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Numerous studies have shown that humans automatically react with congruent facial reactions, i.e., facial mimicry, when seeing a vis-á-vis' facial expressions. The current experiment is the first investigating the neuronal structures responsible for differences in the occurrence of such facial mimicry reactions by simultaneously measuring BOLD and facial EMG in an MRI scanner. Therefore, 20 female students viewed emotional facial expressions (happy, sad, and angry) of male and female avatar characters. During picture presentation, the BOLD signal as well as M. zygomaticus major and M. corrugator supercilii activity were recorded simultaneously. Results show prototypical patterns of facial mimicry after correction for MR-related artifacts: enhanced M. zygomaticus major activity in response to happy and enhanced M. corrugator supercilii activity in response to sad and angry expressions. Regression analyses show that these congruent facial reactions correlate significantly with activations in the IFG, SMA, and cerebellum. Stronger zygomaticus reactions to happy faces were further associated to increased activities in the caudate, MTG, and PCC. Corrugator reactions to angry expressions were further correlated with the hippocampus, insula, and STS. Results are discussed in relation to core and extended models of the mirror neuron system (MNS).
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ORIGINAL RESEARCH ARTICLE
published: 26 July 2012
doi: 10.3389/fnhum.2012.00214
Facial mimicry and the mirror neuron system:
simultaneous acquisition of facial electromyography
and functional magnetic resonance imaging
Katja U. Likowski , Andreas Mühlberger ,Antje B. M. Gerdes ,Matthias J. Wieser ,Paul Pauli and
Peter Weyers *
Department of Psychology, University of Würzburg, Germany
Edited by:
John J. Foxe, Albert Einstein College
of Medicine, USA
Reviewed by:
Matthew R. Longo, University of
London, UK
Yin Wang, The University of
Nottingham, UK
*Correspondence:
Peter Weyers, Department of
Psychology, Julius-Maximilians-
University Würzburg, Marcusstr.
9-11, 97070 Würzburg, Germany.
e-mail: weyers@psychologie.
uni-wuerzburg.de
Present address:
Department of Psychology,
University of Mannheim,
Mannheim, Germany.
Numerous studies have shown that humans automatically react with congruent facial
reactions, i.e., facial mimicry, when seeing a vis-á-vis’ facial expressions. The current
experiment is the first investigating the neuronal structures responsible for differences
in the occurrence of such facial mimicry reactions by simultaneously measuring BOLD
and facial EMG in an MRI scanner. Therefore, 20 female students viewed emotional
facial expressions (happy, sad, and angry) of male and female avatar characters. During
picture presentation, the BOLD signal as well as M. zygomaticus major and M. corrugator
supercilii activity were recorded simultaneously. Results show prototypical patterns of
facial mimicry after correction for MR-related artifacts: enhanced M. zygomaticus major
activity in response to happy and enhanced M. corrugator supercilii activity in response
to sad and angry expressions. Regression analyses show that these congruent facial
reactions correlate significantly with activations in the IFG, SMA, and cerebellum. Stronger
zygomaticus reactions to happy faces were further associated to increased activities in
the caudate, MTG, and PCC. Corrugator reactions to angry expressions were further
correlated with the hippocampus, insula, and STS. Results are discussed in relation to
core and extended models of the mirror neuron system (MNS).
Keywords: mimicry, EMG, fMRI, mirror neuron system
INTRODUCTION
Humans tend to react with congruent facial expressions when
looking at an emotional face (Dimberg, 1982). They react, for
example, with enhanced activity of the M. zygomaticus major (the
muscle responsible for smiling) when seeing a happy expression
of a vis-á-vis’ person or with an increase in M. corrugator super-
cilii (the muscle involved in frowning) activity in response to a
sad face. Such facial mimicry reactions occur spontaneously and
rapidly already after 300–400 ms (Dimberg and Thunberg, 1998)
and even in minimal social contexts (Dimberg, 1982; Likowski
et al., 2008). They appear to be automatic and unconscious,
because they occur without awareness or conscious control and
cannot be completely suppressed (Dimberg and Lundqvist, 1990;
Dimberg et al., 2002); they even occur in response to subliminally
presented emotional expressions (Dimberg et al., 2000). However,
there is up to now no experimental empirical evidence answering
the question about the neuronal structures involved in the occur-
rence of such automatic, spontaneous facial mimicry reactions.
The present study is a first approach to fill this lack of research
by simultaneously acquiring facial electromyography (EMG) and
functional magnetic resonance imaging (fMRI).
According to current literature, the neuronal base of (facial)
mimicry is presumably the “mirror neuron system” (MNS)
(Blakemore and Frith, 2005; Iacoboni and Dapretto, 2006;
Niedenthal, 2007). The discovery of mirror neurons dates from
studies in the macaque where Giacomo Rizzolatti and colleagues
came across a system of cortical neurons in area F5 (premotor
cortex in the macaque)and PF [part of the inferior parietal lobule
(IPL)] that responded not only when the monkey performed an
action, but also when the monkey watched the experimenter per-
forming the same action (di Pellegrino et al., 1992; Gallese et al.,
2002). They named their system of neurons the MNS because
it appeared that the observed action was reflected or internally
simulated within the monkey’s own motor system.
