SN-ENUJ210301 1..17 - ENEURO.0133-21.2021.full.pdf

Cognition and Behavior

Team Flow Is a Unique Brain State Associated with

Enhanced Information Integration and Interbrain

Synchrony

Mohammad Shehata,

1,2

Miao Cheng,

1,3,4

Angus Leung,

Naotsugu Tsuchiya,

5,6,7

Daw-An Wu,

Chia-huei Tseng,

Shigeki Nakauchi,

2,9

and Shinsuke Shimojo

1,2

https://doi.org/10.1523/ENEURO.0133-21.2021

Division of Biology and Biological Engineering, California Institute of Technology, Pasadena 91125, CA,

The

Electronics-Inspired Interdisciplinary Research Institute (EIIRIS), Toyohashi University of Technology, Toyohashi

441-8580, Japan,

The University of Hong Kong, Pokfulam 999077, Hong Kong,

NTT Communication Science

Laboratories, NTT Corporation, Atsugi 243-0198, Japan,

School of Psychological Sciences and Turner Institute for

Brain and Mental Health, Monash University, Melbourne, Victoria 3800, Australia,

Center for Information and Neural

Networks (CiNet), National Institute of Information and Communications Technology (NICT), Suita 565-0871, Japan,

Advanced Telecommunications Research Computational Neuroscience Laboratories, Kyoto 619-0288, Japan,

Research Institute of Electrical Communication, Tohoku University, Sendai 980-8577, Japan, and

Department of

Computer Science and Engineering, Toyohashi University of Technology, Toyohashi 441-8580, Japan

Visual Abstract

Team flow occurs when a group functions in a high task engagement to achieve a goal, commonly seen in

performance and sports. Team flow can enable enhanced positive experiences, as compared with individ-

ual flow or regular socializing. However, the neural basis for this enhanced behavioral state remains un-

clear. Here, we identified neural correlates (NCs) of team flow in human participants using a music rhythm

task with electroencephalogram hyperscanning. Experimental manipulations held the motor task constant

while disrupting the corresponding hedonic music to interfere with the flow state or occluding the partner

’

positive feedback to impede team interaction. We validated these manipulations by using psychometric

ratings and an objective measure for the depth of flow experience, which uses the auditory-evoked

Significance Statement

This report presents neural evidence that teams falling into the flow state (team flow), a highly positive expe-

rience, have a unique brain state distinct from ordinary flow or social states. We established a new objective

neural measure of flow yet consistent with subjective reports. We identified neural markers of team flow at

the left middle temporal cortex (L-MTC). We showed the L-MTC had a unique causality and contributed to

information integration during team flow. Finally, we showed that team flow is an independent interbrain

state with enhanced information integration and neural synchrony. The data presented here suggest a neu-

rocognitive mechanism of team flow.

September/October 2021, 8(5) ENEURO.0133-21.2021 1

–

Research Article: New Research

potential (AEP) of a task-irrelevant stimulus. Spectral power analysis at both the scalp sensors and ana-

tomic source levels revealed higher

power specific to team flow in the left middle temporal cortex (L-

MTC). Causal interaction analysis revealed that the L-MTC is downstream in information processing and

receives information from areas encoding the flow or social states. The L-MTC significantly contributes to

integrating information. Moreover, we found that team flow enhances global interbrain integrated informa-

tion (II) and neural synchrony. We conclude that the NCs of team flow induce a distinct brain state. Our re-

sults suggest a neurocognitive mechanism to create this unique experience.

Key words:

EEG; flow; hyperscanning; in the zone; neural synchrony; teams

Introduction

Flow state, or

“

getting into the zone,

”

is a psychological

phenomenon that develops when balancing the perform-

ance with the challenge of a task and providing clear

goals and immediate feedback (

Csikszentmihalyi, 1975

;

Nakamura and Csikszentmihalyi, 2002

). The flow state is

characterized by intense task-related attention, effortless

automatic action, a strong sense of control, a reduced

sense of external and internal awareness, and a reduced

sense of time (

Nakamura and Csikszentmihalyi, 2002

The flow state is intrinsically rewarding and can positively

affect subsequent experiences (

Csikszentmihalyi, 1975

2014

;

Nakamura and Csikszentmihalyi, 2002

;

Harmat,

2016

). Because of these characteristics, flow is an in-

tensely-studied topic in sports, music, education, work,

and gaming. The flow state can develop during an individ-

ual (solo) activity or a group activity. There is a growing in-

terest in studying flow in group activities, i.e., group flow,

among several fields including psychology, sociology, or-

ganizational behavior, and business (

Sawyer, 2007

;

Walker, 2010

;

Hart and Di Blasi, 2013

;

Salanova et al.,

2014

;

Hari et al., 2015

;

Pels et al., 2018

). Team flow is a

specific case of group flow in which the group forms a

team that is characterized by a common purpose, comple-

mentary skills, clear performance goals, strong commitment,

and mutual accountability (

Katzenbach and Smith, 1993a

;

van den Hout et al., 2018

). The positive subjective experience

during team flow, as in sports teams, music ensembles,

dance squads, business teams, or video gaming teams, is

superior to everyday social interaction or experiencing indi-

vidual flow (

Sato, 1988

;

Hari et al., 2015

;

Pels et al., 2018

A simplistic assumption is that team flow is a simple

combination of the flow and the social states. These two

states are disparate, in other words, acting in a social

context is not necessarily sufficient to get into the flow

state, and vice versa. In prior reports, the neural mecha-

nisms underlying the individual flow state and social experi-

ence have been studied in isolation. For social information

processing, several networks have been implicated. Social

perception, empathy, mentalization, and action observation

networks may provide partially overlapping brain regions in

conjunction with the amygdala, anterior cingulate cortex

(ACC), prefrontal cortex (PFC), inferior frontal gyrus (IFG),

and the inferior and superior parietal lobule (IPL/SPL), re-

spectively (

Ongür and Price, 2000

;

Dodell-Feder et al., 2011

;

Lamm et al., 2011

;

Molenberghs et al., 2012

;

Stanley and

Adolphs, 2013

;

Yang et al., 2015

). Meanwhile, several stud-

ies of individual flow have shown increased activity in the

IFG and the IPL/SPL, and decreased activity in the PFC

(

Klasen et al., 2012

;

Ulrich et al., 2014

2016a

;

Harris et al.,

2017

). We cannot hypothesize that any of the aforemen-

tioned brain regions contribute to team flow since there are

concordant and discordant overlaps. Hence, we posit that

team flow is more than a combination of these two states

and may arise from a unique interaction among these brain

regions, which would reveal new neural correlates (NCs)

that create this unique team flow brain state.

Phenomenologically, the experience of team flow is sub-

jectively more intense than the individual flow state and ordi-

nary social state. However, the underlying neural mechanism

is still unclear. This study directly examines the underlying

neural activity patterns, emerging at both the intrabrain and

interbrains levels during team flow. Using an exploratory ap-

proach, we identified the intrabrain correlates in team flow

that are distinct from ordinary flow or social experiences.

Using causality analysis, integrated information (II), and neural

synchrony data, we propose a model of the neural mecha-

nisms that underlie team flow.

Received March 29, 2021; accepted September 7, 2021; First published

October 4, 2021.

The authors declare no competing financial interests.

Author contributions: M.S., M.C., and S.S. designed research; M.S., M.C.,

and D.-A.W. performed research; M.S., M.C., A.L., N.T., and S.S. analyzed

data; M.S., M.C., A.L., N.T., D.-A.W., C.-h.T., S.N., and S.S. wrote the paper.

