Musical groove modulates motor cortex excitability: A TMS investigation

Article (PDF Available)inBrain and Cognition 82(2):127-136 · May 2013with364 Reads
DOI: 10.1016/j.bandc.2013.03.003 · Source: PubMed
Groove is often described as a musical quality that can induce movement in a listener. This study examines the effects of listening to groove music on corticospinal excitability. Musicians and non-musicians listened to high-groove music, low-groove music, and spectrally matched noise, while receiving single-pulse transcranial magnetic stimulation (TMS) over the primary motor cortex either on-beat or off-beat. We examined changes in the amplitude of the motor-evoked potentials (MEPs), recorded from hand and arm muscles, as an index of activity within the motor system. Musicians and non-musicians rated groove similarly. MEP results showed that high-groove music modulated corticospinal excitability, whereas no difference occurred between low-groove music and noise. More specifically, musicians' MEPs were larger with high-groove than low-groove music, and this effect was especially pronounced for on-beat compared to off-beat pulses. These results indicate that high-groove music increasingly engages the motor system, and the temporal modulation of corticospinal excitability with the beat could stem from tight auditory-motor links in musicians. Conversely, non-musicians' MEPs were smaller for high-groove than low-groove music, and there was no effect of on- versus off-beat pulses, potentially stemming from suppression of overt movement. In sum, high-groove music engages the motor system, and previous training modulates how listening to music with a strong groove activates the motor system.
This article appeared in a journal published by Elsevier. The attached
copy is furnished to the author for internal non-commercial research
and education use, including for instruction at the authors institution
and sharing with colleagues.
Other uses, including reproduction and distribution, or selling or
licensing copies, or posting to personal, institutional or third party
websites are prohibited.
In most cases authors are permitted to post their version of the
article (e.g. in Word or Tex form) to their personal website or
institutional repository. Authors requiring further information
regarding Elsevier’s archiving and manuscript policies are
encouraged to visit:
Author's personal copy
Musical groove modulates motor cortex excitability: A TMS investigation
Jan Stupacher
, Michael J. Hove
, Giacomo Novembre
, Simone Schütz-Bosbach
, Peter E. Keller
Research Group Music Cognition and Action, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
Eberhard Karls University, Tübingen, Germany
Research Group Body and Self, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
MARCS Institute, University of Western Sydney, Australia
article info
Article history:
Accepted 29 March 2013
Auditory-motor interaction
Transcranial magnetic stimulation (TMS)
Corticospinal excitability
Groove is often described as a musical quality that can induce movement in a listener. This study exam-
ines the effects of listening to groove music on corticospinal excitability. Musicians and non-musicians
listened to high-groove music, low-groove music, and spectrally matched noise, while receiving single-
pulse transcranial magnetic stimulation (TMS) over the primary motor cortex either on-beat or off-beat.
We examined changes in the amplitude of the motor-evoked potentials (MEPs), recorded from hand and
arm muscles, as an index of activity within the motor system. Musicians and non-musicians rated groove
similarly. MEP results showed that high-groove music modulated corticospinal excitability, whereas no
difference occurred between low-groove music and noise. More specifically, musicians’ MEPs were larger
with high-groove than low-groove music, and this effect was especially pronounced for on-beat com-
pared to off-beat pulses. These results indicate that high-groove music increasingly engages the motor
system, and the temporal modulation of corticospinal excitability with the beat could stem from tight
auditory–motor links in musicians. Conversely, non-musicians’ MEPs were smaller for high-groove than
low-groove music, and there was no effect of on- versus off-beat pulses, potentially stemming from sup-
pression of overt movement. In sum, high-groove music engages the motor system, and previous training
modulates how listening to music with a strong groove activates the motor system.
Ó 2013 Elsevier Inc. All rights reserved.
1. Introduction
Music and movement are intimately entwined. Music is pro-
duced by movements of an instrumentalist; and in turn, music
can induce movements in a listener. When music ‘feels right’, lis-
teners might want to tap their feet, bob their heads, or even break
into unbridled dance. Movement induction is strongly related to
the musical rhythm. The tight linkage between movement and
auditory rhythm is highly apparent in groove music. Groove is of-
ten described as a musical quality that makes us want to move
with the rhythm or beat (e.g., Iyer, 2002; Janata, Tomic, & Haber-
man, 2012; Madison, 2006; Pressing, 2002; Waadeland, 2001).
Burgeoning areas of research explore the musical factors that
promote groove, the auditory–motor neural links that underlie
movement induction, and how training can modulate these audi-
tory–motor links. However, the interplay between these research
areas, the neural response to groove, and how groove utilizes these
auditory–motor neural links still remain unknown. In this study,
we addressed these questions directly by investigating how the
perception of high- vs. low-groove music modulates the excitabil-
ity of the motor control system in musicians and non-musicians as
probed by transcranial magnetic stimulation.
The close connection between groove and movement has been
established by ethnomusicologists and music cognition research-
ers (e.g., Iyer, 2002; Janata et al., 2012; Madison, 2006; Pressing,
2002; Waadeland, 2001). The concept of groove is widely under-
stood to relate primarily to how music ‘‘makes you want to move’’
and is also associated with a strong beat, pleasure, and enjoyment
(Janata et al., 2012). When participants are asked to rate a song’s
groove, the ratings are highly consistent between individuals
(Janata et al., 2012; Madison, 2006), indicating that listeners ‘‘know
a good groove when they hear it’’ (Zbikowski, 2004). While the
term ‘‘groove’’ could be more associated with musical styles that
cultivate danceable rhythms such as soul and R&B (Janata et al.,
2012), groove ratings emerged consistently across a wide range
of styles suggesting that groove reflects psychological factors inde-
pendent of musical style (Madison, 2006). Similar phenomena to
groove might be captured by more genre-specific terms such as
‘‘swing’’ in jazz, the ‘‘push’’ in polka, or Hodeir’s (1956) more
0278-2626/$ - see front matter Ó 2013 Elsevier Inc. All rights reserved.
Corresponding author. Address: Max Planck Institute for Human Cognitive and
Brain Sciences, Stephanstrasse 1a, 04103 Leipzig, Germany. Present address: MGH/
Harvard Medical School, Department of Psychiatry, 149 13th Street Charlestown,
MA 02129, United States.
E-mail address: (M.J. Hove).
These authors contributed equally.
Brain and Cognition 82 (2013) 127–136
Contents lists available at SciVerse ScienceDirect
Brain and Cognition
journal homepage:
Author's personal copy
general ‘‘vital drive’’ (Keil, 1995), which evoke the quality or feeling
that makes music come alive and induce movement (Butterfield,
In addition to consistent subjective understanding, groove has
recently been shown to affect movement. After establishing groove
ratings for many musical examples, Janata et al. (2012) showed
that the high groove songs yielded more accurate movement syn-
chrony in a tapping task, and induced more spontaneous move-
ment such as foot tapping. This underscores that groove is
largely a phenomenon of sensorimotor coupling (Janata et al.,
2012), and that groove reflects the music’s ‘‘efficiency for entrain-
ment’’ (Madison, Gouyon, Ullén, & Hörnström, 2011).
1.1. Musical aspects of groove
Many musical factors relate to groove, but most generally,
groove is strongly linked to rhythm and timing. In many forms of
music, groove is predominantly shaped by drums (or percussion)
and bass (Butterfield, 2010; Iyer, 2002; Keil, 1995; Pressing,
2002). Musicians have developed many techniques to promote
groove, and some specific musical features are thought to underlie
groove and movement induction. One key factor in groove is a
repetitive rhythm that can increase engagement and attention
(Pressing, 2002) and improve the ability to predict and synchronize
with a beat (Madison et al., 2011). Perfect predictability, however,
can undermine the groove, and many musicians (and drum ma-
chines) purposefully introduce subtle deviations within a rhythmic
structure to increase groove (Danielsen, 2006; Prögler, 1995).
Expressive timing deviations or ‘‘microtiming’’ (e.g., Desain & Hon-
ing, 1993) can highlight musical structure and imply motion (e.g.,
Iyer, 2002), and are a commonly discussed factor in groove (e.g.,
Iyer, 2002; Keil, 1995; Naveda, Gouyon, Guedes, & Leman, 2011;
cf. Madison et al., 2011). For example, ensemble musicians often
purposely play slightly apart from each other in time (Keil,
1995), with one player consistently leading by some tens of milli-
seconds (Butterfield, 2010; Friberg & Sundström, 2002; Prögler,
1995). Slightly asynchronous onsets can create a sense of collective
participation (Iyer, 2002; Waadeland, 2001), and have been shown
to improve movement synchrony for musically trained partici-
pants (Hove, Keller, & Krumhansl, 2007).
