Leader Emergence Through Interpersonal Neural Synchronization Jing Jiang

Jing Jiang (蒋静)a,b,c,d, Chuansheng Chen (陈传升)e, Bohan Dai (代博涵)a,b,f,g, Guang Shi (时光)a,b, Guosheng Ding (丁国盛)a,b, Li Liu (刘丽)a,b, and Chunming Lu (卢春明)a,b,1
a) State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875,
China;
b) Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China;
c) Berlin School of Mind and Brain, Humboldt University, 10117 Berlin, Germany;
d) Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany;
e) Department of Psychology and Social Behavior, University of California, Irvine, CA 92697;
f) Max Planck Institute for Psycholinguistics, Nijmegen 6500 AH, The Netherlands; and
g) International Max Planck Research School for Language Sciences, Nijmegen 6500 AH, The Netherlands

Edited by Susan T. Fiske, Princeton University, Princeton, NJ, and approved March 2, 2015 (received for review December 1, 2014)
The neural mechanism of leader emergence is not well understood. This study investigated (i) whether interpersonal neural
synchronization (INS) plays an important role in leader emergence,
and (ii) whether INS and leader emergence are associated with the
frequency or the quality of communications. Eleven three-member
groups were asked to perform a leaderless group discussion (LGD)
task, and their brain activities were recorded via functional near
infrared spectroscopy (fNIRS)-based hyperscanning. Video recordings of the discussions were coded for leadership and communication. Results showed that the INS for the leader–follower (LF)
pairs was higher than that for the follower–follower (FF) pairs in
the left temporo-parietal junction (TPJ), an area important for social mentalizing. Although communication frequency was higher
for the LF pairs than for the FF pairs, the frequency of leaderinitiated and follower-initiated communication did not differ significantly. Moreover, INS for the LF pairs was significantly higher
during leader-initiated communication than during follower-initiated
communications. In addition, INS for the LF pairs during leaderinitiated communication was significantly correlated with the
leaders’ communication skills and competence, but not their communication frequency. Finally, leadership could be successfully
predicted based on INS as well as communication frequency early
during the LGD (before half a minute into the task). In sum, this
study found that leader emergence was characterized by highlevel neural synchronization between the leader and followers
and that the quality, rather than the frequency, of communications was associated with synchronization. These results suggest
that leaders emerge because they are able to say the right things
at the right time.
leader emergence
|neural synchronization|
babble hypothesis|
quality of communication|
communication skill
L
eadership is a ubiquitous feature of all social species, including
human and nonhuman animals (1, 2). However, the neural
mechanism of leader emergence is still not well-understood.
Evolutionary theories suggest that, whereas both human and
nonhuman animals have evolved tendencies to compete for
dominance over access to survival-related resources (3–5), human leaders also play an important role in maintaining group
cohesion (6). Thus, human leaders need to take into account
not only their own needs but also the needs of their followers
to facilitate cooperation among group members (7–9). Interestingly, recent imaging evidence indicates that the neural activities
of two individuals are more synchronized when they perform
a cooperative rather than a competitive task (10). Moreover,
the level of interpersonal neural synchronization (INS) is closely
associated with the level of understanding between partners (11). It
is unknown, however, whether INS is involved in leader emergence.
Previous evidence has shown that communication plays an
important role in the increase of INS (12). However, the role
of communication in leader emergence has been extensively
debated. On the one hand, the so-called“babble”hypothesis
postulates that the most talkative member of a group often
becomes the group’s leader (13, 14). Indeed, there is evidence
that the frequency of communication (regardless of its usefulness) is a better predictor of leader emergence than other factors
such as the quality of communication (15). It is suggested that
communication frequency is probably one of the main factors
that increase the probability for initiating group action (16).
On the other hand, various recent studies have suggested that
the quality of communication is a more important predictor of
leader emergence than is the frequency of communication (17–
20). Consistent with this“quality-of-communication”hypothesis,
evidence shows that the frequency of communication has no
real effect on leader emergence (20). Although the frequency of
communication boosts leadership ratings, it does so only when
the content is of high quality (17). Furthermore, in task-oriented
groups, the quality rather than the quantity of communication is
a better predictor of leader emergence (18, 19). Research has
also suggested that high-quality communication tends to involve
a high level of mentalizing: i.e., the ability to read social situations and to alter one’s own behavior to fit in and act appropriately (21). Indeed, communication skills have been considered
to be an important part of leader competence in modern societies (22). It is likely that leaders emerge when they possess tactful
communication skills and competence: i.e., being able to say the
right things at the right time.
