Sample Library Research Brief Communications Neural Synchronization during Face-to-Face Communication Jing Jiang, Bohan Dai, Danling Peng, Chaozhe Zhu,

Sample Library Research Brief Communications

Neural Synchronization during Face-to-Face
Communication

Jing Jiang, Bohan Dai, Danling Peng, Chaozhe Zhu, Li Liu, and Chunming Lu
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, P.R. China

Although the human brain may have evolutionarily adapted to face-to-face communication, other modes of communication, e.g., tele-
phone and e-mail, increasingly dominate our modern daily life. This study examined the neural difference between face-to-face commu-
nication and other types of communication by simultaneously measuring two brains using a hyperscanning approach. The results
showed a significant increase in the neural synchronization in the left inferior frontal cortex during a face-to-face dialog between partners
but none during a back-to-back dialog, a face-to-face monologue, or a back-to-back monologue. Moreover, the neural synchronization
between partners during the face-to-face dialog resulted primarily from the direct interactions between the partners, including multi-
modal sensory information integration and turn-taking behavior. The communicating behavior during the face-to-face dialog could be
predicted accurately based on the neural synchronization level. These results suggest that face-to-face communication, particularly
dialog, has special neural features that other types of communication do not have and that the neural synchronization between partners
may underlie successful face-to-face communication.

Introduction
Theories of human evolution converge on the view that our brain
is designed to cope with problems that occurred intermittently in
our evolutionary past (Kock, 2002). Evidence further indicates
that during evolution, our ancestors communicated in a face-to-
face manner characterized by a behavioral synchrony via facial
expressions, gestures, and oral speech (Boaz and Almquist, 1997).
Plausibly, many of the evolutionary adaptations of the brain for
communication involve improvements in the efficiency of face-
to-face communication. Today, however, other communication
modes, such as telephone and e-mail, increasingly dominate the
daily lives of many people (RoAne, 2008). Modern technologies
have increased the speed and volume of communication, whereas
opportunities for face-to-face communication have decreased
significantly (Bordia, 1997; Flaherty et al., 1998). Thus, it would
be interesting to determine the unique neural mechanistic fea-
tures of face-to-face communication relative to other types of
communication.

Two major features distinguish face-to-face communication
from other types of communication. First, the former involves
the integration of multimodal sensory information. The part-
ner’s nonverbal cues such as orofacial movements, facial expres-

sions, and gestures can be used to actively modify one’s own
actions and speech during communication (Belin et al., 2004;
Corina and Knapp, 2006). Moreover, infants show an early spe-
cialization of the cortical network involved in the perception of
facial communication cues (Grossmann et al., 2008). Alteration
of this integration can result in interference in speech perception
(McGurk and MacDonald, 1976).

Another major difference is that face-to-face communication
involves more continuous turn-taking behaviors between part-
ners (Wilson and Wilson, 2005), a feature that has been shown to
play a pivotal role in social interactions (Dumas et al., 2010).
Indeed, turn-taking may reflect the level of involvement of a
person in the communication. Research on nonverbal commu-
nication has shown that the synchronization of the brain activity
between a gesturer and a guesser was affected by the level of
involvement of the individuals involved in the communication
(Schippers et al., 2010).

Despite decades of laboratory research on a single brain,
the neural difference between face-to-face communication
and other types of communication remains unclear, as it is
difficult for single-brain measurement in a strictly controlled
laboratory setting to reveal the neural features of communica-
tion involving two brains (Hari and Kujala, 2009; Hasson et
al., 2012). Recently, Stephens et al. (2010) showed that brain
activity was synchronized between the listener and speaker
when the speaker’s voice was aurally presented to the listener.
Furthermore, Cui et al. (2012) established that functional
near-infrared spectroscopy (fNIRS) can be used to measure
brain activity simultaneously in two people engaging in non-
verbal tasks, i.e., fNIRS-based hyperscanning. Thus, the cur-
rent study used fNIRS-based hyperscanning to examine the
neural features of face-to-face verbal communication within a
naturalistic context.

