Social hierarchy modulates neural responses of empathy for pain
Social hierarchy modulates neural responses of empathy for pain
Chunliang Feng 1 2
Zhihao Li 2
Xue Feng 1
Lili Wang 0
Tengxiang Tian 1
Yue-Jia Luo 2 3
0 School of Educational Science, Huaiyin Normal University , Huaian , China
1 State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University , Beijing , China
2 Institute of Affective and Social Neuroscience, School of Psychology and Sociology, Shenzhen University , Shenzhen , China
3 Collaborative Innovation Center of Sichuan for Elder Care and Health, Chengdu Medical College , Chengdu , China
Recent evidence indicates that empathic responses to others' pain are modulated by various situational and individual factors. However, few studies have examined how empathy and underlying brain functions are modulated by social hierarchies, which permeate human society with an enormous impact on social behavior and cognition. In this study, social hierarchies were established based on incidental skill in a perceptual task in which all participants were mediumly ranked. Afterwards, participants were scanned with functional magnetic resonance imaging while watching inferior-status or superior-status targets receiving painful or non-painful stimulation. The results revealed that painful stimulation applied to inferior-status targets induced higher activations in the anterior insula (AI) and anterior medial cingulate cortex (aMCC), whereas these empathic brain activations were significantly attenuated in response to superior-status targets' pain. Further, this neural empathic bias to inferior-status targets was accompanied by stronger functional couplings of AI with brain regions important in emotional processing (i.e. thalamus) and cognitive control (i.e. middle frontal gyrus). Our findings indicate that emotional sharing with others' pain is shaped by relative positions in a social hierarchy such that underlying empathic neural responses are biased toward inferior-status compared with superior-status individuals.
social hierarchy; empathy; anterior insula (AI); anterior medial cingulate cortex (aMCC); Toronto Alexithymia Scale-20 Items (TAS-20); functional connectivity
Empathy reflects the ability to identify and share the emotions
and feelings of others
(Decety and Jackson, 2004; Zaki, 2014)
Numerous functional imaging studies have explored the neural
signatures underlying empathy with experimental paradigms
in which participants were exposed to the pain experience of
(Singer et al., 2004; Jackson et al., 2005; Lamm and Decety,
2008; Gu et al., 2012, 2013)
. A recent meta-analysis has revealed
that the bilateral anterior insula (AI) and anterior medial
cingulate cortex (aMCC) are most consistently involved in the
perception of others’ pain, and thereby are identified as the core
network of empathy for pain
(Lamm et al., 2011)
. Notably, these
brain regions have also been implicated in representing
affective-motivational aspects of the first-person physical and social
(Peyron et al., 2000; Rainville, 2002; Eisenberger
et al., 2003)
. Therefore, the engagement of the aMCC and AI in
perceiving others’ pain is thought to subserve emotional
sharing with others and constitute the affective aspects of empathy
(Singer et al., 2004; Xu et al., 2009; Lamm et al., 2011)
Empathic neural responses to others’ pain often occur
automatically; however, they are also tremendously modulated by
various individual and situational factors
(Decety and Jackson,
2004; Goubert et al., 2005; De Vignemont and Singer, 2006; Cheng
et al., 2007; Zaki, 2014)
. For instance, brain activity underlying
the perception of others’ pain is modulated according to
perceiver’s empathic ability, which is often measured by the
interpersonal reactivity index (IRI) (Davis, 1980). The higher
perceiver’s scores on dispositional empathy, the stronger aMCC
and AI responses to the pain of others
(Singer et al., 2004, 2006)
Notably, the ability to identify one’s own feelings also
modulates empathic neural responses, such that people with
difficulties in identifying and describing their own emotions (i.e.
alexithymia) show attenuated activations in AI while
introspecting their own feelings and while empathizing with others’
(Silani et al., 2008; Bird et al., 2010)
. These findings are
consistent with the ‘shared representations’ account of empathy,
positing that neural networks engaged by the first-person
pain experience also underpin the sharing of others’ pain
Vignemont and Singer, 2006; Lamm et al., 2011; Ru¨ tgen et al.,
Regarding the context-dependent empathic responses, it has
been revealed that neural responses of the aMCC and AI to the
pain of others are constrained by top-down attention and
(Gu and Han, 2007; Lamm et al., 2007a,b)
Further, the empathic neural responses of aMCC and AI are
modulated by interpersonal relations such that they are
attenuated while observing disliked others or out-group members in
(Singer et al., 2006; Xu et al., 2009)
. Likewise, Meyer et al.
