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Brains in Competition: Improved Cognitive Performance and Inter-Brain Coupling by Hyperscanning Paradigm with Functional Near-Infrared Spectroscopy

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Brains in Competition: Improved Cognitive Performance and Inter-Brain Coupling by Hyperscanning Paradigm with Functional Near-Infrared Spectroscopy

Abstract and Figures

Hyperscanning brain paradigm was applied to competitive task for couples of subjects. Functional Near-Infrared Spectroscopy (fNIRS) and cognitive performance were considered to test inter-brain and cognitive strategy similarities between subjects (14 couples) during a joint-action. We supposed increased brain-to-brain coupling and improved cognitive outcomes due to joint-action and the competition. As supposed, the direct interaction between the subjects and the observed external feedback of their performance (an experimentally induced fictitious feedback) affected the cognitive performance with decreased Error Rates (ERs), and Response Times (RTs). In addition, fNIRS measure (oxyhemoglobin, O2Hb) revealed an increased brain activity in the prefrontal cortex (PFC) in post-feedback more than pre-feedback condition. Moreover, a higher inter-brain similarity was found for the couples during the task, with higher matched brain response in post-feedback condition than pre-feedback. Finally, a significant increased prefrontal brain lateralization effect was observed for the right hemisphere. Indeed the right PFC was more responsive with similar modalities within the couple during the post-feedback condition. The joined-task and competitive context was adduced to explain these cognitive performance improving, synergic brain responsiveness within the couples and lateralization effects (negative emotions).
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ORIGINAL RESEARCH
published: 31 August 2017
doi: 10.3389/fnbeh.2017.00163
Brains in Competition: Improved
Cognitive Performance and
Inter-Brain Coupling by
Hyperscanning Paradigm with
Functional Near-Infrared
Spectroscopy
Michela Balconi* and Maria E. Vanutelli
Research Unit in Affective and Social Neuroscience, Department of Psychology, Catholic University of Milan, Milan, Italy
Edited by:
Nelly Alia-Klein,
Icahn School of Medicine at Mount
Sinai, United States
Reviewed by:
Christos Frantzidis,
Aristotle University of Thessaloniki,
Greece
Anna Song Huang,
Icahn School of Medicine at Mount
Sinai, United States
*Correspondence:
Michela Balconi
michela.balconi@unicatt.it
Received: 05 April 2017
Accepted: 17 August 2017
Published: 31 August 2017
Citation:
Balconi M and Vanutelli ME
(2017) Brains in Competition:
Improved Cognitive Performance and
Inter-Brain Coupling by
Hyperscanning Paradigm with
Functional Near-Infrared
Spectroscopy.
Front. Behav. Neurosci. 11:163.
doi: 10.3389/fnbeh.2017.00163
Hyperscanning brain paradigm was applied to competitive task for couples of
subjects. Functional Near-Infrared Spectroscopy (fNIRS) and cognitive performance
were considered to test inter-brain and cognitive strategy similarities between subjects
(14 couples) during a joint-action. We supposed increased brain-to-brain coupling and
improved cognitive outcomes due to joint-action and the competition. As supposed,
the direct interaction between the subjects and the observed external feedback of
their performance (an experimentally induced fictitious feedback) affected the cognitive
performance with decreased Error Rates (ERs), and Response Times (RTs). In addition,
fNIRS measure (oxyhemoglobin, O2Hb) revealed an increased brain activity in the
prefrontal cortex (PFC) in post-feedback more than pre-feedback condition. Moreover,
a higher inter-brain similarity was found for the couples during the task, with higher
matched brain response in post-feedback condition than pre-feedback. Finally, a
significant increased prefrontal brain lateralization effect was observed for the right
hemisphere. Indeed the right PFC was more responsive with similar modalities within
the couple during the post-feedback condition. The joined-task and competitive
context was adduced to explain these cognitive performance improving, synergic brain
responsiveness within the couples and lateralization effects (negative emotions).
Keywords: hyperscanning, competition, emotion, cognition, fNIRS
INTRODUCTION
Competition essentially implies a social dynamic that requires a comparison between two or
more subjects during an interpersonal performance. Previous research suggested a crucial role
of competitive social interactions in achieving accurate self-representation of our social position.
Conversely, it was found that social perception affects performance during situations that require
to compare our own behavior with that of others (Munafò et al., 2005). That is, the analysis of our
social role in competition may influence the cognitive performance by improving or decreasing the
actual outcomes (Munafò et al., 2005).