Received June 20, 2012; revised Sept. 3, 2012; accepted Sept. 17, 2012.
Author contributions: J.J., D.P., and C.L. designed research; J.J. and B.D. performed research; J.J., B.D., C.Z., and

C.L. analyzed data; J.J., L.L., and C.L. wrote the paper.
This work was supported by the National Natural Science Foundation of China (31270023), National Basic

Research Program of China (973 Program; 2012CB720701), and Fundamental Research Funds for the Central
Universities.

The authors declare no financial conflicts of interest.
Correspondence should be addressed to Chunming Lu, State Key Laboratory of Cognitive Neuroscience and

Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, P.R. China. E-mail:
luchunming@bnu.edu.cn.

DOI:10.1523/JNEUROSCI.2926-12.2012
Copyright © 2012 the authors 0270-6474/12/3216064-06$15.00/0

16064 • The Journal of Neuroscience, November 7, 2012 • 32(45):16064 –16069

Materials and Methods
Participants
Twenty adults (10 pairs, mean age: 23 � 2) participated in this study.
There were four male–male pairs and six female–female pairs, and all
pairs were acquainted before the experiment. The self-rated acquain-
tance level did not show significant differences between the partners (t(18)
� �0.429, p � 0.673). Written informed consent was obtained from all
of the participants. The study protocol was approved by the ethics com-
mittee of the State Key Laboratory of Cognitive Neuroscience and Learn-
ing, Beijing Normal University.

Tasks and procedures
For each pair, an initial resting-state session of 3 min served as a baseline.
During this session, the participants were required to keep still with their
eyes closed, relax their mind, and remain as motionless as possible (Lu et
al., 2010).

Four task sessions immediately followed the resting state session. The
four tasks were as follows: (1) face-to-face dialog (f2f_d), (2) face-to-face
monologue (f2f_m), (3) back-to-back dialog (b2b_d), and (4) back-to-
back monologue (b2b_m). It was assumed that the comparison between
f2f_d and b2b_d would reveal the neural features specific to multimodal
sensory information integration, and that the comparison between f2f_d
and f2f_m would reveal the neural features specific to continuous turn-
taking during communication. b2b_m served as a control. The sequence
of the four task sessions was counterbalanced across the pairs. For each
task session, there were two 30 s resting-state periods located at the
beginning and ending phases to allow the instrument to reach a steady
state. The overall procedures were video recorded.

The pairs of participants sat face-to-face during f2f_d and f2f_m (Fig.
1 A); during b2b_d and b2b_m, they sat back-to-back and could not see
each other (Fig. 1 B). Two hot news topics were used during f2f_d and
b2b_d and the participants were asked to talk with each other about the
topic for 10 min. The sequence of the two topics was counterbalanced
across the pairs. An assessment of the familiarity level of the topics was
performed using a five-point scale (1 representing the lowest level, and 5
representing the highest level). No significant differences were found
between f2f_d and b2b_d (t(19) � �0.818, p � 0.434) or between part-
ners, either during f2f_d (t(18) � �0.722, p � 0.48) or b2b_d (t(18) �
�0.21, p � 0.836). Additionally, the participants were allowed to use
gestures and/or expressions if they so chose during the dialogs.

Immediately after f2f_d and b2b_d, the participants were required to
assess the quality of their communication using the five-point scale de-
scribed above. A significant difference in the assessment scores between
f2f_d and b2b_d (t(9) � 2.449, p � 0.037) was found for the quality of the

communication, but no significant difference
in the assessment scores was found between the
two partners for either f2f_d (t(18) � 1.342, p �
0.196) or b2b_d (t(18) � 1.089, p � 0.291).
These results suggested that f2f_d represented
a higher quality of communication than b2b_d
and that the two participants of the pair had
comparable opinions about the quality of their
communication.