(2012, 2015) recently observed that empathy for the social
exclusion (i.e. social pain) of friends as compared with strangers
relied more heavily on affective pain regions including aMCC
and AI. Taken together, these findings indicate that affective
sharing with others’ pain is modulated according to both
personal and situational factors. However, specific to situational
factors, few studies have examined how empathy and
underlying brain functions are modulated by social hierarchies that are
ubiquitous to human societies
(Cheng et al., 2014)
In human societies, social hierarchies can be readily
established according to many dimensions, such as knowledge, skill
and physical strength. For instance,
Zink et al. (2008)
social hierarchies among experimental participants based on their
performances in a simple perceptual task; and they
demonstrated that people are strongly engaged in this hierarchical
context. Employing similar procedures, previous studies have shown
modulations of social hierarchies on human socioemotional
(Boksem et al., 2012; Hu et al., 2014, 2015)
(Santamarı´a-Garc´ıa et al., 2013; Breton
et al., 2014; Feng et al., 2015)
. More relevant to empathy
modulation, the knowledge that others are superior often conflicts with
positive self-views and provokes negative feelings due to upward
(Smith et al., 1996; Takahashi et al., 2009)
These negative feelings in turn may preclude empathy for
superior-status individuals. Indeed, people tend to eliminate
emotional sharing or even feel pleasure when imagined misfortune
happens to advantaged targets
(Smith et al., 1996; Brigham et al.,
1997; van Dijk et al., 2006; Takahashi et al., 2009)
. Therefore, it is
likely that empathic neural responses in the aMCC and AI are
diminished in response to painful stimulation applied to
superiorstatus compared with inferior-status targets.
To test this hypothesis, we examined empathic neural
responses with functional magnetic resonance imaging (fMRI) in
a hierarchical context. Prior to fMRI scanning, we followed the
Zink et al. (2008)
to create social hierarchies based
on incidental skill in a game setting. Specifically, subjects
performed a perceptual task and were told that all participants
were ranked according to performance on the task. Covertly, all
participants were told that they were medially ranked (‘two-star
players’). Thus, self-identities of hierarchical positions were set
experimentally. During subsequent fMRI scanning, participants
were asked to empathize with inferior-status (‘one-star players’)
or superior-status (‘three-star players’) targets receiving painful
or non-painful stimulation
(Xu et al., 2009)
. With this
experimental design, we assessed modulations of social hierarchy on
empathic neural responses with both a regions-of-interest
(ROIs) analysis and an exploratory voxel-wise whole-brain
analysis. The ROI analysis focused on the bilateral AI and aMCC for
their consistent involvement in empathy for pain
(Lamm et al.,
With these neuroimaging measurements, we first examined
whether empathic brain responses were attenuated in
perceiving pain of superior-status as compared with inferior-status
targets. Furthermore, we investigated whether neural responses of
AI and aMCC to others’ pain were modulated according to
behavioral measures such as empathy-related personality traits
and subjective ratings to others’ pain. For this purpose,
personality measures reflecting participants’ ability to understand
their own feelings (manifested as low level of alexithymia) and
others’ emotions (reflected by high scores on the IRI) were
examined due to their demonstrated associations with
empathic neural responses
(Singer et al., 2004; Bird et al., 2010)
Taken together, our study examined both context-specific and
person-specific empathic neural responses to others’ pain.
Materials and methods
Twenty-two individuals (11 females) (mean age 6 s.d.:
22.23 6 1.85) participated in this study and completed fMRI
scanning for monetary compensation. All participants were
right-handed, had normal or corrected-to-normal vision, and
had no history of neurological or psychiatric disorder. Written
informed consents were collected for all participants. The study
was conducted according to the ethical guidelines and
principles of the Declaration of Helsinki and was approved by the
Institutional Review Board at Beijing Normal University.
A set of 64 color photographs, showing faces of four targets (two
females) unknown to all participants, was employed in this
study. These photographs, 16 for each target, were derived from
video clips used in a previous study
(Xu et al., 2009)
et al. (2009)
have demonstrated that these stimuli are adequate
to elicit empathy-related brain activity and subjective empathic
feelings. For each target, eight photographs depicted faces
receiving painful stimulation (needle penetration) to the left or
right cheek; and the other eight photographs showed faces
receiving non-painful stimulation (Q-tip touch) (Figure 1a and
b). Each photograph was set to the same size of 298 298 pixels.