Research is needed to investigate how communications are
related to INS, which in turn may be related to leader emergence. Considering the interactive nature of leader emergence,
Significance
Great leaders are often great communicators. However, little is
known about the neural basis of leader–follower communication. Only recently have neuroscientists been able to examine
interpersonal neural synchronization (INS) between leaders
and followers during social interactions. Here, we show that
INS is significantly higher between leaders and followers than
between followers and followers, suggesting that leaders
emerge by synchronizing their brain activity with that of the
followers. Moreover, the quality rather than frequency of the
leaders’ communications makes a significant contribution to
theincreaseofINS.Thisresultsupportsthe“quality of communication” hypothesis in leader emergence. Finally, our results
show that leadership can be predicted shortly after the onset
of a task based on INS as well as communication behaviors.
Author contributions: J.J., C.C., G.D., L.L., and C.L. designed research; J.J., B.D., and G.S.
performed research; J.J., C.C., B.D., G.S., and C.L. analyzed data; and J.J., C.C., and C.L.
wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
1
To whom correspondence should be addressed. Email: luchunming@bnu.edu.cn.
This article contains supporting information online atwww.pnas.org/lookup/suppl/doi:10.
1073/pnas.1422930112/-/DCSupplemental.
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it is imperative to adopt the “second-person approach”:i.e.,
measuring two or more persons’ brain activities simultaneously
(23). This approach is also termed “hyperscanning” and has
proven to be promising in the field of social neuroscience (23–25).
By using an EEG-based hyperscanning approach, recent evidence
showed that, during guitar playing, the a priori-assigned leaders
showed higher levels of delta-phase locking than did the followers
and that INS from the leaders to the followers was stronger than
that from the followers to the leaders (26, 27). Evidence further
showed implicit synchronization of both body movements and
neural activity between a priori-assigned leaders and followers
during social interactions (28). However, previous hyperscanning
studies did not examine the neural mechanism of leader–follower
(LF) communications and did not compare the INS between the
LF and the follower–follower (FF) pairs. Thus, it is still unknown
whether and how INS is involved in leader emergence. In addition,
EEG is sensitive to motor artifacts and suffers from poor spatial
resolution. In contrast, functional near infrared spectroscopy
(fNIRS) is more tolerant of movements and is able to measure
local hemodynamic effect. These advantages make it particularly
suitable for testing the role of communication in leader emergence
in a realistic situation.
This study examined whether and how INS was involved in
leader emergence by using the fNIRS-based hyperscanning
approach. During the experiment, three-person groups were
recruited to perform a leaderless group discussion (LGD) task.
This task has been used successfully in many studies to induce
a discussion-oriented, problem-solving situation (19). INS of neural
activity was computed. It was hypothesized that INS of the LF pairs
would be higher than that of the FF pairs. Based on the babble
hypothesis, it was expected that (i) leaders would initiate more
communications than the followers and (ii) the increased INS
for the LF pairs would be mainly due to the emerging leaders’
communication frequency and would occur in language-related
brain areas. Alternatively, based on the quality-of-communication hypothesis, leaders would not initiate more communications
than the followers, and INS for the LF pairs would be associated
with the emerging leaders’ communication skills and competence, rather than the frequency, and would occur in brain areas
associated with social mentalizing. Finally, using a Fisher linear
discrimination analysis, we investigated how early during the
LGD session the INS data and communication behaviors could
predict the emergence of leaders.
Results
Interpersonal Neural Synchronization. The experimental setup is
illustrated in Fig. 1A. For each session, three participants sat
face-to-face in a triangle and were given a topic for an LGD
(see Materials and Methodsfor details). Their brain activities
were simultaneously recorded with an fNIRS system (Fig. 1B).
The discussion was video-taped and coded by independent
judges for leadership, communication skills and competence,
initiation of communications, and frequencies of verbal and
nonverbal communications.
For the LF pairs, a significant INS increase compared with
the resting-state condition was identified at channel 6 (CH6),
which roughly covered the left temporo-parietal junction (TPJ)
[t
(10) =4.62,P=0.001, false discovery rate (FDR) correction]
(Fig. 1C). No INS increase was found for any channel of the FF
pairs (Fig. 1D). Group differences between the LF and FF pairs
were significant for CH6 (t
(20)=3.51,P=0.002), but not for any
other CHs.
