J Neurophysiol 112: 792– 801, 2014.
First published May 7, 2014; doi:10.1152/jn.00901.2013.
The response of the anterior striatum during adult human vocal learning
Anna J. Simmonds,1 Robert Leech,1 Paul Iverson,2 and Richard J. S. Wise1
1
Computational, Cognitive and Clinical Neuroimaging Laboratory (C3NL), Division of Brain Sciences, Imperial College
London, United Kingdom; and 2Department of Speech, Hearing and Phonetic Sciences, Division of Psychology and Language
Sciences, University College London, United Kingdom
Submitted 20 December 2013; accepted in final form 2 May 2014
fMRI; vocal learning; striatum; non-native
motor-sensory process, involving rapid sequential motor movements. It relies on the integration of feedforward motor and feedback sensory signals, with
online self-monitoring guiding rapid modification of motor
commands to the larynx, pharynx, and articulators, requiring
coordination of up to 100 muscles (Ackermann and Riecker
2010; Levelt 1989). This integration allows the maintenance of
intelligible speech, even under adverse speaking conditions,
and depends on motor (frontal), auditory (temporal), and somatosensory (parietal) cortex as well as the insular cortices,
subcortical nuclei, and cerebellum (Argyropoulos et al. 2013;
Golfinopoulos et al. 2010; Guenther 2006; Ventura et al. 2009).
Vocal learning covers a range of vocal behaviors including
contextual learning (comprehension and usage) as well as
production learning (Janik and Slater 2000), and the present
study focuses on the latter. Children are remarkable in their
ability to learn the complex motor sequences that allow them to
SPEECH PRODUCTION IS A COMPLEX
Address for reprint requests and other correspondence: A. J. Simmonds, Computational, Cognitive and Clinical Neuroimaging Laboratory (C3NL), Imperial
College London, 3rd Floor, Burlington Danes Bldg., Hammersmith Hospital, Du
Cane Road, London W12 0NN, UK (e-mail: [email protected]).
792
speak (Guenther 1994). Acquisition of the native language
(L1) occurs by matching speech sounds to articulatory gestures
required to produce those sounds (Buchsbaum et al. 2005),
starting with the babbling stage. By 1 yr, babbling turns to
speech and the accent of the child is clearly that of a native
speaker. In contrast, although humans are capable of vocal
learning throughout life, when a second language (L2) is
learned after L1 has been established, there is no babbling
stage, and the learners acquire new words with the explicit
knowledge about their meaning. There is also a strong tendency to translate a heard word in L2 into its corresponding
word in L1 (Thierry and Wu 2007; Wu and Thierry 2010).
Second language learners may feel that they have developed
L2 sufficient for their purpose at a relatively early stage, and
they may not strive to tune their auditory cortex to generate
accurate, long-term representations of L2 syllables, words, and
phrases as spoken by a native speaker. Therefore, the online
perceptual monitoring of what they are producing is never
sufficiently fine-tuned to drive improvements in the long-term
motor representations of articulation, and late-acquired languages are spoken with an accent determined by the native
speech (Flege 1995; Simmonds et al. 2011).
Repeating non-native bisyllabic words involves generating
previously unlearned sequences of movements of the larynx
and supralaryngeal articulators during the pronunciation of the
first of the two syllables. Novel sensorimotor mappings for the
non-native words depend on the accuracy of both the sensory
“templates” created for each word and the neural coding for the
new sequences of motor commands (Golfinopoulos et al. 2010;
Guenther 2006; Guenther and Vladusich 2012). Although the
most important sensory component may be auditory, somatosensation also plays a role in the control of voice production (Lametti et al. 2012; Simmonds et al. 2014).
Vocal learning is not unique to humans and is particularly
evident in parrots and oscine songbirds (Mooney 2009; Petkov
and Jarvis 2012). A limited capacity for vocal learning may
also exist in mice (Arriaga and Jarvis 2013). During a critical
period, hatchling songbirds create memories of target songs by
listening to a tutor bird. By trial-and-error experimentation,
they then modify their vocal output until the auditory feedback
matches the memorized auditory templates (Olveczky et al.
2005), similar to the mechanism that operates in human infants
as they learn to speak (Doupe and Kuhl 1999). A recent study
has also demonstrated that both songbirds and human infants
take a stepwise approach to the acquisition of vocal transitions,
rather than undergoing a single a developmental shift (Lipkind
et al. 2013). Many songbirds, including the most commonly
studied song learning bird, the zebra finch, learn to sing their
single song as a juvenile, and this song remains constant
0022-3077/14 Copyright © 2014 the American Physiological Society
www.jn.org
Downloaded from http://jn.physiology.org/ by 10.220.33.4 on June 17, 2017
Simmonds AJ, Leech R, Iverson P, Wise RJ. The response of
the anterior striatum during adult human vocal learning. J Neurophysiol 112: 792– 801, 2014. First published May 7, 2014;
doi:10.1152/jn.00901.2013.—Research on mammals predicts that
the anterior striatum is a central component of human motor learning.
However, because vocalizations in most mammals are innate, much of
the neurobiology of human vocal learning has been inferred from
studies on songbirds. Essential for song learning is a pathway, the
homolog of mammalian cortical-basal ganglia “loops,” which includes the avian striatum. The present functional magnetic resonance
imaging (fMRI) study investigated adult human vocal learning, a skill
that persists throughout life, albeit imperfectly given that late-acquired
languages are spoken with an accent. Monolingual adult participants
were scanned while repeating novel non-native words. After training
on the pronunciation of half the words for 1 wk, participants underwent a second scan. During scanning there was no external feedback
on performance. Activity declined sharply in left and right anterior
striatum, both within and between scanning sessions, and this change
was independent of training and performance. This indicates that adult
speakers rapidly adapt to the novel articulatory movements, possibly
by using motor sequences from their native speech to approximate
those required for the novel speech sounds. Improved accuracy
correlated only with activity in motor-sensory perisylvian cortex. We
propose that future studies on vocal learning, using different behavioral and pharmacological manipulations, will provide insights into
adult striatal plasticity and its potential for modification in both
educational and clinical contexts.
STRIATAL RESPONSE IN HUMAN VOCAL LEARNING
METHODS
Participants
Twenty-one right-handed monolingual native speakers of English
(10 women), average age 26 yr (range 19 – 40 yr) participated after
giving informed written consent. The study was approved by the local
research ethics committee. In the second scanning session there were
technical difficulties in one of the three runs for three separate
participants, so those runs were excluded from the analyses. In total,
data were analyzed from 6 runs for 18 participants and from 5 runs for
3 participants. Participants were paid £10 per hour for the 5 training
sessions, each lasting 1 h, giving a total payment of £50 per participant. The subjects were aware that they would be paid irrespective of
their response to training; that is, the final reward was not linked to
improved non-native word repetition.
speakers (e.g., Mandarin: “bu4 cha1”; Spanish: “jaba”; German
“Höhle”). The native stimuli consisted of bisyllabic non-words, also
with a CVCV structure and matched for number of phonemes, starting
with either /b/, /p/, or /t/ (e.g., “beetoo”). Native speakers of each
language, one man and one woman, produced the stimuli. Audiovisual
recordings were made of the stimuli for inclusion in the training
materials, but audio-only versions were used in the scanning sessions.