There is now evidence that an equivalent system exists in
humans. According to a review by Iacoboni and Dapretto (2006),
the human MNS should comprise the ventral premotor cortex
(vPMC, i.e., the human homolog of the monkey F5 region),
the inferior frontal gyrus (IFG) and the IPL. These regions fit
nicely to the macaque’s MNS. Further mirror neuron activity has
been detected in the superior temporal sulcus (STS) (Iacoboni
and Dapretto, 2006) which is seen as the main visual input to
the human MNS. However, recent studies reveal a slightly more
complex picture of the brain areas that show shared activity dur-
ing observation and execution of the same behavior. In an fMRI
study with unsmoothed single subject data, Gazzola and Keysers
(2009) examined shared voxels that show increased BOLD activ-
ity both during observing and executing an action and found a
wide range of areas containing such shared voxels. Those were
classical mirroring regions like the vPMC (BA6/44) and the IPL,
but also areas beside the MNS like the dorsal premotor cortex
(dPMC), supplementary motor area (SMA), middle cingulate
cortex (MCC), somatosensory cortex (BA2/3), superior parietal
lobule (SPL), middle temporal gyrus (MTG) and the cerebellum.
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HUMAN NEUROSCIENCE
Likowski et al. Facial mimicry and the MNS
Additionally, Mukamel et al. (2010) reported mirror activities in
further brain regions, namely the hippocampus and the parahip-
pocampal gyrus. Yet, Molenberghs et al. (2012) concluded in their
broad review of 125 human MNS studies that consistent activa-
tions could be found in the classical regions like the IFG, IPL,
SPL, and vPMC. They termed these regions the “core network”.
However, they also identified activations in other areas depend-
ing on the respective modality of the task and stimuli, e.g., for
emotional facial expressions enhanced activity in regions known
to be involved in emotional processing like the amygdala, insula,
and cingulate gyrus.
There are several studies supporting the assumption that the
human MNS is involved in facial mimicry. Accordingly, there
is evidence for activation in Brodmann area 44 when partici-
pants deliberately imitate other people’s facial expressions (Carr
et al., 2003). van der Gaag et al. (2007) could further show com-
mon activations in the IFG and IPL (both termed “classical”
MNS sites) as well as the STS, MTG, insula, amygdala, SMA,
and somatosensory cortex (called the “extended” MNS) during
both the observation and execution (i.e., conscious imitation) of
emotional facial expressions. Further studies could show similar
relationships between the conscious imitation of facial expres-
sions and activity of parts of the MNS (Leslie et al., 2004; Dapretto
et al., 2006; Lee et al., 2006).
Whereas all these studies examined conscious imitation of
facial expressions, other authors are interested in the relation-
ship between the MNS and unconscious facial mimicry. In a TMS
study, Enticott et al. (2008) could show that accuracy in facial
emotion recognition was significantly associated with increased
motor-evoked potentials during perception of the respective facial
expressions. Because facial mimicry is supposed to be related to
emotion recognition (Niedenthal et al., 2001; Oberman et al.,
2007) the authors interpret this enhanced activation of the MNS
as connected to an internal simulation of the observed expres-
sion comparable to facial mimicry. On the other hand, Jabbi and
Keysers (2008) interpret similar results in a different fashion. They
found a causal connection of a prominent part of the MNS, i.e.,
the IFG, with a region encompassing the anterior insula and the
frontal operculum which is known to be responsible for the expe-
rience and sharing of emotions like disgust. The authors conclude
that this finding reflects a fast and covert motor simulation of
perceived facial expressions by the MNS and that this covert sim-
ulation might be sufficient to trigger emotional sharing without
the need for overt facial mimicry.
These results, however, provide only indirect evidence for or
against a relation between the MNS and unconscious mimicry.
So far, there is only one study directly examining the neuronal
correlates of unconscious and spontaneous facial reactions to
facial expressions. Studies examining conscious mimicry usually
instruct their participants to imitate a seen facial expression delib-
erately and compare reactions in that condition with those from
a passive viewing condition. However, in such a passive viewing
condition participants should also show mimicry, i.e. uncon-
scious facial mimicry. Hence, Schilbach et al. (2008) assessed
spontaneous facial muscular reactions via EMG and blood oxygen
level dependent (BOLD) responses to dynamic facial expres-
sions of virtual characters via fMRI in two separate experiments.
Participants in both of their experiments were instructed to just
passively view the presented expressions. They found enhanced
activity of the precentral cortex, precuneus, hippocampus, and
cingulate gyrus in the time window in which non-conscious facial
mimicry occurred. Unfortunately, Schilbach et al. (2008)didnot
assess muscular activity and BOLD response in the same partici-
pants and at the same point in time. Thus, there is up to now no
certain empirical evidence about the neuronal structures involved
in automatic, spontaneous mimicry.
Therefore, the present study is a first approach to investi-
gate whether the MNS is indeed responsible for differences in
unconscious and spontaneous facial mimicry reactions. Following
the studies by Gazzola and Keysers (2009), Molenberghs et al.
(2012), Mukamel et al. (2010), Schilbach et al. (2008), and van
der Gaag et al. (2007)weconstructedasingleMNS-regionof
interest (ROI) for the current experiment consisting of follow-
ing parts of the MNS: IFG, vPMC, IPL, SMA, cingulate cortex,
SPL, MTG, cerebellum, somatosensory cortex, STS, hippocam-
pus, parahippocampal gyrus, precentral gyrus, precuneus, insula,
amygdala, caudate, and putamen. Activity in this region will be
related to participants’ congruent facial muscular reactions to
examine which parts of the MNS show significant co-activations
with the respective facial mimicry.