About the brain contribution, it was observed that an extended neural network, including
limbic areas, the prefrontal cortex (PFC) and striatal structures, may represent the behavioral
cognitive and emotional and correlates of social interactions, respectively (Levitan et al., 2000).
Frontiers in Behavioral Neuroscience | www.frontiersin.org 1August 2017 | Volume 11 | Article 163
Balconi and Vanutelli Brains in Competition
Preliminary evidence in support of this neural mechanism of
the social brain comes from previous studies exploring the
structures and functions of brain areas associated with social
representation, social ranking and self-efficacy. Specifically, both
dorsal (DLPFC) and ventral (VLPFC) portions of the lateral
PFC are generally involved in response to social status inference
and interpersonal tasks (Chiao et al., 2009b; Balconi and Pagani,
2014, 2015). The activation of DLPFC and VLPFC during
social interactions probably represents the recruitment of brain
areas that apply top-down control over some processes, such
as emotional behavior in response to social demands (Marsh
et al., 2009; Balconi and Vanutelli, 2016). Indeed these brain
areas are generally associated with socio-emotional regulation
and behavioral inhibitory mechanisms.
Recent research on cooperation/competition also showed
enhanced cortico-cortical communication and interconnections
between these prefrontal areas. For example, the effects of
competitive tasks in more than one brain was recently explored
(Decety et al., 2004; Liu et al., 2015; Cui et al., 2016). In
addition, some studies confirmed that the social context jointly
affects subjects’ reactions to their environment and consequently
their brain activity. For example it was noted that one’s own
action planning is facilitated during cooperation since others’
actions are joined with our actions, in opposition to competitive
conditions (Knoblich and Jordan, 2003; Sebanz et al., 2003).
Therefore the cortical activity is also modulated based on
the different forms of social interaction, since competitive or
cooperative situations are qualitatively distinct contexts. Indeed,
it should be noted that cooperation and competition are two basic
types of interpersonal interaction (Decety et al., 2004). That is,
based on the interactive condition (cooperation vs. competition)
people may either facilitate or hinder the goals of others.
Specifically competition, as a social-evaluative phenomenon, can
increase the amount of cognitive resources beyond what is
needed to simply execute the task demands. In particular, the
cognitive effort could be increased in competition when subjects
have potentially contrasting goals (De Cremer and Stouten, 2003;
Decety et al., 2004). We previously focused on cooperation
with specific measures (EEG and neuroimaging near-infrared
spectroscopy (NIRS); Balconi and Vanutelli, 2017a,b) and
then we applied EEG and Functional NIRS (fNIRS) to study
competition.
It was also found that competition may improve the
effective cognitive performance and the self-perception of
higher social position (Goldman et al., 1977). The higher
demand may explain how competition affects performance
(with an ‘‘improving effect’’) and brain responsiveness due to
the attendant modifications of the subject’s mental condition
and underlying neural activities (Rietschel et al., 2011). More
specifically, the self-perception during competition may affect
the subjective internal judgment and manifest as an increased
cerebral responsiveness in those areas related to competitive
conditions, and positively affect the performance outcomes.
However, whereas the available previous results indicate that
social exchanges involve a specific network of cortical areas,
further analysis is required to clarify the specific contribution
of the brain structures in different social conditions, i.e., when
subjects compete toward a personal goal during a joint action.
Second, the presence of a real interlocutor may affect the inter-
brain responsiveness and cognitive outcomes, as suggested by
hyperscanning research (Konvalinka and Roepstorff, 2012). In
a recent study Cui et al. (2016) have measured the prefrontal
activation during cooperative and competitive tasks by using
NIRS. Dyads of participants were asked to press two keys either
simultaneously (to obtain synchronized action in cooperative
condition), or as fast as possible to obtain a better result than
their partner during competitive condition. The participants
showed increased inter-brain synchronization in the right
superior frontal areas during cooperation, but not competition:
such result emerged because of the necessity to model others’
behavior during a cooperative task. It should also be considered
that the increase in cortico-cortical communication was high
and significant, and involved heightened responses between
all non-motor areas with strategy planning regions (such as
prefrontal areas).
Third, it should be considered whether and how an increase
in brain activity and cognitive performance is specifically
promoted by an external feedback which is able to manipulate
the cognitive performance though self-other evaluation. In fact
no previous research has considered the social environment
and the cognitive outcomes by using a direct competitive task.