To validate that the above results could not have been obtained
by chance, we assessed the likelihood of obtaining significant INS increases for any random pairings of the participants.
Specifically, we reanalyzed the data after randomizing the LF
pairing both within and between discussion groups. The first was
the within-group permutation: Each of the two followers was
assigned to be the“leader”and the INS data were reanalyzed.
The second approach was the between-group permutation: All
33 participants were randomly assigned to 11 three-member
groups, and the INS analysis was then reconducted. This permutation was conducted 1,000 times. Both approaches showed
no significant INS increases for any CHs. Fig. 1EandFandG
andHshows the results of typical within- and between-group
validation analyses, respectively. Complete results for CH6 from
1,000 permutations of between-group validation analyses are
shown inFig. S1. These results suggested that the significant INS
increase in the left TPJ was specific to the particular LF relationship in the LGD context.
Who Synchronized with Whom?Granger causality analysis (GCA)
was conducted on the time series of CH6 to determine whether
it was the leader who synchronized with the followers or whether
it was the other way around. One-sample t tests on the pairwiseconditional causalities showed that the mean causalities of both
directions were significantly higher than zero: from the leaders to
the followers (t
(10)=10.001,P<0 .001="" and="" br="" followers="" from="" the="" to="">the leaders (t
(10)=7.272,P<0 .001="" br="" however="" test="" two-samplet="">showed that the mean causality from the leaders to the followers
was significantly higher than that from the followers to the
leaders (t
(10)=2.177,P=0.027). These results indicated a more
important role of the leaders than the followers in the INS increase in the LF pairs at CH6.
Communication Behaviors and INS. Both verbal and nonverbal
communication frequencies were significantly higher for the LF
pairs than for the FF pairs: (t
(20) =3.873,P=0.001) for verbal
and (t
(20)=4.565,P<0 .001="" br="" communications="" for="" ig.="" nonverbal="">2A). However, the leaders did not differ significantly from the
followers in the frequency of communication initiation (t
(10) =
−1.602,P=0.125). To investigate whether the role of leaders in
communication initiation might have changed as the discussion
progressed, the initiation data were reanalyzed by the first and
the second halves of the LGD session. Still, no differences were
found between the LF and FF pairs: (t
(10) =−0.433,P=0.674)
Fig. 1. Experimental procedure and the increase of interpersonal neural
synchronization (INS). (A) For each group, three persons sat in a triangle.
Two cameras were placed in opposite positions. The figure shows two sample
frames from the cameras in the opposite directions. Participants were asked to
discuss a topic for 5 min and then to choose a leader to report their conclusion.
(B) The optode probe set was placed on the left frontal, temporal, and parietal
cortices. T3 corresponds to a position in the international 10–20 system.
(CandD) Shown are tmaps for results of the original pairs (i.e., real data).
(EandF) Shown aretmaps for the permutation results of pairs with a follower
from the same group randomly assigned as the leader. (GandH) Shown aret
maps for the permutation results of randomized pairs from across groups. [C,E,
andGaretmaps for averaged leader–follower (LF) pairs;D,F,andHaretmaps
for the follower–follower (FF) pairs.]
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PSYCHOLOGICAL AND
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for the first half and (t
(10) =−0.858,P=0.411) for the second
half of the LGD session (Fig. 2B). These results suggested that,
although communication frequency was higher for the LF pairs
than for the FF pairs, leaders and followers contributed equally
throughout the LGD session.
We next examined INS that accompanied different types of
communications (verbal, nonverbal, and no communications).
For the LF pairs, INS during verbal communications (INS-V)
differed significantly from both INS during nonverbal communication (INS-NV) (t
(10) =2.951,P=0.015) and INS when no
communications occurred (INS-NC) (t
(10)=2.758,P=0.02) (Fig.
2C). Fig. 3 shows the correspondence between INS (coherence
value) and video frame for a typical LF pair at CH6. No significant results were found for the FF pairs. Group difference in
INS-V between the LF and FF pairs was also significant (t
(20) =
3.178,P=0.005). No significant group differences were found
for INS-NV (t
(20)=−0.24,P=0.813) and INS-NC (t
(20)=0.982,
P=0.338). These results indicated that the INS difference was
specific for verbal communication between the leaders and
the followers.