During f2f_m and b2b_m, one of the partic-
ipants was required to narrate his/her life expe-
riences to his/her partner for 10 min, while the
partner was required to keep silent during the
entire task and not to perform any nonverbal
communication. The sequence of narrators
was balanced across the pairs. To ensure the
listeners attended to the speakers’ speech dur-
ing these tasks, the participants were required
to repeat the key points of the speaker’s mono-
logue immediately after the task. All of the par-
ticipants were able to repeat the key points
adequately.

fNIRS data acquisition
The participants sat in a chair in a silent room
during the fNIRS measurements, which were

conducted using an ETG-4000 optical topography system (Hitachi Med-
ical Company). A group of customized optode probe sets was used. The
probe was placed only on the left hemisphere because it is well established
that the left hemisphere is dominant for the language function and that
the left inferior frontal cortex (IFC) and inferior parietal cortex (IPC)
form the most thoroughly studied nodes of the putative mirror neuron
system for joint actions, including verbal communication (Rizzolatti and
Craighero, 2004; Stephens et al., 2010).

Two optode probe sets were used on each participant in each pair.
Specifically, one 3 � 4 optode probe set (six emitters and six detector
probes, 20 measurement channels, and 30 mm optode separation) was
used to cover the left frontal, temporal, and parietal cortices. Channel 2
(CH2) was placed just at T3, in accordance with the international 10-20
system (Fig. 1C). Another probe set (two emitters and two detectors, 3
measurement channels) was placed on the dorsal lateral prefrontal cortex
(Fig. 1 D). The probe sets were examined and adjusted to ensure the
consistency of the positions between the partners of each pair and across
the participants.

The absorption of near-infrared light at two wavelengths (695 and 830
nm) was measured at a sampling rate of 10 Hz. Based on the modified
Beer–Lambert law, the changes in the oxyhemoglobin (HBO) and deoxy-
hemoglobin concentrations were obtained for each channel. Because
previous studies showed that HBO was the most sensitive indicator of
changes in the regional cerebral blood flow in fNIRS measurements, this
study only focused on the changes in the HBO concentration (Cui et al.,
2012).

Imaging data analysis
Synchronization. During preprocessing, the initial and final periods of the
data were removed, leaving 500 s of task data. Wavelet transform coher-
ence (WTC) was used to assess the relationships between the fNIRS
signals generated by a pair of participants (Torrence and Compo, 1998).
As previously indicated (Cui et al., 2012), WTC can be used to measure
the cross-correlation between two time series as a function of both fre-
quency and time (for more details about WTC, see Grinsted et al., 2004).
We used the wavelet coherence MatLab package (Grinsted et al., 2004).
Specifically, for each CH from each pair of participants during f2f_d, two
HBO time series were obtained simultaneously. WTC was applied to the
two time series to generate a 2-D coherence map. According to Cui et al.
(2012), the coherence value increases when there are cooperative tasks
between partners, but decreases when there are no tasks, i.e., resting-state
condition. Based on the same rationale, the average coherence value
between 0.01 and 0.1 Hz was then calculated to remove the high- and
low-frequency noise. Finally, the coherence value was time-averaged.

Figure 1. Experimental procedures. A, Face-to-face communication. B, Back-to-back communication. C, The first optode probe
set placed on the frontal, temporal, and parietal cortices. D, The second optode probe set placed on the dorsal lateral prefrontal
cortex.

Jiang et al. • Neural Synchronization during Communication J. Neurosci., November 7, 2012 • 32(45):16064 –16069 • 16065

The same procedure was applied to the other
conditions (f2f_m, b2b_d, b2b_m, and resting
state).

The averaged coherence value in the resting-
state condition was subtracted from that of the
communication conditions, and the difference
was used as an index of the neural synchroni-
zation increase between the partners. For each
channel, after converting the synchronization
increase into a z value, we performed a one-
sample t test on the z value across the partici-
pant pairs and generated a t-map of the neural
synchronization [p � 0.05, corrected by false
discovery rate (FDR)]. The t-map was
smoothed using the spline method.