There were four different sounds from each non-native language. Of
the four sounds for each language, the pronunciation of two was
trained and that of the other two was untrained. For Mandarin, the
words involved four different tones (trained: t1 and t4; untrained: t2
and t3); for Spanish, four different consonants were used (trained: /j/
and /y/; untrained: /d/ and /r/); and for German, four different vowel
sounds (trained: /u/ and /ü/; untrained: /o/ and /ö/). The native sounds
were all untrained (/b/, /p/, /t/) because despite being a novel combination of phonemes (since the non-words would not have been
produced by the participants before), the sounds themselves were all
highly familiar to the native English participants and no training was
required.
Off-Line Pronunciation Training
In between the two scanning sessions, participants underwent a
week of computer-based training, 1 h per day, with different exercises
each day. Training materials were developed specifically for the
purposes of this study. The first part of the training introduced
participants to the main articulators involved in speech production,
using interactive exercises and multiple-choice tests. The next three
sessions looked at each of the speech sound groups (Spanish consonants, German vowels, and Mandarin tones) in turn, with the two
speech sounds for each language. The non-native speech sounds were
also presented using interactive exercises, including learning of the
main articulators used in each sound and phoneme discrimination
tasks. In addition, participants watched video clips of a native speaker
producing the sound (1 audiovisual presentation of each word).
Participants were instructed to record their repetition attempts and
were given guidance on creating spectrograms of their speech and
comparing those to spectrograms from the native speakers (1 example
of each word). The final training session involved many repetitions of
all the non-native speech sounds. The training focused on producing
the sounds multiple times, with at least 15 attempts on each word. A
listen-and-repeat paradigm was used in training, with participants
recording their attempts to confirm that they complied with the
training.
Off-Line Phoneme Discrimination Testing
With such unfamiliar phonemes as used in this study, it is possible
that participants would not be able to discriminate between different
sounds and might produce one attempt for a variety of phonemes. To
ensure that participants were able to discriminate between the phonemes, their perception of the different sounds used in the study was
tested, using pairs of non-native words in a forced-choice same/
different task. This test was performed before the first scanning
session, and therefore prior to any training.
Stimuli
For the non-native condition the stimuli came from three different
languages, Mandarin, Spanish, and German, based on four phonemes
per language (Mandarin: tones 1, 2, 3 and 4; Spanish: /d/, /r/, /j/, /y/;
German: /u/, /ü/, /o/, /ö/). The non-native stimuli were specifically
chosen to manipulate a different place and manner of articulation in
each language (by manipulating either pitch, vowel sounds, or consonants). The words were real bisyllabic words with a consonantvowel-consonant-vowel (CVCV) structure and were matched for
number of phonemes, with the target phoneme and stress on the first
syllable and the rest of the word easy to produce for native English
Functional MRI Experimental Tasks
In both fMRI scanning sessions, participants listened to and repeated native non-words and non-native words. There were two main
conditions: “listen-and-repeat,” presented in this report, and “listen
only,” not included in this report. A baseline condition of “rest” was
also included, during which the subjects fixated on a crosshair without
any additional task. During speech trials participants saw two white
circles on the screen and heard a word (or non-word). Participants
were instructed that if the two circles remained unchanged, it was a
listening-only trial and they were not required to speak. If the second
J Neurophysiol • doi:10.1152/jn.00901.2013 • www.jn.org
Downloaded from http://jn.physiology.org/ by 10.220.33.4 on June 17, 2017
throughout life (Eda-Fujiwara et al. 2012). Although this is
clearly more limited than the unbounded nature of human
speech, other species, such as Northern mockingbirds, continue
to learn, even in adulthood, and the song repertoire of the
brown thrasher can extend into the thousands (Boughey and
Thompson 1981; Kroodsma and Parker 1977). Therefore, research on the genetic, molecular, and neural mechanisms
underlying song acquisition in birds may inform human speech
acquisition (Bolhuis et al. 2010; Brainard and Doupe 2002;
Doupe and Kuhl 1999; Doupe et al. 2005; Enard 2011; Jarvis
2007). This has been made possible by a clearer understanding
of the anatomic homologies between avian and mammalian
brains (Jarvis et al. 2005). The anterior striatum is the homolog
of avian “area X,” the basal ganglia region central to song
learning. Neuroscientists studying vocal learning in songbirds
have predicted that the human anterior striatum plays a prominent role in infant vocal learning (Jarvis 2004, 2007).
The present study used functional magnetic resonance imaging (fMRI) to investigate the neural networks supporting
vocal sensorimotor learning in adults, with an emphasis on
anterior striatal function. The study observed the effects of 1
wk’s training on the pronunciation of non-native words,
changes in brain activity during and between the scanning
sessions performed before and after training, and the relationship between performance and brain activity. It was expected
that training on repeating non-native words would result in an
increase in accuracy of behavioral performance. However,
whereas it may reasonably be assumed that infants maintain
anterior striatal activity during the acquisition of their first
language, the hypothesis was that adults would demonstrate a
rapid decline in the anterior striatal activity as they quickly
adapted to the novelty of the new speech motor sequences,
mapping them onto their existing prepotent motor sequences
for native speech.
793
794
STRIATAL RESPONSE IN HUMAN VOCAL LEARNING
ysis of Mixed Effects) stage 1 (Beckmann et al. 2003; Woolrich et al.
2004).
Data Analysis
Speech ratings. Speech recordings from participants’ scanning
sessions, presented randomly across participants and sessions, were
rated by two native speakers of each of the three languages (6
individuals). These independent native speakers listened to each
speech trial for their respective language for each participant and gave
it a score out of seven. A 7-point scale was used to judge the degree
of native-like performance (1 ⫽ non-native-like, very strong foreign
accent, 7 ⫽ native-like, no foreign accent). The speech score was
calculated by each utterance from each participant being given a score
from each of the two native raters, and the mean value for each trial
was calculated. These speech trials were then grouped according to
the specific speech sound involved (from the 4 in each language), and
a group mean for each speech sound was calculated, before combining
the two trained sounds and two untrained sounds for each language.
The mean score from the two raters for the trained and for the
untrained sounds for each participant was used in further analyses.
Repetition performance during the scanning sessions was very high, at
over 94% for all participants. Rare trials in which five of the participants failed to produce a word during a repeat trial were excluded
from further analyses.
Filtering out CSF and WM. As well as removing variance associated with the six motion variables and the motion outliers on an
individual basis, variance associated with the time courses of white
matter (WM) and cerebrospinal fluid (CSF) from the whole brain
functional data was removed using ordinary least-squares linear regression. To calculate the time course for the WM and CSF, a
3-mm-radius sphere was created based on the MNI coordinates ⫺26,
⫺22, 28 and MNI 2, 10, 8, respectively, and the mean time course
across the sphere was calculated.