This work was supported by the Program for Promoting the Enhancement of

Research Universities funded to Toyohashi University of Technology and

Grants-in-Aid for Scientific Research (Fostering Joint International Research

(B), Grant Number 18KK0280) (M.S. and S.N.), Sponsored Research by

Qneuro, Inc. (M.S. and S.S.), Translational Research Institute through NASA

Cooperative Agreement NNX16AO69A (M.S. and S.S.), and by the Japan

Science and Technology (JST)-CREST Grant JPMJCR14E4 (to S.S.). M.C. is

supported by the University of Hong Kong Postgraduate Scholarship Program.

C.-h.T. is supported by the University of Hong Kong General Research Fund

and the Cooperative Research Project Program of the Research Institute of

Electrical Communication, Tohoku University. N.T. is supported by Australian

Research Council Discovery Projects Grants DP180104128 and

DP180100396. A.L. is supported by an Australian Government Research

Training Program Scholarship.

Acknowledgements: We thank Dr. Charles Yokoyama (University of Tokyo,

Japan), Dr. Simone Shamay-Tsoory (University of Haifa, Israel), Dr. Katsumi

Watanabe (Waseda University, Japan), and Dr. Makio Kashino (NTT

Communications Science Laboratories, Japan) for their comments on this

manuscript. We also thank Shota Yasunaga (Pitzer College, CA), Jessica Ye

(California Institute of Technology, CA), Naomi Shroff-Mehta (Scripps College,

CA), and Salma Elnagar (University of Cambridge, UK) for help with data

collection and analysis and Wenqi Yan (Monash University, Australia) for

preliminary data analysis with integrated information.

Correspondence should be addressed to Mohammad Shehata at

mohammad.

shehata@gmail.com

https://doi.org/10.1523/ENEURO.0133-21.2021

This is an open-access article distributed under the terms of the

Creative

Commons Attribution 4.0 International license

, which permits unrestricted use,

distribution and reproduction in any medium provided that the original work is

properly attributed.

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Materials and Methods

Participants

We recruited 78 participants for the screening process.

In the main EEG experiment, 15 participants (five males;

age: 18

–

35 years) attended and formed 10 pairs (three

male pairs), of which five participants (one male) were

paired twice. Written informed consent was acquired from

all participants. Human subjects were recruited at a loca-

tion which will be identified if the article is published. All

the procedures were approved by the Institutional Review

Board of California Institute of Technology.

Task

We used a commercial music rhythm game called

“

O2JAM U

”

(version 1.6.0.11, MOMO Co) played on an

iPad air (model No. MD786LL/B, system, iOS 10.3.2). The

basic structure of this game follows the most common

structure in the music rhythm genre. Two consecutive

screenshots of the game are shown in

Figure 1

. Visual

cues (notes) moves in lanes from the top to the bottom of

the screen where the tapping area is located. There are

two kinds of cues: short and long ones. A player

’

s task is

to tap when a short cue reaches the tapping area, and to

tap and hold for the duration a long cue is at the tapping

area. The cues are designed to give the impression of

playing a musical instrument, which produces much of

the positive experience of the game. The game displays

two types of real-time feedback on the players

’

perform-

ance. The first feedback type includes a semantic judg-

ment expression (

“

EXCELLENT,

”“

GOOD,

”

“

MISS

”

)

together with a numerical score presented at the center

and the top corners of the screen (Extended Data

Fig.

1-1

). We made the first feedback type invisible to the

participants, using a privacy screen protector, to enhance

participants

’

focus on the tapping area. The second feed-

back type is a flashing visual effect that appears at the

tapping area each time the player taps at the correct tim-

ing with the cue (Extended Data

Fig. 1-1

). We kept this

Figure 1.

Behavioral establishment of team flow.

, Diagram of the finger-tapping music rhythm game. Participants must tap when

animated cues moving from the top of the screen reach the tapping area.

, Manipulations: team flow is predicted when the partici-

pants are playing the unmodified song and they can see the partner

’

s positive feedback (Team Flow). The flow state is disrupted

through scrambling the music (Team Only). Team interaction is disrupted by hiding the partner

’

s positive feedback using an occlu-

sion board (Flow Only). See

Table 1

for details.

, Sequence of the trials, showing which song and condition per trial was assigned

during the main experiment.

, Trial analysis: participants were sitting still while listening to a background music during the resting

phase and played the game in the playing phase. The electroencephalogram was epoched for objective assessment of flow i.e., the

AEP analysis of the task-irrelevant beeps (orange bar) and for the NCs analysis (green bar). After each trial, participants answered

the questionnaire for the subjective assessment of flow. Extended Data

Figure 1-1

shows detailed analysis pipeline.

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feedback type visible to the participants as positive rein-

forcement (

Movie 1

). The game provides two modes of

play: a two-lane or four-lane mode, in which either two or

four lanes of moving cues are presented. We used the

two-lane mode during individual screening with the par-

ticipant responsible for both lanes. We used the four-lane

mode during the main experiment in which a pair of par-

ticipants played with each participant responsible for two

adjacent lanes.

After playing each song (trial), the game displays a per-

formance report on the screen, including a final numerical

score, the total number of cues, and the number of

missed cues. The performance report of each trial was

hidden from the participants until they finish answering

their subjective experience psychometric ratings. The

percentage of the missed cues per the total number of

cues was used as a metric for the performance of each

pair of participants.

Manipulations

To create the team flow condition, the iPad was tilted

and positioned, using a custom-made holder, equidistant

from the pair of participants. Participants were instructed

to sit on two chairs at a fixed distance, to keep their heads

on chin rests, and to minimize their body movements ex-

cept for finger movement. The iPad was connected to a

pair of stereo speakers placed horizontally and equidis-

tant from the iPad and the pair of participants. A pair of

participants played in the four-lane mode.

Previous studies controlled the flow experience by ma-

nipulating the skill-challenge level in the experimental

setup, either passively by free task performance and ret-

rograde classification of certain time periods into several

flow levels (

Klasen et al., 2012

) or by actively controlling

the task level to be either too easy (boredom), adaptive

(flow), or too hard (overload;

Ulrich et al., 2014

2016a

One of the issues with modifying the skill-challenge level

to study flow is that this changes other cognitive func-

tions, such as attention, sensory information processing,

and cognitive load necessary to perform each task, as

well as gross changes in motor behavior. Therefore, ma-

nipulating the skill-challenge level complicates the ability

to distinguish the neural mechanisms underlying team

flow interaction. To avoid this complexity, we kept the

task identical in all conditions using the same sequence of

tapping cues. We manipulated the intrinsic reward/enjoy-

ment dimension of flow by scrambling the game music

and hence disrupting the pleasant experience (

Table 1

To create the team only condition, participants played

the same song (i.e., an identical sequence of moving

cues) as the team flow using the same setup; however, a

reversed and shuffled version of the music was played

from the same speakers (

Table 1

). The music for each

song was reversed through an online audio editing web-

site (

https://audiotrimmer.com/online-mp3-reverser/

) and

then cut into 5-s fragments through an online audio cutter

(

http://mp3cut.net/

). We randomly shuffled the fragments

and rejoined them through an online audio joiner (

http://

audio-joiner.com/

To create the flow only condition, participants played

the same song (i.e., an identical sequence of moving

cues) as the team flow using the same setup; however, a

black foam board (1 cm

1.5 m

75cm) was placed be-

tween the chairs to completely block the participants

’

view of each other, and a black piece of cardboard was

placed across the iPad screen with an opening to show

the visual cues but not the tapping area (Extended Data

Fig. 1-1

Screening process

The participants were first tested using a selected song

(269 cues per the two lanes) in the two-lane mode to

exclude unexperienced participants. Participants were

qualified to complete the study if they missed no more

than 10 cues. Out of the 78 participants recruited for

the first screening test, 54 participants were qualified and

the remaining 24 participants were excluded. The 54

qualified participants were also tested using other se-

lected songs with a higher number of cues to confirm their

Movie 1.