The rhythmic structure of a groove is typically hierarchical and
contains subdivisions that can serve to reinforce the beat (Madison
et al., 2011), and improve synchronization precision (Repp, 2003).
The right amount of rhythmic complexity is important for groove;
rhythms that are too simple or too complex are unlikely to groove
(Witek, Clarke, Wallentin, Kringelbach, & Vuust, 2011). Syncopa-
tion is another important component of groove (Holm & Isaksson,
2010); and musicians ‘‘enjoy’’ syncopated rhythms more than
unsyncopated rhythms (Keller & Schubert, 2011).
Groove has also been shown to depend on sonic factors of mu-
sic. Madison et al. (2011) examined acoustic features of many
songs and could quantitatively establish that beat density and beat
salience were strong predictors of groove. Another recent study
found that highly rhythmic periodicities, clear pulses, and energy
in low frequency bands were especially powerful for inducing
movement in participants (Burger et al., 2012). The musical con-
vention that the bass and the bass drum often set the musical beat
might stem from the propensity for low-frequencies to induce
movement or give timing cues. Low tones also generate a vestibu-
lar response (Todd, 2001), and the vestibular system has been
shown to play an important role in perceiving musical rhythm
(Trainor, Gao, Lei, Lehtovaara, & Harris, 2009).
Finally, groove can be facilitated by playing at a tempo that af-
fords easy synchronization (Madison et al., 2011). Tempi of dance
music have a very clear peak around 120 bpm (Moelants, 2002),
which aligns with spontaneous tapping and preferred movement
tempo (e.g., Fraisse, 1982; MacDougall & Moore, 2005), making it
both easy to synchronize with and energizing. In Janata et al.
(2012), higher groove ratings occurred for songs in the faster cate-
gory (mean tempo = 115.6 bpm) than the slower category (mean
tempo = 90.8 bpm). And in a recent fMRI study, tempo strongly
influenced aesthetic ratings of rhythms, and listening to rhythms
at a participant’s preferred tempo increasingly activated his or
her motor-related brain areas (Kornysheva, von Cramon, Jacobsen,
& Schubotz, 2010). These preferred tempo effects suggest en-
hanced sensorimotor simulation of the beat and that auditory–
motor resonance is key for groove.
1.2. Auditory-motor links and music
Experimental evidence indicates that listening to music is
tightly linked to neural processes associated with the motor sys-
tem (for a review see Zatorre, Chen, & Penhune, 2007). Numerous
fMRI investigations have shown that perception of auditory
rhythms without actual movement activates motor regions,
including premotor cortices, supplementary motor areas (SMA),
and the basal ganglia (Bengtsson et al., 2009; Chen, Penhune, & Za-
torre, 2008; Chen, Zatorre, & Penhune, 2006; Grahn & Brett, 2007;
Kornysheva et al., 2010; Schubotz, Friederici, & von Cramon, 2000).
For example, simply listening to an auditory rhythm, as well as
tapping along with that rhythm, similarly activate areas of the
SMA and premotor cortex (Chen et al., 2008). Such activity in mo-
tor regions suggests that temporal features of music might induce
people to ‘‘tune in to its beat’’ (cf. Kornysheva et al., 2010). Thus,
the musical factors that contribute to groove including rhythm,
timing, sonic features, and tempo may do so by directly engaging
movement via auditory–motor links.
Fine-grained temporal correlations between dynamic aspects of
auditory rhythms and activity in the brain have been observed
using experimental methodologies with high temporal resolution
including EEG (e.g., Nozaradan, Peretz, Missal, & Mouraux, 2011)
and MEG (e.g., Fujioka, Trainor, Large, & Ross, 2012). The temporal
dynamics of music can be observed in motor regions during pas-
sive listening. For instance, using MEG, Popescu, Otsuka, and Ioan-
nides (2004) showed that when listening to music, the time course
of motor-related areas (lateral premotor areas, SMA, and somato-
motor areas) correlated with the fine temporal structures of the
sound, indicating the synchronization of external and internal
Combined transcranial magnetic stimulation (TMS) and electro-
myography (EMG) methodologies offer another approach to inves-
tigate the precise temporal dynamics of such sensorimotor
processes and moreover to localize observed effects at the motor
level. Delivering single-pulse TMS over primary motor cortex
(M1) and using EMG to measure the resulting response in contra-
lateral muscle activity (a motor evoked potential, MEP) gives a
measure of corticospinal excitability at the time of pulse. The
amplitude of a MEP is known to be influenced by activity in other
cortical motor regions including premotor and the supplemen-
tary motor areas (Guillot & Collet, 2010) and therefore this index
can be used to make direct inferences about the excitability of the
human motor system with high temporal resolution. For example,
Wilson and Davey (2002) used TMS pulses in time with music to
examine how listening to a song with a strong beat modulated
the MEPs in ankle flexor and extensor muscles. No difference in
MEPs was observed for on-beat vs. off-beat TMS pulses when par-
ticipants relaxed and listened to music (cf. Cameron, Stewart,
Pearce, Grube, & Muggleton, 2012). However correlations between
the antagonist muscles were lower during music trials than white
noise trials, possibly indicating that corticospinal excitability of the
flexor and extensor were time-locked to the music, but out of
phase (Wilson & Davey, 2002).
128 J. Stupacher et al. / Brain and Cognition 82 (2013) 127–136
Author's personal copy
Most of the aforementioned studies showing tight auditory–
motor coupling were observed in non-musicians. Long-term musi-
cal training can further strengthen auditory–motor coupling.
Behavioral and neuroimaging investigations have shown that the
musician’s brain develops multiple sensorimotor associations be-
tween sensory and motor areas, as a result of musical training
(e.g., Bangert & Altenmüller, 2003; Bangert et al., 2006; D’Ausilio
et al., 2006; Drost, Rieger, Brass, Gunter, & Prinz, 2005; Haslinger
et al., 2005; Haueisen & Knösche, 2001; Lahav, Saltzman, & Schl-
aug, 2007). In an MEG study Haueisen and Knösche (2001) com-
pared motor activations in pianists and non-pianists while
listening to piano pieces. They found an increase of activity in mo-
tor cortex in pianists, but not in non-pianists. This result indicates
that musicians develop tight auditory–motor links through
In the present experiment, we investigated whether musical
groove utilizes auditory–motor links and modulates motor cortex
excitability. Musicians and non-musicians listened to commer-
cially available high- and low-groove music (as previously assessed
by Janata et al., 2012) while receiving single-pulse TMS over the
left primary motor cortex (M1). MEPs were recorded from muscles
of the right hand and the right forearm, and their amplitude was
examined to assess corticospinal excitability. We hypothesized
that listening to high-groove music would increase corticospinal
excitability more than listening to low-groove music or spectrally
matched noise, as high-groove music is known to induce move-
ment and entrain the listener. Moreover, given the evidence for
especially strong audio-motor coupling in musicians, we examined
potential differences between musicians’ and non-musicians’ re-
sponse to groove music.
2. Method
2.1. Participants
Fifteen adults (8 female) participated in the experiment (mean
age = 25.1, SD = 2.9). One additional participant was excluded due
to a technical failure. Seven participants (4 female) were musicians
Table 3
Mean MEPs for each condition for each individual participant. Instrument names are abbreviated: piano = pno; percussion = perc; guitar = gtr; vocals = voc; saxophone = sax.
Expertise Instruments Mean MEP high-groove Mean MEP low-groove Mean MEP noise
Musician pno, perc 1.083 .953 1.042
Musician pno, gtr, voc, perc .578 .470 .292
Musician pno, gtr, voc, perc .515 .422 .396
Musician pno .473 .399 .449
Musician pno .324 .250 .286
Musician gtr, pno .326 .328 .276
Musician pno, sax .927 .969 .721
Non-musician .764 .980 .784
Non-musician .552 .678 .765
Non-musician .294 .417 .340
Non-musician 1.438 1.542 1.531
Non-musician 1.527 1.578 1.616
Non-musician .788 .801 .860
Non-musician .613 .612 .604
Non-musician .708 .684 .707
Table 1
The 8 musical clips and their respective groove rating (based on Janata et al. (2012)), vocals, meter signature, beats per minute (BPM) and absolute peak dB level. High- and low-
groove songs were selected as pairs matched as closely as possible in terms of instrumentation, meter signatures, and tempo. The high-groove and low-groove matched pairs are
Superstition/Cheeseburger in Paradise, Look-Ka Py Py/Ray Dawn Balloon, Bad Tune/Bryter Layter and If I Ain’t Got You/Yes I Am (indicated by superscript symbols).
