and we identified relevant areas by
comparing activation for potentially intelligible conditions with both
fully intelligible and completely unintelligible conditions. In
addition, because differences in intelligibility between conditions were included in the model, elevated activity for distorted speech is
statistically independent of a response that is correlated with
intelligibility. A distortion-elevated response was observed in
left-hemisphere areas, including the middle and superior temporal gyri,
the opercular part of the inferior frontal gyrus, the lateral inferior
frontal gyrus, the posterior middle frontal gyrus (premotor cortex),
and the ventral anterior nucleus of the thalamus (Fig. 7a; Table 1, bottom). Both a
distortion-elevated response and a correlation with intelligibility was
observed in the left temporal cortex and left frontal operculum.
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Figure 7.
Areas showing an elevated response to distortion.
Activations are shown superimposed on the mean EPI image across
subjects and thresholded at p < 0.001, uncorrected
for multiple comparisons. a, Sagittal sections depicting
areas in which activation is significantly elevated for distorted
speech relative to clear speech and signal-correlated noise and
covaried for intelligibility (distortion-elevated response).
b, The activation pattern shown in a but
with a mask to show voxels that are sensitive to the acoustic
properties of the stimulus (showing a distortion-elevated response that
is form dependent). The mask is shown in graded color
scale as in Figure 5. Areas exhibiting distortion-elevated
activation that is form independent are shown in blue
and listed in Table 1 (bottom). Arrow indicates the approximate
location of the form-independent left inferior frontal voxel from which
data are plotted in d. c, Axial sections
through Heschl's gyrus showing distortion-elevated responses in
temporal and frontal lobe regions. Form-dependent (graded
color) and form-independent (blue) responses are
observed in temporal and frontal lobe regions. d, The
graph shows the response (percentage of signal change)
of a voxel () in left inferior gyrus (56, 16, 6) against
word-report score. Error bars indicate SEM across subjects.
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As for the correlation with intelligibility, the distortion-elevated
response was masked with a combined map showing sensitivity to
different forms of distortion. Regions surviving this exclusive masking
procedure (at p > 0.00851, uncorrected for each of six contrasts, equivalent to a combined p > 0.05) are
considered to be insensitive to acoustic form (Fig.
7b,c, blue; for the response profile
in a typical voxel, see d). This analysis also revealed that
a subset of the areas showing a distortion-elevated response was also
sensitive to acoustic form (Fig. 7b,c,
red). Interestingly, the distortion-elevated response in the
temporal lobe was primarily form dependent, except for its most
posterior, anterior, and inferior aspects. As discussed before (and
shown in Fig. 6a), this form-dependent response arises from
an elevated response to segmented speech. In contrast, most of the
frontal-lobe distortion-elevated activation was form independent,
except for a region in the frontal operculum, just lateral to the
insula. This region exhibited a significantly elevated response for
vocoded speech compared with segmented speech (Fig. 6c).
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Discussion |
Our observation of intelligibility-sensitive regions in the
lateral temporal lobe replicates and extends the findings of previous functional imaging studies (Binder et al., 2000; Scott et al., 2000;
Vouloumanos et al., 2001). These studies used subtractive designs;
regions that were active for intelligible speech were identified by
comparison with nonspeech baseline conditions that may not be of
equivalent acoustic structure or complexity. Although Scott et al.
(2000) used more than one form of intelligible speech with a well
controlled baseline, their design did not permit them to fractionate
areas that respond to speech intelligibility into regions that are
involved in low-level acoustic and higher-level nonacoustic processing.
Our correlational design reduces the importance of subtractions from
baseline conditions. Comparing among different forms of distortion
allows us to test for acoustic processes within intelligibility-responsive areas.
Sound (compared with silence) produced activation in the probable
location of primary auditory cortex (Rivier and Clarke, 1997; Morosan
et al., 2001; Rademacher et al., 2001). Importantly, activation in
primary auditory cortex did not correlate reliably with
intelligibility; instead, the bilateral temporal-lobe region in which
activation correlated with intelligibility is adjacent to primary
auditory cortex. The form-dependent portion of this intelligibility-sensitive region includes both auditory belt and parabelt areas (and beyond) and, therefore, probably more than one
processing stage (Rauschecker et al., 1995; Rauschecker, 1998; Kaas and
Hackett, 2000), although these data cannot speak to further functional segregation.