A few seconds of game-play in the Team Flow and

Team Only conditions. The

“

beeps

”

word at the bottom right in-

dicates the timing of the task-irrelevant beep sound presenta-

tion. These words are overlaid in the video for illustration and

were not present during the experiment. The scores and other

indicators at the center and at the top right and left corners

were hidden from the participants. [

View online

]

Table 1. Comparison of the stimuli across the experimental conditions

Conditions

Team Flow

Team Only

Flow Only

Cues sequence (visual stimulus)

Self

Constant (visible to both participants)

Partner

Constant (visible to both participants)

Positive Feedback

Self

Visible (performance dependent)

Partner

Visible

Not visible

Song (auditory stimulus)

Original

Scrambled

Original

Beeps (task-irrelevant stimulus)

Constant

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skill level. Because the positive experience of this game

depends on individual preference for the music rhythm,

we prepared 11 songs (500

–

960 cues per the four lanes)

in various genres. The 54 qualified participants were

asked to rate their preferences for all the 11 songs sepa-

rately on a seven-point scale (one for

“

not like it at all,

”

seven for

“

like very much

”

). Although the duration of the

songs varied from 110 to 160 s, each song was played

only for 1 min, which was long enough to ensure that par-

ticipants had heard the main rhythm of the song. We fixed

the duration for all songs to ensure the accuracy of prefer-

ence ratings. To avoid possible influence from the experi-

menter, the experimenter maintained a neutral attitude by

avoiding eye contact with participants or a physical re-

sponse to the music. Participants were paired based on

their skill level and song preference rating.

The second step of the process was to screen paired

participants for their preference to the team set-up (team

flow) versus playing in the team with board set-up (flow

only) or a single-player set-up (solo flow) in a behavior

pilot experiment. For the solo flow condition, we used the

two-lane mode of the game. At the end of the pilot experi-

ment, we presented the following question:

“

Based on

how much you enjoyed the performance and you want to

play it again, please rank the following experiences: sin-

gle-player set-up, team set-up, the team with board set-

up.

”

We presented a scale from 1 (least preferred) to 7

(strongly preferred) in front of each set-up, and the rank-

ing was not a forced-choice one. We excluded partici-

pants who ranked the team with board set-up or single-

player set-up higher than the team set-up. Out of the 54

participants from the first screening step, 38 prosocial

participants were invited for the main experiment on an-

other day, and the remaining 24 participants were ex-

cluded. For both the screening and the main experiment,

we paired only same gender participants, and the main

pairing criteria for qualified participants were their skill

level and song preferences. As friends were encouraged

to pair up to participate in this experiment, we preferred

pairing friends into the main experiment if they satisfied

the main criteria. Participants reported their relation with

the partner by answering the question

“

Generally, how

much time do you spend with the other player?

”

Pairs

who answered

“

first time to meet

”

“

only meet in the last

experiment

”

were categorized as strangers; pairs who an-

swered

“

less than 5 h per week,

”“

–

20 h per week,

”

“

more than 20 h per week

”

were categorized as friends. In

total, 17 participants consisted of six pairs of strangers

and five pairs of friends.

Main experiment

After setting the EEG cap, the electrode positions were

co-registered with the T1-magnetic resonance imaging

(MRI) using the Brainsight TMS Navigation system (Rogue

Resolutions Ltd). Then the paired participants, seated

on two chairs at a fixed distance, underwent a beep-only

trial. In this trial, they were instructed to passively listen to

the task-irrelevant beep stimulus for 2.5 min, to keep their

heads on chin rests and their eyes open, and to minimize

their body movements. This trial was to check the EEG

recording quality and verify that we could obtain a clear

AEP response. The paired participants then performed a

practice trial in the flow only condition to become familiar-

ized with the procedure. Then each pair of participants

was required to play six songs each at the team flow,

team only, or flow only conditions forming 18 trials (

Fig.

). One pair played only five songs because of time

availability. The sequence of songs and conditions were

pseudorandomized (

Fig. 1

). To keep participants

’

con-

tinuous interest, the consecutive songs were always differ-

ent (

Fig. 1

). To control practice and carryover effects, we

arranged each condition to have an equal chance of being

before or after the other two conditions (

Fig. 1

). All trials

included a resting phase and a playing phase (

Fig. 1

During the resting phase, participants were instructed to

passively listen to the task-irrelevant beep sound and the

game background music for 30 s, to keep their heads on

chin rests and their eyes open, and to minimize their body

movements. Then, the experimenter asked participants to

click the game-play icon on the iPad to start the playing

phase.

During the playing phase, participants were instructed

to keep their heads on chin rests, to minimize their body

movements except for finger movement, and to minimize

vocal sounds that could distract their partner. The partici-

pants were allowed to give verbal comments related to

the game. The participants did not comment while playing

the game. They were only allowed to give verbal com-

ments after answering the psychometric ratings and re-

vealing the participants

’

final scores. We video recorded a

top-view of the iPad and the participants

’

hands using an

iPhone fixed

;

50cm above the iPad where all the types

of feedbacks were visible. After the playing phase of each

trial, participants were given access to private screens

and keyboards to freely answer the psychometric ratings

on the flow experience and team interaction experience.

Then, the pair were allowed to jointly view the perform-

ance report. The experimenter asked the participants

whether they wanted to proceed to the next trial or if they

needed some rest to minimize the effect of fatigue on per-

formance or EEG recording quality.

Task-irrelevant stimulus

A task-irrelevant auditory stimulus (a beep sound) was

pseudorandomly presented to probe the strength of the

participants

’

selective attention to the game and was

used as an objective measure of flow. We presented beep

trains played at 5Hz for 1 s (i.e., each train consisted of

five beeps). Each beep was at 500 Hz and lasted for 10

ms. The beep trains simulated the sound of someone

knocking on a door to make the stimulus as natural as

possible. The interval between the beep train varied from

4 to 8 s. The beeps were generated by MATLAB 2012

(The MathWorks) and delivered through another pair of

speakers placed equidistant from the iPad.

Anatomical MRI acquisition

To increase the accuracy of source estimation for corti-

cal activity, individual head anatomy from each

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participant, who passed the screening and agreed to par-

ticipate in the main experiment, was acquired with MRI. A

3 Tesla Siemens Trio scanner and standard radio fre-

quency coil was used for the entire MRI scanning. High

resolution structural images were collected using a stand-

ard MPRAGE pulse sequence, providing full brain T1-

weighted 3D structural images.

Psychometric ratings and calculation of experience

indices

From subjective reports, we calculated the flow, team,

and team flow indices, by calculating the arithmetic mean

of the ratings for each trial, to estimate subjective experi-

ence for flow state, positive team interaction, and team flow,

respectively (Extended Data

Fig. 2-1

). For assessing flow ex-

perience, we used psychometric ratings related to the skill-

demand balance (Q1 and Q2), feeling in control (Q3), auto-

maticity (Q4), enjoyment (Q5), and time perception (Q6) di-

mensions of flow (

Nakamura and Csikszentmihalyi, 2002

For assessing team interaction, we used psychometric rat-

ings related to awareness of partner (Q7), teamwork (Q8),

and coordination (Q9) dimensions of positive team interac-

tion. Psychometric ratings assessing competition (Q10) and

distraction (Q11) were used to confirm the absence of nega-

tive team interactions and were not included in any index.

The team flow index was calculated by averaging the flow

and team indices. In addition, we tested the effect of friend-

ship between the two players (friend or stranger) on the

subjective rating of flow. There was no significant differ-

ence between friend-pairs and stranger-pairs in flow

index, team index or team flow index (two-way repeated

measures ANOVA, main effect of relation for flow index,

(1,18)

=0.6853,

= 0.4186; for team index,

(1,18)

0.1557,

=0.6978; for team flow index,

(1,18)

=1.4992,

=0.2366). Therefore, we combined friend-pairs and

stranger-pairs in the following analysis.