Validation of the synchronization. To verify
that the neural synchronization increase was
specific for the pairs involved in the communi-
cation, the data for the 20 participants were
randomly paired so that each participant was
paired to a new partner who had not commu-
nicated with him/her during the task. The an-
alytic procedures described above were applied
to these new pairs. It was assumed that no sig-
nificant synchronization increase would be
found for any of the four communication
conditions.

Contribution to the synchronization. The
CHs that showed significant synchronization
increases during f2f_d compared with the
other types of communication were selected
for further analysis to examine whether face-
to-face interactions between the partners con-
tributed to the neural synchronization. First, to
identify the video frames corresponding to the
coherence time points, the time course of the
coherence values was downsampled to 1 Hz.
Second, for each pair of participants, the videos for f2f_d and b2b_d were
analyzed as follows: the time points of the video showing the interactions
between partners, i.e., turn-taking behavior, body language (including
orofacial movements, facial expressions, and gestures), were marked;
and the coherence values that corresponded and those that did not cor-
respond to these time points were separately averaged to obtain two
indexes, one for synchronization that occurred during the interaction
(SI) and another for synchronization that did not occur during the in-
teraction (SDI). Finally, SI and SDI were compared across the pairs using
a two-sample t test for each of the two tasks.

Prediction of communicating behavior. The predictability of communi-
cating behavior on the basis of neural synchronization during f2f_d was
examined. Equal numbers of SI and SDI data points were randomly
selected from the identified CHs. The coherence value was used as the
classification feature, whereas the SI and SDI marks were used as the
classification labels. Fisher linear discrimination analysis was used and
validated with the leave-one-out cross-validation method. Specifically,
for a total of N samples, the leave-one-out cross-validation method trains
the classifier N times; each time, a different sample is omitted from the
training but is then used to test the model and compute the prediction
accuracy (Zhu et al., 2008). For the outputs, the sensitivity and specificity
indicated the proportions of SI and SDI that were correctly predicted,
whereas the generalization rate indicated the overall proportions of SI
and SDI that were correctly predicted.

Results
Synchronization during
communication
During f2f_d, a higher synchronization was found in CH3 than
during the resting-state condition, suggesting a neural synchro-
nization increase in CH3 during f2f_d (Fig. 2 A). As shown in

Figure 1C, CH3 covered the left IFC.
No significant neural synchronization increase was found

during b2b_d, f2f_m, or b2b_m (Fig. 2B–D). Thus, the increase
of neural synchronization in the left IFC (i.e., CH3) was specific
for the face-to-face communication.

A further analysis of CH3, which showed a significant neural
synchronization increase during f2f_d, showed that the synchro-
nization increase during f2f_d differed significantly from that
during b2b_d (t(9) � 4.475, p � 0.002), but did not differ from
that during f2f_m (t(9) � 1.547, p � 0.156) or b2b_m (t(9) � 1.85,
p � 0.097) after an FDR correction at the p � 0.05 level.

In addition, during b2b_d, a lower synchronization was found
in CH13 than during the resting-state condition (Fig. 2C). How-
ever, the synchronization of CH13 during b2b_d did not differ
significantly from any other condition after an FDR correction at
the p � 0.05 level (f2f_d: t(9) � 2.877, p � 0.018; f2f_m: t(9) �
1.198, p � 0.262; b2b_m: t(9) � �0.474, p � 0.647).

Validation of the synchronization
No CHs showed a significant increase in neural synchronization
between the randomly paired participants under any of the four
communication conditions (Fig. 2E–H ).

Contribution to the synchronization
To specify which characteristics of f2f_d contributed to the neu-
ral synchronization in the left IFC (i.e., CH3), we further exam-
ined the time course of the coherence value for each pair of
participants. Two raters independently coded the SI and SDI for
the video of each pair. The intraclass reliability was 0.905 for f2f_d

Figure 2. Neural synchronization increase. t-maps are for the original pairs (A–D), random pairs (E–H ), face-to-face dialog (A,
E), face-to-face monologue (B, F ), back-to-back dialog (C, G), and back-to-back monologue (D, H ). The warm and cold colors
indicate increases and decreases in neural synchronization, respectively. The black rectangle highlights CH3, showing a significant
increase in neural synchronization during the face-to-face dialog.