This question shall be answered via the simultaneous mea-
surement of facial muscular activity via (EMG) and the BOLD
response via fMRI. To our knowledge, until now no study with
such a design has been published. In a first approach, Heller
et al. (2011)measuredM. corrugator supercilii activity in response
to affective pictures between interleaved scan acquisitions; that
means that they analyzed muscle activity only for time periods
in which no echoplanar imaging (EPI) sequences were collected
because EPI collection produces intense electromagnetic noise.
However, with this method it is only possible to measure the
neuronal activity before and after the EMG recordings but not
in exactly the same time window in which the facial reactions
occur. Furthermore, with such a sequential recording BOLD and
EMG are measured in two different contexts. Especially the noise
that differs between EPI and non-EPI sequences but also other
influences like repeated presentations or the quality of the pre-
ceding stimulus are significant differences between the BOLD
and the EMG recording phases that hamper a valid detection of
connections between brain activations and muscular reactions.
Therefore, in the present study we will measure muscular activ-
ity and BOLD simultaneously, i.e., during the collection of EPI
images.
METHODS
PARTICIPANTS
Twenty-three right-handed female participants were investigated.
Only female subjects were tested because women show more pro-
nounced, but not qualitatively different mimicry effects than male
subjects (Dimberg and Lundqvist, 1990). Informed consent was
obtained from all subjects prior to participation and is archived
by the authors. All participants received 12C allowance. Three
participants had to be excluded from the analysis due to incom-
plete recordings or insufficient quality of the MRI data. Therefore,
analyses were performed for 20 participants, aged between 20 and
Frontiers in Human Neuroscience www.frontiersin.org July 2012 | Volume 6 | Article 214 |2
Likowski et al. Facial mimicry and the MNS
30 years (M=23.50, SD =3.05). The experimental protocol was
approved by the institution’s ethics committee and conforms to
the Declaration of Helsinki.
STIMULI AND APPARATUS
Facial stimuli
As facial stimuli avatar facial emotional expressions are used.
Avatars (i.e., virtual persons or graphic substitutes for real per-
sons) provide a useful tool for research in emotion and social
interactions (Blascovich et al., 2002), because they allow better
control over the facial expression and its dynamics, e.g., its inten-
sity and temporal course, than pictures of humans (Krumhuber
and Kappas, 2005). Furthermore, due to the possibility to use the
same prototypical faces for all types of characters there is no need
to control for differences in liking and attractiveness between
the conditions and a reduced amount of error variance can be
assumed. How successfully avatars can be used as a research tool
for studying interactions has been demonstrated by Bailenson
and Yee (2005). Subjects rated a digital chameleon, i.e., an avatar
which mimics behavior, more favorably even though they were
not aware of the mimicry. Thus, an avatar’s mimicry created liking
comparable to real individuals (Chartrand and Bargh, 1999).
Stimuli were created with Poser software (Curious Labs,Santa
Cruz, CA) and the software extension offered by Spencer-Smith
et al. (2001) to manipulate action units separately according to the
facial action coding system (Ekman and Friesen, 1978). Notably,
Spencer-Smith et al. (2001) could show that ratings of quality and
intensity of the avatar emotional expressions were comparable
to those of human expressions from the Pictures of Facial Affect
(Ekman and Friesen, 1976).
The stimuli were presented on a light gray background
via MRI-compatible goggles (VisuaStim; Magnetic Resonance
Technologies, Northridge, CA). Four facial expressions were cre-
ated from a prototypic female and a prototypic male face: a
neutral, a happy, a sad and an angry expression (for details see
Spencer-Smith et al., 2001). Each male and female emotional
expression was then combined with three types of hairstyles
(blond, brown, and black hair), resulting in twenty-four stimuli
(for examples see Figure 1).
Facial EMG
Activity of the M. zygomaticus major (the muscle involved in
smiling) and the M. corrugator supercilii (the muscle respon-
sible for frowning) was recorded on the left side of the face
using bipolar placements of MRI-compatible electrodes (MES
Medizinelektronik GmbH, Munich, Germany) according to the
guidelines established by Fridlund and Cacioppo (1986). In order
to cover the recording of muscular activity participants were
told that skin conductance would be recorded (see e.g., Dimberg
et al., 2000). The EMG raw signal was measured with an MRI-
compatible BrainAmp ExG MR amplifier (Brain Products Inc.,
Gilching, Germany), digitalized by a 16-bit analogue-to-digital
converter, and stored on a personal computer with a sampling
frequency of 5000 Hz. The EMG data were post-processed offline
using Vision Analyzer software (Version 2.01, Brain Products
Inc., Gilching, Germany). EMG data recorded in the MR scan-
ner is contaminated with scan-pulse artifacts, originating from
the switching of the radio-frequency gradients. To remove these
artifacts the software applies a modified version of the aver-
age artifact subtraction method (AAS) described by Allen et al.
(2000). This MRI-artifact correction has originally been devel-
oped for combined EEG/fMRI recordings (for applications see
e.g., Jann et al., 2008; Musso et al., 2010)andcannowalso
be applied for EMG data. Thereby, a gradient artifact template
is subtracted from the EMG using a baseline corrected average
of all MR-intervals. Data were then down-sampled to 1000 Hz.