In terms of the role of communication initiation, leaderinitiated communications induced a higher level of INS than the
ones initiated by the followers (t
(20)=2.176,P=0.042) (Fig. 2D).
This result suggested that leader-initiated communications were
likely to be of higher quality (and thus led to increased INS).
This conjecture was further supported by two other results. First,
leaders’ communication skills and competence were more highly
rated (M=25.279, SD=0.800) than those of the followers (M=
22.020, SD=1.112) (t
(20) =7.894,P<0 .001="" 2e="" br="" ig.="" second="">there was a significant correlation between INS during leaderinitiated communications and judge-rated leaders’ communication skills and competence (r=0.697,P=0.017) (Fig. 2F). The
correlation between INS during leader-initiated communications
and the leaders’ initiation frequency was not significant (r =
0.247,P=0.465). This difference in correlation coefficients was
in favor of the quality-of-communication hypothesis over the
babble hypothesis although a larger sample of leaders would be
needed to allow for a statistical test of the difference.
Prediction of Leadership.To investigate how early the leaders
emerged during the LGD, Fisher linear discrimination analyses
were conducted. Fig. 4Ashows the time course of the prediction
accuracy in the discriminant analysis based on the INS data,
which differentiated the LF pairs from the FF pairs. The analysis
included three indexes: sensitivity (percentage of LF pairs correctly predicted, red line), specificity (percentage of FF pairs
correctly predicted, blue line), and the generalization rate of
accuracy (overall proportions of LF and FF pairs correctly predicted, green line). A moving-window analysis (window size=9s)
revealed that the prediction accuracy was sporadic during the
initial period, but the prediction accuracy of all three indexes was
stably higher than the chance level starting at 23 s (P<0 .05="" br="">corrected by FDR) [see the purple section above the chancelevel (0.50) line in Fig. 4A)]. A similar discriminant analysis was
conducted based on the communication frequency (Fig. 4B). The
results showed that the prediction accuracy of all three indexes
was stably higher than the chance level starting at 29 s (P<0 .05="" br="">corrected by FDR) (see the purple section above the chancelevel line in Fig. 4B). In sum, the INS and communication frequency data were able to discriminate the leaders from the followers less than half a minute into the LGD task.
Discussion
This study used an fNIRS-based hyperscanning approach to test
the hypothesis that INS was involved in leader emergence. The
results demonstrated that INS increased from the baseline more
significantly for the LF pairs than for the FF pairs. Further
analysis revealed that, although the communication initiation
frequency of leaders and followers did not differ significantly,
leader-initiated communication induced greater INS than did
follower-initiated communication. The INS increase during leaderinitiated communications was also associated with leaders’
communication skills and competence. These results suggest that
quality rather than quantity (or frequency) of communication is
more important in leader emergence. These results are discussed
sequentially below.
First, results of this study confirmed our hypothesis that the
LF relationship in the LGD context would be characterized by
a high level of INS. We derived our hypothesis from integrating
Fig. 2. (A) Verbal and nonverbal communication frequencies during the
task. The averaged frequency of the two leader–follower (LF) pairs (black)
was higher than the frequency of the follower–follower (FF) pairs (white).
(B) There were no significant differences in leader-initiated (L→F) vs. follower-initiated (F→L) verbal communications. (C) LF pairs’ INS during verbal
communication (INS-V) was higher than INS for all other situations. NC, no
communication occurred; NV, nonverbal communication; V, verbal communication. (D) INS during leader-initiated communication was higher than
that during follower-initiated communication. (E) Leaders’ communication
skills and competence were more highly rated than those of the followers.
(F) INS during leader-initiated communication was positively associated with
ratings of communication skills and competence (Upper), but not with
leader-initiated communication frequency (Lower). *P<0 .05.="" br="">Fig. 3. The correspondence between INS at CH6 and coded communication
behaviors. (A) A time course of INS for one randomly selected LF pair. (B)The
corresponding communication behaviors coded from video frames. Blue
points, follower-initiated verbal communications; green points, nonverbal
communications; red points, leader-initiated verbal communications. The
sections of the line without color points represent no communications. The
numbers 1, 2, and 3 inAhighlight time points that correspond to videoframe examples inB.
