A neural link between affective understanding and interpersonal attraction

Abstract

Significance
Humans interacting with other humans must be able to understand their interaction partner’s affect and motivations, often without words. We examined whether people are attracted to others whose affective behavior they can easily understand. For this, we asked participants to watch different persons experiencing different emotions. We found the better a participant thought they could understand another person’s emotion the more they felt attracted toward that person. Importantly, these individual changes in interpersonal attraction were predicted by activity in the participant’s reward circuit, which in turn signaled how well the participant’s “neural vocabulary” was suited to decode the other’s behavior. This research elucidates neurobiological processes that might play an important role in the formation and success of human social relations.

Full text

A neural link between affective understanding and
interpersonal attraction
Silke Anders
a,1
, Roos de Jong
a
, Christian Beck
a
, John-Dylan Haynes
b,c,d
, and Thomas Ethofer
e,f
a
Social and Affective Neuroscience, Department of Neurology, Universität zu Lübeck, 23562 Luebeck, Germany;
b
Bernstein Center for Computational
Neuroscience, Charité Universitätsmedizin Berlin, 10115 Berlin, Germany;
c
Berlin Center for Advanced Neuroimaging, Charité Universitätsmedizin Berlin,
10117 Berlin, Germany;
d
Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, 10099 Berlin, Germany;
e
Department of Biomedical Magnetic
Resonance, University of Tübingen, 72076 Tuebingen, Germany; and
f
Clinic for Psychiatry and Psychotherapy, University of Tübingen, 72076 Tuebingen,
Germany
Edited by Susan T. Fiske, Princeton University, Princeton, NJ, and approved February 11, 2016 (received for review August 20, 2015)
Being able to comprehend another person’s intentions and emotions
is essential for successful social interaction. However, it is currently
unknown whether the human brain possesses a neural mechanism
that attracts people to others whose mental states they can easily
understand. Here we show that the degree to which a person feels
attracted to another person can change while they observe the
other’s affective behavior, and that these changes depend on the
observer’s confidence in having correctly understood the other’saf-
fective state. At the neural level, changes in interpersonal attraction
were predicted by activity in the reward system of the observer’s
brain. Importantly, these effects were specific to individual observer–
target pairs and could not be explained by a target’s general attrac-
tiveness or expressivity. Furthermore, using multivoxel pattern
analysis (MVPA), we found that neural activity in the reward system
of the observer’s brain varied as a function of how well the target’s
affective behavior matched the observer’s neural representation of
the underlying affective state: The greater the match, the larger
the brain’s intrinsic reward signal. Taken together, these findings
provide evidence that reward-related neural activity during social
encounters signals how well an individual’s“neural vocabulary”is
suited to infer another person’s affective state, and that this intrin-
sic reward might be a source of changes in interpersonal attraction.
affective communication
|
confidence
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intrinsic reward
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multivoxel
pattern analysis
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human social relations
Finding the “right”cooperation partner is an important task for
individuals living in complex environments that require social
interaction and cooperation. To accomplish a common goal, in-
teraction partners must understand and continuously update in-
formation about their partner’s current intentions, motivation, and
affect, anticipate the other’s behavior, and adapt their own behavior
accordingly. From a sociobiological point of view, one thus might
expect that evolution has favored a neural mechanism that permits
individuals to select other individuals as their cooperation partners
whose behavior and communication signals they can easily decode.
However, the neural mechanisms that control human interpersonal
attraction and the selection of cooperation partners are not well-
understood.
Several influential theories in social psychology have stressed the
role of reward in interpersonal attraction (1, 2). The idea is that if a
social encounter with another person is rewarding, then the reward
will become associated with the other person, resulting in in-
terpersonal attraction (2–4). Until recently, neuroscientific research
into interpersonal attraction has focused mainly on determining the
neural mechanism underlying the evaluation of others based on the
physical attractiveness of their faces (e.g., 5–11). These studies con-
sistently show that neural activity in the ventral striatum and medial
orbitofrontal cortex (mOFC), core regions of the brain’sreward
system that also respond to food and money (12, 13), increases in
response to faces that are perceived as attractive. Other studies show
that these brain regions also respond to another person’s prosocial
behavior (14–20). Although this research documents the role of the
brain’s reward system in interpersonal attraction, it does not explain
why social encounters often result in relational effects in interper-
sonal attraction (21) such that one individual is particularly attracted
to one person whereas another individual is more attracted to an-
other person. Here we focus on the role of nonverbal understanding
in interpersonal attraction. Specifically, we ask whether the human
brain possesses a neural mechanism that permits individuals to select
and approach other individuals as interaction partners whose affec-
tive behavior they can easily understand.
Recent work on perceptual learning provides a first hint that the
brain’s reward system might play an important role not only in
signaling facial attractiveness but also in the individual adjustment
of interpersonal attraction during social interaction. This work
suggests that whenever the brain evaluates sensory information, it
generates a neural signal in the ventral striatum that reflects the
amount of evidence available for stimulus evaluation (22) and that,
at the experiential level, is associated with subjective confidence
(23). Importantly, it has been proposed that such intrinsic confi-
dence signals can act as positive reinforcement signals (24). We
hypothesized that a similar confidence signal, reflecting the amount
of evidence available to decode another person’snonverbalbe-
havior, might serve as an intrinsic reward that becomes associated
with the interaction partner and thereby increases or decreases the
perceiver’s interpersonal attraction toward the interaction partner
during social encounters.
Considering further evidence from social neuroscience, we
reasoned that an individual’s confidence in their understanding of
the other’s behavior might reflect how well the individual’s“neural
Significance
Humans interacting with other humans must be able to un-
derstand their interaction partner’s affect and motivations, often
without words. We examined whether people are attracted to
others whose affective behavior they can easily understand. For
this, we asked participants to watch different persons experi-
encing different emotions. We found the better a participant
thought they could understand another person’s emotion the
more they felt attracted toward that person. Importantly, these
individual changes in interpersonal attraction were predicted by
activity in the participant’s reward circuit, which in turn signaled
how well the participant’s“neural vocabulary”was suited to
decode the other’s behavior. This research elucidates neurobio-
logical processes that might play an important role in the for-
mation and success of human social relations.