Regions of Interest
Following a whole brain 2 ⫻ 2 ANOVA looking at language
(native and non-native) and session (pre- and posttraining), the strongest activation for the language ⫻ session interaction was observed in
the basal ganglia. Region of interest (ROI) analyses using anatomic
putamen and caudate masks from the Harvard-Oxford probabilistic
atlas were subsequently carried out. A single voxel was selected from
within these masks, in each hemisphere separately, for the anterior
putamen (left: ⫺22, 8, ⫺2; right: 18, 8, ⫺2) and the caudate (left:
⫺14, 12, 12; right: 14, 12, 12). A 5-mm sphere was then created
around each of those four voxels. The spheres were then constrained
by the interaction functional result to create the four ROI masks.
RESULTS
Twenty-one participants were required to repeat bisyllabic
non-native words taken from three languages (German, Spanish, and Mandarin). There was no training on the meaning of
the words, to exclude parallel lexical semantic learning. Each
word had a target phoneme in the first syllable, with the second
syllable represented in the native (English) language. The
target phonemes differed in terms of the vowel (German),
consonant (Spanish), or pitch (Mandarin). Four phonemes were
chosen for each language, and all were included in two scanning sessions. Functional MRI was performed before and after
1 wk of training on the pronunciation of two target phonemes
per language, with no training on the other two phonemes. The
baseline task was repeating bisyllabic non-words formed from
native (English) syllables. No online feedback was provided
during the repetition trials.
J Neurophysiol • doi:10.1152/jn.00901.2013 • www.jn.org
Downloaded from http://jn.physiology.org/ by 10.220.33.4 on June 17, 2017
circle turned black, that was the cue to repeat the word. One important
point to note from the stimuli presentation design is that when the
participants first heard the stimuli, they did not know whether the trial
was for a listen-only or a listen-and-repeat condition. In each run (3 in
each of the 2 scanning sessions), there were 20 repeat trials for each
of the non-native language groups, 10 native repeat trials, and 15 rest
trials. For the non-native words, both trained and untrained words
were included. In the first session there were words the participants
were going to be trained on and words they were not going to be
trained on. In the second session, there were words the participants
had been trained on (“trained”), along with words they had not been
trained on (“untrained”). For the repeat trials, participants heard the
word (2,500 ms) and were then cued to repeat it (2,500 ms), followed
by a fixation cross (2,200 ms) while the scan was acquired. The
fixation cross then remained on the screen until the start of the next
trial. Also included in this paradigm were 20 listening trials, which are
not included in the present analyses (6 listen trials for each non-native
language group and 2 native listen trials per run).
Functional MRI acquisition and analysis. MRI data were obtained
on a Philips Intera 3.0-Tesla scanner, using dual gradients, a phasedarray head coil, and sensitivity encoding with an undersampling factor
of 2. Thirty-two axial slices with a slice thickness of 3.25 mm and an
interslice gap of 0.75 mm were acquired in ascending order (resolution, 2.19 ⫻ 2.19 ⫻ 4.00 mm; field of view, 280 ⫻ 224 ⫻ 128 mm).
Quadratic shim gradients were used to correct for magnetic field
inhomogeneities within the anatomy of interest. There were three
runs, each of 105 volumes. Functional MR images were obtained
using a T2-weighted, gradient-echo, echoplanar imaging (EPI) sequence with whole brain coverage (repetition time ⫽ 8 s, acquisition
time ⫽ 2 s, giving 6 s for the subjects to speak during silence; echo
time ⫽ 30 ms; flip angle ⫽ 90°). A “sparse” fMRI design was used
to minimize movement- and respiratory-related artifact associated
with speech studies (Hall et al. 1999), as well as to minimize auditory
masking. High-resolution, T1-weighted images were also acquired for
structural reference. Stimuli were presented visually using E-Prime
software (Psychology Software Tools) run on an IFIS-SA system (In
Vivo).
Functional MRI was analyzed with FEAT (FMRI Expert Analysis
Tool) version 5.98, using standard within-subject and across-subject
analysis pipelines and default settings unless otherwise specified.
Preprocessing included motion correction using MCFLIRT (Jenkinson et al. 2002), non-brain removal using BET (Brain Extraction
Tool) (Smith 2002), spatial smoothing using a Gaussian kernel of
FWHM (full width half-maximum) 5 mm; grand-mean intensity
normalization of the entire four-dimensional (4D) data set by a single
multiplicative factor; high-pass temporal filtering (Gaussian-weighted
least-squares straight line fitting, with ␴ ⫽ 50.0 s). The blood oxygen
level-dependent (BOLD) response was modeled using a doublegamma hemodynamic response function (Glover 1999). In addition,
the FSL tool Motion Outliers was used, which identifies time points
with a high amount of residual intensity change from the motion
corrected data. A confound matrix of outliers is created, and this is
included in the FEAT analysis as an additional confound variable.
This is more beneficial than simply removing volumes with high
levels of motion because it does not require any adjustments to the rest
of the model with regard to timing, and it correctly accounts for signal
changes on either side of the excluded time point, as well as correctly
adjusting the degrees of freedom. Time-series statistical analysis was
performed using FILM (FMRIB’s Improved Linear Modeling) with
local autocorrelation correction (Woolrich et al. 2001). Z (Gaussianized T/F) statistic images were thresholded using clusters determined
by z ⬎ 2.3 and a corrected cluster significance threshold of P ⫽ 0.05.
Registration to high-resolution structural and standard space images
was carried out using FLIRT (FMRIB’s Linear Image Registration
Tool) (Jenkinson et al. 2002; Jenkinson and Smith 2001). Higher-level
group analysis was carried out using FLAME (FMRIB’s Local Anal-
STRIATAL RESPONSE IN HUMAN VOCAL LEARNING
795
Behavioral Results
Downloaded from http://jn.physiology.org/ by 10.220.33.4 on June 17, 2017
Off-line phoneme discrimination. Before the first scan, a
within-language pairwise comparison task (chance score 50%)
determined whether the participants could discriminate between the phonemes employed in the study. The mean score
across all languages was 91.4% correct (range 72.0 –98.0%).
The scores for German and Mandarin were identical (93.3%;
range 58.8 –100%), although individual participants were better at either German or Mandarin. The scores were only a little
lower for Spanish (mean 87.5%, range 68.7–100%). Because
performance was above chance, and the most common type of
error was misidentifying a matching pair as a mismatch, this
behavioral measure demonstrated that participants were able to
perceive the differences between unmatched pairs. Although
this is not a measure of the accuracy of the auditory memories
(“templates”) the participants created, these scores indicated
that the subjects were able to detect acoustic differences
between the phonemes.
Rating scores of online speech production. Repetition performance during the scanning sessions was very high, at over
94% for all participants. Sixteen participants repeated back
each non-native word and native non-word that they heard.
Five participants occasionally failed to produce a word during
a repeat trial, and the data for these trials were excluded from
the fMRI analyses. Speech recordings from each participant at
both scanning sessions were presented randomly to two native
speakers for each of the three languages (see METHODS). The
raters scored accuracy of pronunciation on a 7-point scale,
from 1 (very strong foreign accent) to 7 (native pronunciation).