Following gradient artifact correction raw data were rectified and
filtered with a 30 Hz low cutoff filter, a 500 Hz high cutoff fil-
ter, a 50 Hz notch filter, and a 125 ms moving average filter. The
EMG scores are expressed as change in activity from the pre-
stimulus level, defined as the mean activity during the last second
before stimulus onset. Trials with an EMG activity above 8 μV
during the baseline period and above 30 μV during the stimuli
presentation were excluded (less than 5%). Before statistical anal-
ysis, EMG data were collapsed over the 12 trials with the same
emotional expression, and reactions were averaged over the 4 s
of stimulus exposure. An example snapshot of the raw and the
filtered zygomaticus and corrugator EMG data can be seen in
Figure 2.
FIGURE 1 | Examples of avatars with different emotional facial expressions (happy, neutral, sad, angry).
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Likowski et al. Facial mimicry and the MNS
FIGURE 2 | Representative snapshot of raw zygomaticus and corrugatorEMG data acquired simultaneously with fMRI. (A) Top panel is raw, unfiltered
EMG data. (B) Bottom panel shows filtered EMG data.
IMAGE ACQUISITION
Image acquisition followed the standard procedure in our lab
(Gerdes et al., 2010; Mühlberger et al., 2011): Functional and
structural MRI was performed with a Siemens 1.5 T MRI whole
body scanner (SIEMENS Avanto) using a standard 12-channel
head coil and an integrated head holder to reduce head move-
ment. Functional images were obtained using a T2—weighted
single-shot gradient EPI sequence (TR: 2500 ms, TE: 30 ms, 90
flip angle, FOV: 200mm, matrix: 64 ×64, voxel size: 3.1×
3.1 ×5mm
3). Each EPI volume contained 25 axial slices (thick-
ness 5 mm, 1 mm gap), acquired in interleaved order, covering
the whole brain. The orientation of the axial slices was paral-
lel to the AC–PC line. Each session contained 475 functional
images. The first eight volumes of each session were discarded
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Likowski et al. Facial mimicry and the MNS
to allow for T1 equilibration. In addition, a high-resolution T1-
weighted magnetization-prepared rapid gradient-echo imaging
(MP-RAGE) 3D MRI sequence was obtained from each subject
(TR: 2250 ms, TE: 3.93 ms, 8flip angle, FOV: 256 mm, matrix:
256 ×256, voxel size: 1 ×1×1mm
3).
IMAGE PREPROCESSING AND ANALYSES
Data were analyzed by using Statistical Parametric Mapping soft-
ware (SPM8; Wellcome Department of Imaging Neuroscience,
London, UK) implemented in Matlab R2010a (Mathworks Inc.,
Sherborn, MA, USA). Functional images were slice-time cor-
rected and realignment (b-spline interpolation) was performed
(Ashburner and Friston, 2003). To allow localization of functional
activation on the subjects’ structural MRIs, T1-scans were coreg-
istered to each subject’s mean image of the realigned functional
images. Coregistered T1 images were then segmented (Ashburner
and Friston, 2005) and in the next step, EPI images were spatially
normalized into the standard Montreal Neurological Institute
(MNI) space using the normalization parameters obtained from
the segmentation procedure (voxel size 2 ×2×2mm
3)and
spatially smoothed with an 8 mm full-width-half-maximum
(FWHM) Gaussian kernel. Each experimental condition (happy,
neutral, sad, and angry) and the fixation periods were modeled
by a delta function at stimulus onset convolved with a canonical
hemodynamic response function. Parameter estimates were sub-
sequently calculated for each voxel using weighted least squares
to provide maximum likelihood estimates based on the non-
sphericity assumption of the data in order to get identical and
independently distributed error terms. Realignment parameters
for each session were included to account for residual movement
related variance. Parameter estimation was corrected for temporal
autocorrelations using a first-order autoregressive model.
For each subject, the following t-contrasts were computed:
“happy >fixation cross”, “sad >fixation cross”, “angry >fixation
cross”, “happy +sad +angry >fixation cross”, “happy >neu-
tral”, “sad >neutral” and “angry >neutral”. We did not analyze
the contrast “neutral >fixation cross” because no facial mimicry
reactions are expected in response to neutral faces and thus no
neural correlates of facial mimicry can be computed. For a ran-
dom effect analysis, the individual contrast images (first-level)
were used in a second-level analysis. FMRI data were analyzed
specifically for the ROI (MNS-ROI, see above). To investigate the
brain activity in relation to the facial muscular reactions, we per-
formed six regression analyses with estimated BOLD responses
of individual first-level contrast images (“happy >fixation cross”,
“happy >neutral”, “sad >fixation cross, “sad >neutral”, “angry
>fixation cross”, “angry >neutral”) as dependent variable and
the according congruent facial reactions (zygomaticus to happy
expressions, corrugator to sad expressions, corrugator to angry
expressions) as predictors.
The WFU Pickatlas software (Version 2.4, Wake Forest
University, School of Medicine, NC) was used to conduct the
small volume correction with pre-defined masks in MNI-space
(Tzourio-Mazoyer et al., 2002; Maldjian et al., 2003, 2004). For
the ROI analysis, alpha was set to p=0.05 on voxel-level, cor-
rected for multiple comparisons (family-wise error–FWE) and
meaningful clusters exceeding 5 significant voxels.
PROCEDURE
After arriving at the laboratory, participants were informed about
the procedure of the experiment and were asked to give informed
consent. They were told that the experiment was designed to
study the avatars’ suitability for a future computer game to cover
the true purpose of the experiment in order to avoid deliberate
manipulation of the facial reactions. The EMG electrodes were
then attached and participants were placed in the MRI scanner.