16066 • J. Neurosci., November 7, 2012 • 32(45):16064 –16069 Jiang et al. • Neural Synchronization during Communication

and 0.932 for b2b_d. Further statistical tests of the coherence
value showed a significant difference between SI and SDI during
f2f_d (t(9) � 3.491, p � 0.007) but not during b2b_d (t(9) �
�0.363, p � 0.725), indicating that the synchronization in the left
IFC was primarily contributed to by the face-to-face interaction
rather than simply the verbal signal transmission (Fig. 3A). Fig-
ure 3B illustrates the distribution of SI across the time course for
a randomly selected pair of participants. Figure 3C focuses on a

portion of the time course and presents the recorded video im-
ages corresponding to SI, with most of the SI events being distrib-
uted around the peak or along the increasing portion of the time
course of the coherence value.

Two validation analyses were conducted. First, the coherence
value in CH3 was randomly split into two parts for each partici-
pant pair. A paired two-sample t test was then conducted to eval-
uate the difference between the two parts; no significant

Figure 3. Contributions of nonverbal cues and turn-taking to the neural synchronization during the face-to-face and back-to-back dialogs. A, Statistical comparisons between SI and SDI. Error
bars indicate SE. **p � 0.01. B, Distribution of SI (yellow points) across the entire time course of coherence values in a randomly selected pair of participants. C, A portion of the time course and the
corresponding video images recorded during the experiment. The dialog content at that point is transcribed in blue. R and L, Right and left persons, respectively. The type of communication behavior
is indicated in black below the image.

Jiang et al. • Neural Synchronization during Communication J. Neurosci., November 7, 2012 • 32(45):16064 –16069 • 16067

difference was found (t(9) � �0.513, p � 0.62). Second, two
additional CHs, i.e., CH13, which covered the premotor area, and
CH15, which covered the left IPC, were also examined using the
above procedure, and no significant difference between SI and
SDI was found during either f2f_d (CH13: t(9) � 1.004, p � 0.342;
CH15: t(9) � 1.252, p � 0.242) or b2b_d (CH13: t(9) � 0.067, p �
0.948; CH15: t(9) � 1.104, p � 0.298). These results suggested that
the synchronization increase in this region was primarily contrib-
uted to by the face-to-face interaction rather than simply the
verbal signal transmission.

Prediction of communicating behavior
The leave-one-out cross-validation showed that the average ac-
curacy of the prediction of communicating behavior during f2f_d
was 0.74 � 0.13 for the sensitivity, 0.96 � 0.07 for the specificity,
and 0.86 � 0.07 for the generalization rate. The statistical tests
showed that all three indexes exceeded the chance level (0.5)
(sensitivity: t � 5.745, p � 0.0001; specificity: t � 20.294, p �
0.0001; generalization rate: t � 16.162, p � 0.0001; Fig. 4). These
results suggested that the neural synchronization could accu-
rately predict the communicating behavior.

Discussion
The current study examined the unique neural mechanistic fea-
tures of face-to-face communication compared with other
types of communication. The results showed a significant in-
crease in the neural synchronization between the brains of the
two partners during f2f_d but not during the other types of
communication. Behavioral coupling between partners dur-
ing communication, such as the structural priming effect, has
been well documented: the two partners involved in a communi-
cation will align their representations by imitating each other’s
choice of grammatical forms (Pickering and Garrod, 2004). Re-
cent studies suggest that behavioral synchronization between
partners may rely on the neural synchronization between their
brains (Hasson et al., 2012). It was found that successful commu-
nication between speakers and listeners resulted in a temporally
coupled neural response pattern that decreased if the speakers
spoke a language unknown to the listeners (Stephens et al., 2010).
Moreover, neural synchronization between brains was con-
firmed in nonverbal communication protocols (Dumas et al.,

2010; De Vico Fallani et al., 2010; Schippers et al., 2010; Cui et al.,
2012). Thus, it can be concluded that the level of neural synchro-
nization between brains is associated with behavioral synchroni-
zation and underlies successful communication.