The inter-rater reliability was significant for each of the three
languages (Mandarin: r ⫽ 0.594, P ⬍ 0.001; German: r ⫽
0.543, P ⬍ 0.01; Spanish: r ⫽ 0.483, P ⬍ 0.01). Therefore, the
mean of the two raters’ individual scores for each language and
each participant was used in the analyses (Fig. 1).
Performance in the two sessions varied for the three different
non-native languages. Highest scores were for the German
vowels, closely followed by the Mandarin tones, with Spanish
consonants scoring the least (Fig. 1). This pattern was the same
in both sessions. For averaging across all sounds in each
language group, there were significant improvements for all
languages, although Spanish and Mandarin improved more
than German.
When each participant’s external rating score was used as
the dependent variable, a 3 (language) ⫻ 2 (session) ⫻ 2
(training) ANOVA revealed significant main effects for language [F(2,40) ⫽ 83.4, P ⬍ 0.001], session [F(1,20) ⫽ 9.0, P ⬍
0.01], and training [F(1,20) ⫽ 99.9, P ⬍ 0.001]. In addition,
there were significant interactions between language and training [F(2,40) ⫽ 53.7, P ⬍ 0.001] and between session and
training [F(1,20) ⫽ 5.4, P ⬍ 0.05], and a significant three-way
interaction between language, session, and training [F(2,40) ⫽
5.7, P ⬍ 0.01].
Post hoc t-tests (Fig. 1) revealed that after the scores were
combined for both trained and untrained words in each language, there was a small but significant improvement in pronunciation between scanning sessions. When performance was
assessed on the trained words alone, Spanish consonants and
Mandarin tones, but not German vowels, improved significantly between sessions. On the untrained words, there was no
Fig. 1. Speech scores from all languages in both sessions. Mean scores from session
1 are shown in black and from session 2 in gray. Error bars display 95% confidence
intervals. A: average scores for the 3 languages in both sessions. One-tailed paired
t-tests revealed significant improvements across session for all languages (P ⬍ 0.05).
B: scores for the trained sounds only for each of the 3 languages. One-tailed paired
t-tests revealed significant improvements for the trained sounds in Spanish (P ⬎ 0.005)
and Mandarin (P ⬍ 0.0005). C: scores for the untrained sounds only. There were no
significant differences across session for the untrained sounds. The table (bottom)
presents post hoc 2-tailed paired t-tests following a 3 (language) ⫻ 2 (session) ⫻ 2
(training) ANOVA for the behavioral scores from the functional magnetic resonance
imaging (fMRI) experimental task.
J Neurophysiol • doi:10.1152/jn.00901.2013 • www.jn.org
796
STRIATAL RESPONSE IN HUMAN VOCAL LEARNING
improvement for any language, although there was a trend
(P ⫽ 0.08) for improvement on the German words.
Within the first session, comparing the first and third runs,
there was a significant improvement for the non-native languages averaged together (P ⫽ 0.05), which was driven by
improvements for Mandarin (P ⫽ 0.001). Spanish and German
did not show any within-session significant changes.
Overall, the behavioral results demonstrated that participants
were able to detect acoustic differences between the non-native
phonemes included in the study, and there was a small but
significant improvement in pronunciation between scanning
sessions.
Imaging Results
Fig. 2. Whole brain ANOVA. A: sequential axial MRI brain
slices showing the corrected statistical maps of the main
effect of language (non-native ⬎ native; i), the main effect
of session (first ⬎ second; ii), and the language ⫻ session
interaction (iii). Results are displayed on a standard brain
template (MNI152), and axial slices are shown with left
shown on the left. In each section slices are shown in 4-mm
decrements from 14 to ⫺2. Z statistic images were thresholded using clusters determined by z ⬎ 2.3 and a corrected
cluster significance threshold of P ⬍ 0.05. Because of the
extensive activation at the given threshold for the main
effect of language, images in i thresholded at z ⬎ 7 and P ⬍
0.01. B: the main effect of language at a threshold of z ⬎ 5
and P ⬍ 0.01 on a single coronal slice (y ⫽ 25; i) demonstrating the cingulo-opercular (salience) network and a single axial slice (z ⫽ 4; ii) demonstrating the frontoparietal
(central executive control) network. C: the language ⫻
session interaction at a threshold of z ⬎ 4 and P ⬍ 0.05 to
demonstrate the peaks in both the left and right caudate
nucleus [single coronal slice (y ⫽ 4); i] and anterior putamen [single axial slice (z ⫽ 4); ii]. D: a single axial slice
(z ⫽ 4) demonstrating the interaction of session ⫻ language
for each non-native language separately with native. Activity for Spanish consonants (blue; i) was only visible at a
lower threshold of z ⬎ 2. Activity for Mandarin tones (pink;
ii) and German vowels (green; iii) is shown at a threshold of
z ⬎ 2.3 and P ⬍ 0.05.
J Neurophysiol • doi:10.1152/jn.00901.2013 • www.jn.org
Downloaded from http://jn.physiology.org/ by 10.220.33.4 on June 17, 2017
There were two types of trial, listen only and listen-andrepeat (see METHODS). Only the data from the repeat trials are
presented in this report. A visual cue signaled to the participant
to repeat the non-native word or native non-word that had just
been heard, after which the scan data were acquired over 2 s.
A low-level baseline condition, when the participants fixated
on a crosshair, was also included.
This study was designed to investigate training effects of
producing non-native speech sounds. A 2 (language: native and
non-native, each against the silent rest baseline, with trained
and untrained word sets combined) ⫻ 2 (session: pre- and
posttraining) factorial design was used, and a whole brain
ANOVA was performed. The main effect of language was
observed in extensive bilateral cortical and subcortical regions
(Fig. 2Ai). In addition to activity in bilateral auditory, premotor, and sensorimotor cortices and in the basal ganglia, thalami,
and cerebellum, much of which can be attributed to sensorymotor processes involved in hearing and repeating a word,
there was activity in two separate task-dependent, domaingeneral networks (Dosenbach et al. 2007, 2008; Seeley et al.
2007). The first, the cingulo-opercular or salience network
(Fig. 2Bi), encompasses, in both cerebral hemispheres, the
midline dorsal anterior cingulate cortex and adjacent superior
frontal gyrus, anterior lateral prefrontal cortex, frontal operculum and adjacent anterior insular cortex, and thalamus. The
second, the frontoparietal or central executive network (Fig.
2Bii), encompasses, in both cerebral hemispheres, the dorsolateral prefrontal cortex, dorsal inferior parietal cortex and
adjacent intraparietal sulcus, middle cingulate cortex, and precuneus. The cerebellar hemispheres are functionally connected
to these two networks. In contrast, the structures showing both
a main effect of session (Fig. 2Aii) and a language ⫻ session
interaction (Fig. 2Aiii) were the left and right basal ganglia.
The main peaks for the interaction were located in the left and
right caudate nuclei and anterior putamina (Fig. 2C).