Following this the functional MRI session started. Each of the four
expressions was repeated 24 times, i.e., a total of 96 facial stim-
uli were presented in a randomized order. Faces were displayed
for 4000 ms after a fixation-cross had been presented for 2000 ms
to ensure that participants were focusing on the center of the
screen. The inter-trial interval varied randomly between 8750 and
11,250 ms. Participants were instructed to simply view the pic-
tures without any further task. After the functional MRI the struc-
tural MRI (MP-RAGE) was recorded. Then, participants were
taken out of the scanner and electrodes were detached. Finally
participants completed a questionnaire regarding demographic
data, were debriefed, paid and thanked.
RESULTS
EMG MEASURES
A repeated measures analysis of variance with the within-subject
factors muscle (M. zygomaticus major vs. M. corrugator super-
cilii) and emotion (happy vs. neutral vs. sad vs. angry) was
conducted. A main effect of emotion, F(3,17)=4.17, p=0.02,
η2
p=0.20, and a significant Muscle ×Emotion effect, F(3,17)=
9.38, p<0.01, η2
p=0.33, occurred. The main effect muscle did
not gain significance, p>0.36. To further specify the Muscle
×Emotion interaction, separate follow up ANOVAs for the
M. zygomaticus major and the M. corrugator supercilii were
calculated.
M. zygomaticus major
As predicted, activity in M. zygomaticus major was larger to happy
compared to neutral, sad, and angry faces (see Figure 3). This was
verified by a significant emotion effect, [F(3,17)=3.91, p=0.04,
η2
p=0.176]. Following t-tests revealed a significant difference
FIGURE 3 | Mean EMG change from baseline in µVforM. zygomaticus
major in response to happy, neutral and sad faces. Error bars indicate
standard errors of the means.
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Likowski et al. Facial mimicry and the MNS
between M. zygomaticus major reactions to happy faces (M=
0.17) as compared to neutral (M=0.02), t(19)=2.64, p=0.02,
sad (M=−0.02), t(19)=2.09, p=0.05, and angry expres-
sions (M=0.01), t(19)=3.57, p<0.01. No other significant
differences were observed, all ps>0.41.
M. corrugator supercilii
As predicted, activity in M. corrugator supercilii was larger to
sad and angry faces as compared to neutral and positive faces
(see Figure 4). This was verified by a significant emotion effect,
[F(3,17)=7.58, p<0.01, η2
p=0.28]. Following t-tests revealed
a significant difference between M. corrugator supercilii reactions
to sad faces (M=0.32) as compared to happy (M=−0.31),
t(19)=3.12, p<0.01, and neutral expressions (M=0.05),
t(19)=2.56, p=0.02. In a similar vein, reactions to angry
faces (M=0.50) differed from reactions to happy, t(19)=2.91,
p<0.01, and neutral faces, t(19)=2.41, p=0.03. Furthermore,
M. corrugator supercilii reactions in response to happy expres-
sions differed from reactions to neutral faces, t(19)=2.53,
p=0.02. Reactions to sad and angry faces did not differ,
p>0.13.
Additionally, one-sample t-tests against zero revealed that the
M. zygomaticus major reaction to happy faces was indeed an
increase in activity, t(19)=2.13, p=0.04. Furthermore, the
M. corrugator supercilii reaction to happy expressions was a
significant decrease in activity, t(19)=2.33, p=0.03, whereas
reactions to sad and angry faces both occurred to be signifi-
cant activity increases, t(19)=2.35, p=0.03 and t(19)=2.19,
p=0.04. Therefore, all these reactions can be seen as congruent
facial reactions. All other reactions did not differ from zero, all
ps>0.41.
fMRI DATA
ROI analyses were performed for the contrasts comparing the
brain activation during viewing of emotional expressions with
the activation during the fixation crosses, i.e., “expression>
“fixation cross”. These analyses revealed for all expression con-
trasts (“happy >fixation cross”, “sad >fixation cross”, “angry >
fixation cross”) significant activations (FWE-corrected, p<0.05,
FIGURE 4 | Mean EMG change from baseline in µVforM. corru gator
supercilii in response to happy, neutral, and sad faces. Error bars
indicate standard errors of the means.
minimum cluster size of k=5 voxels) in numerous classical
(core) as well as extended parts of the MNS. Those were IFG,
IPL, MTG, STS, precentral gyrus, cerebellum, hippocampus,
amygdala, caudate, putamen, insula, and posterior cingulate cor-
tex (PCC). Additionally, the contrast “happy >fixation cross”
revealed activations in the MCC, the parahippocampal gyrus,
the precuneus and the SMA. The contrast “sad >fixation cross”
revealed further significant activations in the precuneus. ROI
analyses for the contrast “happy +sad +angry >fixation cross”
as well as all contrasts comparing the emotional expressions with
activation during the neutral expression (“happy >neutral”, “sad
>neutral” and “angry >neutral”) did not reveal any signifi-
cant clusters (FWE-corrected, p<0.05, minimum cluster size of
k=5voxels).