Generally, previous studies implied only single subjects and their
isolated performance in abstract social tasks, since they did
not include paired joint actions and interactive tasks. In other
cases research explored the response in asynchronous conditions
(subsequential response by the participants) by two or more
subjects interacting each other (Boone et al., 1999; Decety et al.,
2004). In this regard, the hyperscanning approach introduces an
innovative perspective to explore two interacting brains (Holper
et al., 2012; Konvalinka and Roepstorff, 2012). However, when
an hyperscanning paradigm was used, it was applied only in
response to cognitive performance without a specific interactive
feedback (Saito et al., 2010; Dommer et al., 2012; Cui et al., 2016).
Therefore, to summarize, compared to previous research,
two relevant aspects were underestimated and deserve to be
considered to evaluate inter-subjective brain activity and the
cognitive performance during competition: the presence of a dual
interaction, and the feedback furnished by the social context
to (fictitiously) represent the effectiveness of the joint action.
That is, the effect of an external feedback (positive or negative
feedback about the competitive performance) on the inter-
brain responses and cognitive performance was not adequately
considered. An external feedback is supposed to modify the self-
representation, the effective cognitive outcomes and the brain
responsiveness to social contexts (Montague et al., 2002). In
the present study the performance was manipulated in a vis-
à-vis competitive situation which stressed the subjects’ ability
to win and to perform better than the partner. Compared to
other studies (Zink et al., 2008), we planned a more ecological
and realistic scenario where subjects were asked to directly
compare their outcomes with the other partner by monitoring
their performance. Specifically this request underlined the
necessity to increase subject’s effectiveness during the task
(‘‘your performance is better than. . .’’). In this regard we
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Balconi and Vanutelli Brains in Competition
FIGURE 1 | Experimental procedure: task structure and oxyhemoglobin (O2Hb) measures.
formulated a clear and reinforced social condition based on
cognitive skill during a dyadic interaction. Second, brain-to-
brain coupling effect induced by the competitive task has to
be explored, by using an adequate hyperscanning paradigm,
which is able to reveal the common strategies applied by the
participants to obtain a better performance and the effects
of this synergic planning. To test these double effects, fNIRS
was applied to acquire subjects’ brain response during a
task performed simultaneously in paired subjects. Classical
neuroimaging approach (i.e., functional Magnetic Resonance,
fMRI) was not able to exhaustively show the social nature
of the inter-personal processes since the temporal course of
such activation was scarcely addressed. fNIRS measure has a
resolution which is considered high enough for monitoring
event-related fNIRS responses (Elwell et al., 1993; Montague
et al., 2002; Decety et al., 2004; Dötsch and Schubö, 2015).
More importantly, fNIRS proved to be much more suitable
for ecological hyperscanning applications since it imposes
significantly milder physical burdens than other techniques such
as fMRI, it is not noisy or uncomfortable, and is robust to
exogenous noise thus permitting interactive contexts (Balconi
and Molteni, 2015).
Therefore, based on our hypotheses, the artificially increased
performance during competition may effectively modulate the
behavioral performance in social contexts, with improved
outcomes mainly after receiving the feedback. Therefore,
a consistent better performance should be found for the
post-feedback condition (that is in the case of perception
of improved outcomes), as a result of a higher reinforcing
situation. Second, the cortical effect of these social and cognitive
representation processes are hypothesized to be supported by the
PFC (Hall et al., 2005; Chiao et al., 2009a; Balconi and Pagani,
2014, 2015), with significant higher responsiveness of the PFC
mainly after the feedback. Third, we intended to study inter-brain
activity in competitive conditions and in relationship with the
positive (improved) feedback. Indeed we expected a higher brain-
to-brain coupling induced by the feedback, which may induce a
more synergic activity between the subjects.
MATERIALS AND METHODS
Subjects
Fourteen couples of subjects (28 subjects, all undergraduate
students: M= 23.78, SD = 1.98 years old) were recruited
for the present research. Each couple was composed by two
players of the same gender matched for age. The subjects were
all right-handed, with normal or corrected-to-normal visual
acuity. To exclude history of psychopathology Beck Depression
Inventory (BDI-II, Beck et al., 1996) was administered to the
participants or immediate family. Moreover State-Trait-Anxiety-
Inventory (STAI, Spielberger et al., 1970) was submitted in
the post-experimental session. No neurological or psychiatric
pathologies were revealed. The research was approved by
the local ethics committee of the Department of Psychology,
Catholic University of Milan. The subjects gave informed
written consent to participate in the study in accordance with
the 1964 Helsinki Declaration and its later amendments or
comparable ethical standards. No payment was provided for
subjects’ performance.