When 2 ⫻ 2 ANOVAs were performed separately for the
trained and untrained words in all three non-native languages
as one level of the language factor and the native non-words as
STRIATAL RESPONSE IN HUMAN VOCAL LEARNING
Correlations of imaging results with speech score. To investigate how changes in brain activity were linked to improvements in
behavioral score, the contrast of non-native repeating ⬎ rest
difference across sessions was run with each participant’s
individual speech score difference as an additional variable. It
was assumed that activity during the rest condition did not
change between sessions. Changes in activity in response to
repeating non-native words between the two sessions were
correlated with changes in the raters’ scores of proficiency at
pronunciation for all words, trained and untrained. When all
non-native languages were considered together, no significant
linear increases in activity were observed, but activity declined
in a number of cerebral and cerebellar cortical regions, the
main clusters being located in left central operculum (ventral
motor-sensory cortex), anterior and posterior left superior temporal gyrus, and right lateral cerebellum (Fig. 4). Within this
network of regions with changes during repeating in response
to proficiency, each language contributed differently. There
was some overlap between Spanish and German within left
primary sensorimotor cortex and left frontal operculum,
whereas improvement in Mandarin sounds was responsible for
reduction in activity in left anterior superior temporal sulcus.
Improvement in the Spanish sounds correlated with a reduction
in activity in left posterior superior temporal sulcus. Figure 4B
presents the whole brain correlations for individual languages,
with correlation plots for the peak voxels. The correlation plots
in Fig. 4B confirm that as the speech score improved, activity
decreased.
DISCUSSION
This study required participants to learn to pronounce single
non-native words, with repeated practice on the required articulatory movements and associated self-corrections based on
anticipated and actual sensory feedback. This resulted in overall improvement on the trained words, but with variable success. The prescanning phoneme discrimination task demonstrated that participants could detect auditory differences be-
Fig. 3. Regions of interest (ROI) for native and non-native repeating. A: ROI masks are shown in both hemispheres for the anterior putamen (red) and caudate
(blue) on a coronal slice (y ⫽ 10). ROI masks are displayed on a standard brain template (MNI152). L, left; R, right. B: across-session effects are shown with
the blood oxygen level-dependent (BOLD) response for non-native (black and gray) and native (open and hatched) repeating in both sessions (session 1, black
and open; session 2, gray and hatched). Error bars represent 95% confidence intervals. Caudate ROIs are shown in i and anterior putamen ROIs in ii.
C: within-session effects are shown with the mean time series for native and non-native repeating in each of the 3 runs for both scanning sessions for the left
(i) and right caudate (ii) and the left (iii) and right anterior putamen (iv).
J Neurophysiol • doi:10.1152/jn.00901.2013 • www.jn.org
Downloaded from http://jn.physiology.org/ by 10.220.33.4 on June 17, 2017
the other, the language ⫻ session interaction was observed for
both word sets. Separate ANOVAs for each non-native language (combining trained and untrained word sets) with the
native non-words confirmed a language ⫻ session interaction
in the anterior striatum for all three languages, although for
Spanish it was only visualized when the statistical threshold
was reduced to a z value of 2 (Fig. 2D).
Spherical ROIs, 5 mm in diameter, were used to demonstrate
the profiles of activity across conditions in the anterior striata.
The positioning of the ROIs, described in METHODS and illustrated in Fig. 3A, was based on the whole brain ANOVA. The
only statistics performed on these profiles were to investigate
within-session and between-language effects, although 95%
confidence intervals for the effect sizes are included for illustrative purposes. Activity in the anterior striata was present
during the repetition of native non-words, relative to the
baseline condition of rest, but was considerably greater during
the repetition of non-native words. These profiles also illustrated the sharp decline in activity between sessions 1 and 2
during the repetition of non-native words (Fig. 3B).
To further investigate how rapidly changes in anterior striatal activity occurred, activity across the three runs in each of
the two scanning sessions were investigated on the ROI data
(Fig. 3C). To limit the number of statistical comparisons, only
the data from the left striatal ROIs (caudate nucleus and
anterior putamen) were analyzed. Language (native non-words
and non-native words) ⫻ run (first and second) ANOVAs
revealed that there was a language ⫻ run interaction in both
sessions, although less marked in session 2. Thus, in session 1,
there was a significant interaction in anterior putamen [F(1,20) ⫽
23.6, P ⬍ 0.001] and caudate nucleus [F(1,20) ⫽ 9.4, P ⬍ 0.01],
with a corrected level for significance set at P ⫽ 0.01. In
session 2, the interactions were significant at an uncorrected
level in anterior putamen [F(1,17) ⫽ 6.2, P ⬍ 0.05] and caudate
nucleus [F(1,17) ⫽ 4.85, P ⬍ 0.05]. In both sessions, paired
t-tests confirmed that activity when native non-words were
repeated, relative to that at rest, did not change across runs
(P ⬎ 0.5).
797
798
STRIATAL RESPONSE IN HUMAN VOCAL LEARNING
tween the non-native speech sounds included in this study,
with performance near ceiling for Mandarin and German and
well above chance for Spanish. Therefore, we have concluded
that the participants were able to form accurate auditory templates of the non-native speech sounds they were required to
repeat. Based on this assumption, the vocal training the participants received was directed at the formation of these auditory memories and then modification of their speech output
pathways to match more closely accuracy of articulation to
these novel templates.
The most striking finding was the sharp focal decline in
activity in the anterior striatum over time. However, the results
indicate that this decline was dissociated from individual variability in performance and training. The change in anterior
striatal function was not a nonspecific effect related to rapid
habituation to task novelty, because repeating native nonwords was also novel. Although repeating bisyllabic native
non-words required only habitual sequential movements to
produce each syllable, the combinations of syllables had not
previously been encountered by the participants. Although the
response of the anterior striatum during native non-word repetition was less than that to non-native word repetition, activity
was significantly greater than during the rest condition. Importantly, it did not change across runs in the first session.
J Neurophysiol • doi:10.1152/jn.00901.2013 • www.jn.org
Downloaded from http://jn.physiology.org/ by 10.220.33.4 on June 17, 2017
Fig. 4. Whole brain correlation with speech score. A: results for all non-native languages averaged together. Top: 2 sagittal slices of a standard brain template
(MNI152) are shown, x ⫽ ⫺52 and 52, revealing activity in bilateral frontal opercula (1), left primary sensorimotor cortex (2), and anterior and posterior left
superior temporal gyrus (3). Z statistic images were thresholded using clusters determined by z ⬎ 2.3 and a corrected cluster significance threshold of P ⬍ 0.05.
Bottom: axial slices (z ⫽ ⫺34, ⫺10, 6, 26, 50) and an orthogonal sagittal view. B: whole brain correlations with mean speech score improvements across sessions
for individual languages. Activity for Spanish consonants is shown in cyan, Mandarin tones in violet, and German vowels in green. Activity is shown on axial
slices (z ⫽ ⫺34, ⫺10, 6, 26, 40) with an orthogonal sagittal slice. Correlation plots at right show the BOLD difference across sessions and the speech score
improvement for each language separately (n ⫽ 21). The peak voxel correlations between the BOLD difference across sessions and the speech score improvement
were r ⫽ ⫺0.77 for Spanish consonants (voxel coordinates ⫺58, ⫺14, 14), r ⫽ ⫺0.81 for Mandarin tones (voxel coordinates ⫺64, ⫺10, ⫺6), and i⫽ ⫺0.59
for German vowels (voxel coordinates ⫺40, ⫺26, 28).