4276 | www.pnas.org/cgi/doi/10.1073/pnas.1422930112 Jiang et al.
recent imaging evidence that cooperation between persons led to
a high level of INS (10, 28) with recent perspectives about human
leaders’ role as the coordinators who help their groups to solve
various tasks, including resource sharing and decision making
(9, 29). According to the service-for-prestige theory of leadership
(9), human leaders and followers are involved in reciprocal exchange: Leaders may incur costs to provide followers with public
goods, and, in return, followers incur costs to provide leaders
with prestige, particularly in a relatively small group. We interpret
the higher INS for the LF pairs as a reflection of their closer
cooperation and social exchange.
Second, we found that the level of INS was increased specifically during verbal communications between the leaders and
followers, not during nonverbal or no communications, nor for
any type of communications involving the FF pairs. This result
was consistent with previous studies showing that verbal communication was one of the main factors that affected leader
emergence (13, 17–19). The present results further suggest that
verbal communication affects leader emergence by modulating
the neural synchronization. Because of the importance of verbal
communications in INS, this particular route of leader emergence
may be specific to humans (e.g., the service-for-prestige theory) (9).
Nonhuman animals typically establish leadership via dominance
(e.g., displays of physical strength), so it would be interesting to
investigate whether they also show INS.
Third, although the leaders and followers contributed equal
numbers of communications, leader-initiated verbal communications were found to lead to higher INS than did followerinitiated ones. Moreover, the GCA results showed that INS was
bidirectional but was significantly stronger from the leaders to
the followers than the other direction. These results suggested
that dynamic social interactions played an important role in
leader emergence. Indeed, as Schilbach et al. (23) suggested,
dynamic social interaction is a key constituent of grasping the
minds of others. An action by an“initiator”may lead to closer
monitoring of the outcome of the interaction, including the
responses by other individuals (23). In our study, the leaders initiated the communications, monitored the followers’ responses,
and closely synchronized their brain activities with those of the
followers. This speculation was further supported by the significant correlation between communication skills and competence
and INS. It seems that a leader is someone who would say the
right things at the right time to increase neural synchronization with the followers.
Fourth, the increased INS for the LF pairs was found in the
left TPJ, but not in the language area [i.e., left inferior frontal
cortex (IFC)]. This result was consistent with previous evidence
that high quality of communication is associated with high-level
mentalizing (21), which was partly subserved by the left TPJ.
Specifically, previous evidence has shown that interpersonal coordination or communication is facilitated by the mutual abilities
to predict each other’s subsequent action (i.e., high-level mentalizing) (30). Researchers have debated about which specific
parts of the left or right TPJ or both are involved in mentalizing
and understanding and reasoning about the beliefs and intentions of others (31–33). In one study, a lesion in the left TPJ was
found to affect the representations of someone else’s beliefs
(33). In another study, the posterior part of the right TPJ and the
parietal cortex were found to be involved in social cognition and
memory retrieval whereas the anterior part of the right TPJ as
well as the motor cortex and insula were involved in attention
(32). Although the poor spatial resolution of fNIRS did not allow
us to precisely locate the position of the INS increase, the most
likely area would be the posterior part of the left TPJ (for highlevel mentalizing) because no motor cortex was involved in
this study.
Finally, discriminant analyses showed that, shortly after the
start of the LGD task, the INS data and communication
behaviors could successfully distinguish the LF from the FF
pairs. These results further supported the quality-of-communication hypothesis by suggesting that the communication
frequency matters when the quality is of high level (17). These
results also confirmed previous findings (26–28, 34) that
neural activity (as well as interactive communication behaviors)
could be used to differentiate reliably the leaders from the followers. It is worth noting that different studies have found different earliest time points for successful discrimination based on
neural activity: before the onset of the interactions in Sänger
et al. (26, 27) and Konvalinka et al. (34) and about half a minute
into the interaction in our study. One possible explanation of
these variations is that the time point for successful discrimination depends on how the leaders emerge. In Sänger et al. (26,
27), leaders were assigned a priori; in Konvalinka et al. (34),
leaders emerged through a number of repeated trials; and, in the
present study, leaders emerged during a single LGD task. Future
research should specifically examine the role of neural activity or
INS in predicting different types of leader emergence.