Author contributions: S.A. and R.d.J. designed research; S.A. and R.d.J. performed re-
search; S.A. analyzed data; C.B. provided analysis tools; and S.A., J.-D.H., and T.E. wrote
the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Freely available online through the PNAS open access option.
1
To whom correspondence should be addressed. Email: silke.anders@neuro.uni-luebeck.de.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1516191113/-/DCSupplemental.
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vocabulary”is suited to decode and interpret the target’s behavior.
Neurobiological accounts of social cognition suggest that when
humans evaluate the inner state of another person, they implicitly
use neural representations of their own states as reference (e.g.,
25–28), and empirical studies have provided evidence that is
consistent with this idea (e.g., 29–39). Thus, we predicted that an
individual’s intrinsic confidence that they correctly understood
another person’s affective state would reflect how well the target’s
behavior matches the observer’s neural representation of the
underlying state.
To examine the role of mutual understanding in interpersonal
attraction, we conducted two experiments, a behavioral experi-
ment (experiment I) and a combined behavioral–fMRI (functional
magnetic resonance imaging) experiment (experiment II) with two
independent samples of volunteers. Participants in both experi-
ments were shown short video clips of six different female targets
who experienced and facially expressed two different emotions,
fear or sadness. After each video, participants were asked to judge
the target’s affective state (fear or sadness) and to indicate how
confident they were that they had correctly understood the target’s
affective state (Fig. 1). Before and after emotion observation,
we assessed the participants’interpersonal attraction toward
each target at two levels. First, we asked participants to enlarge
a small picture of each target on a computer screen by re-
peatedly pressing a button until the picture had a size that
corresponded to a subjectively pleasant conversational dis-
tance.Thenumberofbuttonpressesexecutedtoenlargethe
picture of a given target was taken as measure of the partici-
pant’s approach behavior toward that target (modified after ref.
6). Next, participants were given three statements about each
target and asked to indicate how much they agreed with each
of these statements (Table 1). This way, we derived a moti-
vational–behavioral measure (approach behavior) and a self-
report measure of each participant’s interpersonal attraction
toward each target before and after emotion observation. This
experimental design allowed us to link the participants’confidence
that they correctly understood the targets’affective state to
individual changes in interpersonal attraction. In experiment
II, we additionally measured the participants’brain activity
during emotion observation. This enabled us to identify neural
activity in the brain’s reward system that predicted the par-
ticipants’self-reported confidence in their emotion judgments
and to link this neural activity to changes in interindividual
attraction. Finally, participants in experiment II completed an
emotion experience task immediately after the emotion ob-
servation task in which they were asked to experience and
express the two emotions (fear and sadness) themselves, using
instructions similar to those that were used when recording
the videos of the targets (37). This permitted us to compare
the patterns of neural activity elicited during emotion obser-
vation to those associated with the observer’s own emotional
experience. We refer to the level of correspondence between
these patterns as neural observation–experience matching (NOE
matching).
Experiment I tested whether observing another person’s
affective behavior can lead to individual changes in interpersonal
attraction, and whether these changes are predicted by the ob-
server’s subjective confidence that they correctly understood the
other’s affective state. Experiment II validated the findings of
experiment I and additionally investigated the neural mecha-
nisms that might mediate between affective understanding and
interpersonal attraction. To this end, we first examined whether
the participants’subjective confidence in their emotion judg-
ments was predicted by neural activity in the reward system of
their brains. Second, we used multivoxel pattern analysis (MVPA)
(40) to examine the relation between subjective confidence, neural
confidence signals, and NOE matching. Both analyses were per-
formed in a cross-validated hierarchical approach, using whole-
brain analyses to identify relevant brain regions, followed by
region-of-interest (ROI) analyses to examine the relation be-
tween neural signals within these regions and individual changes
in interpersonal attraction (Fig. S1).
Table 1. Statements used to assess interpersonal attraction
Original statement (German) Translation
Willingness to meet
Ich würde Sie gerne im echten Leben treffen I would like to meet her in real life
Expectation of intimate communication
Ich habe das Gefühl, dass sie mich verstehen würde I feel that she would understand me
Ich glaube, dass ich mit ihr über persönliche Probleme reden könnte I think I could discuss personal problems with her
Participants were asked to indicate how much they agreed with each statement on a Likert-type 7-point scale ranging from 1 (not at all) to 7 (definitely).
The first statement estimated the participant’s overt willingness to meet the target; the last two questions were averaged to estimate the participant’s
expectation that they could have an intimate communication with the target.
Fig. 1. Experimental design. To measure changes in interpersonal attraction during emotion observation, the participants’attraction toward each target was
assessed before (Left) and after (Right) emotion observation. (Middle) A sample emotion observation trial is shown (time intervals and screen shots are taken
from experiment II). A trial consisted of a facial observation period during which a short video clip of the target experiencing fear or sadness was shown,
followed by a fixation cross, an emotion judgment period, and a confidence rating period. Responses were given with a button box and fed back to the
observer (orange frame around the selected emotion and orange dot on the confidence scale).
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Results
Experiment I.
Emotion judgments and self-reported confidence. In the behavioral
experiment, observers (21 women, 19 men) correctly labeled the
target’s emotional state in the majority of trials (hit rate 74 ±3.6%
[mean ±SEM], T[39] =21, P<0.001), and the observers’average
self-reported confidence for a given target closely reflected the
actual correctness of their emotion judgments for this target
(mean r=0.71 ±0.07 [back-transformed mean of Fisher-trans-
formed correlation coefficients], T[39] =9.8, P<0.001). This
indicates that observers had a valid internal model that allowed
them to infer the targets’affective state and to accurately estimate
the correctness of their understanding. A two-way ANOVA with
between-subject factor observer (40 levels), within-subject factor
target (6 levels), and the observers’self-reported confidence as
dependent variable (n=40 ×6×16 =3,840 trials) revealed, in
addition to a significant main effect of target (F[5,195] =95,
P<0.001, eta
2
=0.71), a significant observer-by-target interaction
(F[195,3600] =2.4, P<0.001, eta
2
=0.12). This indicates that an
observer’s subjective confidence that they correctly understood a
target’s affective state did not only depend on the observer’s
general ability to recognize facial emotions, or the target’sgeneral
ability to express their emotion, but also on how well a particular
observer could “tune in”to a particular target’s affect.