The form-dependent profile observed in these periauditory areas arises
from an increased response to segmented speech compared with other
forms of distortion that are matched on intelligibility. This may
reflect differential sensitivity of neurons to particular acoustic
features of speech. For instance, neurons sensitive to rapid spectral
changes or formant transitions that are present in clear speech might
respond more strongly to segmented speech. Alternatively, neurons that
are sensitive to transitions between periodic and aperiodic sounds
might respond more strongly to segmented speech, because these
transitions are absent from the other forms of distortion.
Surrounding this periauditory form-dependent region anteriorly,
posteriorly, and inferolaterally, we observed areas in which activation
correlated significantly with intelligibility but was insensitive to
acoustic differences among types of distortion. We conclude that
these form-independent areas are involved in processing speech at more
abstract nonacoustic levels of representation. The hierarchical
structure that we infer from these results is consistent with cognitive
accounts of spoken language comprehension (McClelland and Elman, 1986;
Gaskell and Marslen-Wilson, 1997) in which lexical and semantic
processes are driven by the output of lower-level acoustic and phonetic
processes. This finding also mirrors what is known of the anatomical
and functional organization of the auditory system in nonhuman
primates. Whereas form-dependent responses were observed in both core
and belt areas of auditory cortex, it is only in the parabelt and more
distant polymodal cortex that we see a form-independent response to the
intelligibility of speech signals.
A stream of processing, specialized for sound-object identification,
has been documented previously in nonhuman primates. This extends
anteriorly within lateral temporal neocortex (Rauschecker and Tian,
2000; Tian et al., 2001), similar to the anterior temporal portion of
the form-independent intelligibility response. Future work to determine
the functional specialization of these anterior temporal regions might
therefore focus on whether responses in these regions are affected by
the lexical and semantic content of sentences. An additional inferior
frontal area exhibited a similar form-independent intelligibility
profile. This area in humans, as in other primates, may receive
projections from anterior auditory areas and anterior temporal lobe,
extending the anteroventral-processing stream into ventrolateral
frontal cortex (Hackett et al., 1999; Romanski et al., 1999a,b; Mamata
et al., 2002).
We also observed a form-independent, intelligibility-related response
in left posterior superior temporal gyrus and left angular gyrus. These
activations may be indicative of other parallel streams of processing,
extending posteriorly from auditory and form-dependent regions (Hickok
and Poeppel, 2000; Scott and Johnsrude, 2003). Although the functional
significance of such posterior streams has yet to be firmly
established, one proposal common to these accounts is that a stream
running dorsally to the sylvian fissure may play a role in linking the
perception and production of speech. In support of this account, a
number of previous cognitive models have proposed separate processing
pathways involved in phonological versus lexical processing of speech
(Gaskell and Marslen-Wilson, 1997; Norris et al., 2000).
An additional region in which the BOLD signal was correlated with
intelligibility in a form-independent manner was the left anterior
hippocampus. The medial temporal-lobe structures of the left (usually
language-dominant) hemisphere are known to be required for the encoding
and retention of verbal material (Milner, 1958; Johnsrude, 2001;
Strange et al., 2002). Results from neuroimaging studies suggest that
activation in the left anterior hippocampus is sensitive to the
presence of meaning in verbal stimuli (Martin et al., 1997; Otten et
al., 2001). The correlation that we observe may thus reflect the
increasing memorability of sound sequences as they become more meaningful.