Procedure
Subjects were seated with monitor in front of them (positioned
approximately 60 cm) in a moderately darkened room.
Participants were seated side-by-side and separated by a black
screen in order to not seeing each other. They performed a
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Balconi and Vanutelli Brains in Competition
cognitive task for sustained selective attention (modified version
of Balconi and Pagani, 2014).
Task
Subjects were taught that specific attentional measures were
registered to evaluate the subjective skills. They were required
to recognize target stimuli from non-targets, based on four
different combinations of shape and color: circles or triangles,
green or blue. The target remained on the video until subjects
were able to memorize it. Then, stimuli were displayed one
after another. Size and color features changed every experimental
block, each composed by 25 trials. Subjects were asked to
press a left/right button after each stimulus to decide for
target/non target. Each stimulus was displayed on the screen
for 500 ms (300 ms inter-stimulus interval, ISI), and each
trial was constituted by three stimuli. After each trial a
feedback appeared on the screen in the form of two up-arrows
(better performance than the competitor); a dash (comparable
performance); or two down-arrows (worse performance). The
feedback lasted 5000 ms, followed by an inter-trial interval
(ITI) of 5000 ms. The task was subdivided in two sub-sessions:
the first without a specific feedback to subject’s performance
(four blocks of stimuli before the feedback, for a total of
100 trials); the second preceded by the feedback about the
performance (four blocks of stimuli with the feedback, for a total
of 100 trials; Figure 1).
To increase subjects’ intrinsic motivation, they were told that
accuracy, number of errors (Error Rate, ER) and response times
(RTs) were usually used to screen future professional career
success in term of teamwork abilities. Moreover, participants
were told that the final cognitive outcome was based on the ability
to produce a better performance than the competitor, in order to
highly stress the competitive nature of the task.
Halfway, participants received also a synthetic feedback of
their cognitive performance. Both feedbacks (both trial-related
and general feedback) were prearranged (without the awareness
of the participants), and participants were told that their outcome
was ‘‘well above (score with 91% in terms of speed, and 92% in
terms of accuracy)’’. In addition they were pressed to maintain
their higher performance level during the course of the task
(‘‘The measures recorded till now reveal that your performance
is very good. Your response profile is well superior to your
competitor’s one. If you want to win, keep going like this in
the following part’’). Trial feedbacks constantly reinforced them
about their high and competitive performance by presenting the
up-arrows (70% of the cases) and the dash or the down-arrows
(30% of the cases, and they were mainly positioned at the
beginning of the task), to make the outcome more credible and
plausible.
Finally, after each block, subjects were required to rank
their outcome and perceived self-efficacy on a 7-point Likert
scale (1 = most decreased performance; 7 = most improved
performance). As reported in a post-experimental phase, the
participants were strongly engaged in the social and ranking
process. Participants were also requested to self-report their
degree of trust based on the external feedback. They showed very
FIGURE 2 | The location of near-infrared spectroscopy (NIRS) channels. The
emitters were placed on positions FC3-FC4 (purple, number 1 and 2) and
F1-F2 (number 3 and 4), while detectors were placed on FC1-FC2 (blue,
number 3 and 4) and F3-F4 (number 1 and 2). Resulting channels are
displayed in pink color.
high trust (92%) and a self-represented relevance of the task for
their social position (94%).
Performance Scoring
The RTs (ms) measures were registered from the stimulus
onset, and ERs were calculated as the total number of incorrect
detections out of the total trial, for each experimental category
(therefore higher values represented higher number of incorrect
responses).
fNIRS
fNIRS recordings were conducted with NIRScout System (NIRx
Medical Technologies, LLC. Los Angeles, CA, USA) with
an 8-channel array of optodes placed on the prefrontal area
(four light sources/emitters and four detectors). Emitters
were placed over FC3-FC4 and F1-F2 positions, while
detectors were placed on FC1-FC2 and F3-F4 positions
(Figure 2). Emitter-detector distance was maintained at
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Balconi and Vanutelli Brains in Competition
30 mm for contiguous optodes and near-infrared light
of two wavelengths (760 and 850 nm) were considered.
According to the international 10/5 system, NIRS optodes
were placed on the subject’s head using a NIRS-EEG
compatible cup. Changes in the concentration of oxygenated
(O2Hb) and deoxygenated hemoglobin (HHb) from a
120 s resting baseline were acquired. By using NIRStar
Acquisition Software, Signals obtained from the eight NIRS
channels were acquired (sampling rate of 6.25 Hz), then
transformed in values for the changes in the concentration of
oxygenated and deoxygenated HHb in each channel (scaled in
mmolmm).