Changes in interpersonal attraction. Overall, observers conducted
more button presses to “approach”the observed targets after
than before emotion observation (T[39] =3.7, P<0.001; please
see Table S1 for results for self-reported interpersonal attrac-
tion). This is in line with previous research that shows that fa-
miliarity increases interpersonal attraction (e.g., 41). However,
the critical question in the current study was whether emotion
observation can lead to changes in interpersonal attraction that
differ between observers, such that one observer feels more
attracted to a particular target after emotion observation
whereas another observer feels less attracted to the same target,
even though both observers saw exactly the same behavior. In-
triguingly, this was the case. Between-subject variability of the
number of button presses observers conducted to approach a
given target (measured as the width of the 66% interval of button
presses for each target) was significantly larger after emotion
observation (mean width of the 66% interval for each target,
7.4 ±0.6 button presses) than before emotion observation
(mean width of the 66% interval for each target, 6.2 ±0.4
button presses) (T[5] =2.7, P=0.040; Fig. 2; please see Table S1
for results for self-reported interpersonal attraction).
Self-reported confidence and individual changes in interpersonal
attraction. Next, we asked whether the observed changes in in-
terpersonal attraction were predicted by the observers’confidence
that they correctly understood the targets’affective state. To test
this, we computed partial correlations between each observer’s
confidence ratings and postobservation attraction scores for each
target. Importantly, to ensure that this correlation was not driven
by the observer’s initial attraction toward the targets, any variance
that could be explained by preobservation attraction (Table S2)
was removed from confidence ratings and postobservation attrac-
tion scores. This revealed significant positive partial correlations
between the observer’s confidence and postobservation attraction
both at the behavioral–motivational level (approach behavior)
and at the level of self-report (Table 2). To further control for
potential differences between targets in physical attractiveness
and facial expressivity, we removed average confidence ratings
and average attraction scores for each target (“general target
effects,”ref. 21) from each individual dataset and performed the
same partial correlation analyses as above. As predicted, partial
correlations between self-reported confidence and self-reported
interpersonal attraction remained significant after general target
effects had been removed (Table 2). This indicates that the link
between confidence and changes in interpersonal attraction
cannot be fully explained by a target’s general attractiveness or
expressivity.
Experiment II.
Behavioral data: Emotion judgments, self-reported confidence, and individual
changes in interpersonal attraction. Behavioral data of experiment II
largely replicated those of experiment I. Observers (28 women, 24
men) correctly labeled the target’s emotional state in the majority of
trials (mean hit rate 75 ±2.5%, T[51] =30, P<0.001), and their
self-reported confidence closely reflected the actual correctness of
their emotion judgments for a given target (mean back-transformed
r=0.42 ±0.01, T[51] =4.1, P<0.001). Furthermore, there was a
significant observer-by-target interaction in self-reported confidence
similar to that observed in experiment I (two-way ANOVA with
between-subject factor observer [52 levels], within-subject factor tar-
get [6 levels], and the observers’confidence ratings as dependent
variable [n=52 ×6×16 =4,992 trials]; main effect target, F[5,255] =
41, P<0.001, eta
2
=45; observer-by-target interaction, F[255,4624] =
2.9, P<0.001, eta
2
=0.14). As in experiment I, observers
conducted more button presses to approach the observed tar-
gets after than before emotion observation (T[51] =2.7,
P=0.005), and between-subject variability of the number of button
presses observers conducted to approach a given target increased
significantly from preobservation (mean width of the 66% interval
for each target 7.8 ±0.5 button presses) to postobservation (mean
width of the 66% interval for each target 9.1 ±0.5 button presses)
Fig. 2. Interindividual variability in the observers’approach behavior toward the targets before and after emotion observation. Data were first centered,
separately for each target and pre- and postobservation runs (i.e., the mean number of button presses executed for each target in each run was set to 0), and
then averaged across all targets, separately for pre- and postobservation runs.
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(T[5] =4.2, P<0.001; Fig. 2; please see Table S1 for results for self-
reported interpersonal attraction). Finally, the pattern of correla-
tions between self-reported confidence and interpersonal attraction
closely reflected that of experiment I, with partial correlations be-
tween self-reported confidence and self-reported interpersonal at-
traction remaining significant after general target effects had been
removed from each individual dataset (Table 2).
Neural confidence signals in the brain’s reward system. In the first step
of the fMRI data analysis, we examined whether the observers’
subjective confidence that they correctly understood the target’s
affective state was associated with neural activity in reward-related
brain regions. For this, we used whole-brain univariate correlation
analyses. First, we computed the correlation between each ob-
server’s trial-by-trial confidence ratings and trial-by-trial neural
activity during the facial observation period. This revealed a sig-
nificant positive correlation between self-reported confidence and
neural activity in the right ventral striatum (x=18, y=6, z=−15;
T[51] =5.7, P=0.022, familywise error [FWE]-corrected at voxel
level; Fig. 3 Aand B). Second, we computed the correlation be-
tween each observer’s trial-by-trial confidence ratings and trial-by-
trial neural activity during the emotion judgment period. This
revealed a significant positive correlation between self-reported
confidence and neural activity in the mOFC (x=−3, y=39, z=
−18; T[51] =4.9, k=503, P<0.001, FWE-corrected at cluster
level; Fig. 3 Eand F) (please see Table S3 for brain regions
outside the reward system where neural activity increased signifi-
cantly with increasing subjective confidence).
ROI analysis: Neural confidence signals in the brain’s reward system and
individual changes in interpersonal attraction. Having shown that the
observer’s self-reported confidence reflected neural activity in
the brain’s reward system, we next asked whether these confi-
dence-related neural signals would predict changes in interper-
sonal attraction. For this, we averaged the neural activity within
the two clusters in the ventral striatum and mOFC, separately for
each observer and target, and performed a partial correlation
analysis between this neural activity and the observer’s post-
observation attraction scores for each target (with variance
explained by preobservation attraction removed from both var-
iables). This revealed significant positive partial correlations
between confidence-related neural activity and the observer’s
approach behavior in both clusters (Fig. 3 Cand Gand Table 2).