Hyperscanning EEG recording and preprocessing

Electroencephalogram (EEG) was recorded simulta-

neously from both participants using a dual BioSemi

ActiveTwo system (BioSemi Inc.). Each participant wore

a cap holding 128 scalp Ag/AgCl electrodes. Signals

were amplified by two daisy-chained ActiveTwo AD

boxes where one AD box was connected to the control

PC and worked as a master controlling the other AD

box to ensure synchronization. Electrode impedance

waskeptbelow10k

. For each cap, an active common

mode sense (CMS) electrode and a passive driven right

leg (DRL) electrode positioned near the vertex served

as the ground electrodes. EEG signals were recorded at

asamplingrateof2048Hz(laterdown-sampledto

256Hz). During recording, the A1 electrode, or A2 elec-

trode in three participants served as a reference. In the

ABC layout (a Biosemi designed equiradial system),

these electrodes overlap with the Cz location of the in-

ternational 10

–

20 system. Signals were recorded and

saved using ActiView/LabView software (version 8.04,

BioSemi Inc.) installed on the control PC. Another mas-

ter PC was used to generate the task-irrelevant beep

sound and to send signals to the EEG data receiver

marking the onset of each beep train (event triggers).

The event triggers were used to align the EEG data with

the resting and the playing phases by using a real-time

projection of the top-view video recording to the control

PC. The experimenter confirmed that all the onsets of

the beep trains happened during the resting or the play-

ing phase periods.

To analyze the auditory-evoked potentiation (AEP), EEG

data were epoched

0.5

–

1 s (1.5-s total) flanking the

beep train onsets (AEP epochs). To analyze the NCs of

game play experience, EEG data were epoched 2

–

5 s (3-s

total) after the beep train onset (NC epochs;

Fig. 1

). EEG

data were bandpass filtered at 0.5

–

50Hz, using the

Parks-McClellan FIR filter, and re-referenced to the aver-

age of all channels. After this initial preprocessing, we did

a visual inspection for artifacts, including EMG, then per-

formed artifact-rejection using automatic independent

component analysis (ICA) rejection using the FASTER

toolbox (

Nolan et al., 2010

). Bad channels showing line

noise noted during recording sessions were rejected and

interpolated during the FASTER preprocessing.

Auditory-evoked potential (AEP) analysis

To select the channels maximally responsive to the

task-irrelevant auditory stimuli, we analyzed the AEP

epochs during the resting phase. We calculated the

event-related spectral perturbation (ERSP) and the intere-

poch coherence (IEC) using the EEGLAB toolbox (version

14.1.1;

Brunner et al., 2013

). Both ERSP and IEC showed

changes in

activity (3

–

7 Hz) at 100

–

350 ms postonset,

with a peak increase at 150

–

250ms postonset (Extended

Data

Fig. 2-2

). Topographical analysis in the

band

showed strong positive activity in the 14 central channels

from 200

–

260 ms postonset (Extended Data

Fig. 2-2

The frequency, time, and topographical frames of our

AEP were consistent with previous reports (

van Driel et

al., 2014

;

Stropahl et al., 2018

). For each trial, we used

IEC in the

band during the resting phase to select chan-

nels showing stable AEP. IEC was averaged across the 14

central channels, and channels showing IEC lower than

one standard error below the mean were excluded from

further AEP analysis for that trial. We then analyzed

power from

200 to 500ms flanking the beep train onsets

during the resting and the playing phase (Extended Data

Fig. 2-2

). AEP peak amplitude was calculated accord-

ing to the method described by a previous simulation

report showing that event-related potential measured

based on the mean amplitude surrounding the group la-

tency is the most robust against background noise

(

Clayson et al., 2013

). Therefore, we calculated the N1,

P2, and N2 peak latencies averaged across all conditions

during the resting phase (Extended Data

Fig. 2-2

). The

individual N1, P2, and N2 mean peak amplitudes

40ms

surrounding the calculated peak latencies were obtained

during the playing phase (Extended Data

Fig. 2-2

). This

resulted in the following time windows: N1 (110

–

150ms),

P1 (210

–

250 ms), and N2 (310

–

350ms). The amplitude

peaks at these time windows were averaged, considering

polarity [i.e., (P2-N1-N2)/3], and used as AEP (

Fig. 2

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Anatomically-defined source estimations

FreeSurfer (

Reuter et al., 2012

) was used for automatic

segmentation and reconstruction of the MRI images. MRI

images were used to compute each individualized head

model using the boundary element model (BEM) imple-

mented in OpenMEEG withinthe BrainStorm software

package (version 3.4) using the default parameters

(

Gramfort et al., 2010

;

Tadel et al., 2011

). MRI registration

with EEG electrode-positions were aligned with each par-

ticipant

’

s BEM model, and sources were computed (ver-

sion 2018) using BrainStorm for each NC epoch in the

playing phase. Maps of cortical activity density were ob-

tained across the BEM mesh using the distributed mini-

mum-norm estimate (MNE) method, with constrained

dipole orientations and no baseline noise correction. For

cortical region-based analysis, brain regions were defined

according to the anatomic parcellation of the Destrieux

atlas as implemented in FreeSurfer and available in

BrainStorm (

Destrieux et al., 2010

). The time series of

source activities from the 15,002 vertices and the aver-

aged activity of the predefined 148 regions of interest

(ROIs) were exported for further analysis.

Power spectrum analysis

The power spectral density (PSD) estimate was calcu-

lated using Welch

’

s overlapped segment averaging

estimator as implemented in the MATLAB 2016a signal

processing toolbox within the EEGLAB toolbox using de-

fault parameters (

Welch, 1967

;

Brunner et al., 2013

). The

normalized PSD was calculated for each NC epoch then

averaged within each trial yielding trial PSD data at each

of the 128 channels, the 148 brain region sources, and the

15,002 mesh vertex sources. For each song played, the indi-

vidual

’

s mean PSD across the three conditions was calcu-

lated. The normalized power was calculated by subtracting

the individual

’

smeanPSDfromthePSDateachcondition.

The normalized power was averaged within the following fre-

quency bands:

–

3Hz),

–

7Hz),

12 Hz),

(13

–

30 Hz),

(31

–

120Hz), and lower

(31

–

50 Hz). We started

with exploratory analysis by checking the normalized power

grand-averaged across all channels for each frequency band

(Extended Data

Fig. 3-1

). We found significant differences

across conditions in the

(31

–

120Hz; one-way repeated

measures ANOVA,

(2,57)

=5.1445,

= 0.0105) band, and

showing a trend in the

12Hz; one-way repeated meas-

ures ANOVA,

(2,57)

= 2.3661,

=0.1075) and

(13

–

30 Hz;

one-way repeated measures ANOVA,

(2,57)

= 2.0504,

0.1427) bands. The

–

3Hz; one-way repeated measures

ANOVA,

(2,57)

=0.5378,

=0. 5884) and

–

7Hz; one-

way repeated measures ANOVA,

(2,57)

=0.1129,

=0.8936)

bands were not significant. For the topographical analysis,

the normalized power for the 128-channel data and the per-

mutation statistics with Bonferroni multiple comparison

Figure 2.

Assessment of the flow state.

–

, Subjective assessment of flow: psychometric rating indices as a measure of subjective

flow (flow index;

), team interaction (team index;

), or team flow (team flow index;

) experiences (Extended Data

Fig. 2-1

shows

the detailed psychometric ratings for each question). Friedman test with Conover

’

post hoc

test; *

0.05, **

0.01, ***

0.001.

Error bars represent mean

SEM;

= 15.

, Objective assessment of flow.

, The mean AEP calculated by averaging the follow-

ing time windows: N1 (110

–

150ms), P1 (210

–

250ms), and N2 (310

–

350ms), considering polarity. The non-flow condition (Team

Only) showed statistically significant higher AEP than the flow conditions. One-way repeated measures ANOVA with Bonferroni

post

hoc

test; *

0.05. Error bars represent mean

SEM;

= 15.