In addition to establishing anatomical specialization for speech
comprehension, we wanted to identify brain areas that exhibited an
increased response to degraded speech stimuli compared with both clear
speech and an unintelligible baseline. We observed a left-lateralized
frontal and temporal lobe system that showed this profile. This network
of areas is consistent with anatomical connectivity. Auditory belt and
parabelt are known (from work in nonhuman primates) to be reciprocally
connected with prefrontal areas, including premotor cortex and areas in
the inferior frontal gyrus, such as Brodmann area 45 (Hackett et al.,
1999; Romanski et al., 1999a,b). These connections may provide a means
by which frontal areas can modulate the operation of lower-level
auditory areas in the temporal lobe during effortful comprehension of
spoken language, thereby assisting in the recovery of meaning from
distorted speech input.
Mechanisms to compensate for degraded input need not be speech
specific. Low-level auditory or attentional processes that segregate
speech from noise or restore continuity to speech briefly masked by
noise (Cherry, 1953; Warren, 1970; Bregman, 1990) may assist in
perceiving speech heard in noisy environments. Because the
distortion-elevated activation in the temporal lobe was primarily form
dependent (i.e., sensitive to distortion type) and particularly pronounced in areas adjacent to the probable location of primary auditory cortex, this response may reflect increased allocation of
attention to spoken input (Grady et al., 1997). Because the response of
this temporal lobe region was particularly pronounced for segmented
speech, we speculate that perceiving speech in the "gaps" between
noise bursts places a particular demand on this attentional system (cf.
Warren, 1970; Bashford et al., 1996).
In contrast to the response profile observed in temporal lobe regions,
the elevated response to distorted speech in the left frontal cortex
was primarily insensitive to the form of distortion applied, as might
be expected for compensatory processes that apply at a nonacoustic
level. Although some of these frontal regions may not be involved in
processes specific to language comprehension (such as decision
processes involved in assessing intelligibility), we propose that a
restricted portion of this activated area, the inferior frontal region
in which responses were also correlated with intelligibility,
contributes to the linguistic processes involved in accessing and
combining word meanings (Thompson-Schill et al., 1997; Wagner et al.,
2001). All three forms of distortion might be expected to draw more
heavily on processes in which semantic or syntactic context is used to
recover words and meanings that cannot be identified from bottom-up
information alone (Miller et al., 1951; Gordon-Salant and Fitzgibbons,
1997). The results of previous work, in which sentence comprehension is
challenged by the inclusion of more complex grammatical structures or
lexical ambiguity, are consistent with this hypothesized role for left inferior frontal regions (Kaan and Swaab, 2002; Rodd et al., 2002).
Finally, we observed a focal region in the frontal operculum that
showed an elevated response to distortion that was form dependent
(particularly sensitive to noise-vocoded speech). This was the only
form-dependent region that we observed outside of the temporal lobe and
may correspond to an area previously identified electrophysiologically
in macaques, which responds specifically to auditory stimuli, including
both vocal and nonvocal sounds (Romanski and Goldman-Rakic, 2002).
Additional behavioral work investigating comprehension of noise-vocoded
speech may be informative in assessing the role of this region of
elevated activation.
| |
FOOTNOTES |
Received Sept. 5, 2002; revised Jan. 7, 2003; accepted Jan. 7, 2003.
This work was supported by the Medical Research Council (UK). We thank
Paul Boersma and Chris Darwin for assistance with Praat scripts, Philip
Dilks and Iain Turnbull for their help with the pilot study, the staff
of the Wolfson Brain Imaging Centre, University of Cambridge, for their
help with data acquisition, Matthew Brett and Ian
Nimmo-Smith for advice on image processing and statistical analysis, and Brian Cox for his assistance with figures. We are also
grateful to Daniel Bor, John Duncan, Stefan Kohler, William Marslen-Wilson, Dennis Norris, Sophie Scott, and anonymous
reviewers for comments and suggestions. Example sounds can be found on
the Internet at: www.mrc-cbu.cam.ac.uk/~matt.davis/jneurosci/.
Correspondence should be addressed to Matt Davis, Medical Research
Council Cognition and Brain Sciences Unit, 15 Chaucer Road, Cambridge,
UK CB2 2EF. E-mail: matt.davis{at}mrc-cbu.cam.ac.uk.
| |
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TOP
ABSTRACT
Introduction