Again, these partial correlations remained significant when
general target effects (i.e., average levels of confidence-related
neural activity and average attraction scores for each target
across all observers) were removed from each individual dataset
(Fig. 3 Dand Hand Table 2). The only brain region outside the
reward system that showed a similar pattern of partial correla-
tions was the lingual gyrus (Table S4).
To ensure that the observed partial correlations between confi-
dence-related neural activity and postobservation attraction were
not due to the fact that we used the observer’s confidence ratings
(which we already knew predicted changes in interpersonal attrac-
tion) to identify clusters in the reward system that showed confide nce-
related neural activity, we performed a split-half cross-validation
analysis (42, 43) (see SI Materials and Methods for details). This
analysis replicated the significant partial correlations between con-
fidence-related neural activity and postobservation attraction in the
ventral striatum and mOFC (ventral striatum, mean back-trans-
formed r=0.17, T[51] =1.9, P=0.033; mOFC, mean back-
transformed r=0.23, T[50] =2.9, P=0.003) and the significant
partial correlation between confidence-related neural activity and
postobservation attraction after general target effects had been re-
moved in the mOFC (mean back-transformed r=0.11, T[50] =1.7,
P=0.047). This indicates that the observed correlations between
neural confidence signals in the reward system and interpersonal
attraction cannot be explained by nonindependencies.
NOE matching. In the next step of our fMRI data analysis, we asked
whether the observer’s self-reported confidence and neural
confidence signals are linked to the degree to which the patterns
of neural activity elicited during emotion observation matched
those associated with the observer’s own emotional experience.
For this, we used searchlight-based MVPA (44, 45), a technique
that allows estimation of the level of correspondence between
local patterns of neural activity within spherical neighborhoods
(the “searchlights”) across the entire brain volume (we refer to
this level of correspondence as neural observation–experience
matching; please see Materials and Methods for details). NOE-
matching maps were computed for each trial and observer.
These maps were then subjected to whole-brain correlation
analyses with (i) the observer’s trial-by-trial confidence ratings
Table 2. Partial correlations between self-reported confidence, neural confidence signals in the brain’s reward system, neural
observation–experience matching in the anterior insula, and interpersonal attraction after emotion observation
Postobservation attraction
Partial correlations
Residual partial correlations
(general target effects removed)
Approach
behavior
Willingness
to meet
Expected
intimacy of
communication
Approach
behavior
Willingness
to meet
Expected
intimacy of
communication
Measure r T r T r T r TrTrT
Experiment I (n=40)
Self-reported confidence 0.45* (4.5) 0.58* (5.4) 0.61* (7.5) 0.03 (0.3) 0.23* (1.7) 0.24* (1.7)
Experiment II (n=52)
Self-reported confidence 0.44* (4.5) 0.45* (5.2) 0.59* (6.9) 0.17 (1.6) 0.19* (1.7) 0.30* (2.8)
Confidence signals in VS 0.20*
,†
(2.2) 0.06 (0.1) 0.12 (1.3) 0.21* (2.0)
Confidence signals in mOFC 0.26*
,†
(3.1) −0.03 (−0.3) 0.01 (0.1) 0.22*
,†
(2.8)
NOE matching (cluster 1) 0.10 (1.2) 0.02 (0.2) 0.04 (0.4)
NOE matching (cluster 2) 0.11 (1.2) 0.00 (0.0) −0.02 (−0.2)
Variance that can be explained by the observer’s initial interpersonal attraction toward the targets is removed from both variables in all analyses. Residual
correlations (i.e., correlations after general target effects are removed from both variables) are only reported for significant main correlations. r, back-
transformed average partial correlation coefficients; T,tvalues at random-effects group level; VS, ventral striatum. Please see Fig. 3 for the location of the
two clusters in the anterior insula.
*Significant correlations (P<0.05, one-tailed).
†
Correlations that remain significant in the split-half analysis (P<0.05, one-tailed) (see SI Materials and Methods for details).
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and (ii) the observer’s trial-by-trial confidence-related neural
activity in the reward system (ventral striatum and mOFC, re-
spectively). This revealed (i) a significant positive correlation
between the observer’s self-reported confidence and NOE
matching in a cluster in the anterior insula (x=−33, y=21, z=
−9; T[51] =3.7, P=0.001, FWE-corrected at cluster level) and
(ii) a significant positive correlation between neural confidence
signals in the mOFC and NOE matching in a second, adjacent,
cluster in the anterior insula (x=−27, y=30, z=−12; T[51] =
4.2, P=0.001, FWE-corrected at cluster level) (Fig. 4; please
note that the actual overlap of the two clusters in the anterior
insula is much larger than shown in Fig. 4 because each voxel
represents a 9-mm spherical searchlight). No significant corre-
lation was observed between confidence-related activity in the
Fig. 3. Confidence-related neural activity in the brain’s reward system and individual changes in interpersonal attraction. (A) Brain regions where neural activity
during the facial observation period covaried with self-reported confidence. (B) Scatter plot illustrating the correlation between neural activity in the right ventral
striatum and self-reported confidence. (C) Scatter plot illustrating the partial correlation between confidence-related neural activity in the right ventral striatum and
the observer’s postobservation approach behavior (variance that can be explained by preobservation approach behavior is removed). (D) Scatter plot illustrating the
partial correlation between confidence-related neural activity in the right ventral striatum and the observer’s postobservation approach behavior (variance that can be
explained by preobservation approach behavior and general target effects are removed). (E) Brain regions where neural activity during the emotion judgment period
covaried with self-reported confidence. (F) Scatter plot illustrating the correlation between neural activity in the mOFC and self-reported confidence. (G) Scatter plot
illustrating the partial correlation between confidence-related neural activity in the mOFC and the observer’s postobservation approach behavior (variance that can be
explained by preobservation approach behavior is removed). (H) Scatter plot illustrating the partial correlation between confidence-related neural activity in the mOFC
and the observer’s postobservation approach behavior (variance that can be explained by preobservation approach behavior and general target effects are removed).