STRIATAL RESPONSE IN HUMAN VOCAL LEARNING
the first few years of life. Studies on motor learning in humans,
non-human primates, and rats have demonstrated that the
anterior striatum is involved in motor learning, but once
sequences of motor movements become habitual, the maintenance of these procedural memories is dependent on the
posterior putamen (Graybiel 2005; Jueptner and Weiller 1998;
Miyachi et al. 1997; Yin et al. 2009; Yin and Knowlton 2006).
We did not observe a change in activity in the posterior
striatum, even during the later phases of learning, but this may
be because the pronunciation of non-native words was not
overlearned after 5 days of training. Of particular interest
would be to determine whether activity never declines to the
level of L1, even though it may reduce with increasing use of
late-acquired L2. This possibility is suggested by cortical,
thalamic, and cerebellar regions remaining more active when
skilled late bilinguals speak in their L2 compared with L1
(Simmonds et al. 2011).
The whole brain ANOVA demonstrated a main effect of
repeating non-native words, one that involved extensive bilateral cortical and subcortical, cerebral, and cerebellar regions,
comprising motor, sensory, and higher order systems. This
replicates the finding from our previous work on bilingual
speech production (Simmonds et al. 2011). As well as premotor and sensorimotor regions, there was activity in cinguloopercular (salience) and frontoparietal (central executive) networks (Dosenbach et al. 2007, 2008; Seeley et al. 2007). These
domain-general regions were strongly active during the repetition of non-native words relative to activity during the repetition of native non-words. It has been proposed that the
frontoparietal network initiates and adjusts control during each
trial of a task, whereas the cingulo-opercular network maintains task set over many trials, so-called adaptive and stable
task control, respectively (Dosenbach et al. 2008). Evidence
from other studies that have investigated temporal ordering and
“chunking” of novel motor sequences (whether the movements
involve the fingers or the articulators) also observed activity in
bilateral inferior frontal and parietal networks (Bohland and
Guenther 2006; Jubault et al. 2007; Koechlin and Jubault 2006;
Majerus et al. 2006). The greater activity in these frontoparietal
systems during non-native word repetition did not change
significantly after 1 wk of training, for either trained or untrained words. Therefore, overall cortical “effort” was not
modulated by either familiarity with the repetition task or
modest improvement in performance on the trained words.
Correlations between behavioral improvement and changes
in activity across sessions for all languages together were
observed only in cortical regions, the left and right frontal
opercula, left ventral sensorimotor cortex, left superior temporal sulcus (STS), and right lateral cerebellum. The specific
requirements of the different types of non-native sounds resulted in different patterns of activation for the correlations
between the BOLD response and improvement in behavioral
performance. Improvement in producing the novel pitch shifts
associated with the tonal words of Mandarin correlated with a
reduction in activity in the anterior STS, known to be sensitive
to the intelligibility of an auditory stimulus. In contrast, improvement in behavioral performance on the novel vowel
sounds in German and novel consonant sounds in Spanish,
probably requiring greater dependence on the subtle placement
of the articulators, correlated with a decline in activity in
ventral sensorimotor cortex. However, precisely which aspects
J Neurophysiol • doi:10.1152/jn.00901.2013 • www.jn.org
Downloaded from http://jn.physiology.org/ by 10.220.33.4 on June 17, 2017
No previous functional neuroimaging study in humans has
demonstrated novel language learning in relation to striatal
activity. Evidence from bilingual studies has demonstrated that
the striatum is involved in non-native speech production, but
previous studies have investigated languages that have already
been acquired. The caudate has been shown to be involved in
the cognitive control of multiple languages and in language
switching (Abutalebi and Green 2007; Costa and Santesteban
2004; Crinion et al. 2006; Hernandez et al. 2000; Price et al.
1999; Rodriguez-Fornells et al. 2002) and the putamen in
articulation (Abutalebi et al. 2013; Frenck-Mestre et al. 2005;
Golestani et al. 2006; Klein et al. 1994, 1995, 1999). Activity
in the left putamen may be related to language proficiency,
with increased activity observed when controlling articulation
in a less proficient language (Abutalebi et al. 2013), and may
also be attributed to the precise motor timing of speech output,
which is less automatic and more “effortful” in a language
acquired later in life (Klein et al. 1994).
The analogy from the birdsong literature about human language learning relates specifically to infants acquiring their
first language (Jarvis 2004). One obvious difference between
the infant and adult human brain is the absence and presence of
prepotent articulatory motor sequences, ones that are habitual
to the adult. Therefore, if it were possible to perform fMRI
scans on awake babbling infants, it is expected that anterior
striatal activity would be maintained across scanning runs and
sessions. The results presented here on adult brains are compatible with initial activation of the anterior striatum in response to the task demand to attempt completely novel articulatory motor sequences, followed by a rapid decline as the
new sequences are incorporated, as an approximation, to the
habitual motor sequences of the first language. This is in
accordance with the observation that once birdsong has been
acquired, ablation of area X has only a small impact on
continued “crystallized” song production.
However, even though the role of area X is reduced in
crystallized song production, the use of gene expression studies
and electrophysiology has shown that area X, a core component of the anterior forebrain pathway (AFP), is still active
during singing in adulthood (Hilliard et al. 2012; Jarvis 2004).
The output from the AFP to the dorsal song production pathway introduces trial-to-trial variability and is required to make
small changes in adult song (Goldberg et al. 2012), and it has
been shown that variability in the neural activity in the AFP
correlates with performance variability (Kao et al. 2005).
Although it is difficult to directly compare the fMRI activation
with gene expression and electrophysiological measures, this is
one possible difference between humans and song learning
birds.
The rapid decline in anterior striatal activity within and
between scans by adult vocal learners may have demonstrated
a rapid “habituation” to the pronunciation of non-native words
even when there had been no objective improvement of articulatory performance. This was probably the product of imperfect self-monitoring, resulting in a form of motor learning in
which the learners settled for a less than proficient (native-like)
performance. This finding may account for the persistence of a
foreign accent in adult vocal learners, which is “good enough”
for communication when making do with greater or lesser
modifications of the habitual sequences of movements used for
articulating the first language that had became consolidated in
799
800
STRIATAL RESPONSE IN HUMAN VOCAL LEARNING
of the speech production tasks result in specific reductions in
activity in these regions must be speculative without further
studies.
One prediction from this study is that first language learning
in early childhood is associated with much more sustained
anterior striatal activity, but this hypothesis will be difficult to
determine given the limited cooperation of toddlers and the
intimidating environment of MRI scanners. However, the results from this study suggest behavioral changes to training
(e.g., external feedback with reward on each correct trial), and
observations on subjects who demonstrate a wide range of
proficiency at learning accurate pronunciation of a novel language may add more information about the modulation of
anterior striatal function.