Prior to fMRI scanning, participants’ personality traits were
measured by the IRI
and TAS-20 Items
(Bagby et al.,
. IRI is a self-administered questionnaire measuring the
empathetic ability in four aspects: (i) empathic concern, feeling
of warmth and concern for others; (ii) perspective taking,
adopting the perspective of other people; (iii) fantasy, identifying with
fictitious characters in books or movies and (iv) personal
distress, feelings of discomfort and anxiety when witnessing
the negative experiences of others. TAS-20 is a
self-administered questionnaire consisting of 20 items, which are scored on
a 5-point scale from ‘strongly disagree’ to ‘strongly agree’, with
higher scores indicating greater levels of alexithymia. TAS-20
provides an overall measure of deficiency in understanding,
processing or describing emotions
(Bagby et al., 1994)
, and it
consists of three subscales: difficulty identifying emotions,
difficulty describing emotions and externally oriented thinking.
The associations between alexithymia and empathy have been
previously observed, suggesting that the awareness of one’s
own emotions is a prerequisite to share emotions of others
(Moriguchi et al., 2007; Grynberg et al., 2010)
To establish the social hierarchy, all participants were asked
to perform a dot-estimation task. In this task, participants were
presented with 100 red dots in a white background and asked to
judge which side (left or right) of the field had more dots
et al., 2013)
. Participants were told that their performance in this
task would be evaluated by both speed and accuracy and would
be compared with other players for ranking purpose. They were
also told that more than 650 people had already performed this
task as a part of a cognitive ability test, and all of them were
ranked as inferior (‘one star players’), medium (‘two star
players’) or superior (‘three star players’) status according to their
performance. Covertly, outcomes of this dot-estimation task
were always fixed, such that all participants were told that they
were mediumly ranked based on their performance. Similar
procedures to establish social hierarchy were initially employed
in a landmark study by
Zink et al. (2008)
, and these authors
demonstrated that individuals are strongly engaged in the
hierarchical context in this paradigm. Accordingly, this paradigm has
been widely employed in the current literature
(Boksem et al.,
2012; Santamarı´a-Garcı´a et al., 2013; Breton et al., 2014)
After the dot-estimation task, participants were told that a
fraction of the aforementioned 650 players had agreed to take
part in a sensory test in which they had received painful or
nonpainful stimulation. Participants were then told that they would
view faces of four of these players in the other two social
positions: two (one female) superior players and two (one female)
inferior players. For the four targets employed, the
combinations of targets and hierarchies were counterbalanced across
subjects to control for potential confounding factors such as
attractiveness. To ensure that participants believed that the four
players had received both painful and non-painful stimulation,
participants were asked to watch video clips that vividly
depicted needle penetration or Q-tip touch applied to each target.
In cases where a participant questioned about the ranking
procedure or stimulation applied, the experiment was terminated
and such participants (three females and two males, not
included in the present sample of N ¼ 22) were excluded from
On the fMRI session, each trial of the fMRI task consisted of a
central fixation (1 s) followed by a photograph (3 s) of either
inferior or superior player (Figure 1c). On each photograph,
participants were instructed to empathize and judge how much pain
the depicted target was feeling
(de Greck et al., 2012)
. No overt
response was required from participants for minimizing
(Decety et al., 2008; Akitsuki and Decety,
. Afterwards, an optimized jitter generated by an fMRI
simulator software (http://www.cabiatl.com/CABI/resources/
fmrisim/) was presented with minimum of 1 s and average of
3 s. Each scanning run consisted of 64 trials and lasted for 448 s.
Each participant completed two scanning runs with each
photograph being non-repetitively presented once in each run.
In the post-scan session, participants were asked with two
evaluation scales for each photograph presented: (i) ‘how
painful do you think the target feels’ (pain intensity: 1 ¼ not at all,
9 ¼ extremely painful) and (ii) ‘how pleasant do you feel when
observing the photograph’ (pleasantness: 1 ¼ extremely
unpleasant, 9 ¼ extremely pleasant). Finally, participants were
asked whether they believed that their own and the four viewed
targets’ social positions were based on their performances in
the dot-estimation task and whether the painful/non-painful
stimulations applied to targets were real. The debriefing
received positive confirmations from all participants that
completed the fMRI scanning.