Song name Artist Groove
Vocals Meter BPM Peak (dB)
Stevie Wonder High Male 4/4 101 1
Look-Ka Py Py
The Meters High None 4/4 87 1
Bad Tune
Earth, Wind and Fire High None 4/4 118 1
If I Ain’t Got You
Alicia Keys High Female 6/8 41 1
Cheeseburger in Paradise
Jimmy Buffett Low Male 4/4 140 1
Ray Dawn Balloon
Trey Anastatio Low None 4/4 80 1
Bryter Layter
Nick Drake Low None 4/4 119 1
Yes I Am
Melissa Etheridge Low Female 6/8 53 1
Based on Janata et al. (2012).
Table 2
Mean ratings of groove, liking and familiarity. Possible values range from 0 to 127.
Song name Groove rating (SD) Groove rating (Janata et al., 2012) Liking Familiarity
Superstition 120.5 (13.6) 108.7 101.1 57.5
Look-Ka Py Py 94.9 (33.3) 92.5 94.5 30.5
Bad Tune 86.9 (41.3) 86.2 89.7 35.3
If I Ain’t Got You 70.5 (36.6) 98.7 91.0 90.0
Cheeseburger in Paradise 93.7 (27.6) 48.6 87.1 43.5
Ray Dawn Balloon 34.0 (31.3) 38.5 77.9 22.3
Bryter Layter 38.5 (31.6) 40.4 65.8 26.3
Yes I Am 45.9 (35.9) 40.2 74.7 46.9
J. Stupacher et al. / Brain and Cognition 82 (2013) 127–136
Author's personal copy
with musical performance experience ranging from 8 to 18 years
(M = 13.4 years, SD = 4.2). Their starting age ranged from 4 to
12 years (M = 7.0, SD = 2.3). Most musicians had experience playing
multiple instruments; all seven had piano training, and other
instruments were guitar, saxophone, and percussion (see Table 3
for details). The 8 remaining participants (4 female) were non-
musicians who had no musical training. The experiment was run
in Leipzig, Germany, and all participants were German speakers.
All participants were right-handed, as assessed by the Edinburgh
Handedness Inventory (Oldfield, 1971). A Mann–Whitney test re-
vealed no significant difference of laterality quotient between
musicians and non-musicians, z = .78, p = .433. The mean time
of listening to music did not differ between musicians (M = 8.4 h/
week, SD = 6.2) and non-musicians (M = 6.5 h/week, SD = 3.8),
t(13) = .72, p = .483. Both musicians and non-musicians reported
that they usually dance more often than once a month, but less of-
ten than once a week. The experiment was approved by the local
ethics committee. Participants gave informed consent and were
paid for participating. They were naïve with regard to the purpose
of the study.
2.2. Musical stimuli
Four high-groove and four low-groove songs clips were selected
from over 150 songs that were recently rated on groove (Janata
et al., 2012). The song clips were free 30-s previews available from
the iTunes Music Store ( from the genres
pop, rock and soul. High- and low-groove songs are shown in Ta-
ble 1, and were selected as pairs matched as closely as possible
in terms of instrumentation, meter signatures, and tempo (which
did not significantly differ between categories, z = .58, p > .5).
Additionally, the clips were equated in loudness using the Software
Nuendo (Steinberg, Hamburg, Germany), so that the peak dB levels
were at 1 dB.
As a control condition, eight noise clips were created with the
same frequency spectrum profile as a corresponding song. The
noise clips contained no beat-like structure or temporal modula-
tion (other than the narrow-band filter characteristics of the fre-
quency envelope). A pre-study with 9 participants (3 female, 6
male; mean age = 24.6 years, SD = 2.6) established the subjective
equal loudness of the noise compared to the songs. Stimuli were
presented over Sennheiser IE 6 in-ear headphones (Sennheiser,
Wedemark-Wennebostel, Germany).
The overall loudness of the song and noise clips during the TMS
experiment was adjusted to be loud and clear but still comfortable.
To ensure that the stimuli were pleasant, prior to the experiment,
participants were asked about stimuli loudness; as a result, loud-
ness was turned down slightly for four participants and was not
adjusted further during the experiment. Additionally, at the end
of the entire experimental session, participants rated the global
perceived loudness of music and noise on a 7-point Likert-scale
(1 = much too quiet; 7 = much too loud). Subjective ratings showed
that participants judged the loudness to be comfortable for both
noise (M = 4.13; SD = .35) and music (M = 4.33; SD = .62), with no
difference between the two, t(14) = 1.15, p = .271. The stimuli
were not audible to the experimenters, who were blind to the
experimental conditions.
2.3. TMS and EMG recordings
Focal single TMS pulses (Magstim 200, Whitland, UK; 70 mm
figure-of-eight stimulation coil) and EMG recordings were com-
bined in order to measure corticospinal excitability. We examined
changes in MEP amplitudes, which were measured simultaneously
from right hand (first dorsal interosseous, FDI) and right forearm
(extensor carpi radialis, ECR) muscles, as previous studies investi-
gating motor representations of music have commonly recorded
MEPs from FDI (e.g., Giovannelli et al., 2013; Novembre, Ticini,
Schütz-Bosbach, & Keller, 2012) and ECR (e.g., D’Ausilio et al.,
2006; Novembre et al., 2012).
The EMG signal was recorded using Ag/AgCl surface electrodes
placed in a belly-tendon montage. A ground electrode was placed
on the back of the right hand. The signal was amplified 1000 times,
band-pass filtered (10–1000 Hz) with a mains hum notch filter at
50 Hz and digitized at 5 kHz.
TMS was delivered over the left M1, which was identified by
moving the coil over the left motor cortex while sending pulses
with constant time interval (of 6 s) and constant intensity until
the optimal scalp position (i.e., which elicited maximal MEP ampli-
tudes from both right FDI [hand] and ECR [forearm] muscles) was
identified and marked with a pencil. For two participants it was not
possible to identify a scalp position that would reliably elicit MEPs
from both ECR and FDI muscles (this was presumably related to
structural–functional individual differences). Hence, for these par-
ticipants, MEPs were only recorded from ECR.
The intensity of the stimulation was set at 120% of each individ-
ual motor threshold, which was defined as the lowest intensity at
which 5 out of 10 TMS pulses produced MEPs with an amplitude
greater than 50
V. The stimulation intensity ranged from 37% to
58% (M = 44.47, SD = 6.56) of the maximum stimulator output.
MEP amplitudes were constantly monitored visually.
2.4. Procedure
Participants sat in a cushioned seat with armrests. The right
armrest was adjusted to ensure that the muscles were relaxed. A
green fixation cross appeared on screen during presentation of
auditory stimuli, and it turned white during the 3 s between trials.
Participants were instructed to sit relaxed. They were explicitly in-
structed not to move while listening to the auditory stimuli; this
was monitored and corroborated by the two experimenters in
the room, and was empirically confirmed by looking at the muscle
activity prior to each pulse (see below).
Each presentation of a 30-s audio clip was accompanied by four
TMS pulses. Metrical positions of pulses alternated within each clip
between on-beat and off-beat (counterbalanced in order between
clips). On-beat pulses were delivered on the beat (i.e., on the quar-
ter notes in 4/4 clips and on the eighth notes in the 6/8 clips); and
off-beat pulses were delivered between the beats (i.e., on the
eighth notes in the 4/4 clips, and on the sixteenth notes in 6/8
clips). The inter-pulse interval was approximately 6 s
(SD = 307 ms). The first pulse in a trial started either approximately
5 or 7 s into the trial in order to avoid stimulating on the same
beats on every presentation of a music clip. Each participant re-
ceived 48 pulses for each of the experimental conditions (high-
groove, low-groove, spectrally matched noise), resulting in a total
of 144 pulses through the entire experiment.
The experiment consisted of three blocks, each containing the
12 auditory stimuli in random order (4 high-groove songs, 4 low-
groove songs, 4 spectrally matched noise recordings). Noise
recordings were counterbalanced between subjects, so that each
participant heard only 4 noise recordings (2 high- and 2 low-
groove spectrally matched noise recordings). The blocks lasted
7 min each, and were separated by short breaks.