Note: SPMs (height threshold T[51] =5.5, P=0.05, FWE-corrected at voxel level in A; height threshold T[51] =3.2, extent threshold k=100 voxels, P=0.001, FWE-
corrected at cluster level in E) are superimposed onto a rendered surface and coronal/axial sections of a T1-weighted map of a standard brain (MNI space). For the
scatter plots in Band F, trialwise data of each observer (i.e., 96 data points) were z-standardized and rank-ordered according to BOLD parameter estimates and then
averaged across observers, separately for each rank. For the scatter plots in C,D,G,andH, targetwise data of each observer (i.e., 6 data points) were z-standardized
and rank-ordered according to BOLD parameter estimates and then averaged across observers, separately for each rank. Error bars represent SEMs.
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ventral striatum and NOE matching (Table S5). As above, a
split-half cross-validation analysis, performed to ensure that the
correlation between NOE matching and confidence-related
neural activity in the mOFC was not due to nonindependencies,
replicated this effect (x=−27, y=30, z=−12; T[51] =3.5, P=
0.040, FWE-corrected at cluster level).
ROI analysis: NOE matching and individual changes in interpersonal
attraction. Finally, we tested whether NOE matching in the an-
terior insula also predicted changes in interpersonal attraction
directly. Interestingly, this was not the case (Table 2). This is in
line with our hypothesis that NOE matching is not directly as-
sociated with changes in interpersonal attraction but that these
changes are mediated by neural confidence signals in the brain’s
reward system.
Discussion
The goal of this study was to examine whether the human brain
possesses a neural mechanism that attracts individuals to other in-
dividuals whose nonverbal signals they can easily understand. To
pursue this goal, we conducted two experiments. In line with our
first prediction, data of experiment I and behavioral data of ex-
periment II show that an individual’s interpersonal attraction to-
ward another person can change after a few minutes of emotion
observation, depending on the individual’s subjective confidence
that they correctly understood the other’s affective state. fMRI data
from experiment II provide an initial understanding of the neural
processes that mediate between subjective understanding and in-
terpersonal attraction. First, we found that individual changes in
interpersonal attraction were predicted by confidence-related neu-
ral signals in the ventral striatum and the mOFC, core regions of the
brain’s reward system (12, 13). Second, we found that both the
observer’s subjective confidence and neural confidence signals in
the mOFC covaried with the degree of similarity between patterns
of neural activity elicited during emotion observation and those
associated with the observer’s own emotional experience (NOE
matching). This suggests that an individual’s confidence in their
interpersonal judgments of affect, and ensuing changes in in-
terpersonal attraction, is partly determined by how well the other
person’s affective behavior matches the observer’s neural rep-
resentation of the underlying state.
Confidence Signals in the Brain’s Reward System. The first important
finding of the current study is that confidence-related neural activity
in the brain’s reward system can act as an intrinsic reward signal,
predicting individual changes in interpersonal attraction. Previous
studies have shown that neural activity in the ventral striatum signals
internal confidence when subjects make perceptual judgments
about physical stimuli such as circles, lines, and moving dots (23,
24). The current study shows that neural activity in the ventral
striatum/mOFC covaries with subjective confidence when partici-
pants try to infer another person’s current affective state from their
facial expression. This underlines a modality-independent role of
the ventral striatum/mOFC in signaling confidence. Furthermore,
confidence-related neural activity in the ventral striatum/mOFC
predicted changes in the observer’s interpersonal attraction toward
the target, providing behavioral evidence that confidence-related
activity in the brain’s reward system can act as a positive
reinforcement signal (24).
Three details of these findings are worth further discussion. First,
we observed a temporal dissociation of neural confidence signals in
the ventral striatum and in the mOFC: Confidence-related neural
activity in the ventral striatum occurred during the facial observa-
tion period of each trial, whereas confidence-related neural activity
in the mOFC occurred later, during the emotion judgment period.
This is in line with previous work that has shown a similar temporal
dissociation of neural activity in the ventral striatum/mOFC during
face evaluation, which has led to the suggestion that the mOFC has
a particular role in holding the outcome of stimulus evaluations
online for further processing (9).
Second, neural confidence signals in both the ventral striatum
and the mOFC were more closely associated with subsequent
changes in the observer’s approach behavior toward the target
Fig. 4. Neural observation–experience matching (NOE matching), self-reported confidence, and neural confidence signals in the mOFC. (A) Brain regions
where NOE matching covaried with self-reported confidence (orange, cluster 1) and neural confidence signals in the mOFC (red, cluster 2), respectively. (Band
C) Scatter plots illustrating the correlation between NOE matching in each cluster and self-reported confidence (orange)/neural confidence signalsinthe
mOFC (red). Note: SPMs (height threshold T[51] =3.2, extent threshold k=10 voxels, P=0.05, FWE-corrected at cluster level) are superimposed onto a
rendered surface and coronal/axial sections of a T1-weighted map of a standard brain (MNI space). For the scatter plots in Band C, trialwise data of each
observer (i.e., 96 data points) were z-standardized and rank-ordered according to NOE matching and then averaged across observers, separately for each
rank. Error bars represent SEMs.
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than with changes in self-reported interpersonal attraction,
whereas the observer’s subjective confidence that they correctly
understood the target’s affective state was more closely associ-
ated with changes in self-reported interpersonal attraction. This
is in line with previous studies that have stressed the role of the
ventral striatum in motivated behavior (6) and suggests a partial
dissociation between neural processes underlying motivational–
behavioral and cognitive components of interpersonal attraction.
Third, confidence-related activity in the mOFC, but not in the
ventral striatum, reflected the degree of similarity between pat-
terns of neural activity elicited during emotion observation and
those associated with the observer’s own emotional states (NOE
matching). Again, this supports a particular role of the mOFC in
holding the outcome of stimulus evaluations online for further
processing (9).