Imaging data were acquired with a 3T Siemens Trio scanner
equipped with a 12-channel transmit/receive head coil. A
T2weighted gradient-echo echo-planar-imaging (EPI) sequence was
used to acquire functional images (TR/TE ¼ 2000 ms/30 ms, flip
angle ¼ 90 , number of axial slices ¼ 33, slice thickness ¼ 3.5 mm,
gap between slices ¼ 0.7 mm, matrix size ¼ 64 64, FOV ¼ 224
224 mm). High-resolution anatomical images covering the entire
brain were also obtained by a magnetization prepared rapid
acquisition with gradient-echo (MPRAGE) sequence (TR/TE ¼ 2530/
3.39 ms, flip angle ¼ 7 , number of sagittal slices ¼ 144, slice
thickness ¼ 1.33 mm, matrix size ¼ 256 256, FOV ¼ 256 256 mm).
Behavioral data analysis. Behavioral data analyses were carried
out using SPSS 16.0 (IBM, Somers, USA) with a significance
threshold of P < 0.05 (two-tailed). Subjective ratings of pain intensity and
pleasantness were submitted to a 2 (social hierarchy: superior
status vs inferior status) 2 (stimulation valence: painful stimulation
vs non-painful stimulation) repeated measures analysis of
fMRI data analysis. Functional neuroimaging data analyses were
performed with SPM8
(http://www.fil.ion.ucl.ac.uk/spm/software/spm8/). Preprocessing of functional data included
slicetiming correction, realignment through rigid-body registration
to correct for head motion, spatial normalization to the
Montreal Neurological Institute (MNI) template, spatial
smoothing (FWHM ¼ 5 mm) and temporal high-pass filtering (removal
of low frequency drift of T > 80 s).
A two-level general linear model (GLM) was used to analyze
functional data. The first-level modeling included regressors
defined for each subject. These regressors modeled blood
oxygenation level-dependent (BOLD) responses to the fixation and
the four task conditions of painful stimulation applied to
inferior players (Inferior-Pain), non-painful stimulation applied to
inferior players (Inferior-NoPain), painful stimulation applied to
superior players (Superior-Pain) and non-painful stimulation
applied to superior players (Superior-NoPain). The six
movement parameters obtained from the realignment (three
translations, three rotations) were also included in the design matrix
as nuisance regressors. Each task regressor was generated by
convolving the corresponding boxcar stimulus function with a
canonical hemodynamic response function (HRF)
(Bu¨ chel et al.,
. To improve noise estimation, the GLM also considered
signal temporal autocorrelations with a first-order
(Bullmore et al., 1996)
. In the first-level GLM,
regression coefficients (or beta values) for each regressor were
computed at every voxel within the brain.
With the obtained parameter estimates, we performed a
region-of-interest (ROI) analysis based on a priori hypotheses
(Poldrack, 2007; Poldrack and Mumford, 2009)
. This ROI analysis
focused on the aMCC and bilateral AI as they are most
consistently implicated in perceiving others’ pain, thereby constituting
the core network of empathy for pain (Lamm et al., 2011). To
determine the ROIs independently of the present data, the regions
of bilateral AI (Figure 2a) and aMCC (Figure 3a) were defined as
spheres (radius ¼ 10 mm) centered at MNI coordinates (x/y/
z ¼ 40/22/0 mm, 39/23/ 4 mm and 2/23/40 mm) reported in a
(Lamm et al., 2011)
. The average
parameter estimates across all voxels in each ROI were extracted
from each subject for all experimental conditions using SPM
REX toolbox (https://www.nitrc.org/projects/rex/). These data
were then compared between conditions with a 2 (social
hierarchy: superior status vs inferior status) 2 (stimulation
valence: painful stimulation vs non-painful stimulation) repeated
The ROI analysis was supplemented with an exploratory
whole-brain analysis using voxel-wise repeated measures
ANOVA. This analysis was employed to confirm the interaction
between social hierarchy and stimulation valence in the
predefined ROIs as well as to explore the same interaction in other
brain regions. To correct for false positives yielded by multiple
comparisons, statistical maps were clipped with a joint threshold
at both the voxel level and the cluster level. The cluster threshold
was determined using a Monte Carlo simulation-based estimator
implemented in Matlab
(Slotnick et al., 2003; Slotnick and
. On the basis of simulations (5000 iterations) and
the estimated spatial smoothness of FWHM ¼ 9 mm, a