After the TMS experiment, participants rated all 8 music clips
first in terms of familiarity, then liking, then groove (which was de-
fined as ‘‘an aspect of music that makes you want to move’’). They
could start and stop the currently rated clip individually by press-
ing the space bar of a computer keyboard. A mouse was used to ad-
just a horizontal slider on a computer screen corresponding to
their rating. Low ratings (on the left) and high ratings (on the right)
130 J. Stupacher et al. / Brain and Cognition 82 (2013) 127–136
Author's personal copy
ranged in value from 0 to 127, respectively, but participants could
not see these numerical values.
The entire experiment, including preparation, music listening,
and questionnaires lasted approximately 80 min per participant.
2.5. Data processing
The absolute distance between the maximum and minimum
MEP values (peak-to-peak) within a time window between
10 ms and 80 ms after the TMS pulse was calculated separately
for both muscles. To control for actual movement, the mean pre-
pulse EMG activity in the 50 ms window prior to a TMS pulse
was calculated after taking the absolute value of all samples in this
window. If this pre-pulse EMG mean was larger than .075
V (indi-
cating movement), or if an MEP was more than 3 standard devia-
tions from the mean MEP for each participant, muscle, condition
and block, that MEP was excluded (.57% of total MEPs). MEPs from
spectrally matched high-groove noise and low-groove noise were
collapsed, as an ANOVA with the factors noise (high-groove
noise/low-groove noise), muscle (FDI/ECR) and expertise (musi-
cians/non-musicians) revealed no main effect of noise and no
interactions (ps > .2).
Since an ANOVA with the factors muscle (FDI[hand]/ECR[fore-
arm]), condition (high-groove/low-groove/noise) and expertise
(musicians/non-musicians) revealed no main effect or interactions
of muscle (all ps > .1), the mean MEP of both muscles was used as
the main dependent variable (for the two participants whose FDI
could not be stimulated, ECR data were used). The noise condition
was used as a baseline to normalize the high- and low-groove con-
ditions. Thus we subtracted the mean MEP in the noise condition
from the mean MEP in the high-groove and low-groove conditions
for each participant.
To ensure that possible differences observed in MEP amplitudes
were not preceded by a difference in the EMG signal, mean pre-
pulse EMG activity was calculated for each MEP (after having fil-
tered out the large pre-pulse EMG trials that would stem from ac-
tual movement). Mean pre-pulse EMG was calculated by averaging
the absolute values of all samples in the 50 ms window before a
pulse. The EMG data were normalized with the noise condition
as a baseline using the same procedure used for the MEPs: pre-
pulse EMG activity of the noise condition was subtracted from
pre-pulse EMG activity of the high- and low-groove conditions.
Additionally although groove is a complex musical feature
resulting from the interplay of many factors as an exploratory
step to identify audio features that might differ between high-
and low-groove music, we extracted a set of audio features from
the music clips using Matlab’s (Mathworks, Natick, MA) MIR tool-
box (Lartillot & Toiviainen, 2007). The audio features we extracted
were: event density (the number of note onsets per second); pulse
clarity (strength of the beats); fluctuation (rhythmic periodicity);
low energy (measure of dynamics in percentage of less-than-aver-
age energy); spectral flux (distance between the spectrum of suc-
cessive frames); and sub-band spectral flux in eight frequency
3. Results
3.1. Subjective ratings
Every high-groove clip was rated higher than the matched low-
groove clip in the groove rating and in the liking rating. Mean rat-
ings of groove, liking, and familiarity are shown in Table 2. With
exception of the songs ‘‘Cheeseburger in Paradise’’ and ‘‘If I Ain’t
Got You’’, the ratings of the present study aligned very well with
the ratings of Janata et al. (2012). Groove ratings significantly cor-
related with liking ratings in both musicians and non-musicians,
r(53) = .37, p = .005, and r(61) = .55, p < .001, respectively. Groove
rating and familiarity correlated in non-musicians, r(61) = .32,
p = .012, but not in musicians, r(53) = .22, p = .114.
An ANOVA on the groove ratings with the factors groove cate-
gory (high-groove/low-groove) and expertise (musicians/non-
musicians) revealed a main effect of groove category,
F(1,13) = 30.97, p < .001,
= .70, indicating that the perceived
groove ratings for high-groove clips were indeed higher than for
low-groove clips (see also Fig. 1). Significant main effects of groove
category in the same direction were also found in separate ANOVAs
on liking ratings, F(1, 13) = 5.67, p = .033,
= .30, and on familiar-
ity ratings, F(1, 13) = 13.25, p = .003,
= .51. No between-subject
effects and no interactions between musicians and non-musicians
occurred for groove ratings, liking, or familiarity.
3.2. MEPs
Normalized MEPs of high- and low-groove clips are shown for
musicians and non-musicians in Fig. 2. A clear difference in high-
groove MEPs can be observed between musicians and non-musi-
cians. High- and low-groove clips affected the MEPs of musicians
and non-musicians differently, as indicated by a significant inter-
action between groove category and expertise in the 2 2 ANOVA,
F(1,13) = 13.55, p = .003,
= .51. For musicians, high-groove clips
produced larger MEPs than low-groove clips, t(6) = 2.67, p = .037;
whereas for non-musicians, high-groove clips produced smaller
MEPs than low-groove clips, t(7) = 2.66, p = .032. The main effect
of expertise was significant, F(1,13) = 5.09, p = .042,
= .28, indi-
cating that musicians showed larger overall MEPs than non-musi-
cians when listening to music (relative to the noise baseline). The
main effect of groove category was not significant F(1, 13) = .14,
p = .718,
= .01, due to the opposite effects observed for musi-
cians and non-musicians. Table 3 displays MEPs by condition of
each individual participant.
We next compared the mean MEPs between the groove catego-
ries and spectrally matched noise (y =0,inFig. 2) using t-tests. In
musicians, high-groove clips produced larger MEPs than noise,
t(6) = 2.86, p = .029, whereas low-groove clips did not show a
Fig. 1. Mean groove ratings of musicians and non-musicians. Error bars represent
±.5 SE.
J. Stupacher et al. / Brain and Cognition 82 (2013) 127–136
Author's personal copy
significant difference, t(6) = 1.00, p = .357. In non-musicians, high-
groove clips produced smaller MEPs than noise, t(7) = 2.60,
p = .036, and again, low-groove clips did not differ from noise,
t(7) = .34, p = .748. Therefore, high-groove clips, and not low-
groove, lead to a concrete modulation of corticospinal excitability.
In order to examine the high- versus low-groove MEP differ-
ences between musicians and non-musicians, we compared musi-
cians’ and non-musicians’ MEPs for each song clip individually.
Results for each independent samples t-test on the normalized
MEPs are shown by song in Table 4. For every high-groove clip, a
significant difference occurred between musicians and non-musi-
cians, with musicians having larger MEPs. Conversely, for the
low-groove clips, no significant differences in MEPs occurred be-
tween musicians and non-musicians.
Finally, possible effects of on- and off-beat pulses were exam-
ined. Two separate ANOVAs with the factors beat (on-/off-beat)
and groove category (high-/low-groove) were performed on mean
MEPs for musicians and non-musicians. There was no main effect
of beat for musicians, F(1, 6) = .10, p = .768,
= .02, or for non-
musicians, F(1,7) = 1.11, p = .328,
= .14. However in musicians,
the interaction between beat and groove category approached sig-
nificance, F(1,6) = 5.87, p = .052,
= .50, indicating that on-beat
and off-beat MEPs tended to differ between high- and low-groove
conditions. Follow-up t-tests showed that on-beat MEPs were lar-
ger for high-groove clips than for low-groove clips, t(6) = 2.79,
p = .031, whereas off-beat MEPs did not differ, t(6) = .35, p = .738
(see Fig. 3). Thus, the musicians’ modulation of motor excitability
mirrored the beat structure. Interestingly, in non-musicians no
on-beat/off-beat differences between high- and low- groove clips
were evident in the interaction, F(1, 7) = .24, p = .638,
= .03.
3.3. Pre-pulse EMG activity
Mean pre-pulse EMG activity is shown for musicians and non-
musicians in Fig. 4. For the non-musicians, pre-pulse EMG differed
between high- and low-groove clips, with higher activity associ-
ated with high-groove clips t(7) = 2.39, p = .048. Musicians, how-
ever, showed no significant difference in pre-pulse EMG activity
for high- and low groove clips, t(6) = .53, p = .616. Further analysis
comparing high- or low-groove and noise were not significant for
musicians or non-musicians (ps > .3).