Several limitations of this study need to be noted. First, our
findings from the LGD task may not be generalized to other
types of situations for leader emergence. The process of leader
emergence from a free discussion among equals (all college
students) may be different from one involving members who are
of different ages, genders, social status, etc. In addition, the
phenomenon of INS may also be different for leader emergence
than for situations with a leader assigned a priori, as discussed
earlier. Second, our sample size was adequate for the examination of group differences, but not as satisfactory for individual
differences in leaders. Similarly, the statistical power was limited
when we tested the babble hypothesis because of both the small
sample size and the somewhat limited verbal behaviors from the
short period of the LGD task. Third, we did not measure other
important characteristics of leadership, such as charisma (35),
which should be considered in future research for their role in
INS. Finally, because of the poor spatial resolution of fNIRS,
it was difficult to identify exactly which brain areas were responsible for the responses at CH6.
In summary, leadership is an important feature of human society, but little is known about the neural basis of leader emergence. Using the fNIRS-based hyperscanning approach in a
realistic interpersonal-communication context, the current study
found evidence that human leaders cooperated with their followers to achieve group decision by synchronizing their brain
Fig. 4. Time course of prediction accuracy. (A) Prediction results based on
the cumulative INS data. (B) Prediction results based on cumulative communication frequency. There were a total of 274 time points forAafter
shifting 6 s toward the left due to fNIRS signal delay (Materials and Methods) and 280 time points for B. The time courses were smoothed by using
a moving average method (span=9 s). The purple line above the chancelevel line indicates the time points where all three accuracy indexes were
significantly higher than the chance level (0.50).
Jiang et al. PNAS | April 7, 2015 | vol. 112 | no. 14 | 4277
PSYCHOLOGICAL AND
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activities with those of the followers through their tactful communication skills and competence. We further found that it was
possible to predict leadership based on the INS data as well
as communication behaviors early in their interactions. These
findings contribute to the theoretical discussion about the importance of communications in leader emergence and advance our
understanding of the neural mechanism of leaderemergence. The
results also potentially may be used in neuro-feedback or neurointervention during leadership training.
Materials and Methods
Participants.Thirty-six healthy adults (mean age 22±2 y) participated in this
study. They were pseudorandomly split into 12 three-person groups. For
each group, the members had to be of the same sex (to avoid a potential
confound of intergender interactions) and were total strangers to one another. There were 6 female groups and 6 male groups. One female group
was excluded because of data collection failure.
Written informed consent was obtained from all participants. The study
protocol was approved by the Institutional Review Board of the State Key
Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University.
Tasks and Procedures.For each group, an initial resting-state session of 5 min
served as the baseline. During this session, the participants were required to
remain as motionless as possible with their eyes closed and mind relaxed (36).
After the resting-state session, each group was instructed to perform the
LGD task. Two additional 30-s resting-state periods (one at the initial phase
and the other at the ending phase of the LGD) were used to allow the imaging instrument to reach a steady state.
During the LGD, the three participants of each group sat face-to-face in
a triangle. Two digital video cameras were placed at opposite positions so
that all three participants could be recorded (see Fig. 1Afor two sample
frames). Participants received the following topic for discussion: “An airplane crash-landed on a deserted island. Only 6 persons survived: A pregnant
woman, an inventor, a doctor, an astronaut, an ecologist, and a vagrant.
Whom do you think should be given the only one-person hot-air balloon to
leave the island?”The participants were asked to read and think about the
topic for 5 min without interacting with one another. Afterward, each
group was instructed to discuss the topic for 5 min. Each group was then
required to choose a member to report their conclusion to the experimenter.
The reporting session lasted 1 min. The whole procedure was video recorded
for subsequent coding.
Determination of the Leaders and Evaluation of Communication Skills and
Competence.After the experiment, an additional group of eight graduate
students was recruited to view the video recordings of the discussion session
and to judge who the leader was for each group. Judges were asked to use
their own criteria to make the judgment. For each group, the member with
votes from more than half of eight judges was defined as the leader. The
average vote for the leaders was 77.3±15.6%. The intraclass reliability (ICC)
among judges was 0.874 (P<0 .001="" 11="" 9="" br="" for="" groups="" judges="" of="" the="">choice of the leader agreed with the group members’ own choice (i.e., the
person who gave the report). For subsequent analyses, we used the more
objective choices by the judges.