, Spearman

’

s correlation between AEP and flow index. AEP is nega-

tively correlated with the flow index in the team flow condition (Spearman

’

s Rho =

0.48,

= 0.03), showing a negative correlation

trend in the flow only condition (Spearman

’

s Rho =

0.29,

= 0.22), and no correlation in the team only condition (Spearman

’

Rho= 0.11,

= 0.64). The lines indicate the regression lines. Shaded areas indicate a 95% confidence interval;

= 20. Extended

Data

Figure 2-2

shows the detailed AEP analysis.

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correction were projected to topographical maps using

EEGLAB toolbox. As detecting high-

power (

50 Hz) using

noninvasive EEG might be prone to artifacts (

Völker et al.,

2018

), we only considered the combined

and low-

(

)

band (13

–

50Hz) for further analysis. We used one-way re-

peated measures ANOVA across conditions for determining

the significance in each anatomic-source

power effect.

We set the significance threshold to

0.00034 (i.e., 0.05/

148 ROIs) to correct for multiple comparisons (Bonferroni-

corrected critical value).

Unsupervised clustering analysis

We clustered the 15,002 mesh vertex sources based on

their

power. We used scikit-learn, a Python machine

learning library, and implemented the unsupervised ag-

glomerative clustering approach (

Abraham et al., 2014

Agglomerative clustering uses a bottom-up hierarchical

approach where vertices are progressively linked together

into clusters based on their feature similarity. We used

three features for clusters which are the grand averaged

normalized power at each of the three conditions.

We used the Euclidean distance as a similarity measure

and the complete linkage criteria, which minimizes the

maximum distance between observations of pairs of clus-

ters. We have tried setting the number of clusters into 3

–

40 clusters. We selected the minimum number of clusters,

seven in our case, that shows trends for flow-related and

team-related clusters. When we set the number of clusters

to three up to six clusters, the anatomic resolution was not

clear. When we set the number of clusters to more than

seven clusters, the anatomic resolution was more clear,

and we obtained higher significant clusters even after mul-

tiple comparison. When we set the number of clusters to

seven clusters (cls; Extended Data

Fig. 4-2

), we detected

two cls distributed over the anterior part of the frontal cor-

tex, where the

power was higher in the team only con-

dition than the other conditions (Extended Data

Fig. 4-2

; cls 1 and 2). This pattern was significant in cl 2 (one-way

repeated measures ANOVA,

(2,57)

= 3.6125,

=0.033),

while cl 1 showed a trend (one-way repeated measures

ANOVA,

(2,57)

=1.5916,

=0.2125). The suppressed activ-

ity in these clusters is specific to the flow experience, re-

gardless of the social context, which is consistent with a

neural representation of the automaticity-dimension of flow

(

Klasen et al., 2012

;

Ulrich et al., 2014

). We also detected

two clusters distributed mostly over the middle and inferior

frontal cortex and the left occipital cortex (OC), where the

power was lower in the flow only condition than the

other conditions (Extended Data

Fig. 4-2

; cls 3 and 4).

This pattern was significant in cl 4 (one-way repeated

measures ANOVA,

(2,57)

= 7.4841,

= 0.0013), while cl

three showed a trend (one-way repeated measures

ANOVA,

(2,57)

=2.4288,

=0.0972). The increased ac-

tivity in these clusters is specific to team interactions,

regardless of the flow state. The remaining clusters

were distributed mostly over the temporal, parietal, and

occipital cortices, where the

power was higher

in the team flow condition than the other conditions

(Extended Data

Fig. 4-2

;cls5

–

7). This pattern was

significant in all three cls: cl 5 (one-way repeated measures

ANOVA,

(2,57)

=11.8753,

=0.000049), cl 6 (one-way re-

peated measures ANOVA,

(2,57)

=9.548,

=0.00027), and

cl 7 (one-way repeated measures ANOVA,

(2,57)

=6.9256,

=0.002). The increased activity in these clusters was spe-

cific to team flow.

Grouping of ROIs

First, the anatomically-defined ROIs that showed signif-

icant

normalized power across conditions, as shown

in Extended Data

Figure 3-2

–

, were grouped as RG7

regardless of their cluster composition. Second, for the

remaining anatomic-defined ROI, we calculated the clus-

ter composition as the percentage of the flow-related

clusters (cls 1

–

2), team-related clusters (cls 3

–

4), and

team flow-related clusters (cls 5

–

7). We checked whether

the anatomically-defined ROIs can be spatially subdi-

vided into smaller ROIs with clear tendencies for a certain

activity-dependent cluster composition (Extended Data

Fig. 4-1

). This check was done by calculating a cumula-

tive cluster composition curve to define a threshold for

subdividing the ROIs (Extended Data

Fig. 4-1

). We pre-

sented the superior frontal cortex as an example of the

subdivided ROIs (Extended Data

Fig. 4-1

). Finally, we

grouped anatomically-defined ROIs or their subdivisions

into six regions (RGs) per hemisphere based on the major

activity-dependent cluster composition (Extended Data

Fig. 4-1

). Therefore, the total number of RGs was 14

RGs (seven RGs per hemisphere). For each of the 14 RGs,

the activity-dependent cluster composition is summarized

in Extended Data

Figure 4-3

and the anatomic composi-

tion is summarized in Extended Data

Figure 4-4

. The time

series from all the 15,002 vertices were averaged based

on the new 14 RGs and hence reduced into 14 time series

for each trial per participant.

Intrabrain causal interactions analysis

We used the Source Information Flow Toolbox (SIFT) to

fit an adaptive multivariate autoregressive (AMVAR)

model for the 14 RGs activities for each subject

’

s trial

using the Vieira

–

Morf algorithm (

Delorme et al., 2011

). We

fitted the NC epoch with a sliding window length of

500ms and a step size of 25 ms (

Wang et al., 2014

Model order was selected by minimizing the Akaike

Information criterion. We validated each fitted model

using tests included in SIFT for consistency, stability, and

whiteness of residuals. To estimate causal interactions,

we used three directed model-based linear frequency-do-

main Granger-causality (GC) measures (

Wang et al.,

2014

). These measures are the normalized partial directed

coherence (nPDC;

Baccalá and Sameshima, 2001

), the

direct directed transfer function (dDTF;

Korzeniewska et

al., 2003

), and the Granger

–

Geweke causality (GGC;

Geweke, 1982

;

Bressler et al., 2007

). For each connectiv-

ity measure, we averaged across trials for each partici-

pant per condition, then averaged across the NC epoch

time interval (3 s) and across the

(13

–

50Hz) fre-

quency. Finally, to quantify the degree by which an RG

sends or receives information, we calculated the ratio of

sending (to) divided by receiving (from) for each RG-RG

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interaction and then average these ratios for each RG per

condition per participant (to/from ratio). A two-way re-

peated measures ANOVA was used as statistical test. To

calculate the information senders for RG-RG causal inter-

actions, we used the Log to/from GGC ratio for each RG-

RG connection. Top information senders were calculated

by setting a threshold with a

value of 0.064. The RG-RG

connections above this threshold were represented on a

circular graph.