Regression analyses
Regression analyses with the contrasts “expression >fixation
cross” as dependent and the respective congruent facial reactions,
measured simultaneously via EMG, as predictor variable were
computed to investigate which brain activations were related to
the occurrence of facial mimicry. The corresponding ROI regres-
sion analysis with BOLD contrast “happy >fixation cross” as
dependent variable and zygomaticus reactions to happy expres-
sions as predictor revealed significant co-activations in the cau-
date, cerebellum, IFG, PCC, SMA, and MTG (see Figure 5).
ROI regression analysis with BOLD contrast “sad >fixation
cross” as dependent and corrugator reactions to sad expressions
as predictor variable revealed no significant co-activations. ROI
regression analysis with the BOLD contrast “angry >fixation
cross” as dependent variable and the corrugator reactions to
angry expressions as predictor variable revealed significant co-
activations in the cerebellum, IFG, hippocampus, insula, SMA,
and STS (see Figure 6).
Finally, the three ROI regression analyses with BOLD contrasts
“emotional expression >neutral expression (“happy >neutral”,
“sad >neutral”, “angry >neutral”) as dependent and the accord-
ing congruent facial reactions as predictors revealed no significant
co-activations.
DISCUSSION
The present experiment is a first approach revealing the neu-
ronal structures responsible for differences in automatic and
spontaneous facial mimicry reactions in a clear and experimen-
tal fashion. In a first step it was shown that a broad network
of regions with mirroring properties is active during the per-
ception of emotional facial expressions. This network included
for all expressions the IFG, IPL, MTG, STS, precentral gyrus,
cerebellum, hippocampus, amygdala, caudate, putamen, insula,
and PCC as well as for happy expressions the MCC, the parahip-
pocampal gyrus, the precuneus and the SMA, and for sad expres-
sions additionally the precuneus. These findings replicate earlier
studies showing an involvement of both classical and “extended”
mirror neuron regions in the observation and execution of (facial)
movements (e.g., van der Gaag et al., 2007; Molenberghs et al.,
2012).
More importantly, in a second step we explored which of
these brain regions show a direct relation with the individual
Frontiers in Human Neuroscience www.frontiersin.org July 2012 | Volume 6 | Article 214 |6
Likowski et al. Facial mimicry and the MNS
FIGURE 5 | Statistical parametric maps for the ROI regression analyses
with BOLD-contrast “happy >fixation cross” as dependent variable and
zygomaticus reactions to happy expressions as predictor. FWE-corrected,
alpha =0.05, k5 voxels. Coordinates x, y, and zare given in MNI space.
Color bars represent the T-values. (A) Significant co-activation in the right
caudate, (x=16, y=12, z=16 ; t=4.55; k=6voxel).(B) Significant
co-activation in the left cerebellum, (x=−14, y=−52, z=−42; t=4.58;
k=29 voxel). (C) Significant co-activation in the right inferior frontal gyrus,
(x=40, y=38, z=2; t=5.82; k=18 voxel). (D) Significant co-activation in
the left posterior cingulate cortex, (x=−14, y=−60, z=14; t=5.03; k=6
voxel). (E) Significant co-activation in the right supplementary motor area,
(x=14, y=8, z=70; t=6.27; k=6voxel).(F) Significant co-activation in
the right middle temporal cortex, (x=60, y=−58, z=2; t=5.44; k=5
voxel).
strength of facial mimicry reactions by regressing the BOLD
data on the simultaneously measured facial EMG reactions.
The EMG measurement proved to deliver reliable and signifi-
cant data comparable to earlier studies on attitude effects on
facial mimicry (Likowski et al., 2008). It was found that both
zygomaticus reactions to happy expressions and corrugator reac-
tions to angry faces correlate significantly with activations in
the right IFG, right SMA, and left cerebellum. Stronger zygo-
maticus reactions to happy faces were further associated with
an increase in activity in the right caudate, the right MTG as
well as the left PCC. Corrugator reactions to angry expres-
sions were also correlated with the right hippocampus, the right
insula, and the right STS. This shows that although a wide range
of regions assumed to belong to the core and the extended
MNS is active during the observation of emotional facial expres-
sions only a small number actually seems to be related to the
observed strength of facial mimicry. The correlated regions are
on the one hand regions concerned with the perception and
execution of facial movements and their action representations.
For example, the STS codes the visual perception, the MTG is
responsible for the sensory representation (Gazzola and Keysers,
2009), the IFG is responsible for coding the goal of the action
(Gallese et al., 1996), and the SMA is concerned with the execu-
tion of the movement (Cunnington et al., 2005). On the other
hand, we also observed associations of mimicry and regions
involved in emotional processing. We found co-activations in
the insula which connects the regions for action representa-
tion with the limbic system (Carr et al., 2003)andthecaudate
Frontiers in Human Neuroscience www.frontiersin.org July 2012 | Volume 6 | Article 214 |7
Likowski et al. Facial mimicry and the MNS
FIGURE 6 | Statistical parametric maps for the ROI regression analyses
with BOLD-contrast “angry >fixation cross” as dependent variable and
corrugator reactions to angry expressions as predictor. FWE-corrected,
alpha =0.05, k5 voxels. Coordinates x, y, and zare given in MNI space.
Color bars represent the T-values. (A) Significant co-activation in the left
cerebellum, (x=−10, y=−48, z=−32; t=6.24; k=43 voxel).
(B) Significant co-activation in the right inferior frontal gyrus, (x=42, y=40,
z=0; t=6.25; k=25 voxel). (C) Significant co-activation in the right
hippocampus, (x=30, y=−34, z=−6; t=5.91; k=8voxel).