The present findings extend previous evidence by showing
significant neural synchronization in the left IFC during f2f_d
but not the other types of communication. Compared with
b2b_d, f2f_d involved verbal signal transmission and also non-
verbal signal transmission, including orofacial movements, facial
expressions, and/or gestures. This multimodal information
would facilitate the alignment of behavior between partners at
various levels of communication, resulting in higher-level neural
synchronization during f2f_d (Belin et al., 2004; Corina and
Knapp, 2006).

One possible explanation for this facilitation effect is the func-
tion of the action–perception system (Garrod and Pickering,
2004; Rizzolatti and Craighero, 2004; Hari and Kujala, 2009).
Previous evidence has shown that the left IFC, in addition to
several other brain regions, is the site where mirror neurons are
located (Rizzolatti and Arbib, 1998). The mirror neurons re-
spond to observations of an action, to a sound associated with
that action, or even to observations of mouth-communicative
gestures (Kohler et al., 2002; Ferrari et al., 2003). In the current
study, no CHs that covered the left IPC showed a significant
synchronization increase during f2f_d. This result indicated that
the left IFC might be involved in such an action–perception sys-
tem (Nishitani et al., 2005) and also that it might specifically
provide a necessary bridge for human face-to-face communica-
tion (Fogassi and Ferrari, 2007).

Further analysis revealed that the difference between f2f_d
and b2b_d was primarily based on the direct interactions between
partners, i.e., turn-taking and body language, rather than simply
on verbal signal transmission. This finding is consistent with pre-
vious evidence regarding the acquisition of communication. Re-
search on infant language development has found that the native
language is acquired through interactions with caregivers (Gold-
stein and Schwade, 2008). Interactions between caregivers and
infants can help maintain proximity between the caregivers and
the infants and reinforce the infants’ earliest prelinguistic vocal-
izations. Thus, f2f_d offers important features for the acquisition
of communication that b2b_d does not (Goldstein et al., 2003;
Goldstein and Schwade, 2008).

The importance of turn-taking behavior during communi-
cation was further confirmed by the comparison between
f2f_d and f2f_m, i.e., the left IFC showed a significant neural
synchronization increase during f2f_d but not during f2f_m.
This finding extended the previous evidence of neural syn-
chronization in unidirectional emitter/receiver communica-
tion to dynamic bidirectional transmission communication
and suggested that turn-taking, in addition to other types of
interactions during f2f_d, contributes significantly to the neu-
ral synchronization between partners during real-time dy-
namic communication.

Based on the features of neural synchronization, two commu-
nication behaviors that included or that did not include interac-
tive communication, such as turn-taking and body language,
could be successfully predicted. This finding further validates
that during face-to-face communication, multimodal sensory
information integration and turn-taking behavior contribute
to the neural synchronization between partners, and that com-
municating behavior can be predicted above chance level
based on the neural synchronization. These results can also
provide a potential approach for helping children with com-

Figure 4. Prediction accuracy for communication behavior based on the neural synchroni-
zation of CH3 during face-to-face dialog. Each black point denotes one pair of participants. The
dashed line indicates the chance level (0.5). Error bars are SE. ***p � 0.001.

16068 • J. Neurosci., November 7, 2012 • 32(45):16064 –16069 Jiang et al. • Neural Synchronization during Communication

munication disorders through neurofeedback techniques (i.e.,
a brain– computer interface).

It has been suggested that the human brain is evolutionarily
adapted to face-to-face communication (Boaz and Almquist,
1997; Kock, 2002). However, such technologies as telephone and
e-mail have changed the role of traditional face-to-face commu-
nication. The current study showed that, compared with other
types of communication, face-to-face communication is charac-
terized by a significant neural synchronization between partners
based primarily on multimodal sensory information integration
and turn-taking behavior during dynamic communication.
These findings suggest that face-to-face communication has im-
portant neural features that other types of communication lack,
and also that people should take more time to communicate
face-to-face.

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