3.4. Audio features
Separate Mann–Whitney tests revealed significant differences
between high- and low-groove clips (with higher values for high-
groove music clips) for spectral flux, z = 2.02, p = .043, and sub-
band flux in the low frequency bands: band 1 [20–100 Hz],
z = 2.02, p = .043, and band 3 [216–467 Hz], z = 2.02, p = .043,
(a similar trend occurred in band 2 [100–216 Hz], z = 1.44,
p = .149). Sub-band flux in mid- and high-frequency bands did
not differ between groove categories, nor did the other extracted
audio features (ps > .2).
4. Discussion
Groove has been established as a musical quality associated
with movement induction (e.g., Iyer, 2002; Janata et al., 2012;
Madison, 2006; Pressing, 2002; Waadeland, 2001). This study pro-
vides direct evidence of neural modulation of the motor system
while listening to high-groove music, but not low-groove music.
High-groove clips modulated corticospinal excitability more than
low-groove and noise clips for both musicians and non-musicians.
As expected, musicians showed a higher excitability for high-
groove clips compared to low-groove clips and noise. Two musical
training effects were observed: (1) corticospinal excitability with
high-groove clips was higher in musicians than in non-musicians,
whereas no difference between groups was found for low-groove
clips; and (2) excitability was affected by on- versus off-beats in
musicians, but not in non-musicians. Rather unexpectedly, non-
musicians showed lower corticospinal excitability for high-groove
clips than for low-groove clips and noise, despite higher subjective
groove ratings for the high-groove clips. Additionally, for the non-
musicians, the high-groove clips elicited higher pre-pulse EMG
activity than low-groove clips.
Fig. 2. Normalized MEPs of musicians and non-musicians. Y = 0 represents the
mean MEP size during listening to spectrally matched noise clips. Error bars
represent ±.5 SE.
Table 4
Independent samples t-tests comparing musicians’ and non-musicians’ normalized MEPs for the 8 song clips.
Song name t(13) p (Two-tailed) Mean difference SE difference
2.74 .017
.178 .065
Look-Ka Py Py
3.39 .005
.187 .055
Bad Tune
2.85 .014
.112 .039
If I Ain’t Got You
2.47 .028
.222 .090
Cheeseburger in Paradise 0.54 .598 .030 .055
Ray Dawn Balloon 0.56 .589 .027 .049
Bryter Layter 1.01 .330 .110 .108
Yes I Am 0.36 .725 .036 .100
High-groove song clips.
p < .05.
p < .01.
132 J. Stupacher et al. / Brain and Cognition 82 (2013) 127–136
Author's personal copy
The musical stimuli in the current study had been previously
established as high- or low-groove in a recent study by Janata
et al. (2012). In the current study, the subjective groove ratings
of the high-groove clips were indeed rated higher than matched
low-groove clips, indicating the robustness of the groove construct.
Groove ratings here aligned well with Janata et al. (2012), with the
exception of the music clips ‘‘Cheeseburger in Paradise’’ and ‘‘If I
Ain’t Got You.’’ Differences in ratings for these two music clips
may be due to effects of enculturation and context. Participants
in Janata et al. (2012) rated 148 music clips (compared to only 8
in our study), and thus they maybe developed better defined
groove categories. Regardless, in line with previous studies of
Janata et al. (2012) and Madison (2006), the participants in the cur-
rent study had highly consistent subjective ratings of groove.
Groove ratings were also associated with likeability, as well as a
modulation of motor system activity (relative to baseline). Groove
ratings were associated with familiarity only for the non-musi-
cians, and unlikely drive the effect (as many unfamiliar songs were
rated high in groove in Janata et al. (2012)); future work however
should consider familiarity when selecting stimuli. More interest-
ing is the connection between likeability and motor system modu-
lation. While we cannot discern the causal direction of the
likeability/motor system effects, other work suggests a possible
role of the motor system in aesthetic appreciation (e.g., Calvo-Mer-
ino, Jola, Glaser, & Haggard, 2008; Cross & Ticini, 2012).
In musicians, motor system activity, as measured by MEPs, was
consistent with their groove ratings: High-groove music elicited
larger MEPs than low-groove music and noise. Motor system activ-
ity did not differ between low-groove music and noise. High-
groove clips positively modulated the corticospinal excitability in
musicians, and the extent of corticospinal excitability aligned with
the subjective ratings of ‘‘wanting to move to the music.’’ This re-
sult is in line with other studies that describe groove as a phenom-
enon of sensorimotor coupling (Janata et al., 2012) and the music’s
‘‘efficiency for entrainment’’ (Madison et al., 2011).
More detailed analyses of MEPs in musicians showed that the
difference in motor excitability between high- and low-groove mu-
sic is driven by differences related to the fine-grained temporal
dynamics of the music. On-beat MEPs were larger for high-groove
than low-groove music, whereas off-beat MEPs were nearly identi-
cal for high- and low-groove music. This provides evidence that the
corticospinal excitability of musicians is related to the temporal
dynamics of music. Activity in motor-related brain areas was
shown to correlate with dynamic aspects of a solo piano piece
(Popescu et al., 2004); and motor excitability was recently found
to be influenced by metrical structure (Cameron et al., 2012). How-
ever, one must note that these studies tested only participants
with little to no musical experience, and that musicians might
show even stronger effects.
Through musical training, musicians shape their sensory and
motor representations and develop strong auditory–motor links
(e.g., Bangert & Altenmüller, 2003; Bangert et al., 2006; D’Ausilio
et al., 2006; Drost et al., 2005; Haslinger et al., 2005; Haueisen &
Knösche, 2001; Lahav et al., 2007). After many years of training
auditory–motor connections, simply hearing music can activate
the motor system (e.g., Haueisen & Knösche, 2001). Musicians’ mo-
tor systems were highly activated when listening to the high-
groove music, and the motor system’s modulation in time with
the music could stem from the well-trained auditory–motor links.
All participants trained on instruments requiring hand movements,
the area where the modulation of excitability was observed. How-
ever, this effect is surely not limited to the hand and arm areas of
M1; increased corticospinal excitability likely extends throughout
large parts of the motor system including premotor and supple-
mentary motor regions.
In addition to automatic auditory–motor coupling, the current
results could also relate to musicians’ advantages in auditory
encoding and rhythm processing. Mismatch negativity (MMN)
investigations have shown that musicians are more sensitive in
encoding rhythmic regularities than non-musicians (van Zuijen,
Sussman, Winkler, Näätänen, & Tervaniemi, 2005; Vuust, Osterg-
aard, Pallesen, Bailey, & Roepstorff, 2009), and that MMNs elicited
by music can be enlarged by auditory–motor training (Lappe, Her-
holz, Trainor, & Pantev, 2008). Additionally, musicians have faster
and larger brainstem responses to auditory stimuli than non-musi-
cians, indicating a more precise subcortical representation of tim-
ing (Musacchia, Sams, Skoe, & Kraus, 2007). Together, musicians’
advantages in rhythm processing and auditory encoding could con-
tribute to the musical rhythm’s increased modulation of motor
The musicians’ increased corticospinal excitability in time with
the music might also relate to more efficient motor control. During
Fig. 3. Normalized high-groove and low-groove MEPs of on- and off-beat TMS
pulses in musicians and non-musicians. Y = 0 represents the mean MEP size during
listening to spectrally matched noise clips. Error bars represent ±.5 SE.
Fig. 4. Pre-pulse EMG activity of musicians and non-musicians for high- and low-
groove clips. Y = 0 represents the mean pre-pulse EMG activity during listening to
spectrally matched noise clips. Error bars represent ±.5 SE.
J. Stupacher et al. / Brain and Cognition 82 (2013) 127–136
Author's personal copy
motor tasks, piano players have lower motor cortical activations
than non-musicians, indicating musicians’ greater functional effi-
ciency within cortical motor areas (Jäncke, Shah, & Peters, 2000;
Krings et al., 2000). Functional efficiency of the motor systems
might relate to the control of a motor action threshold, which dif-
ferentiates imagined or prepared movement from executed move-
ment. In general, imagined and executed movements share similar
neural networks (e.g., Gerardin et al., 2000; Jeannerod & Frak,
1999), but differ in whether commands are sent to effectors. Imag-
ined or simulated movement have been shown to increase MEPs,
indicating sub-threshold motor system activity (Fadiga et al.,
1999; Hashimoto & Rothwell, 1999; Kasai, Kawai, Kawanishi, &
Yahagi, 1997). Thus, musicians’ increased MEPs while listening to
high-groove music (especially on the beat) are consistent with in-
creased motor simulation. Musicians often use motor simulation,
for example in ensemble performance (Keller, 2008; Keller, Knob-
lich, & Repp, 2007; Novembre et al., 2012), and this experience
might allow them to decouple the covert simulation system from
the overt execution system. Additionally, musicians’ more efficient
motor system would enable simulation without overt movement
as they can presumably approach the motor action threshold with-
out ‘‘overstepping’’ it.