(D) Significant co-activation in the right insula, (x=42, y=8, z=2; t=6.54;
k=19 voxel). (E) Significant co-activation in the right supplementar y motor
area, (x=14, y=6, z=70; t=5.26; k=5voxel).(F) Significant
co-activation in the right superior temporal sulcus, (x=58, y=−32, z=12;
t=5.65; k=35 voxel).
and the cingulate cortex which are involved in processing pos-
itive and negative emotional content (Mobbs et al., 2003; Vogt,
2005).
These results fit nicely with assumptions of the MNS. It is
widely assumed that the function of the MNS is to decode
and to understand other people’s actions (Carr et al., 2003;
Rizzolatti and Craighero, 2004; Iacoboni and Dapretto, 2006;but
see Decety, 2010; Hickok and Hauser, 2010 for a discussion).
Accordingly, Carr et al. (2003) suggest that the activation of areas
concerned with action representation and emotional content
helps to resonate, simulate and thereby recognize the emotional
expression and to empathize with the sender. This assump-
tion overlaps with theories on the purpose of facial mimicry.
According to embodiment theories congruent facial reactions
are part of the reenactment of the experience of another per-
son’s state (Niedenthal, 2007). Specifically, embodiment theories
assume that during an initial emotional experience all the sen-
sory, affective and motor neural systems are activated together.
This experience leads to interconnections between the involved
groups of neurons. Later on, when one is just thinking about the
event or perceiving a related emotional stimulus, the activated
neurons in one system spread their activity through the inter-
connections that were active during the original experience to all
the other systems. Thereby the whole original state or at least the
most salient parts of the network can be reactivated (Niedenthal,
2007; Oberman et al., 2007; Niedenthal et al., 2009). Embodiment
theories state that looking at an emotional facial expression
means reliving past experience associated with that kind of face.
Frontiers in Human Neuroscience www.frontiersin.org July 2012 | Volume 6 | Article 214 |8
Likowski et al. Facial mimicry and the MNS
Thus, perceiving an angry face can lead to tension in the muscles
used to strike, a rise in blood pressure or the enervation of facial
muscles involved in frowning (Niedenthal, 2007). Accordingly,
congruent facial reactions reflect an internal simulation of the
perceived emotional expression. The suggested purpose of such
simulation is like for mirror neurons understanding the actor’s
emotion (Wallbott, 1991; Niedenthal et al., 2001; Atkinson and
Adolphs, 2005).
Contrary to expectations, no correlations of MNS activities
and facial mimicry were found in response to sad expressions.
The reason for that is unclear. We observed proper mimicry reac-
tions in the corrugator muscle, comparable to those to angry
expressions. Also the number of significant clusters and their
respective sizes were comparable for all emotional expressions.
Maybe the low arousal of sad facial expressions (see e.g., Russell
and Bullock, 1985) compared to other negative stimuli ham-
pered the detection of co-activations in this case. However,
this is pure speculation and should be investigated in further
studies.
The contrasts “emotional expression >neutral expression” as
well as the regression analyses with these contrasts revealed no
significant clusters in the reported ROIs. We attribute this to the
finding that many of the regions involved in processing the emo-
tional expressions (happy, sad, angry) are also activated during
perception of the neutral expressions (as revealed by the contrast
“neutral >fixation”). Such overlapping clusters probably reflect
activations of general face processing and might be responsible
for the lower contrast effects and thereby also for lower variances
which presumably prevented our regressions from showing valid
and significant effects. One might now argue that the overlap in
activations in response to emotional as well as neutral expres-
sions suggests that we just observed general and unspecific face
processing regions. Importantly, we can proof that this is not the
case. The fact that our regression results are only significant for
the congruent pairings of BOLD and muscular activation but not
for incongruent pairings (like e.g., BOLD to happy expressions
and corrugator activity to sad expressions) clearly shows that we
observed specific relations of regions with mirror properties and
facial muscularreactions. Furthermore, we canconclude from the
non-significant contrast “happy + sad + angry >fixation cross”
that the effects of the three separate contrasts “happy >fixa-
tion cross”, “sad >fixation cross” and “angry >fixation cross”
appear to be rather specific regarding the locations of the relevant
clusters.
Taken together, the results of this experiment are the first to
show successful simultaneous recording of facial EMG and func-
tional MRI. Thus, it was possible to examine which specific parts
of the MNS were associated with differences in the occurrence
of facial mimicry, i.e., the strength of congruent facial muscu-
lar reactions in response to emotional facial expressions. It was
found that mimicry reactions correlated significantly with promi-
nent parts of the classic MNS as well as with areas responsible
for emotional processing. These results and the here introduced
methods for simultaneous measurement may provide a promis-
ing starting point for further investigations on moderators and
mediators of facial mimicry.
ACKNOWLEDGMENTS
This research was supported by the German Research Foundation
(DFG Research Group “Emotion and Behavior” FOR605, DFG
WE2930/2-2). The publication was funded by the German
Research Foundation (DFG) and the University of Würzburg
within the funding program Open Access Publishing. We are
grateful to the editor John J. Foxe and the two reviewers Matthew
R. Longo and Yin Wang for their fruitful comments on earlier
drafts of this paper.
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Conflict of Interest Statement: The
authors declare that the research
was conducted in the absence of any
commercial or financial relationships
that could be construed as a potential
conflict of interest.
Received: 24 April 2012; acce pted: 02 July
2012; published online: 2 6 July 2012.