“Common Coding”and Success of Affective Communication. The
second important finding of the current study is that both the ob-
server’s subjective confidence and neural confidence signals in the
mOFC reflected the level of correspondence between patterns of
neural activity elicited in the anterior insula during emotion ob-
servation and those associated with the observer’s own emotional
experience (NOE matching). This extends previous studies that
observed overlapping activity in the anterior insula when partici-
pants experienced and observed pain (46, 47), disgust (30), or joy
(32) and more recent studies that used MVPA to examine whether
one’s own pain and emotional experience and another person’spain
and emotional experience are encoded in similar patterns of neural
activity (37, 38).
Importantly, the results of the current study not only provide
evidence that confidence can signal correspondence, they also
indicate that confidence and correspondence covary across in-
dividual observer–target pairs. This provides empirical evidence
for theoretical models of social interaction and communication
that propose that the more similar the observer’s and the target’s
internal model of a given behavior, the easier it should be for the
observer to understand the target’s inner state and to react ac-
cordingly (25, 48).
A similar link between correspondence of neural activity and
success of communication has been observed in the medial
prefrontal cortex (mPFC). A study using pseudohyperscanning
(a technique where a “sender”and a “perceiver”are scanned
one after the other in the same scanner but are connected by
audio or video recordings such that their brain activity can be
temporally aligned after scanning) showed that neural activity in
the mPFC is time-locked between speakers and listeners in-
volved in verbal communication. Strikingly, the listener’s se-
mantic understanding of the story told by the speaker varied as a
function of the degree to which neural activity in the mPFC of
the listener’s brain at time point t
1
predicted the speaker’s neural
activity in that region at time point t
2
(49). However, in that
study, all stories were told by a single speaker, so it remains
unclear whether there was a specific listener-by-speaker in-
teraction, similar to the observer-by-target interaction observed
in the current study.
Success of Communication and Interpersonal Attraction. Until re-
cently, neuroscientific research into interpersonal attraction has
been guided by the view that an individual’s primary goal when
evaluating other individuals must be to identify potential mating
partners who possess high genetic fitness and fertility (e.g., 11, 50).
This research builds on a large literature that links physical at-
tractiveness to genetic fitness (for a review, see, e.g., ref. 51).
However, for species that live in complex environments that re-
quire social interaction and cooperation to maximize reproductive
success, being able to identify the right cooperation partners might
be equally important. The current study provides evidence that
potential cooperation partners qualify as right not only by their
willingness and competence to cooperate (14–17) (for a theoret-
ical account see, e.g., ref. 52) but also by the degree to which their
communication signals can be reliably decoded by the other in-
dividual. Importantly, unlike interpersonal attraction due to a
target’s fitness-signaling physical features, which seems to be fairly
consistent across perceivers (53, 54), the confidence-dependent
adjustment of interpersonal attraction found in the current study
seems to be specific for specific interaction partners. Indeed, ob-
servers showed more disagreement about which target they felt
attracted to after than before emotion observation. In social psy-
chology it has long been recognized (21) that attraction between
individuals is not only determined by general target effects (e.g., a
target’s physical attractiveness) but also by specific perceiver-by-
target effects (relational effects) such as the match between a
target’s affective behavior and a perceiver’s neural vocabulary
we describe here. Interestingly, it has been suggested that such
interaction partner-specific effects could underlie the forming
of social cliques within larger groups (1, 55). The current study
provides evidence that the brain’s reward system, signaling
how well one’s neural vocabulary is suited to decode another
person’s behavior, might play an important role in these social
processes.
Conclusion. In sum, we have shown that subjective understanding
during social interaction can modulate interpersonal attraction. In-
terestingly, the findings of the current study suggest that the neural
mechanisms underlying individual adjustments of interpersonal at-
traction during social encounters might act through internal reward
signals that are partly independent of external feedback, which
makes them perhaps less prone to cheating by potential cooperation
partners. To investigate the interaction between intrinsic confidence
signals and other—honest or manipulative—signals sent back and
forth between communication partners and to examine the neural
determinants of the dynamics of human social relations in larger
groups (“social connectomes”) remain challenging tasks for future
studies. The current study suggests that mutual understanding is an
important factor in interpersonal attraction, and that further re-
search into the role of a common neural vocabulary in interpersonal
attraction will lead to a better understanding of the neurobiological
factors that define human social relations.
Materials and Methods
Participants. Forty volunteers (21 women, 19 men, all Caucasian, mean age
22.3 y, range 18–30 y) completed experiment I, and 54 volunteers completed
experiment II. In experiment II, data of two participants were discarded
because of estimated head movements >3 mm within a functional imaging
run. The final sample in experiment II comprised 52 participants (28 women,
24 men, all right-handed and Caucasian, mean age 25.3 y, range 18–35 y).
Participants reported no history of neurological or psychiatric disorders and
had normal or corrected-to-normal vision. All participants gave written
consent before participation and both studies were approved by the local
ethics committee (Universität zu Lübeck).
Stimuli. Videos of women experiencing fear or sadness were recorded in a
previous fMRI study in which participants were asked to imagine and sub-
merge themselves into a cued emotional situation and to facially express their
feeling to their romantic partner (36). Using prerecorded videos of women
who experienced and facially expressed fear and sadness toward their ro-
mantic partner ensured that all participants saw exactly the same behavior,
and allowed us to exclude the possibility that individual changes in the
participants’interpersonal attraction toward the targets were due to dif-
ferences in the target’s behavior toward different participants. Women
were chosen as targets because women have been shown to express their
emotions more accurately than men (56, 57). For the current study, videos of
fear and sadness of six different women (all Caucasian, age 20–25 y) were
selected. Videos were cut into short clips, each covering the first 8 s of a 20-s
emotional period. The final set consisted of 48 different video clips (4 videos
for each target and emotion). Each of the 48 emotion video clips was shown
twice, resulting in a total of 96 emotion observation trials per participant.
For preexperiment familiarization with each target and the assessment of
Anders et al. PNAS Early Edition
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interpersonal attraction (see below), a still picture of each target was cut
from the original recordings, showing the target’s face during a 20-s
rest period.