The raw O2Hb and HHb data from each channel were
digitally band-pass filtered at 0.01–0.3 Hz. Then, the mean
concentration of each channel was calculated (from the trial
onset for the following 5 s) as the average across trials. According
to the mean concentrations in the time series, the effect size was
calculated for each channel and participant in every experimental
condition. The Cohen’s z effect sizes were obtained as the
difference of the means of the baseline and trial divided by
the standard deviation (SD) of the baseline, as reported in
the formula: d= (m1 m2)/s. m1 and m2 were the mean
concentration values during baseline and trial, respectively, and s
the SD of the baseline. The baseline was calculated considering
the 5 s period immediately before the trial beginning. Then,
in order to increase the signal-to-noise ratio, the effect sizes
obtained from the eight channels were averaged. Although
NIRS raw data were originally relative values and for his
reason they could not be directly averaged across experimental
conditions (subjects or channels), effect sizes normalized data
could be averaged regardless of the unit since the effect size is
not affected by differential pathlength factor (DPF; Schroeter
et al., 2003; Matsuda and Hiraki, 2006; Shimada and Hiraki,
2006).
Data Analysis
Three levels of analyses were performed for behavioral (ER; RTs)
and neurophysiological (fNIRS, O2Hb measures) measures.
For the first level of analysis, a repeated measure ANOVA
with one factor (Condition, Cond: pre vs. post feedback) was
applied to ERs and RTs data. A second ANOVA was applied
to O2Hb dependent measure, with repeated factors Cond
and Lateralization (Lat: left vs. right). This preliminary set of
ANOVAs was finalized to test the general effect of Condition (for
ERs and RTs) and Condition and Lateralization (for O2Hb) in
the whole sample.
For the second level, a similarity measure for continuous data
was applied to each couple of subjects in pre- and post-feedback
condition (for the 100 trials). These similarity measures for
interval data, i.e., Pearson correlation as a measure of distance
between vectors, finds the ratio between the covariance and
the SD of both subjects (Sheldon, 2014). By using this
measure specific similarities between each couple of subjects was
monitored for cognitive (ERs and RTs) and O2Hb dependent
measures.
For the third level, to analyze the systematic effect of the
independent within subjects factors Cond on the similarities
FIGURE 3 | Error Rates (ERs; A) and Response Times (RTs; B) modulation as
a function of pre-feedback and post-feedback, with decreased values in
post-feedback. (C) O2Hb modulation (D values) for Condition and
Lateralization. Post-feedback condition revealed increased D values for the
right prefrontal cortex (PFC).
coefficients, repeated measure ANOVAs were applied to the
coefficients calculated for ERs and RTs as dependent variables.
In addition to Cond, Lateralization factor was added in the case
of O2Hb coefficients as dependent measure.
For all the ANOVA, the Greenhouse–Geisser epsilon was
used for degrees of freedom correction where appropriate.
Post hoc comparisons (contrast analyses) were used when
necessary and Bonferroni test was used in the case of multiple
comparisons.
To exclude a possible learning effect due to pre-/post feedback
condition, a preliminary analysis was conducted, comparing
distinctly the first groups of intervals (four pre-feedback
intervals, for each 25 trials) and the second group of intervals
(four post feedback intervals, for each 25 trials) for all the
dependent variables (RTs, ERs, O2Hb). Since no significant
differences among the four intervals, respectively for before and
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Balconi and Vanutelli Brains in Competition
FIGURE 4 | Similarity measures (Pearson coefficients) for couples of subjects for (A) ERs; (B) RTs, a function of pre-feedback and post-feedback; (C) O2Hb variable
as a function of right (C) and left (D) hemisphere.
after feedback condition, were found, we did not include this
factor in the successive phases of the analysis.
RESULTS
ANOVA (Raw Data)
ER and RTs
For ER measure, ANOVA indicated significant effect for Cond
(F(1,27)= 8.90, p0.001, η2= 0.37). Indeed ER decreased in
post-feedback (M= 0.03; SD = 0.009) compared to pre-feedback
(M= 0.05; SD = 0.01; Figure 3A).
For RTs, ANOVA revealed significant main effect for Cond
(F(1,27)= 9.05, p0.001, η2= 0.39), showing reduced RTs for
post-feedback (M= 223; SD = 0.25) compared to pre-feedback
(M= 258; SD = 0.31; Figure 3B).