familywise error (FWE) correction at P < 0.05 is achieved with a cluster
defining threshold of P < 0.005 and a cluster extent of 86
contiguous resampled voxels (688 mm3)
(Janes et al., 2010; Dietsche et al.,
2014; Abel et al., 2015; Henry et al., 2015; Willems et al., 2015)
This joint threshold was applied to all results of whole-brain
Focusing on the predefined ROIs of AI and aMCC, we also
performed an analysis of psychophysiological interaction (PPI)
(Friston et al., 1997)
to examine how social hierarchy modulates
functional connectivity between the AI/aMCC and other regions
of the brain. Specifically, we used the generalized PPI toolbox
(McLaren et al., 2012)
fMRI signal time courses individually extracted from the AI and
aMCC as the seeding signals. These seeding signals were then
deconvolved with the canonical HRF, resulting in estimates of
the underlying neuronal activity
(Gitelman et al., 2003)
Subsequently, the interactions of these estimated neuronal
time-series and vectors representing each of the onsets for the
fixation and four stimulus types (Inferior-Pain, Inferior-NoPain,
Superior-Pain, Superior-NoPain) were computed. Lastly, these
interaction terms were re-convolved with the HRF and entered
into a new GLM along with the vectors for the onsets of each
stimulus type (i.e. the psychological terms), the original average
time-series, and nuisance regressors (i.e. six movement
parameters derived from realignment corrections). Group level
analysis of the PPI data was almost identical to that of activation
data except the beta values used were derived from the
PPI regressors. In this study, we focused on connections that
exhibited a significant interaction effect of social
hierarchy stimulation valence. Namely, those connections with a
different painful vs non-painful contrast between the superior
and inferior status.
Moreover, given previous reports of differential behavioral
responses to superior-status men and women
(Maner et al.,
2007; DeWall, 2008)
, potential effects of target gender were
explored by adding it as a within-subjects factor to the GLMs
described earlier. However, since we did not observe significant
modulations of target gender on either subjective ratings or
empathic AI and aMCC responses (Supplementary Figures S1 and
S2), the current data analyses on the empathic responses were
collapsed across gender of targets.
Finally, to examine potential relationships between the
internal brain activation and external behavior, Spearman’s Rank
non-parametric (i.e. Spearman q) correlations that are more
robust to outliers than Pearson’s linear correlations
and Pernet, 2012)
were computed to determine associations
among dispositional (personality scores), behavioral (subjective
ratings), fMRI (BOLD signal changes) and functional connectivity
(connectivity strengths) measures.
Relative to non-painful stimulation, painful stimulation was
rated with higher scores of pain intensity (F(1, 21) ¼ 123.25,
P < 0.0005) and lower scores of pleasantness (F(1, 21) ¼ 9.84,
P < 0.01). These rating scores of painful and non-painful
stimulations did not differ between superior and inferior status
(all P > 0.05).
fMRI results: ROI analysis
The analysis of BOLD responses in the left AI (F(1, 21) ¼ 17.42,
P < 0.0005), right AI (F(1, 21) ¼ 9.24, P < 0.01) and aMCC
(F(1, 21) ¼ 22.20, P < 0.0005) confirmed the augmented activity for
painful stimulation as compared with non-painful stimulation;
whereas the main effect of social hierarchy was not significant
(left AI: F(1, 21) ¼ 2.61, P > 0.05; right AI: F(1, 21) ¼ 1.40, P > 0.05;
aMCC: F(1, 21) ¼ 0.71, P > 0.05). Moreover, in supporting our
hypothesis, we observed significant interactions of social hierarchy
and stimulation valence in all of these three regions (left AI:
F(1, 21) ¼ 7.27, P < 0.05, Figure 2b; right AI: F(1, 21) ¼ 5.86, P < 0.05,
Figure 2b; aMCC: F(1, 21) ¼ 10.75, P < 0.005, Figure 3b). For
superior-status targets, BOLD responses in these areas did not
significantly differ between painful and non-painful stimulation
(left AI: t(21) ¼ 1.54, P > 0.05; right AI: t(21) ¼ 1.10, P > 0.05;
aMCC: t(21) ¼ 0.73, P > 0.05). For inferior-status targets, however,
painful stimulation elicited higher BOLD responses than
non-painful stimulation (left AI: t(21) ¼ 5.97, P < 0.0005; right
AI: t(21) ¼ 4.15, P < 0.0005; aMCC: t(21) ¼ 5.45, P < 0.0005).