Citation: Likowski KU, Mühlberger A,
Gerdes ABM, Wieser MJ, Pauli P and
Weyers P (2012) Facial mimicry and
the mirror neuron system: simultane-
ous acquisition of facial electromyogra-
phy and functional magnetic resonance
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Copyright © 2012 Likowski,
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and Weyers. This is an open-access
article distributed under the terms of the
Creative Commons Attribution License,
which permits use, dist ribution and
reproduction in other forums, provided
the original authors and source are cred-
ited and subject to any copyright notices
concerning any third-party graphics etc.
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6
... However, an absence of mimicry has sometimes been observed whilst using pictures of faces [15,[22][23]. Considering the nature of the emotion, the congruence between the stimulus emotion and the one expressed by contagion is robust for anger and joy [2,[9][10][12][13][17][18]21,[24][25][26]. Data for sadness and surprise are scarcer [14], or even diverging for fear and disgust [11,[14][15][17][18]. Several variables may impact the presence of mimicry as measured by EMG (e.g. ...
... These studies have several limitations, however. Even though the influence of the emotion emitter's sex has been reported, most studies only include women [9,18,24,26,[40][41][42][43]. The stimuli are often very selective, conveyed by a single material such as images, sounds or videos, and focus on a limited number of emotions. ...
... Most of them are emotionally intense, presented in a static manner [2,9,[12][13]14,25,44] and selected among the "images of facial affect" [45]. In an attempt to overcome some of these drawbacks, some studies used more artificial stimuli, such as avatars [22,26,42] or morphed images [10,20,23,46]. The effect of the task to be accomplished has seldom been considered [47][48][49], the stimuli being often processed in a passive manner, without any specific instructions. ...
... This basic matching mechanism underlie perception of disgust in self and others (Wicker et al., 2003), as well as pain (Timmers et al., 2018), laughter and joy (Caruana et al., 2017). Thus, perceiving others' facial expressions activates motor and somatosensory areas involved in the execution of the same facial behavior (Schilbach et al., 2008;Likowski et al., 2012). ...
... Interestingly, quite robust evidence suggests that the MNS is causally involved in phenomena of facial mimicry and emotional contagion (Hogeveen et al., 2015;Kraaijenvanger et al., 2017;Paz et al., 2022). This has been shown in the last decades through studies that have inquired simultaneously into the activity of the brain with more than one neuroscientific tool (Likowski et al., 2012). The simultaneous use of different neuroscience techniques with different direction of bias is often employed to disambiguate controversial results about causal questions (Tramacere, 2021). ...
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Thesis
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Virtual reality allows users to experience a sense of ownership of a virtual body-a phenomenon commonly known as the body ownership illusion. Researchers and designers aim at inducing a body ownership illusion and creating embodied experiences using avatars-virtual characters that represent the user in the digital world. In accordance with the real world where humans own a body and interact via the body with the environment, avatars thereby enable users to interact with virtual worlds in a natural and intuitive fashion. Interestingly, previous work revealed that the appearance of an avatar can change the behavior, attitude, and perception of the embodying user. For example, research found that users who embodied attractive or tall avatars behaved more confidently in a virtual environment than those who embodied less attractive or smaller avatars. Alluding to the versatility of the Greek God Proteus who was said to be able to change his shape at will, this phenomenon was termed the Proteus effect. For designers and researchers of virtual reality applications, the Proteus effect is therefore an interesting and promising phenomenon to positively affect users during interaction in virtual environments. They can benefit from the limitless design space provided by virtual reality and create avatars with certain features that improve the users' interaction and performance in virtual environments. To utilize this phenomenon, it is crucial to understand how to design such avatars and their characteristics to create more effective virtual reality applications and enhanced experiences. Hence, this work explores the Proteus effect and the underlying mechanisms with the aim to learn about avatar embodiment and the design of effective avatars. This dissertation presents the results of five user studies focusing on the body ownership of avatars, and how certain characteristics can be harnessed to make users perform better in virtual environments than they would in casual embodiments. Hence, we explore methods for inducing a sensation of body ownership of avatars and learn about perceptual and physiological consequences for the real body. Furthermore, we investigate whether and how an avatar's realism and altered body structures affect the experience. This knowledge is then used to induce body ownership of avatars with features connected with high performance in physical and cognitive tasks. Hence, we aim at enhancing the users' performance in physically and cognitively demanding tasks in virtual reality. We found that muscular and athletic avatars can increase physical performance during exertion in virtual reality. We also found that an Einstein avatar can increase the cognitive performance of another user sharing the same virtual environment. This thesis concludes with design guidelines and implications for the utilization of the Proteus effect in the context of human-computer interaction and virtual reality.
... mehreren Interakti-onspartner_innen, wobei Signale oftmals schon nach 0,3-0,4 Sekunden nach deren Auftreten gespiegelt werden. Die vielfach erforschte positive Wirkung von Mimikry auf die frühkindliche Entwicklung, auf zwischenmenschliche Bindungen, auf die Persönlichkeitsentwicklung und auf das Verständnis und Einfühlungsvermögen für das Gegenüber hat in der Wissenschaft das Interesse an diesem nonverbalen Phänomen geweckt (Ashenfelter et al. 2009;Holler 2011;Isabella und Belsky 1991;Likowski et al. 2012;Ramseyer und Tschacher 2008). Auch in der Emotionsforschung fand das Phänomen der Mimikry Einzug. ...
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