Integrated information analysis

Integrated information (II) was used as a measure of

inter-RG bidirectional causal interaction. For every pair of

time courses of the RGs activities, within and between

participants, we operationalized the

“

state

”

of the pair of

RGs by discretizing time-samples into binary values. To

roughly match the frequency range of 13

–

50Hz, we first

down-sampled the RGs activities to give timesteps of

12.8, 17.1, 25.6Hz or 51.2Hz (that is, a time step of 19.5,

39.1, 58.6, or 78.1ms). Using the down-sampled RGs ac-

tivities, we then converted each pair of consecutive time

samples to

“

”

if the RGs activity

’

s voltage was increas-

ing over two time steps and

“

off

”

otherwise. Using the

time series of binarized states, we computed the probabil-

ities of each state transitioning into each other state, con-

structing a transition probability matrix (TPM) which

describes the evolution of the pair of RGs activities across

time. To ensure accuracy of transition probabilities, we

computed these across all trials. As lower time resolutions

give fewer observations with which to compute the proba-

bilities, we repeated the down-sampling for each possible

“

start

”

(i.e., for each time-sample in the first time-bin) and

used all transitions from all shifted-down-sampled time

series to build the TPM. We then submitted the TPM to

PyPhi (1.2.0;

Mayner et al., 2018

), which then constructs a

minimally reducible version of the TPM, assuming inde-

pendence of RGs activities, and compares the original

TPM to the minimally reducible version to compute II

(

Oizumi et al., 2014

;

Mayner et al., 2018

). For each actual

pair, we calculated the normalized II value by subtracting

the absolute value from the average across all conditions

for each RG-RG connection. A three-way repeated meas-

ures ANOVA (condition

RG1

RG2) was used as a sta-

tistical test for normalized II at each RG-RG connection.

The global normalized II was calculated through averaging

normalized II values across all possible RG-RG connec-

tions. A one-way repeated measures ANOVA was used as

a statistical test for global normalized II.

Phase synchrony analysis

The phase-locking value (PLV), or intersite phase clus-

tering (ISPC), was used as an index of neural synchrony.

The distribution of the phase angle differences between

sources was generated at each time point (within the NC

epoch 3-s window) then averaged over (ISPC-trial;

Lachaux et al., 1999

;

Cohen, 2014

). ISPC-trial was calcu-

lated at each frequency and then averaged across the

frequency band of 13

–

50Hz. For each condition, we cal-

culated the ISPC-trial between all sources for the actual

pairs or for each of 10 randomly-assigned pairs. For each

actual or random pair, we calculated the normalized PLV

value by subtracting the PLV value from the average

across all conditions for each RG-RG connection. The

global normalized PLV was calculated through averaging

normalized PLV values across all possible RG-RG con-

nections. A two-way repeated measures ANOVA was

used as a statistical test.

Statistical analysis

All statistics were done using the Statistics and Machine

Learning Toolbox within MATLAB 2016a and JASP (Version

0.14.1). We compared non-overlapping dependent correla-

tions, as described in the article (

https://garstats.wordpress.

com/2017/03/01/comp2dcorr/

), using the Robust Correlation

Toolbox in MATLAB (

http://sourceforge.net/projects/

robustcorrtool/

) which was validated for Spearman

’

correlation (

Wilcox, 2016

). In this section, we give a

parameter justification for each analysis based on the

rationale for doing the analysis.

Screening process

The screening process is necessary in this study to at-

tain reasonable team flow behavioral response. In the

first screening process, we needed to assure that partici-

pants who signed up for this study have enough skill to

fall into the flow state. In the second screening process,

we needed to match participants based on their skill and

song preference. We assumed that this screening would

maximize the chances of finding pairs of participants

who can reach the team flow state.

Sample size

The final number of participants was mainly constrained

by availability after the screening process. We tried to

kept the final number of participations similar to the sam-

ple sizes reported in similar publications (

Yun et al., 2012

Note, during the main experiment, the data collection pro-

cess for one male pair of participants was interrupted be-

cause of a technical error, and the collected data were

excluded from data analysis.

Trial numbers

We limited the number of trials to six per condition to

avoid fatigue which might have compromised the possi-

bility of falling into the flow state in later trials. For one

pair, we could only collect five trials per condition be-

cause of time constraints. For another pair, one of the

trials contained excessive noise, and hence, we ex-

cluded this trial and all corresponding trials in the other

conditions.

Units of analysis

Unless otherwise described, the unit of analysis is par-

ticipant, i.e.,

= 15. For the five participants invited twice,

we averaged the results from the two experiments giving

one data point. In some analyses, the unit of analysis was

participation, i.e.,

= 20. For the performance analysis,

the unit of analysis was the final score for the pair, i.e.,

=10. Data collection was not performed blind to the

conditions of the experiment. Experimental blinding

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was not possible because of the overt and obvious na-

ture of the experimental setup for each manipulation.

Data in all conditions were subjected to identical analy-

sis algorithms.

Data availability

Analysis codes used in the preparation of this article are

available at

https://osf.io/3b4hp

Results

Behavioral paradigm for team flow

We designed a behavioral paradigm to assess team

flow, in which a pair of participants played a popular

music rhythm game. The game

’

s task required responses

by tapping a touch screen when animated visual cues

reached a designated area and delivered instantaneous

positive feedback. The game created the impression of

playing a musical instrument, which increases the likeli-

hood of entering a flow state. Each pair of participants

played as a team by splitting the tapping area and sharing

in task completion with the common goal of obtaining the

best score for the team. We simultaneously recorded their

brain activities using electroencephalogram (EEG;

Fig.

; Extended Data

Fig. 1-1

;

Movie 1

). Participants were

screened to select prosocial highly-skilled participants in

this game and were matched according to their skill level

and song preference (for more details, see Materials and

Methods).

In the primary experimental condition, the team flow

condition, teams played the unmodified songs in an open

interpersonal setting to maximize the team flow experi-

ence (

Fig. 1

, left panel). To fulfill the team characteristics:

(1) common purpose: we instructed each pair of partici-

pants (team) to get the highest score for the team; (2)

complimentary skills: we matched participants based on

skill and song preference; (3) clear performance goals: we

provided the performance feedback at the end of each

trial; (4) keep a strong commitment: we allowed for the

visibility of teammate

’

s instant feedback; and (5) mutual

accountability: we explained that a decrease in perform-

ance from any teammate would affect the total score. We

designed two control conditions to manipulate either the

flow or the social states. To disrupt the flow state, we

manipulated the team only condition by modulating the

intrinsic reward/enjoyment dimension for flow by scram-

bling the game

’

s music. This procedure then interrupted

the sense of immersion and the continuity of the game

(

Fig. 1

, middle panel). To disrupt the social state in the

flow only condition, we used an occlusion board be-

tween the two participants that occluded the partner

’

positive feedback and bodies while leaving all of the cues

visible to both players (

Fig. 1

, right panel; Extended Data

Fig. 1-1

). We designed the manipulations to disrupt one

component of team flow per condition to ensure that any

discovered NC for team flow did not arise from only one of

these components.

To control for stimuli-related neural activities, we kept

all stimuli constant across conditions by asking the teams

to play the same song at the three conditions (

Fig. 1

For each song, the visual stimulus (cue sequence), the

total auditory stimulus presented, task difficulty, and the

sequence of the task-irrelevant beeps were kept constant

across conditions (

Table 1

). Then we normalized the neu-

ral signals per song. The only remaining variables across

conditions were the song

’

s pleasance and the visibility

of the partner

’

s positive feedback. There were no differ-

ences in the participants

’

performances across condi-

tions (repeated measures one-way repeated measures

ANOVA,

(2,27)

= 0.02437,

= 0.976), which ensure no

differences in gross motor responses.

Subjective assessment of team flow

To validate our manipulations, participants performed

psychometric ratings after each trial (

Fig. 1

; Extended

Data

Fig. 1-1

). To assess the dimensions of the flow

state, we presented the participants with the following

psychometric ratings: (1)

“

I had the necessary skill to play

this trial successfully

”

; (2)

“

I will enjoy this trial more if it

has less/more notes

”

; (3)

“

I felt in control while playing this

trial

”

; (4)

“

I made correct movements automatically with-

out thinking

”

; (5)

“

I love the feeling of this trial and want to

play it again

”

; and (6)

“

How time flies during this trial.