Judges were also asked to evaluate the communication skills and competence of each group member on a 5-point scale (Table S1). There were
seven aspects of communication skills and competence (group coordination,
active participation, new perspectives, input quality, logic and analytic
ability, verbal communication, and nonverbal communication). Judges were
given explanations of the above categories and a scoring guide (seeTable
S1for details). Interjudge reliability was determined by ICC, and it was satisfactory to high (ranging from 0.773 to 0.926) for all but one item (new
perspectives, ICC=0.412). Possible reasons for the judges’ lack of consensus
on“new perspectives”might be the low frequencies of relevant behavior or
ambiguity of this construct. This item was removed from further analyses.
For the remaining items, ratings from the eight judges were averaged for
each item. The final scale of communication skills and competence included
six items with high internal consistency (Cronbach alpha=0.930).
Coding of Communication Behaviors.Two additional coders, who were not
involved in the voting of leaders and the evaluations of communication skills
and competence, coded communication behaviors. We used new coders
to avoid the leader voting’s potential contamination of behavior coding.
Communication behaviors included verbal communications, such as turntaking and interjections, and nonverbal communications, such as orofacial
movements, facial expressions, and sign gestures. Each of the 280 s during
the LGD was coded as having either verbal communication, nonverbal
communication, or no communications. If both verbal and nonverbal behaviors occurred for a given second, the dominant behavior was coded.
The frequencies of verbal and nonverbal communications were calculated
as the proportions of time (out of the 280 s) when verbal and nonverbal
communications occurred, respectively. The intercoder reliability (based on
ICC) was 0.930 for verbal communications (vs. no communications) and 0.952
for nonverbal communications (vs. no communications).
In addition, the initiator of each occurrence of verbal communication was
also coded. The frequency of initiations for each member was calculated as
the ratio of time points where a member initiated a communication over the
total number of that member’s verbal communications (ICC=0.949).
FNIRS Data Acquisition.During the experiment, the participants sat in a quiet
room. An ETG-4000 optical topography system (Hitachi Medical Company)
was used to collect imaging data from the three participants of each group
simultaneously. Three sets of the same customized optode probes were used.
The probe was placed on the left hemisphere so as to cover both the left
inferior frontal cortex (an area important for language) (37) and the temporal-parietal junction (TPJ) (an area closely associated with social mentalizing) (31, 33).
The optode probes consisted of 10 measurement channels (four emitters
and four detectors, 30 mm optode separation). CH9 was placed just at T3 in
accordance with the international 10–20 system (Fig. 1B). The probe set was
examined and adjusted to ensure consistency of the positions among the
participants of each group and across the groups.
The absorption of near-infrared light at two wavelengths (695 and 830 nm)
was measured with a sampling rate of 10 Hz. The changes in the oxyhemoglobin (HbO) and deoxy-hemoglobin (HbR) concentrations were recorded
in each channel based on the modified Beer–Lambert law. This study focused
only on the changes in the HbO concentration, which was demonstrated to
be the most sensitive indicator of changes in the regional cerebral blood
flow in fNIRS measurements (38).
Imaging-Data Analysis.
Interpersonal neural synchronization.Data collected during the resting-state and
LGD sessions were entered into the analysis. During preprocessing, data in the
initial and ending periods (30 s resting state plus 10 s LGD, respectively) were
removed, leaving 280 s of data for each session. Wavelet transform coherence
(WTC) was used to assess the cross-correlation between two fNIRS time series
generated by pairs of participants as a function of frequency and time (39).
The wavelet coherence MatLab package was used (40) [for more thorough
information, please see Grinsted et al. (40) and Chang and Glover (41)].
Briefly, three HbO time series were obtained simultaneously for each CH
from the three participants of each group. WTC was applied to each pair of
the time series to generate 2D coherence maps. According to previous
studies (10, 12), the coherence value increases when there are interactions
between persons, compared with that during the resting state. Based on the
same rationale, the average coherence value between 0.02 and 0.2 Hz was
calculated. This frequency band also excluded the high- and low-frequency
noises, such as those associated with respiration (about 0.2–0.3 Hz) and
cardiac pulsation (about 1 Hz), all of which would lead to artificial coherence. Finally, the coherence value was time-averaged.