The non-musicians’ decrease in MEPs in high-groove was rather
surprising given that (1) their subjective groove ratings (i.e., how
much the music ‘‘made them want to move’’) was significantly
higher for the high-groove clips, and (2) other studies show that
non-musicians have increased motor system activation in time
with a musical beat (e.g., Popescu et al., 2004). Given that the
non-musicians reported that the high-groove songs ‘‘made them
want to move,’’ it is likely that they wanted to move, but in order
to follow the ‘‘do not move’’ instructions, they needed to suppress
movement. In line with this idea of movement suppression, a re-
cent study using facilitatory paired-pulse TMS showed that passive
reading of positive sentences related to hand actions (e.g., ‘‘I grab
the handle’’) were associated with corticospinal suppression, com-
pared to negative hand action related sentences (e.g., ‘‘I don’t grab
the handle’’) (Liuzza, Candidi, & Aglioti, 2011). Additionally, there
is evidence that the non-musicians’ below-baseline MEPs for
high-groove clips may reflect a suppression of movements. Sohn,
Dang, and Hallett (2003) showed that corticospinal excitability is
suppressed during the imagination of suppressing TMS-induced
hand movements (negative motor imagery) compared to a control
condition without imagination. It is possible that, although non-
musicians potentially show beat-specific modulations, such modu-
lations are not observable when the motor system is inhibited. If
this was the case, then the lack of excitability in response to
high-groove and the lack of beat-modulation would be due to
non-musicians’ inability to motorically represent groove music
without causing an actual movement.
Non-musicians’ higher pre-pulse EMG activity for high-groove
compared to low-groove was rather unexpected. The excitability
of M1 typically increases with voluntary muscle contraction (e.g.,
Hess, Mills, & Murray, 1987; Ugawa, Terao, Hanajima, Sakai, &
Kanazawa, 1995). However, the non-musicians’ increase of EMG
activity during high-groove music might also reflect the inhibition
of movement, as during a go/no-go task, the peak rate of change of
EMG (reflecting the gain of the corticomotor pathway) was higher
for no-go (inhibition) trials than for go trials (Coxon, Stinear, &
Byblow, 2007). Thus, higher pre-pulse EMG activity with high-
groove music in non-musicians may relate to an attempt to combat
the urge to move by contracting antagonistic pairs of extensor and
flexor muscles. The role of inhibition was not directly tested here,
but could be examined by measuring cortical silent periods evoked
by single-pulse TMS or short-interval intracortical inhibition (SICI)
probed by double-pulse TMS. Additional future work could exam-
ine the precise time-course of corticospinal excitability relative to
the beat by adding pulses slightly before and after the beats, and
looking for potential individual or between-group differences.
The qualitative difference between musicians’ and non-musi-
cians’ motor system activity while listening to high-groove music
is noteworthy because the vast majority of previous work on per-
ceptual and cognitive effects of music has shown only quantitative
differences between musicians and non-musicians. This may sug-
gest that musicians and non-musicians use experience-based audi-
tory–motor links differently when processing music under specific
Finally, the exploratory analysis of audio features revealed sig-
nificant differences between high- and low groove clips in spectral
flux, especially in the low-frequency bands. Null effects of other
audio features, such as event density (which have previously been
shown to correlate with groove ratings (Madison et al., 2011; cf.
Witek et al., 2011)), could in part stem from the small number of
clips examined in the present study. Regardless, the current results
align well with research showing a correlation between spectral
flux and the perceived energy or ‘‘activity’’ of the audio signal
(Alluri & Toiviainen, 2009), and that energy in low frequency bands
is a key feature of movement induction (Burger et al., 2012). Low-
frequency tones have been shown to create vestibular responses
(Todd, 2001), which are important in perceiving musical rhythm
(Trainor et al., 2009), and may be especially powerful in movement
induction and timing. The differences in spectral flux in low-
frequency bands between high- and low-groove clips possibly
relate to the MEP results. However, more studies with naturalistic
music clips (or parametric manipulation of factors) would be
needed to further investigate the influence of acoustic and rhyth-
mic features of music on corticospinal excitability.
In conclusion, our study demonstrates that high-groove music
modulates the motor system activity differently for musicians
and non-musicians, and that modulations of the motor system in
musicians are aligned with the beat during high-groove music.
The human motor system is involved in rhythm processing (e.g.,
Zatorre et al., 2007); and the current results suggest that the
degree of motor involvement relates to the temporal and sonic fea-
tures of musical groove. Music engages on many levels cognitive,
emotional, social (Huron, 2006) and auditory–motor coupling is
one of the most direct ways that music can capture a listener.
The current study shows that groove is a musical quality that is
especially powerful at activating the motor system via auditory–
motor coupling. High-groove music and its power to induce
movement can facilitate movement synchrony between individu-
als. Close interpersonal synchrony is thought to have an adaptive
function (Merker, 1999; Merker, Madison, & Eckerdal, 2009), and
can create strong prosocial effects (e.g., Hove & Risen, 2009).
People all over the world are motivated to synchronize their move-
ments to a shared musical rhythm in social contexts (Clayton,
Sager, & Will, 2005; Koelsch, 2010; Overy & Molnar-Szakacs,
2009). The study of groove and its ability to engage the motor
system gives further insights into this pervasive social and musical
This research was supported by The Max Planck Society. We
thank Jan Bergmann for technical assistance and Felix Haiduk for
help in data collection.
Alluri, V., & Toiviainen, P., 2009. In search of perceptual and acoustical correlates of
polyphonic timbre. In Proceedings of the 7th triennial conference of European
Society for the cognitive sciences of music (ESCOM), Jyväskylä, Finland.
Bangert, M., & Altenmüller, E. (2003). Mapping perception to action in piano
practice: A longitudinal DC-EEG study. BMC Neuroscience, 4,26.
134 J. Stupacher et al. / Brain and Cognition 82 (2013) 127–136
Author's personal copy
Bangert, M., Peschel, T., Schlaug, G., Rotte, M., Drescher, D., Hinrichs, H., et al. (2006).
Shared networks for auditory and motor processing in professional pianists:
Evidence from fMRI conjunction. Neuroimage, 30, 917–926.
Bengtsson, S. L., Ullén, F., Ehrsson, H. H., Hashimoto, T., Kito, T., Naito, E., et al.
(2009). Listening to rhythms activates motor and premotor cortices. Cortex, 45,
Burger, B., Thompson, M. R., Luck, G., Saarikallio, S., & Toiviainen, P., (2012). Music
moves us: Beat-related musical features influence regularity of music-induced
movement. In Proceedings of the 12th International Conference in Music Perception
and Cognition and the 8th Triennial Conference of the European Society for the
Cognitive Sciences for Music, Thessaloniki, Greece.
Butterfield, M. (2010). Participatory discrepancies and the perception of beats in
jazz. Music Perception, 27, 157–176.
Calvo-Merino, B., Jola, C., Glaser, D. E., & Haggard, P. (2008). Towards a sensorimotor
aesthetics of performing art. Consciousness and Cognition, 17, 911–922.
Cameron, D. J., Stewart, L., Pearce, M. T., Grube, M., & Muggleton, N. G. (2012).
Modulation of motor excitability by metricality of tone sequences.
Psychomusicology: Music, Mind, and Brain, 22, 122–128.
Chen, J. L., Penhune, V. B., & Zatorre, R. J. (2008). Listening to musical rhythms
recruits motor regions of the brain. Cerebral Cortex, 18, 2844–2854.
Chen, J. L., Zatorre, R. J., & Penhune, V. B. (2006). Interactions between auditory and
dorsal premotor cortex during synchronization to musical rhythms.
Neuroimage, 32, 1771–1781.
Clayton, M., Sager, R., & Will, U. (2005). In time with the music: The concept of
entrainment and its significance for ethnomusicology. European Meetings in
Ethnomusicology, 11, 3–142.
Coxon, J. P., Stinear, C. M., & Byblow, W. D. (2007). Selective inhibition of movement.
Journal of Neurophysiology, 97, 2480–2489.