Noteworthy, to non-painful stimulation, aMCC showed
stronger responses to superior-status than inferior-status targets
(t(21) ¼ 2.54, P < 0.05).
The correlation analysis revealed that empathic responses
of the left AI to inferior targets (i.e. Inferior-Pain vs
InferiorNoPain) were negatively correlated with participants’ scores of
TAS-20 (Spearman q ¼ 0.44, P < 0.05, Figure 2c); whereas this
correlation was not significant in response to superior-status
targets (P > 0.05). In addition, empathic aMCC responses and
empathic subjective ratings of pain intensity were positively
correlated with each other regarding the difference between
inferior and superior status [i.e. (Inferior-Pain Inferior-NoPain)
(Superior-Pain Superior-NoPain)] (Spearman q ¼ 0.68,
P < 0.005, Figure 3c).
fMRI results: exploratory whole-brain analysis
Whole-brain analysis of neuroimaging data was detailed in the
supplementary materials (Supplementary Figures S3–S5 and
fMRI results: PPI analysis
PPI analysis was performed to assess the interaction effects of
social hierarchy stimulation valence on the functional
connectivity between AI/aMCC and other brain regions. The connectivity
contrast of [(Inferior-Pain vs Inferior-NoPain) > (Superior-Pain vs
Superior-NoPain)] for the left AI as a seed region identified the
following brain regions (P < 0.05 FWE corrected at the cluster
level): the left thalamus ( 14/ 6/10 mm, cluster size ¼ 182 voxels,
T ¼ 5.14) (Figure 4a and b) and the right calcarine (30/ 66/8 mm,
cluster size ¼ 138 voxels, T ¼ 4.03); and the reverse contrast
identified the left middle occipital gyrus ( 28/ 92/0 mm, cluster
size ¼ 126 voxels, T ¼ 4.55). Notably, empathic left AI-thalamus
connectivity and empathic left AI responses were positively
correlated with each other regarding the difference between
inferior and superior status [i.e. (Inferior-Pain Inferior-NoPain)
(Superior-Pain Superior-NoPain)] (Spearman q ¼ 0.58, P < 0.01,
The connectivity contrast of [(Inferior-Pain vs
InferiorNoPain) > (Superior-Pain vs Superior-NoPain)] for the right AI as
a seed region identified the right middle frontal gyrus (MFG) (42/
8/32 mm, cluster size ¼ 91 voxels, T ¼ 4.52) (Figure 5a and b);
whereas no significant cluster was identified with the reverse
contrast. The correlation analysis revealed that scores of
TAS20 were positively correlated with empathic AI-MFG
connectivity changes in response to inferior-status targets (Spearman
q ¼ 0.55, P < 0.01, Figure 5c). Further, the strength of right
AIMFG connectivity in response to the pain of inferior targets
showed a negative correlation with empathic AI responses to
inferior-status targets (Spearman q ¼ 0.46, P < 0.05, Figure 5d).
These correlations were not significant in response to
superiorstatus targets (all P > 0.05).
Finally, aMCC showed significant functional covariation
with the right precuneus (38/ 80/36 mm, cluster size ¼ 91
voxels, T ¼ 4.43) as revealed by the connectivity contrast of
[(Inferior-Pain vs Inferior-NoPain) > (Superior-Pain vs
SuperiorNoPain)], whereas no significant cluster was identified with the
Our study examined the influence of social hierarchy on the
neural responses to others’ pain. We identified brain activation
to the pain of others in the AI and aMCC that are implicated in
affective aspects of empathy for pain
(Lamm et al., 2011)
empathic neural responses in the left AI inversely correlated
with the alexithymia traits, and neural responses in the aMCC
were positively associated with subjective sensitivity to others’
pain. Notably, we observed significant modulations of social
hierarchy on these empathic neural responses, such that they
were evident in the perception of pain of inferior-status targets
but were significantly attenuated in response to the pain of
superior-status targets. Finally, we observed stronger functional
couplings of AI with brain regions implicated in emotional
processing (i.e. thalamus) and cognitive control (i.e. MFG) in
response to the pain of inferior-status than superior-status
targets. Our findings indicate that brain functions underlying
empathy are modulated by the relative positions in a social
hierarchy such that empathic neural responses are biased
toward inferior-status compared with superior-status targets.