The averaged coherence value of the resting-state session was subtracted
from that of the LGD session, and the difference was used as an index of the
INS increase between two persons. Because each group had two LF pairs and
only one FF pair, the INS increases for the two LF pairs were averaged for
matched-samplet tests (SI TextandFig. S2). For each channel, after converting the INS increase into azvalue, a one-samplettest was performed on
thezvalue across the participant pairs, and twotmaps of the INS increase
(P<0 .05="" and="" br="" by="" corrected="" fdr="" for="" generated="" lf="" one="" pairs="" the="" were="">other for the FF pairs. Thetmaps were smoothed using the spline method.
Validation by randomizing the data.To verify that the INS increase was specific to
the LF relationship that emerged during the LGD, two validation approaches
were applied. The first was the within-group permutation: Each of the two
followers was assigned to be the“leader,”and the INS data were reanalyzed.
The second approach was the between-group permutation: All 33 participants were randomly assigned to 11 three-member groups, and the INS
analysis was then reconducted. This permutation was conducted 1,000 times.
Who synchronized with whom?For CHs that showed significant INS increases,
GCA was conducted to determine the direction of synchronization (i.e.,
whether it was the leaders who synchronized with the followers or the other
way around). GCA is a method that uses vector autoregressive models to
measure the causal relationship (i.e., pairwise-conditional causalities from the
4278 | www.pnas.org/cgi/doi/10.1073/pnas.1422930112 Jiang et al.
source to the target) between time series such as the fNIRS data (42). We
computed the pairwise-conditional causalities of both directions: from the
leaders to the followers and from the followers to the leaders. These two
causality indices were statistically tested to see whether they differed from
zero and from each other.
Communication Behaviors and INS.To confirm the contribution of communication to the INS increase during the LGD, the CHs that showed significantly
greater INS increases for the LF pairs than for the FF pairs were selected. First,
the time courses of INS in the selected CHs were downsampled to 1 Hz to
obtain point-to-frame correspondence between the signal’s time course and
video recordings. Second, the time points of the video were marked as
having either verbal or nonverbal or no communications. Third, the corresponding INSs were separately averaged to obtain three indexes: i.e., INS-V,
INS-NV, and INS-NC, for INS during verbal, nonverbal, and no communications, respectively. The INS data were adjusted for the delay-to-peak effect
in the fNIRS signal (about 6 s) (43). Finally, these indexes were statistically
compared for the LF and FF pairs separately (using a paired two-samplet
test), as well as between the LF and FF pairs (using an independent twosamplettest).
To examine the role of the leaders, further analyses were conducted
to clarify whether the results were driven by leader-initiated or followerinitiated communications and whether the increase of INS was associated
with the leaders’ communication skills and competence or communication
frequency. The results were threshholded atP<0 .05="" br="" corrected="" level="">Prediction of Leadership.The time course of INS for the LF and FF pairs during
the LGD session was baseline-corrected by subtracting their respective averaged INS during the resting state. Cumulative INS across the time was
calculated and then used as the neural-classification feature to classify the LF
and FF pairs: i.e., the type of relationship (i.e., LF or FL) was the classification
label. The cumulative INS at time pointnwas computed as a sum of the INS at
time points from 1 to n−1. The discriminant analysis was conducted for
each time point. A leave-one-out cross-validation method was used to obtain the prediction accuracy. Time courses were generated for three indexes
of prediction accuracy: sensitivity, specificity, and the generalization rate of
accuracy. Because the fNIRS signal needs 6 s to reach a peak value after the
presentation of a stimulus (43), the recorded time points did not match the
brain-activity time points (or behavioral time points, such as communications). To adjust for the delay, we deleted the first 6 time points, yielding
a total of 274 time points for INS. Then, a moving window of 9 s was used to
identify the time points when the prediction accuracy differed significantly
from a chance level (0.50). Similar analyses were conducted based on the communication frequency at each time point. Finally, a moving average method
(span =9 s) was used to smooth the time courses of prediction accuracy.
The prediction results based on moment-to-moment INS data and communication frequency are provided inFig. S3, which suggested that the cumulative data provided more stable prediction accuracy than the moment-tomoment data.
ACKNOWLEDGMENTS.This work was supported by National Natural Science
Foundation of China (31270023), National Key Basic Research Program of
China (973 Program, 2012CB720704), National Natural Science Foundation
of China (30900393), Fundamental Research Funds for the Central Universities (2013YB24), the Beijing Higher Education Young Elite Teacher Project, and
the Open Research Fund of the State Key Laboratory of Cognitive Neuroscience
and Learning.
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