Cross, E. S., & Ticini, L. F., 2012. Neuroaesthetics and beyond: New horizons in
applying the science of the brain to the art of dance. Phenomenology and the
Cognitive Sciences, 1–12.
Danielsen, A. (2006). Presence and pleasure: The funk grooves of James Brown and
Parliament. Wesleyan University Press.
D’Ausilio, A., Altenmüller, E., Olivetti Belardinelli, M., & Lotze, M. (2006). Cross-
modal plasticity of the motor cortex while listening to a rehearsed musical
piece. European Journal of Neuroscience, 24, 955–958.
Desain, P., & Honing, H. (1993). Tempo curves considered harmful. Contemporary
Music Review, 7, 123–138.
Drost, U. C., Rieger, M., Brass, M., Gunter, T. C., & Prinz, W. (2005). Action-effect
coupling in pianists. Psychological Research, 69, 233–241.
Fadiga, L., Buccino, G., Craighero, L., Fogassi, L., Gallese, V., & Pavesi, G. (1999).
Corticospinal excitability is specifically modulated by motor imagery: A
magnetic stimulation study. Neuropsychologia, 37, 147–158.
Fraisse, P. (1982). Rhythm and tempo. In D. Deutsch (Ed.), The psychology of music
(pp. 149–180). New York: Academic Press
Friberg, A., & Sundström, A. (2002). Swing ratios and ensemble timing in jazz
performance: Evidence for a common rhythmic pattern. Music Perception, 19,
Fujioka, T., Trainor, L. J., Large, E. W., & Ross, B. (2012). Internalized timing of
isochronous sounds is represented in neuromagnetic beta oscillations. The
Journal of Neuroscience, 32, 1791–1802.
Gerardin, E., Sirigu, A., Lehéricy, S., Poline, J. B., Gaymard, B., Marsault, C., et al.
(2000). Partially overlapping neural networks for real and imagined hand
movements. Cerebral Cortex, 10, 1093–1104.
Giovannelli, F., Banfi, C., Borgheresi, A., Fiori, E., Innocenti, I., Rossi, S., et al. (2013).
The effect of music on corticospinal excitability is related to the perceived
emotion: A transcranial magnetic stimulation study. Cortex, 49, 702–710.
Grahn, J. A., & Brett, M. (2007). Rhythm and beat perception in motor areas of the
brain. Journal of Cognitive Neuroscience, 19, 893–906.
Guillot, A., & Collet, C. (2010). The neurophysiological foundations of mental and motor
imagery. USA: Oxford University Press.
Hashimoto, R., & Rothwell, J. R. (1999). Dynamic changes in corticospinal
excitability during motor imagery. Experimental Brain Research, 125, 75–81.
Haslinger, B., Erhard, P., Altenmüller, E., Schroeder, U., Boecker, H., & Ceballos-
Baumann, A. O. (2005). Transmodal sensorimotor networks during action
observation in professional pianists. Journal of Cognitive Neuroscience, 17,
Haueisen, J., & Knösche, T. R. (2001). Involuntary motor activity in pianists evoked
by music perception. Journal of Cognitive Neuroscience, 13, 786–792.
Hess, C. W., Mills, K. R., & Murray, N. M. (1987). Responses in small hand muscles
from magnetic stimulation of the human brain. The Journal of Physiology, 388,
Hodeir, A. (1956). Jazz: Its evolution and essence. New York: Grove.
Holm, T., & Isaksson, T., 2010. Syncopes, duration and timing properties used to affect
groove in monophonic melodies. Unpublished master’s thesis. Umea University,
Umea, Sweden.
Hove, M. J., Keller, P. E., & Krumhansl, C. L. (2007). Sensorimotor synchronization
with chords containing tone-onset asynchronies. Attention, Perception &
Psychophysics, 69, 699–708.
Hove, M. J., & Risen, J. L. (2009). It’s all in the timing: Interpersonal synchrony
increases affiliation. Social Cognition, 27, 949–960.
Huron, D. (2006). Sweet anticipation: Music and the psychology of expectation.
Cambridge, Massachusetts: MIT Press
Iyer, V. (2002). Embodied mind, situated cognition, and expressive microtiming in
African–American music. Music Perception, 19, 387–414.
Janata, P., Tomic, S. T., & Haberman, J. M. (2012). Sensorimotor coupling in music
and the psychology of the groove. Journal of Experimental Psychology: General,
141, 54–75.
Jäncke, L., Shah, N. J., & Peters, M. (2000). Cortical activations in primary and
secondary motor areas for complex bimanual movements in professional
pianists. Cognitive Brain Research, 10, 177–183.
Jeannerod, M., & Frak, V. (1999). Mental imaging of motor activity in humans.
Current Opinion in Neurobiology, 9, 735–739.
Kasai, T., Kawai, S., Kawanishi, M., & Yahagi, S. (1997). Evidence for facilitation of
motor evoked potentials (MEPs) induced by motor imagery. Brain Research, 744,
Keil, C. (1995). The theory of participatory discrepancies: A progress report.
Ethnomusicology, 39, 1–19.
Keller, P. E., Knoblich, G., & Repp, B. H. (2007). Pianists duet better when they play
with themselves: On the possible role of action simulation in synchronization.
Consciousness & Cognition, 16, 102–111.
Keller, P. E. (2008). Joint action in music performance. In F. Morganti, A. Carassa, &
G. Riva (Eds.), Enacting intersubjectivity: A cognitive and social perspective to the
study of interactions (pp. 205–221). Amsterdam: IOS Press.
Keller, P. E., & Schubert, E. (2011). Cognitive and affective judgements of syncopated
musical themes. Advances in Cognitive Psychology, 7, 142–156.
Koelsch, S. (2010). Towards a neural basis of music-evoked emotions. Trends in
Cognitive Sciences, 14, 131–137.
Kornysheva, K., von Cramon, D. Y., Jacobsen, T., & Schubotz, R. I. (2010). Tuning-in to
the beat: Aesthetic appreciation of musical rhythms correlates with a premotor
activity boost. Human Brain Mapping, 31, 48–64.
Krings, T., Töpper, R., Foltys, H., Erberich, S., Sparing, R., Willmes, K., et al. (2000).
Cortical activation patterns during complex motor tasks in piano players and
control subjects. A functional magnetic resonance imaging study. Neuroscience
Letters, 278, 189–193.
Lahav, A., Saltzman, E., & Schlaug, G. (2007). Action representation of sound:
Audiomotor recognition network while listening to newly acquired actions. The
Journal of Neuroscience, 27, 308–314.
Lappe, C., Herholz, S. C., Trainor, L. J., & Pantev, C. (2008). Cortical plasticity induced
by short-term unimodal and multimodal musical training. The Journal of
Neuroscience, 28, 9632–9639.
Lartillot, O., & Toiviainen, P., 2007. A matlab toolbox for musical feature extraction
from audio. In Proceedings of the 10th int. conference on digital audio effects
(DAFx-07), Bordeaux, France.
Liuzza, M. T., Candidi, M., & Aglioti, S. M. (2011). Do not resonate with actions:
Sentence polarity modulates cortico-spinal excitability during action-related
sentence reading. PLoS ONE, 6, e16855.
MacDougall, H. G., & Moore, S. T. (2005). Marching to the beat of the same
drummer: The spontaneous tempo of human locomotion. Journal of Applied
Physiology, 99, 1164–1173.
Madison, G. (2006). Experiencing groove induced by music: Consistency and
phenomenology. Music Perception, 24, 201–208.
Madison, G., Gouyon, F., Ullén, F., & Hörnström, K. (2011). Modeling the tendency
for music to induce movement in humans: First correlations with low-level
audio descriptors across music genres. Journal of Experimental Psychology:
Human Perception and Performance, 37, 1578–1594.
Merker, B. (1999). Synchronous chorusing and the origins of music. Musicae
Scientiae, Special issue, 1999–2000, 59–73.
Merker, B., Madison, G., & Eckerdal, P. (2009). On the role and origin of isochrony in
human rhythmic entrainment. Cortex, 45, 4–17.
Moelants, D., 2002. Preferred tempo reconsidered. In Stevens, C., Burnham, D.,
McPherson, G., Schubert, E.D., Renwick, J. (Eds.), Proceedings of the 7th
international conference on music perception and cognition. Adelaide: Causal
Musacchia, G., Sams, M., Skoe, E., & Kraus, N. (2007). Musicians have enhanced
subcortical auditory and audiovisual processing of speech and music.
Proceedings of the National Academy of Sciences, 104, 15894–15898.