We first replicated previous findings on the neural
signatures underlying empathy for others’ pain. Among other brain
regions, the AI and aMCC showed stronger responses to the
painful than non-painful stimulation applied to others. The AI
and aMCC responses to others’ pain are thought to represent
feeling states of others
(Lamm et al., 2011; Bernhardt and Singer,
. This assertion has support from the present and previous
observations that empathic aMCC responses were associated
with subjective sensitivity to the pain of others
. Furthermore, the AI and aMCC are also engaged
in affective and motivational aspects of first-person pain
(Peyron et al., 2000; Rainville, 2002)
, leading to the
notion that emotional sharing with others’ pain is based on
shared neural representations for first-person and vicarious
experiences of emotion
(Singer et al., 2004; Lamm et al., 2011;
Bernhardt and Singer, 2012; R u¨tgen et al., 2015)
. This hypothesis
is confirmed by our findings that neural responses of AI to the
pain of inferior-status targets correlated inversely with
alexithymia traits that involve difficulties in understanding one’s
(Silani et al., 2008; Bird et al., 2010)
correlations of the AI and aMCC activations with behavioral
measures were different. The AI responses were correlated with
alexithymia scores, whereas the aMCC responses with pain
intensity ratings. Whether these findings imply different roles of
the AI and aMCC remains to be elucidated, since much of the
research has focused on the commonality of AI and aMCC
also Gu et al., 2010; Bernhardt and Singer, 2012)
. Recent attempts
to dissociate functions of these brain regions have not yet
provided straightforward predictions on the distinct correlations of
their activations with behavioral measures
(Gu et al., 2010, 2012,
We next studied the modulations of social hierarchy on
empathic neural activity in the bilateral AI and aMCC. Our findings
revealed that empathy-related activations in these brain regions
were significantly attenuated in response to the pain of
superiorstatus targets. These results concur with previous observations
that empathic neural responses in the aMCC and AI are
modulated by interpersonal relationship such that they are remarkably
decreased by the knowledge that out-group members or disliked
others are in pain
(Singer et al., 2006; Xu et al., 2009)
. The evidence
that empathic neural responses are modulated by interpersonal
relations supports the context-dependent account of empathy
(De Vignemont and Singer, 2006)
Modulations of social hierarchy on empathic neural
responses might be mediated by the social comparison processes.
For instance, the knowledge that others are better threatens
positive self-views and induces negative affect due to upward
(Major et al., 1993)
. This negative affective
link with superior-status targets in turn may dampen empathy
(Singer et al., 2006)
. This is supported by the stronger aMCC
responses to superior than inferior targets at baseline in the
context of non-painful stimuli. Such an aMCC activation pattern
echoes previous observation that upward social comparison
with advantaged targets induced enhanced activations in the
aMCC, which was thought to reflect painful feelings or conflicts
of positive self-concepts (Takahashi et al., 2009). In Takahashi
et al.’s (2009) study, negative affective link predicts experienced
pleasure and associated brain activations (e.g. ventral striatum)
in response to imagined misfortunes on advantaged
individuals. We did not identify the involvement of reward neural
circuit in response to the pain of superior-status targets. This
might be due to the reason that participants were asked to
intentionally empathize with the pain of others. Noteworthy, this
affective account is very tentative given that we did not collect
participants’ attitudes toward superior-status and
inferiorstatus targets. Alternatively, differential empathic neural
responses to inferior and superior targets might be attributed to
(Gu and Han, 2007; Lamm et al.,
. It has been demonstrated both by previous data
et al., 2008)
and by the main effect of social status in the present
whole-brain analyses that greater attentional resources are
directed to the superior than inferior targets. As such, when
viewing superior targets in pain, the status itself may deter attention
from allocating to pain so that empathic neural response are
(Gu and Han, 2007)
Despite the observed effects of social status on the fMRI
responses to others’ pain, social status had no effect on
participants’ self-reported ratings of pain intensity. This divergence
could be due to attention redirection in the self-paced rating
task, which presumably allowed more time for evaluation as
well as increased attention to the pain-related features.