O2Hb
The successive ANOVAs were applied to d measure for both
O2Hb and HHb-values. Since the analysis on HHb did not show
any significant results only statistical results for O2Hb were
reported. The data over left (Ch1: FC3-F3; Ch2: FC3-FC1; Ch5:
F1-F3; Ch6: F1-FC1) and right (Ch3: FC4-F4; Ch4: FC4-FC2;
Ch7: F2-F4; Ch8: F2-FC2) channels were averaged.
ANOVA showed Lat ×Cond significant interaction effect
(F(1,27)= 11.32, p0.001, η2= 0.40) with increased right
brain responsiveness for post-feedback (M= 0.72; SD = 0.02)
compared to pre-feedback condition (M= 0.38; SD = 0.01;
Figure 3C).
Similarity Measures
ER and RTs
The Pearson similarity coefficients (Fisher’s z transform) were
reported in the following Figures 4A–B for each couple of
subjects in pre- and post-feedback. As indicated in Figure 4A,
for ER, five couples showed significant coefficients for the
pre-feedback condition, whereas 10 couples showed significant
coefficients for the post-feedback condition. Figure 4B indicates
the coefficients for RTs measures. As reported, nine couples
revealed significant joined RTs modulation for the pre-feedback
condition, whereas 13 couples showed significant coefficients in
post-feedback.
O2Hb
Significant Pearson coefficients were found in pre-feedback
condition: six couples in the right and four in the left
hemisphere revealed significant coefficients, whereas 12 couples
were matched in post-feedback in the right side and five couples
in the left side (Figures 4C,D).
ANOVA on Similarity Measures
The third level of analysis considered the Pearson coefficients
derived for ER, RTs and O2Hb as dependent measure in the
repeated measures ANOVAs.
ER and RTs Coefficients
Significant differences in ER were found for Cond (F(1,27)= 9.06,
p0.001, η2= 0.39), with increased Pearson coefficients values
in post-feedback (M= 0.68; SD = 0.01) than pre-feedback
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Balconi and Vanutelli Brains in Competition
FIGURE 5 | Pearson coefficients as a function of pre-feedback and
post-feedback for (A) ERs; (B) RTs measures, with decreased ERs and RTs
for post-feedback; (C) O2Hb. It was observed an increasing of O2Hb for
post-feedback in the right hemisphere.
(M= 0.54; SD = 0.01) condition (Figure 5A). For RTs, a
significant result was found for Cond (F(1,27)= 7.76, p0.001,
η2= 0.34), with increased Pearson values in post-feedback
(M= 0.67; SD = 0.03) than pre-feedback (M= 0.57; SD = 0.02)
condition (Figure 5B).
O2Hb Coefficients
Significant effect was found for Cond (F(1,27)= 8.79, p0.001,
η2= 0.36) and Cond ×Lat (F(1,27)= 7.52, p0.001,
η2= 0.34). Indeed, increased coefficients were revealed in
post-feedback (M= 0.60; SD = 0.02) than pre-feedback condition
(M= 0.49; SD = 0.01). Second, about the interaction effect,
during post-feedback the right hemisphere showed higher
coefficient values (M= 0.67; SD = 0.02) compared to the left
hemisphere (M= 0.57; SD = 0.01; F(1,27)= 7.12, p0.001,
η2= 0.34). In addition the right hemisphere registered increased
Pearson values in post-feedback (M= 0.67; SD = 0.01) than in
pre-feedback (M= 0.47; SD = 0.03; F(1,27)= 7.43, p0.001,
η2= 0.35; Figure 5C).
GENERAL DISCUSSION
The present research explored the effects of a competitive
joint-action on cognitive performance and brain activity
by using a hyperscanning paradigm. Specifically, inter-brain
similarities measures were acquired in couples of subjects
during a competitive task, by using fNIRS. Based on our
results, the following effects were observed. A first main
effect was the systematic prefrontal (PFC) increased activity
when a positive reinforce (post-feedback) was furnished to
the participants about their performance. Indeed significant
increased PFC activity in response to a positively reinforced
joint action was found for all participants when compared
to pre-feedback condition. Second, a better performance for
both RTs and ER measures was revealed after the reinforcing
feedback. Third, a higher inter-brain similarity was found for
the couples after the feedback. Specifically when participants
perceived (experimentally induced) to have performed better,
a homologous and similar brain response was produced, with
higher coherent PFC activity within the couple. Finally, it should
be noted that a significant prefrontal brain lateralization effect
was present, with the right hemisphere being more engaged in
post-feedback condition.