Cover Story and Preexperiment Familiarization. Experimental procedures were
similar in both studies (Fig. 1). Upon arrival in the laboratory, participants
were told that the aim of the study was to investigate the relation between
response times and the neural processing of faces and emotional expres-
sions. Participants were then seated in front of a computer screen using
head phones and a chin rest to avoid distraction and to ensure that they
viewed all facial stimuli at the same distance. To support the cover story and
to familiarize participants with the targets, assessment of interpersonal at-
traction was preceded by a motor task in which participants were required
to press one of two response buttons in response to a visual cue (an arrow
pointing to the left or to the right, or a negative or positive word) as quickly
as possible. Response time trials were intermixed with a total of 96 short
presentations (200 ms) of still pictures of each target (16 presentations per
target) and targets were fully balanced over arrows and words, so that after
completion of the motor task, participants were well-familiarized with each
target. Participants in experiment I completed all parts of the study on the
same computer screen, and participants in experiment II completed all parts
except emotion observation and emotion experience (which were per-
formed during fMRI) on the same computer screen.
Assessment of Interpersonal Attraction. First, to assess the participants’in-
terpersonal attraction toward each target at the motivational–behavioral
level, participants were asked to imagine that they would approach targets,
one after the other, for a casual conversation. At the beginning of each trial,
a small picture of the target appeared on the computer screen (about 40%
of the original picture size) and participants were asked to increase the size
of the picture by repeatedly pressing a button (increase about 4% per
button press, no decrease button) until a pleasant conversational distance
was reached. This task is a modified version of a task originally introduced by
Aharon and colleagues (6) to measure the reward value of faces, except that
the task used in the current study additionally mimics approach behavior by
increasing the size of the target with each button press.
Second, to assess the participants’self-reported attraction toward each
target, they were shown a still picture of each target (1 s) followed by three
statements about their interpersonal attraction toward the target. The
statements were adapted from the “social attraction”items in McCroskey
and McCain (58) and related to the participant’s subjective motivation to
meet the target in real life (willingness to meet) and the participant’sex-
pectation that they could have an intimate communication with the target
(expectation of intimate communication) (Table 1). Participants were asked
to indicate how much they agreed with each statement on a Likert-type
7-point visual scale ranging from 1 (not at all) to 7 (definitely) by pressing
the corresponding key on a keyboard. In both tasks, targets were presented
in different random orders before and after emotion observation.
Emotion Observation. In experiment I, emotion observation was divided into
four runs. During each run, 24 video clips, balanced across the six targets and
two emotions, were presented in randomized order. Each video clip was
followed by an emotion judgment question [“Hat sie Furcht oder Trauer
empfunden?”(“Did she feel fearful or sad?”)]. After the participant had
entered their emotion judgment by pressing the corresponding button on
the keyboard, a confidence question [“Wie sicher bist Du, dass sie Furcht/
Trauer empfunden hat?”(“How confident are you that she felt fearful/sad?”)]
and a 5-point visual scale ranging from 1 (I am guessing) to 5 (I am absolutely
sure) appeared on the screen. Responses were entered by pressing the cor-
responding number on the keyboard. The orientation of the scale (increasing
confidence values from left to right or right to left) was balanced across
participants. Each trial terminated with an intertrial interval of 1 s, during
which a fixation cross was shown.
In experiment II, emotion observation was divided into eight runs. During
each run, 12 video clips, balanced across the six targets and two emotions,
were presented in randomized order. Each video clip was followed by a
fixation cross (1 s), an emotion judgment screen (2 s), and a confidence screen
(3 s). The emotion judgment screen showed the words “Trauer”(sadness) and
“Furcht”(fear) side by side at the center of the screen, indicating that the
participant should convey their emotion judgment by the response button in
their left or right hand, respectively. The order of emotion words (left or
right) was balanced across targets and emotions within participants. After
the participant had entered their response, an orange frame appeared
around the chosen emotion word, providing the participant with feedback
about their response. The confidence screen showed a five-dot visual scale
ranging from 1 (I am guessing) to 5 (I am absolutely sure). An orange dot at
the central position of the scale indicated the starting position of the cursor.
Participants were asked to move the orange dot to the left or to the right by
pressing the button in their left or right hand, respectively, to indicate their
confidence about their emotion judgment. The orientation of the scale
(increasing confidence from left to right or right to left) was balanced across
participants. Each trial terminated with an intertrial interval of 8 or 10 s,
during which a fixation cross was shown (Fig. 1).
Stimulus presentation and response logging were controlled with Pre-
sentation software (Neurobehavioral Systems).
Emotion Experience. After completion of the emotion observation part,
participants in experiment II participated in four additional fMRI runs during
which they were asked to experience and express fear and sadness them-
selves. Participants were informed that the experimental setup during this
part of the experiment would be very similar to that for the women they had
just observed, except that their facial expression would not be recorded, and
that they would be asked to submerge themselves into frightening or sad
situations and to feel and express their feelings as soon as they saw the
corresponding word [Furcht (fear) or Trauer (sadness)] on the screen (please
see SI Materials and Methods for details). Each run (two runs per emotion)
comprised four emotional periods (20 s) and five interspersed periods (20 s),
during which participants were asked to relax. The order of emotions was
balanced across participants.
MRI Data Acquisition. MRI data were acquired on a 3-T scanner (Siemens
MAGNETOM Trio). A T1-weighted magnetization-prepared rapid gradient-
echo (MPRAGE) image [MPRAGE, 176 sagittal slices, resolution 1 ×1×1mm
3
,
field of view (FOV) 256 ×256 mm
2
, flip angle 8°, inversion time 1,100 ms],
used for spatial normalization of individual data, and a T2-gradient echo
image [39 axial slices per volume, slice thickness 3 mm +1-mm gap, in-
terleaved order, in-plane resolution 3 ×3mm
2
, FOV 192 ×192 mm
2
, echo
time (TE) 1 5.19 ms, TE2 7.65 ms, repetition time (TR) 425 ms], used to
compute individual field maps for correction of image distortions, were
obtained from each participant before functional imaging. One hundred
forty-five T2*weighted echoplanar images (EPIs) covering the whole brain
were acquired during each emotion observation run, and 96 EPIs were ac-
quired during each emotion experience run (35 axial slices per volume, slice
thickness 4 mm +0.4-mm gap, interleaved order, in-plane resolution 3 ×
3mm
2
,FOV192×192 mm
2
, TE 30 ms, TR 2,000 ms, generalized autocalibrating
partially parallel acquisition, factor 2). Functional runs were preceded by five
functional images not included in the analysis to allow for T1 saturation.