This work was supported by the Medical Research Council.
DISCLOSURES
No conflicts of interest, financial or otherwise, are declared by the authors.
AUTHOR CONTRIBUTIONS
A.J.S., R.L., P.I., and R.J.W. conception and design of research; A.J.S. and
R.L. performed experiments; A.J.S., R.L., P.I., and R.J.W. analyzed data;
A.J.S., R.L., and R.J.W. interpreted results of experiments; A.J.S., R.L., and
R.J.W. prepared figures; A.J.S., R.L., P.I., and R.J.W. drafted manuscript;
A.J.S., R.L., and R.J.W. edited and revised manuscript; A.J.S., R.L., P.I., and
R.J.W. approved final version of manuscript.
REFERENCES
Abutalebi J, Green D. Bilingual language production: the neurocognition of
language representation and control. J Neurolinguistics 20: 242–275, 2007.
Abutalebi J, Rosa PAD, Castro Gonzaga AK, Keim R, Costa A, Perani D.
The role of the left putamen in multilingual language production. Brain
Lang 125: 307–315, 2013.
Ackermann H, Riecker A. The contribution(s) of the insula to speech
communication: a review of the clinical and functional imaging literature.
Brain Struct Funct 214: 5– 6, 2010.
Argyropoulos GP, Tremblay P, Small SL. The neostriatum and response
selection in overt sentence production: An fMRI study. Neuroimage 82:
53– 60, 2013.
Arriaga G, Jarvis ED. Mouse vocal communication system: Are ultrasounds
learned or innate? Brain Lang 124: 96 –116, 2013.
Beckmann CF, Jenkinson M, Smith SM. General multilevel linear modeling
for group analysis in FMRI. Neuroimage 20: 1052–1063, 2003.
Bohland JW, Guenther FH. An fMRI investigation of syllable sequence
production. Neuroimage 32: 821– 841, 2006.
Bolhuis JJ, Okanoya K, Scharff C. Twitter evolution: converging mechanisms in birdsong and human speech. Nat Rev Neurosci 11: 747–759, 2010.
Boughey MJ, Thompson NS. Song variety in the brown thrasher (Toxostoma
rufum). Z Tierpsychol 56: 47–58, 1981.
Brainard MS, Doupe AJ. What songbirds teach us about learning. Nature
417: 351–358, 2002.
Buchsbaum BR, Olsen RK, Kock P, Berman KF. Human dorsal and ventral
auditory streams subserve rehearsal-based and echoic processes during
verbal working memory. Neuron 48: 687– 697, 2005.
Buckner RL, Andrews-Hanna JR, Schacter DL. The brain’s default network. Ann NY Acad Sci 1124: 1–38, 2008.
Costa A, Santesteban M. Lexical access in bilingual speech production:
evidence from language switching in highly proficient bilinguals and L2
learners. J Mem Lang 50: 491–511, 2004.
Crinion J, Turner R, Grogan A, Hanakawa T, Noppeney U, Devlin JT,
Aso T, Urayama S, Fukuyama H, Stockton K, Usui K, Green DW, Price
CJ. Language control in the bilingual brain. Science 312: 1537–1540, 2006.
J Neurophysiol • doi:10.1152/jn.00901.2013 • www.jn.org
Downloaded from http://jn.physiology.org/ by 10.220.33.4 on June 17, 2017
GRANTS
Dosenbach NUF, Fair DA, Cohen AL, Schlaggar BL, Petersen SE. A
dual-networks architecture of top-down control. Trends Cogn Sci 12: 99 –
105, 2008.
Dosenbach NUF, Fair DA, Miezin FM, Cohen AL, Wenger KK, Dosenbach RA, Fox MD, Snyder AZ, Vincent JL, Raichle ME, Schlaggar BL,
Petersen SE. Distinct brain networks for adaptive and stable task control in
humans. Proc Natl Acad Sci USA 104: 11073–11078, 2007.
Doupe AJ, Kuhl PK. Birdsong and human speech: common themes and
mechanisms. Annu Rev Neurosci 22: 567– 631, 1999.
Doupe AJ, Perkel DJ, Reiner A, Stern EA. Birdbrains could teach basal
ganglia research a new song. Trends Neurosci 28: 353–363, 2005.
Eda-Fujiwara H, Imagawa T, Matsushita M, Matsuda Y, Takeuchi HA,
Satoh R, Watanabe A, Zandbergen MA, Manabe K, Kawashima T,
Bolhuis JJ. Localized brain activation related to the strength of auditory
learning in a parrot. PLoS One 7: e38803, 2012.
Enard W. FOXP2 and the role of cortico-basal ganglia circuits in speech and
language evolution. Curr Opin Neurobiol 21: 415– 424, 2011.
Flege JE. Second language speech learning: theory, findings, and problems.
In: Speech Perception and Linguistic Experience: Issues in Cross-Language
Research, edited by Strange W. Timonium, MD: York, 1995, p. 233–277.
Frenck-Mestre C, Anton JL, Roth M, Vaid J, Viallet F. Articulation in early
and late bilinguals’ two languages: evidence from functional magnetic
resonance imaging. Neuroreport 16: 761–765, 2005.
Glover GH. Deconvolution of impulse response in event-related BOLD fMRI.
Neuroimage 9: 416 – 429, 1999.
Goldberg JH, Farries MA, Fee MS. Integration of cortical and pallidal inputs
in the basal ganglia-recipient thalamus of singing birds. J Neurophysiol 108:
1403–1429, 2012.
Golestani N, Alario FX, Meriaux S, Le Bihan D, Dehaene S, Pallier C.
Syntax production in bilinguals. Neuropsychologia 44: 1029 –1040, 2006.
Golfinopoulos E, Tourville JA, Guenther FH. The integration of large-scale
neural network modeling and functional brain imaging in speech motor
control. Neuroimage 52: 862– 874, 2010.
Graybiel A. The basal ganglia: learning new tricks and loving it. Curr Opin
Neurobiol 15: 638 – 644, 2005.
Guenther F. A neural network model of speech acquisition and motor
equivalent speech production. Biol Cybern 72: 43–53, 1994.
Guenther FH. Cortical interactions underlying the production of speech
sounds. J Commun Disord 39: 350 –365, 2006.
Guenther FH, Vladusich T. A neural theory of speech acquisition and
production. J Neurolinguistics 25: 408 – 422, 2012.
Hall DA, Haggard MP, Akeroyd MA, Palmer AR, Summerfield AQ,
Elliott MR, Gurney EM, Bowtell RW. “Sparse” temporal sampling in
auditory fMRI. Hum Brain Mapp 7: 213–223, 1999.
Hernandez AE, Martinez A, Kohnert K. In search of the language switch:
an fMRI study of picture naming in Spanish-English bilinguals. Brain Lang
73: 421– 431, 2000.