About the first result, previous evidence revealed that
prefrontal areas are crucial in social status monitoring and
joint actions (Karafin et al., 2004; Haruno and Kawato, 2009;
Suzuki et al., 2011). Also, using EEG-based hyperscanning
technique, specific DLPFC activation emerged during
reciprocal interaction in iterated Prisoner’s Dilemma paradigm
(De Vico Fallani et al., 2010). In the present research we observed
a similar effect, with significant increased PFC activity in
response to positively reinforced joint action during the cognitive
task. This prefrontal brain area was hypothesized to have an
evolutionary role in social perception mainly when hierarchy
in social groups is crucial (Chiao et al., 2009a). Therefore
we may suggest that this area has dedicated mechanisms to
perceive social position and interaction significance during an
interpersonal task.
More interestingly, the post-feedback condition induced an
increased PFC responsiveness than pre-feedback condition. In
fact, we observed that the PFC was mainly implicated when
subjects were informed on their efficient interaction. This fact
may indicate a central role of this prefrontal area in the case
of a positive self-perception (to be a good performer) within a
social situation where the competition is relevant and stressed. It
is interesting to note that this ‘‘improved brain effect’’ was also
accompanied by a significant increased cognitive performance
(decreased ER and RTs). Indeed it was found that subjects highly
improved their cognitive outcomes in response to the external
reinforce. Due to these results we may suppose that the improved
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Balconi and Vanutelli Brains in Competition
self-representation in term of social ranking and social position
may have enhanced the real subjective performance. It should
also be noted that the cortical and behavioral data showed to
be matched, with a similar trend of higher activity for both
behavior and cortical activity, which underlined the main effect
of the (artificially) induced positive social reinforce on the inter-
subjective joint performance.
About the inter-brain relation, we observed a consistent
and relevant increased brain-to-brain coupling for the dyads,
mainly in concomitance with the positive social feedback. That
is, this homologous inter-brain activity emerged in post-feedback
condition for most of the couples. Therefore we may state
that the externally induced reinforcing condition influenced the
joint cortical responsiveness. That is, it could be suggested that,
although the task was competitive, the self-perceived efficacy
produced a sort of ‘‘glue’’ between the two brains, orienting the
subjects on the same direction. Therefore, the present results
provides initial evidence for the hypothesis of a significant inter-
brain effect during competitive tasks and offer suggestions for
future studies examining the extent to which the competition
in two brains is selectively related to a better cognitive joint
performance for the two inter-agents.
This fact was further underlined by the significant effect of
positive feedback on the cognitive joint-performance. Indeed the
common strategy was evident also for the cognitive measures
(RTs and ER) in addition to the brain measures. Higher
similarity coefficients were found for the cognitive variables,
thus underlining the impact of the external feedback on both
the hemodynamic and cognitive level. In other terms, we
may suppose that the external reinforce may have modulated
the effective joint-behavior inside the couple, with relevant
convergence of the increased performance by the two inter-
agents.
About inter-subjective joint neural activity it should be
noted that this prominent effect was mainly observed for
the right hemisphere with respect to the left one. This
result may be understood taking into account the social
role of PFC and the lateralized effect observed in previous
studies (Balconi et al., 2012). At this regard, we may
consider the increased responsiveness in the right hemisphere
as a possible marker of the competitive goal, oriented
toward the maximization of the personal profit. Indeed,
as previously demonstrated, the prefrontal asymmetry in
favor of the right hemisphere may represent the withdrawal
motivation in opposition to approach motivation (Davidson,
1993; Jackson et al., 2003; Urry et al., 2004; Balconi and
Mazza, 2010; Harmon-Jones et al., 2010; Koslow et al.,
2013).
An alternative second explanation of the present result
may relate the increased right hemisphere responsiveness to a
significant increasing of more negative and avoidance emotions
toward the competitor, linked to the competitive condition.
As previously shown, the right hemisphere is supporting the
aversive situations where the subjects are required to manage
the conflictual and potential divergent goals (Balconi et al.,
2012). Therefore, a sort of a ‘‘negative echo’’ may be induced
by the individualistic and competitive aims of the task for
each subject, with a significant increasing of more withdrawal
attitudes. Consequently, prefrontal brain activity can be regarded
to be highly involved in the processing of emotional behavior
which affects the competitive context (Adolphs, 2002; Chiao
et al., 2009a). However actually few studies have tried to study
the emotional effects of competition on brain activity, taking
into consideration the role of emotions on the cortical response
(and on inter-brains responsiveness) when it responds to social
situations as competition of cooperation. For this reason future
research should better explore the distinct effect of emotions and
competition on the cortical responsiveness to disambiguate their
reciprocal relation.