”

assess positive social interaction for teams, we pre-

sented the following: (7)

“

I was aware of the other play-

’

sactions

”

;(8)

“

I felt like I was playing with the other

person as a team

”

;and(9)

“

I was coordinating my fingers

with the other player

’

sfingers

”

(Extended Data

Fig. 2-1

Responses were collected on a seven-point Likert scale

and averaged into a flow index by averaging responses

across (1) to (6), a team index by averaging across (7) to

(9), and a team flow index by averaging across (1) to (9).

As expected, the flow index decreased significantly in

the team only condition than the other two conditions

(Friedman test, non-parametric repeated measures ANOVA,

=20.133,

0.001,

=15;

Fig. 2

). The team index de-

creased significantly in the flow only condition than the other

two conditions (Friedman test,

=20.373,

0.001,

=15;

Fig. 2

). The team flow index was significantly higher

in the team flow condition more than the other two condi-

tions (Friedman test,

=22.933,

0.001,

=15;

Fig. 2

The results of the psychometric assessment confirmed ef-

fectiveness of our manipulations to achieve the desired sub-

jective experience for each condition.

Objective assessment for the depth the flow state

To provide objective evidence for the flow state, we de-

veloped a novel neurophysiological measure of flow. We

used the intense task-related attention and the reduced

sense of external awareness dimensions of flow (

Nakamura

and Csikszentmihalyi, 2002

), and the well-known effect of

selective attention on the AEP (

Picton and Hillyard, 1974

During each trial, we presented task-irrelevant beeps to the

participants (

Fig. 1

; Extended Data

Fig. 1-1

). The more

the participants were immersed in the game, the weaker the

strength of the AEP in response to the task-irrelevant beeps.

Thus, this AEP constitutes an objective measure for flow

(

Fig. 2

; Extended Data

Fig. 2-2

). The mean AEP re-

sponse was significantly higher in the team only (mean=

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0.29, 95% confidence interval (CI) [0.22, 0.35]) condition

more than the other two conditions (team flow mean: 0.19,

95%CI [0.13, 0.25]; flow only mean: 0.17, 95%CI [0.11,

0.24]; one-way repeated measures ANOVA,

(2,42)

=6.149,

=0.006,

= 0.305;

Fig. 2

). The higher AEP in the team

only condition indicated that the participants where not fully

engaged, and hence their brains responded more to the

task-irrelevant beep sound. Notably, the AEP was negatively

correlated with the flow index in the team flow condition

(Spearman

’

sRho=

0.48 [

0.76,

0.03],

=0.03), while it

wasonlyweakly(Spearman

’

sRho=

0.29 [

0.66, 0.14],

=0.22) or not correlated (Spearman

’

sRho=0.11[

0.35,

0.55],

= 0.64) with the flow index in the flow only and the

team only condition, respectively (

Fig. 2

).Thenegativecor-

relation of AEP with the flow index was significantly stronger

in the team flow condition than the team only condition

(Spearman

’

s Rho difference =0.59 [

1.05,

0.05],

=0.04).

These results indicate that the experimental manipulations

did produce a deeper flow state in the team flow and flow

only conditions than the team only condition.

Power at the middle temporal cortex (MTC) as a

neural signature for team flow

To detect specific NCs for team flow, we used power

spectral analysis at various domains (Extended Data

Fig.

1-1

). In the frequency domain, we started with explora-

tory analysis by checking the normalized power grand-

averaged across all channels for each frequency band.

We found significant differences across conditions in the

–

12 Hz),

(13

–

30Hz), and

(31

–

120 Hz) bands (for

details, see Materials and Methods, Power spectrum

analysis; Extended Data

Fig. 3-1

). At the topographical

domain level,

power analysis did not show specific sur-

face channels significantly different across conditions

(data not shown). Topographical

-power and

-power

analysis showed four channels at the left temporal

area with significantly higher

and

power in the team

flow condition, more than the other two conditions

(

Fig. 3

). The power spectral analysis, averaged from

these four channels, showed a clear higher normalized

-power and

-power in the team flow condition than the

other conditions (

Fig. 3

). As there are some limitations in the

capability of EEG to accurately detect high-

(

0Hz)power,

we used the combined

and low-

(

)band(13

–

50 Hz)

for further analysis. The

band showed significantly high-

er normalized power in the team flow condition, more than

the other two conditions (

Fig. 3

, one-way repeated

measures ANOVA,

(2,42)

=6.335,

=0.005,

= 0.312;

team flow mean: 0.77, 95%CI [0.32, 1.23]; team only

mean:

–

0.27, 95%CI [

0.72, 0.19]; flow only mean:

–

0.51, 95%CI [

0.96,

0.06]).

At the anatomic-source domain level, we performed a

cortical source localization method, using co-registration

with the individual

’

s structural MRI. The brain was seg-

mented into 148 ROIs based on the Destrieux brain atlas

(

Destrieux et al., 2010

). The anatomic-source

power

analysis, after multiple comparison correction, showed 16

ROIs in the left and right temporal areas with a signifi-

cantly higher

power in the team flow condition com-

pared with the other two conditions (Extended Data

Fig.

3-2

). As a representative example, the normalized

power for the left middle temporal gyrus (L-MTG) is

shown in

Figure 3

(one-way repeated measures ANOVA,

(2,42)

= 6.744,

= 0.004,

= 0.325; team flow mean:

0.76, 95%CI [0.32, 1.2]; team only mean:

–

0.2, 95%CI

[

0.63, 0.24]; flow only mean:

–

0.56, 95%CI [

1.0,

0.12]). Also, the

power of these brain regions

showed higher correlation tendencies with the team

flow index only in the team flow condition. The L-MTG

showed the highest

power correlation with the team

flow index in the team flow condition (Spearman

’

Rho =0.59 [0.21, 0.84],

=0.006) but not in the team

only (Spearman

’

s Rho =

0.19 [

0.67, 0.34],

=0.43) or

the flow only (Spearman

’

sRho=

0.02 [

0.46, 0.42],

=0.95) conditions (

Fig. 3

). The positive correlation of

the

power with the team flow index was significantly

higher in the team flow condition than the team only con-

dition (Spearman

’

s Rho difference=0.78 [

0.01 1.40],

= 0.05) and the flow only condition (Spearman

’

sRho

difference =0.61 [0.00, 1.16],

=0.05).

We note that some ROIs showed a trend unique to the

team only or the flow only conditions, but they did not sur-

vive after the multiple comparison correction. Since the

anatomic-source localization averages source vertices

based on a predefined parcellations method, we devel-

oped a method to give more weight to the distribution of

activity rather than anatomy. We used unsupervised ma-

chine learning to cluster (cl) the source vertices based on

their similarity in the

power pattern (Extended Data

Fig. 3-2

). Using the unsupervised clustering analysis,

we detected cls specific to team flow, team only and flow

only conditions (for details, see Materials and Methods,

Unsupervised clustering analysis). These results indicate

that even during team flow, the brain shows neural activ-

ities related to each isolated experience: the flow and the

social states.

Theresultsfromthepowerspectralanalysesatevery

tested domain provided the first neural evidence that the

team flow experience is a qualitatively different brain state

distinguishable from the flow or social states. In other words,

the team flow state does not result from a simple combination

of the flow and the social states, but it has its own neural sig-

nature, which we posit accounts for the superiority in the sub-

jective experience. Next, we checked for possible unique

interactions between these brain regions during team flow.

Before performing further analyses, we grouped the 148

ROIs into 14 brain regions, seven per hemisphere, using a

combination of the standard anatomic definition and the

functional activity revealed through the cluster analysis

(Extended Data

Figs. 3-2

4-1

4-3

,and

4-4

). These 14

brain regions (RGs) are: the PFC (RG1), the ACC (RG2), the

inferior frontal cortex (IFC, RG3), the superior temporal cortex

(STC; RG4), the central and parietal cortex (CPC; RG5), the

OC (RG6), and the MTC (RG7). The MTC included all the

ROIs that showed a significant effect on team flow (Extended

Data

Fig. 3-2

), regardless of the cluster composition.