Hilliard AT, Miller JE, Horvath S, White SA. Distinct neurogenomic states
in basal ganglia subregions relate differently to singing behavior in songbirds. PLoS Comput Biol 8: e1002773, 2012.
Janik VM, Slater PJ. The different roles of social learning in vocal communication. Anim Behav 60: 1–11, 2000.
Jarvis E. Neural systems for vocal learning in birds and humans: a synopsis.
J Ornithol 148: 35– 44, 2007.
Jarvis ED. Learned birdsong and the neurobiology of human language. Ann
NY Acad Sci 1016: 749 –777, 2004.
Jarvis ED, Gunturkun O, Bruce L, Csillag A, Karten H, Kuenzel W,
Medina L, Paxinos G, Perkel DJ, Shimizu T, Striedter G, Wild JM, Ball
GF, Dugas-Ford J, Durand SE, Hough GE, Husband S, Kubikova L,
Lee DW, Mello CV, Powers A, Siang C, Smulders TV, Wada K, White
SA, Yamamoto K, Yu J, Reiner A, Butler AB; Avian Brain Nomenclature Consortium. Avian brains and a new understanding of vertebrate brain
evolution. Nat Rev Neurosci 6: 151–159, 2005.
Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for
the robust and accurate linear registration and motion correction of brain
images. Neuroimage 17: 825– 841, 2002.
Jenkinson M, Smith S. A global optimisation method for robust affine
registration of brain images. Med Image Anal 5: 143–156, 2001.
Jubault T, Ody C, Koechlin E. Serial organization of human behavior in the
inferior parietal cortex. J Neurosci 27: 11028 –11036, 2007.
Jueptner M, Weiller C. A review of differences between basal ganglia and
cerebellar control of movements as revealed by functional imaging studies.
Brain 121: 1437–1449, 1998.
STRIATAL RESPONSE IN HUMAN VOCAL LEARNING
Price CJ, Green DW, von Studnitz R. A functional imaging study of
translation and language switching. Brain 122: 2221–2235, 1999.
Rodriguez-Fornells A, Rotte M, Heinze HJ, Nosselt T, Munte TF. Brain
potential and functional MRI evidence for how to handle two languages with
one brain. Nature 415: 1026 –1029, 2002.
Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H,
Reiss AL, Greicius MD. Dissociable intrinsic connectivity networks for
salience processing and executive control. J Neurosci 27: 2349 –2356, 2007.
Seghier ML, Price CJ. Functional heterogeneity within the default network
during semantic processing and speech production. Front Psychol 3: 281,
2012.
Simmonds AJ, Wise RJS, Collins C, Redjep O, Sharp DJ, Iverson P,
Leech R. Parallel systems in the control of speech. Hum Brain Mapp 35:
1930 –1943, 2014.
Simmonds AJ, Wise RJS, Dhanjal NS, Leech R. A comparison of sensorymotor activity during speech in first and second languages. J Neurophysiol
106: 470 – 478, 2011.
Smith SM. Fast robust automated brain extraction. Hum Brain Mapp 17:
142–155, 2002.
Thierry G, Wu YJ. Brain potentials reveal unconscious translation during
foreign-language comprehension. Proc Natl Acad Sci USA 104: 12530 –
12535, 2007.
Ventura MI, Nagarajan SS, Houde JF. Speech target modulates speaking
induced suppression in auditory cortex. BMC Neurosci 10: 58 – 69, 2009.
Woolrich MW, Behrens TE, Beckmann CF, Jenkinson M, Smith SM.
Multilevel linear modelling for FMRI group analysis using Bayesian inference. Neuroimage 21: 1732–1747, 2004.
Woolrich MW, Ripley BD, Brady M, Smith SM. Temporal autocorrelation
in univariate linear modeling of fMRI data. Neuroimage 14: 1370 –1386,
2001.
Wu YJ, Thierry G. Chinese-English bilinguals reading English hear Chinese.
J Neurosci 30: 7646 –7651, 2010.
Yin H, Mulcare S, Hilàrio M, Clouse E, Holloway T, Davis M, Hansson A,
Lovinger D, Costa R. Dynamic reorganization of striatal circuits during the
acquisition and consolidation of a skill. Nat Neurosci 12: 333–341, 2009.
Yin HH, Knowlton BJ. The role of the basal ganglia in habit formation. Nat
Rev Neurosci 7: 464 – 476, 2006.
J Neurophysiol • doi:10.1152/jn.00901.2013 • www.jn.org
Downloaded from http://jn.physiology.org/ by 10.220.33.4 on June 17, 2017
Kao MH, Doupe AJ, Brainard MS. Contributions of an avian basal gangliaforebrain circuit to real-time modulation of song. Nature 433: 638 – 643,
2005.
Klein D, Milner B, Zatorre RJ, Meyer E, Evans AC. The neural substrates
underlying word generation: a bilingual functional-imaging study. Proc Natl
Acad Sci USA 92: 2899 –2903, 1995.
Klein D, Milner B, Zatorre RJ, Zhao V, Nikelski J. Cerebral organization
in bilinguals: a PET study of Chinese-English verb generation. Neuroreport
10: 2841–2846, 1999.
Klein D, Zatorre RJ, Milner B, Meyer E, Evans AC. Left putaminal
activation when speaking a second language: evidence from PET. Neuroreport 5: 2295–2297, 1994.
Koechlin E, Jubault T. Broca’s area and the hierarchical organization of
human behaviour. Neuron 50: 963–974, 2006.
Kroodsma DE, Parker LD. Vocal virtuosity in the brown thrasher. Auk 94:
783–785, 1977.
Lametti DR, Nasir SM, Ostry DJ. Sensory preference in speech production
revealed by simultaneous alteration of auditory and somatosensory feedback. J Neurosci 32: 9351–9358, 2012.
Levelt WJ. Speaking: From Intention to Articulation. Cambridge, MA: MIT
Press, 1989.
Lipkind D, Marcus GF, Bemis DK, Sasahara K, Jacoby N, Takahasi M,
Suzuki K, Feher O, Ravbar P, Okanoya K, Tchernichovski O. Stepwise
acquisition of vocal combinatorial capacity in songbirds and human infants.
Nature 498: 104 –108, 2013.
Majerus S, Poncelet M, Van der Linden M, Albouy G, Salmon E,
Sterpenich V, Vandewalle G, Collette F, Maquet P. The left intraparietal
sulcus and verbal short-term memory: focus of attention or serial order?
Neuroimage 32: 880 – 891, 2006.
Miyachi S, Hikosaka O, Miyashita K, Kárádi Z, Rand MK. Differential
roles of monkey striatum in learning of sequential hand movement. Exp
Brain Res 115: 1–5, 1997.
Mooney R. Neural mechanisms for learned birdsong. Learn Mem 16: 655–
669, 2009.
Olveczky BP, Andalman AS, Fee MS. Vocal experimentation in the juvenile
songbird requires a basal ganglia circuit. PLoS Biol 3: e153, 2005.
Petkov CI, Jarvis E. Birds, primates and spoken language origins: behavioral
phenotypes and neurobiological substrates. Front Evol Neurosci 4: 12, 2012.
801