Data Analysis. MRI data were preprocessed with SPM8 (Wellcome Department
of Imaging Neuroscience, University College London; www.fil.ion.ucl.ac.uk/
spm/software/spm8/). Preprocessing followed standard procedures and in-
cluded concurrent spatial realignment and correction of image distortions and
normalization into standard Montreal Neurological Institute (MNI) space (59)
at a spatial resolution of 3 ×3×3mm
3
using DARTEL (60). An additional
receiver coil sensitivity bias correction [using the New Segment tool of SPM8
with very light regularization (0.0001) and 60-mm smoothness] was conducted
after realignment and unwarping that corrected for differences in the scan-
ner’s bias correction between functional runs that occurred due to a technical
problem.
For the analysis of confidence-related neural activity, individual maps of
parameter estimates were computed for each participant based on a stan-
dard generalized linear model (GLM) that accounted for first-order auto-
correlations and low-frequency drifts (high-pass cutoff period 128 s). BOLD
(blood oxygen level-dependent) activity was modeled separately for each
emotion observation trial (n=96) using box car functions (three per trial),
convolved with a standard hemodynamic response function (hrf) that mod-
eled (i) video onset and duration (8 s), (ii) emotion judgment onset and du-
ration (3 s), and (iii) confidence rating onset and duration (3 s) (the latter were
included as regressors of no interest). For the analysis of neural observation–
experience matching (see below), a second set of maps of parameter esti-
mates (n=16, 20-s box car functions convolved with the hrf) was obtained for
the emotion experience runs of each participant.
For the whole-brain analysis of confidence-related neural activity, trial-by-
trial correlation maps (BOLD parameter estimates–self-reported confidence)
were computed for each participant, Fisher-transformed, spatially smoothed
(8-mm isotropic Gaussian kernel), and tested at random-effects group level
(using Tstatistics).
For the analysis of NOE matching, we used a linear support vector machine
(SVM) as implemented in LIBSVM (https://www.csie.ntu.edu.tw/∼cjlin/libsvm)
with a linear kernel and a hard margin. The searchlight radius was set to
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9 mm (123 voxels), and the searchlight was moved in steps of one voxel
through the entire brain volume. The classifier was trained on patterns of
neural activity associated with the participant’s own emotional experience
(n=8 samples per class) and tested on patterns of neural activity elicited
during video observation (n=96). To ensure that classification was based on
multivoxel patterns of neural activity and not on the average level of activity
within a sphere, the spatial mean of each local pattern was set to zero.
Because we reasoned that the classifier’s decision confidence (the distance
between test sample and decision border) would provide a more accurate
estimate of the level of correspondence between a test pattern and the
reference patterns than the classifier’s decision accuracy alone (which is bi-
nary variable), we computed a measure that reflected both the classifier’s
decision accuracy and the classifier’s confidence for this decision, the
weighted decision confidence. Mathematically, the weighted decision con-
fidence is the product of the classifier’s decision accuracy [which was set to
{1} for correct decisions (classifier’s decision matched the participant’s
judgment) and to {−1} for incorrect decisions (classifier’s decision did not
match the participant’s judgment)] and the classifier’s decision confidence
(which is defined for a linear SVM as the distance between the test sample
and the hyperplane that separates the two classes).
For the whole-brain analyses of neural observation–experience matching,
three trial-by-trial correlation maps (NOE-matching–self-reported confidence,
NOE-matching–neural confidence signals in the ventral striatum, NOE-matching–
neural confidence signals in the mOFC) were computed for each participant. As
above, these maps were Fisher-transformed, spatially smoothed (4-mm isotropic
Gaussian kernel; the small kernel size accounted for the fact that a searchlight-
based SVM already introduces some smoothness into the data), and tested at
random-effects group level (using Tstatistics) separately for each comparison.
Statistical significance of all random-effects statistical parametric maps
(SPMs) was assessed allowing for a probability of false positives of P=0.05,
corrected for multiple tests (familywise errors) across the whole volume
according to random-field theory (61). FWE correction was performed at
voxel level for the ventral striatum and at cluster level (using a height
threshold of T[51] =3.2 corresponding to P=0.001) for all other regions.
This accounted for the fact that clusters of activity were expected to be more
distributed in cortical regions than in the ventral striatum.
For the ROI analyses (i.e., neural confidence signals in the ventral striatum–
interpersonal attraction, neural confidence signals in the mOFC–interpersonal
attraction, NOE matching in the anterior insula–interpersonal attraction), tri-
alwise BOLD parameter estimates/trialwise weighted decision confidences
were extracted from the corresponding cluster (identified in the whole-brain
analyses) and averaged separately for each target and participant. For the
cluster in the mOFC, BOLD parameter estimates were extracted from a 3-mm
sphere centered at the peak voxel ([−339−18]) because the mOFC cluster was
a large cluster that extended into the dorsomedial OFC.
All ROI-based correlation analyses were checked for outliers, defined as
values that deviated more than three times the interquartile range from the
first or third quartile. No outliers were detected in the main analysis, and two
outliers were detected in the split-half cross-validation (indicated by degrees
of freedom less than n−1). For all ROI-based analyses, a probability of false
positives of P=0.05 (one-tailed) was accepted unless indicated otherwise,
and exact Pvalues are reported for P<0.200 and P>0.001.
Observer-by-target interactions in self-reported confidence were com-
puted with SPSS (version 22.0.0.1; IBM).
ACKNOWLEDGMENTS. The authors thank N. Dewies, E. Charyasz, and M. Erb
for help with data acquisition, and N. Weiskopf for technical expertise. This
work was funded by Bundesministerium für Bildung und Forschung (German
Federal Ministry of Education and Research) Grant 01GQ1105 (to S.A.).
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