However some limitations could be suggested for the present
study. First, some adjunctive analyses could be used, to elucidate
the gender effect in interactions, since the couples were
composed by males or females. Second, a more accurate analysis
for the dynamical changes of the inter-subjective strategy during
the task should be conducted, in order to verify the progression of
the learning mechanisms related to the inter-brain and cognitive
processes. Finally, future research should better explore the effect
of competitive in comparison with cooperative task, to verify the
significant differences in brain-to-brain coupling and cognitive
performance in response to these two different experimental
conditions. This comparison should also allow to comprehend
the significance of the positive feedback per se, separated by the
competitive/cooperative task effect.
AUTHOR CONTRIBUTIONS
MB designed the research, supervised the experiment, analyzed
the data and wrote the text. MEV realized the experiment,
analyzed the data and wrote the text.
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Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
The reviewer AH and handling Editor declared their shared affiliation, and the
handling Editor states that the process met the standards of a fair and objective
review.
Copyright © 2017 Balconi and Vanutelli. This is an open-access article distributed
under the terms of the Creative Commons Attribution License (CC BY). The use,
distribution or reproduction in other forums is permitted, provided the original
author(s) or licensor are credited and that the original publication in this journal
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reproduction is permitted which does not comply with these terms.
Frontiers in Behavioral Neuroscience | www.frontiersin.org 10 August 2017 | Volume 11 | Article 163
... Out of the 24 review papers, two review papers [46,47] gave a general timeline of hyperscanning research but failed to dive into the research paradigms and indices in hyperscanning. Six papers discussed one methodology [48][49][50][51][52][53] and four papers reviewed multiple modalities [12,[54][55][56]. The applications discussed were joint action [57,58], social neuroscience [59,60], social interaction [13,[61][62][63][64], and interpersonal coordination [65,66]. ...
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The neurophysiological mechanisms underlying social behavior are still poorly understood. An increasing number of international studies uses hyperscanning for simultaneous recording of brain activation from several individuals during social interaction. Despite the outstanding school of Russian social psychology, the number of studies investigating the neurophysiological basis of social behavior in humans is still limited in the Russian literature. The goal of the present work was to review the hyperscanning methods, i.e., methods for simultaneous recording of physiological indices used to investigate inter-brain synchronization during social interactions. The paper discusses methods for recording and analysis of multi-subject data representing the changes in brain activity, existing experimental and naturalistic models, key results, as well as applied and fundamental aspects of the implementation of this technique in social psychology and neuroscience. Introduction of the methods which allow for a better understanding of physiological mechanisms of social interactions may significantly contribute to the development of innovative approaches to improving educational process, teamwork in various professional areas, social welfare, and psychosomatic health of people.
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The ubiquity of technology in today’s world is exemplified by our ability to connect with each other instantly all around the globe. Advances in video conferencing capabilities combined with dramatic socio-dynamic shifts brought about by COVID-19 have redefined the ways in which humans interact in modern society. Human reliance on effective virtual interfacing (e.g., zoom conferencing) is evermore present in today’s COVID-19 world and will undoubtedly expand in the future. This unprecedented rise in digitalization has direct implications on the output and productivity of human interactions across all design (thinking) activities and practices. Working in a virtual environment limits access to traditional design thinking tools such as (analog) “artifacts” or “manipulatives” (e.g., physical prototypes, post-its, etc.). As both neuroscientists and design researchers, we are interested in elucidating the neurobiological signatures that underlie these adapted human-to-human interactions. Our overarching goal is to understand and uncover the differences in collaborative outcomes (e.g., creativity) and inter-brain synchrony in virtual versus in-person interactions using both analog and digital manipulatives. We proposed an emergent technology in brain-imaging—hyperscanning (i.e., measuring two brains simultaneously to derive measures of inter-brain synchrony) with functional near-infrared spectroscopy (fNIRS)—as an ideal brain-imaging technique to tackle this challenge. A better understanding of how the nuances of these dynamics impact inter-brain synchrony during an innovation event will provide new insights for interventions or technology that can help optimize successful interaction in both scenarios. To inform the design of future fNIRS hyperscanning studies, we review the existing fNIRS hyperscanning literature in this book chapter. On the basis of the existing literature, we highlight the current gaps in research regarding virtual interactions. We provide insight into current hurdles regarding fNIRS hyperscanning hardware and methodology and give recommendations on how to advance the field of fNIRS hyperscanning relevant to design research in the digital age.
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