Naveda, L., Gouyon, F., Guedes, C., & Leman, M. (2011). Microtiming patterns and
inter-actions with musical properties in samba music. Journal of New Music
Research, 40, 225–238.
Novembre, G., Ticini, L. F., Schütz-Bosbach, S., & Keller, P. E. (2012). Distinguishing
self and other in joint action. Evidence from a musical paradigm. Cerebral Cortex,
22, 2894–2903.
Nozaradan, S., Peretz, I., Missal, M., & Mouraux, A. (2011). Tagging the
neuronal entrainment to beat and meter. The Journal of Neuroscience, 31,
Oldfield, R. C. (1971). The assessment and analysis of handedness: The Edinburgh
inventory. Neuropsychologia, 9, 97–113.
Overy, K., & Molnar-Szakacs, I. (2009). Being together in time: Musical experience
and the mirror neuron system. Music Perception, 26, 489–504.
Popescu, M., Otsuka, A., & Ioannides, A. A. (2004). Dynamics of brain activity in
motor and frontal cortical areas during music listening: A
magnetoencephalographic study. Neuroimage, 21, 1622–1638.
Pressing, J. (2002). Black Atlantic rhythm: Its computational and transcultural
foundations. Music Perception, 19, 285–310.
Prögler, J. A. (1995). Searching for swing: Participatory discrepancies in the jazz
rhythm section. Ethnomusicology, 39, 21–54.
Repp, B. H. (2003). Rate limits in sensorimotor synchronization with auditory and
visual sequences: The synchronization threshold and the benefits and costs of
interval subdivision. Journal of Motor Behavior, 35, 355–370.
J. Stupacher et al. / Brain and Cognition 82 (2013) 127–136
Author's personal copy
Schubotz, R. I., Friederici, A. D., & von Cramon, D. Y. (2000). Time perception and
motor timing: A common cortical and subcortical basis revealed by fMRI.
Neuroimage, 11, 1–12.
Sohn, Y. H., Dang, N., & Hallett, M. (2003). Suppression of corticospinal excitability
during negative motor imagery. Journal of Neurophysiology, 90, 2303–2309.
Todd, N. (2001). Evidence for a behavioral significance of saccular acoustic
sensitivity in humans. The Journal of the Acoustical Society of America, 110,
Trainor, L. J., Gao, X., Lei, J., Lehtovaara, K., & Harris, L. R. (2009). The primal role of
the vestibular system in determining musical rhythm. Cortex, 45, 35–43.
Ugawa, Y., Terao, Y., Hanajima, R., Sakai, K., & Kanazawa, I. (1995). Facilitatory effect
of tonic voluntary contraction on responses to motor cortex stimulation.
Electroencephalography and Clinical Neurophysiology, 97, 451–454.
van Zuijen, T. L., Sussman, E., Winkler, I., Näätänen, R., & Tervaniemi, M. (2005).
Auditory organization of sound sequences by a temporal or numerical
regularity A mismatch negativity study comparing musicians and non-
musicians. Cognitive Brain Research, 23, 270–276.
Vuust, P., Ostergaard, L., Pallesen, K. J., Bailey, C., & Roepstorff, A. (2009). Predictive
coding of music Brain responses to rhythmic incongruity. Cortex, 45, 80–92.
Waadeland, C. H. (2001). ’It don’t mean a thing if it ain’t got that swing’ Simulating
expressive timing by modulated movements. Journal of New Music Research, 30,
Wilson, E. M. F., & Davey, N. J. (2002). Musical beat influences corticospinal drive to
ankle flexor and extensor muscles in man. International Journal of
Psychophysiology, 44, 177–184.
Witek, M. A. G., Clarke, E. F., Wallentin, M., Kringelbach, M. L., & Vuust, P., 2011.
...and I feel good!’ The relationship between body movement, pleasure and
rhythmic tension in groove. In The neurosciences and music III. University of
Zatorre, R. J., Chen, J. L., & Penhune, V. B. (2007). When the brain plays music:
Auditory–motor interactions in music perception and production. Nature
Reviews Neuroscience, 8, 547–558.
Zbikowski, L. M. (2004). Modelling the groove: Conceptual structure and popular
music. Journal of the Royal Musical Association, 129, 272–297.
136 J. Stupacher et al. / Brain and Cognition 82 (2013) 127–136
    • "This is not only because participants are required by the experiment to perform a specific gesture, but also because that gesture may not be one that arises spontaneously in non-experimental contexts . As groove is an important aspect of motor engagement during music listening [22] and of the quality of sensorimotor coupling [20], the experimental study of movements that occur spontaneously outside of the lab may help improve the understanding of some important aspects of SMS to music in humans. In sum, the way we move has an influence on how we interpret, enjoy and synchronize to musical rhythms. "
    [Show abstract] [Hide abstract] ABSTRACT: The vast majority of humans move in time with a musical beat. This behaviour has been mostly studied through finger-tapping synchronization. Here, we evaluate naturalistic synchronization responses to music-bouncing and clapping-in 100 university students. Their ability to match the period of their bounces and claps to those of a metronome and musical clips varying in beat saliency was assessed. In general, clapping was better synchronized with the beat than bouncing, suggesting that the choice of a specific movement type is an important factor to consider in the study of sensorimotor synchronization processes. Performance improved as a function of beat saliency, indicating that beat abstraction plays a significant role in synchronization. Fourteen percent of the population exhibited marked difficulties with matching the beat. Yet, at a group level, poor synchronizers showed similar sensitivity to movement type and beat saliency as normal synchronizers. These results suggest the presence of quantitative rather than qualitative variations when losing the beat.
    Full-text · Article · Jul 2016
    • "Although tapping and rating trials were completed at separate times, individual correlations for each participant indicated that the variability of tap-to-beat asynchronies tended to be lower with audio clips that were rated higher on groove. This observation supports previous suggestions Audio Features of Groove 27 that the ease of sensorimotor coupling with musical stimuli underlies perceived and experienced groove (Fairhurst, Janata, & Keller, 2013; Janata et al., 2012; Stupacher et al., 2013). In sum, we found some evidence that low bass frequencies can positively affect groove ratings and tapping performances, but these effects are probably not independent from more general musical aspects like instrumentation, songwriting, genre, or mixing and mastering. "
    [Show abstract] [Hide abstract] ABSTRACT: The experience of groove is associated with the urge to move to a musical rhythm. Here we focus on the relevance of audio features, obtained using music information retrieval (MIR) tools, for explaining the perception of groove and music-related movement. In the first of three studies, we extracted audio features from clips of real music previously rated on perceived groove. Measures of variability, such as the variance of the audio signal’s RMS curve and spectral flux (particularly in low frequencies) predicted groove ratings. Additionally, we dissociated two forms of event density, showing that an algorithm that emphasizes variability between beats predicted groove ratings better. In Study 2 we manipulated RMS levels and groove category (low, mid, and high groove) to confirm that perceived groove is not a function of loudness. In Study 3 we utilized novel music clips that manipulated the frequency of bass and bass drum (low vs. high) and attack time (short vs. long). Groove ratings and tapping velocities tended to be higher and tapping variability tended to be lower when the bass instruments had lower frequencies. The present findings emphasize the multifaceted nature of groove by linking audio and musical qualities to subjective experience and motor behavior.
    Full-text · Article · Jun 2016
    • "High-groove music is said to " make you want to move " , to have danceable rhythms [33]. Stupacher et al. [34] demonstrated that high-groove music modulates the motor system activity. The modulations of the motor system in musicians are aligned with the beat during high-groove music. "
    [Show abstract] [Hide abstract] ABSTRACT: The expressive features of music can influence the velocity of walking. So far, studies used instructed (and intended) synchronization. But is this velocity effect still present with non-instructed (spontaneous) synchronization? To figure that out, participants were instructed to walk in their own comfort tempo on an indoor track, first in silence and then with tempo-matched music. We compared velocities of silence and music conditions. The results show that some music has an activating influence, increasing velocity and motivation, while other music has a relaxing influence, decreasing velocity and motivation. The influence of musical expression on the velocity of self-paced walking can be predicted with a regression model using only three sonic features explaining 56% of the variance. Phase-coherence between footfall and beat did not contribute to the velocity effect, due to its implied fixed pacing. The findings suggest that the velocity effect depends on vigor entrainment that influences both stride length and pacing. Our findings are relevant for preventing injuries, for gait improvement in walking rehabilitation, and for improving performance in sports activities.
    Full-text · Article · May 2016
Show more