Another possible account is that subjective ratings of pain
intensity for superior-status targets might be based on cognitive
evaluations or sensory perception
(cf. Xu et al., 2009)
. It is not
without precedent that differential neuropsychological
processes are involved in empathizing with different targets.
Meyer et al. (2012
, 2015) have demonstrated that
empathy for strangers’ social suffering relies more heavily on
mentalizing networks, whereas empathy for friends’ social suffering
relies on networks implicated in emotional sharing and
selfprocessing. In line with this account, our whole-brain analysis
revealed that empathy for the pain of superior-status relative to
inferior-status targets induced stronger responses in the
precuneus, which is implicated in mentalizing
The neural empathic bias to inferior-status targets was
accompanied by enhanced functional connectivity of AI and
aMCC with other brain regions including thalamus and MFG. On
the one hand, the thalamus plays a critical role in affective
aspects of empathy
(Nummenmaa et al., 2008; Hillis, 2014)
Patients with thalamus lesion have shown lower ability in
emotional empathy as measured by the ‘Reading the mind in the
Eyes Test’ (Wilkos et al., 2015). The thalamus might contribute
to emotional empathy by relaying sensory information about
affective experience of others to the insula to shape the
representation of others’ emotion
(Craig, 2002; Hillis, 2014)
. In line
with this viewpoint, our results revealed that stronger
AIthalamus connectivity strengths predicted higher empathic
neural responses of the AI. On the other hand, the MFG is often
involved in cognitive control and presumably contributes to
emotion regulation of empathy
(Decety and Jackson, 2004; Gu
and Han, 2007)
. This is also consistent with our findings that
the stronger right AI-MFG connectivity strengths predicted
lower empathic AI responses to inferior-status targets. In
addition to emotional sharing, emotion regulation constitutes
another crucial component of empathy to manage intersubjective
transactions between self and other
(Decety and Jackson, 2004;
Lamm et al., 2010)
Several limitations warrant consideration. First, our study
did not consider gender of perceivers as a potential moderators
as many previous studies did
(Chiao et al., 2008; Zink et al., 2008;
de Greck et al., 2012)
. It is possible that genders of perceivers and
targets interact with each other to modulate the processing of
social hierarchy and its effects on socio-emotional functioning
(Maner et al., 2007; DeWall, 2008)
. Replication in larger groups of
men and women will help to clarify sex differences in the
modulation of social hierarchy on empathic neural responses.
Second, we deliberately used faces of neutral expressions for
both painful and non-painful stimulations to avoid confounding
effects of emotional contexts on empathic responses (cf. Han
et al., 2009). One may argue that neural responses to painful
stimulation were attributed to conflict resolution. This argument is
not consistent with our results that empathic neural responses
of the AI/aMCC were modulated according to empathy-related
personality traits (i.e. alexithymia) and subjective sensitivity to
others’ pain. Finally, our design did not include a same-status
condition that could help to identify the directions of effects of social
hierarchy on empathic responses. For instance, it is possible that
empathic responses to inferior targets reflect general affective
sharing as in the neutral (e.g. same-status) condition rather than
increased empathy for the pain of inferior targets.
In summary, our findings confirmed the hypothesis that
empathic neural responses are modulated by relative positions in a
social hierarchy. We showed evidence that the affective neuronal
network consisting of the aMCC and AI is engaged in empathic
responses to the pain of inferior-status but not superior-status
individuals. In addition, the AI showed stronger functional
couplings with thalamus and MFG that are respectively associated
with emotional processing and cognitive control in response to
the pain of inferior-status than superior-status targets. These
findings indicate a bias of emotional sharing with inferior-status
compared with superior-status others and complement previous
observations on the effects of social hierarchy on human
social behaviors and cognitive functions
(Koski et al., 2015)
The authors thank Dr Shihui Han for generous sharing of
experimental stimuli and Dr Mac Merritt for improving
This study was supported by the National Natural Science
Foundation of China (31530031, 81471376, 31300869), the
National Basic Research Program of China (973 Program:
2014CB744600), the Natural Science Foundation of Jiangsu
Province of China (BK20130415) and Natural Science
Foundation of SZU (201564).
Supplementary data are available at SCAN online.
Conflict of interest. None declared.
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