The left MTC (L-MTC) receives and integrates information

from brain areas encoding flow or social states

We tested whether the neural signature of team

flow detected in the MTC upstream or downstream in

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information processing. We analyzed the causal infor-

mation interactions across all the brain regions (RGs),

using three frequency-domain GC measures: the GGC,

the dDTF, and the nPDC (

Wang et al., 2014

). In all GC

measures, the causal interaction matrix showed that

MTC receives information (from) more than sending in-

formation to (to) other RGs (Extended Data

Fig. 4-2

Also, we quantified the global to/from ratio for each

Figure 3.

Higher

power at the L-MTC revealed as a unique neural signature for team flow.

, The topographies of the

and

frequencies (13

–

120Hz) computed as the average over normalized power.

, Permutation statistical significance across conditions

with Bonferroni multiple comparison corrections. The black crosses indicate channels with

0.05.

, The normalized power spec-

tral analysis averaged from the four channels in the left temporal area identified in

. Shaded area represent mean

SEM;

= 20.

Extended Data

Figure 3-1

shows the power difference spectral analysis grand averaged across all the 128 channels.

, Averaged

normalized power for the

(13

–

50 Hz) frequency band showing power enhancement in the team flow condition. One-way re-

peated measures ANOVA with Bonferroni

post hoc

test; *

0.05. Error bars represent mean

SEM;

= 15.

, The brain regions

(highlighted in green), as defined by the Destrieux atlas and showing significant

normalized power difference across conditions.

Extended Data

Figure 3-2

shows the average normalized

power for each significant region.

, The average normalized

power at the L-MTG. One-way repeated measures ANOVA with Bonferroni

post hoc

test; **

0.01. Error bars represent mean

SEM;

= 15.

, Condition-specific Spearman

’

s correlations between

power and team flow index at L-MTG as a representative

region. Positive correlation was found in the team flow condition (Spearman

’

s Rho= 0.56,

= 0.006), but not in the team only condi-

tion (Spearman

’

s Rho =

0.19,

= 0.43) or in the flow only condition (Spearman

’

s Rho =

0.02,

= 0.95). The lines indicate the re-

gression lines. Shaded areas indicate a 95% confidence interval;

= 20.

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RG per condition. In all GC measure, global to/from

ratiofortheL-MTCwassignificantlylessthanany

other RG except for the right MTC (R-MTC; Extended

Data

Fig. 4-2

; for GGC, two-way repeated measures

ANOVA,

(26,494)

= 2.9768,

= 0). Hence, the detected

power in L-MTC is a downstream in information

processing during the team flow experience. We then

checked the most important upstream brain regions

that sent information to L-MTC. For each RG-RG

causal interaction, we applied a global threshold to

leave only the top (

;

10%) information senders (

Fig.

). Only in the team flow condition, the top informa-

tion senders to L-MTC include the contralateral R-PFC

and R-IFC. The team only and flow only conditions

showed a similar causality pattern, yet different from

the team flow condition, in which the top information

senders to L-MTC include the contralateral R-CPC.

Interestingly, the differences between the conditions

were observed in the interhemispheric connectivity

rather than the intrahemispheric one.

Figure 4.

Causality and II analyses during team flow.

, Causality analysis showing the top information senders among all RG-RG

causal interactions. For each RG-RG connection, the line color matches the color of the RG name which sends the information.

Notably, only in the team flow condition, L-MTC receives information from R-PFC and R-IFC. Extended Data

Figures 4-1

4-3

4-4

show the method for grouping of ROIs. Extended Data

Figure 4-2

shows detailed causality analysis.

, The mean normalized II

value (Norm II) connectivity matrix for the brain regions (RG1

–

RG7). Normalized II is calculated by subtracting the mean per condi-

tion from the average II across conditions for each RG-RG connection across conditions.

, The mean global Norm II averaged

across all RG-RG connections showing significantly higher interbrain (left panel) and intrabrain (right panel) mean during team flow

condition. One-way repeated measures ANOVA with Bonferroni

post hoc

test; **

0.01. Error bars represent mean

SEM;

= 15.

, RG-RG connections that shows significant (

0.05) Norm II in the team flow condition compared with other conditions. Three-

way repeated measures ANOVA with Bonferroni

post hoc

test. Black lines indicate intrabrain and green line indicates interbrain RG-

RG connections. D-L, dorsal-left.

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The results above indicate that the

power detected

in the L-MTC during team flow might arise from informa-

tion processing that happened earlier in time, and the

sources of this information include brain areas that enco-

des flow state (namely, PFC) and social state (namely,

IFC). We suspected that a downstream brain region plays

a role in integrating information from the brain regions en-

coding each isolated experience. To test this hypothesis,

we used the II theory (

Tononi, 2004

;

Oizumi et al., 2014

We calculated the normalized II value (Norm II) as a metric

for the II. In both intrabrain and interbrain calculations,

there was a general tendency for Norm II to be higher in

the team flow condition than the other conditions (

Fig.

). When we averaged the Norm II across all the RG-RG

connections (global Norm II) from the left hemisphere, the

team flow condition showed significant higher interbrain

global Norm II than other conditions (one-way repeated

measures ANOVA,

(2,42)

=9.310,

0.001,

= 0.399;

team flow mean: 0.56, 95%CI [0.3, 0.83]; team only mean:

–

0.33, 95%CI [

–

0.6,

–

0.06]; flow only mean:

–

0.23, 95%CI

[

–

0.5, 0.04]), while showing a similar trend at the intrabrain

level (one-way repeated measures ANOVA,

(2,42)

=2.496,

= 0.101,

= 0.151;

Fig. 4

Next, we checked for the specific RG-RG connections

that showed a significant Nom II at the team flow condi-

tion compared with other conditions using the three-way

repeated measures ANOVA with Bonferroni

’

s multiple

comparison correction (condition

RG interaction

for intrabrain:

(26,10133)

= 4.7622,

=0, and for interbrain:

(26,10959)

= 3.676,

= 0). Among all RG-RG connections,

we detected significant connections only at the left hemi-

sphere. These connections formed an intrabrain L-IFC-

STC-CPC-MTC subnetwork and an interbrain L-MTC-to-

L-MTC link that showed significantly higher Norm II in the

team flow than the other conditions (

Fig. 4

). These re-

sults indicate that during team flow, the team members

exhibited higher information integration not only within

each player

’

s brain, but also between their brains. More

specifically, L-MTC was the only brain region that showed

significantly higher interbrain II during team flow. These

results indicates that L-MTC plays a critical function in in-

formation integration during the team flow state.

Team flow is associated with higher interbrain neural

synchrony

Enhanced interbrain II might concur with enhanced

neural synchrony between the team

’

s brain regions. To

test for this hypothesis, we calculated the interbrains nor-

malized PLV (Norm PLV) across all the RG-RG connections

for each condition (

Fig. 5

). The results showed a general

tendency for Norm PLV to be higher in the team flow condi-

tion than other conditions. The interbrain Norm PLV calcu-

lated using a randomly shuffled pairs did not show any

difference across conditions (

Fig. 5

). To quantify this effect,

we averaged the Norm PLV for all RG-RG connections (global

Norm PLV) in the left hemisphere. The team flow showed a

significantly higher global Norm PLV than other conditions

only in the actual paired participants but not in randomized

pairs (

Fig. 5

; two-way repeated measures ANOVA, condi-

tion

randomness interaction

(2,84)

= 3.317,

=0.05,

0.092; condition effect

=0.015,

= 0.135, team flow

mean: 0.002, 95%CI [0.0007, 0.003]; team only mean:

–

0.001, 95%CI [

–

0.002, 0.0003]; flow only mean:

–

0.001, 95%

CI [

–

0.002, 0.0003]). Collectively, these results indicate that

during team flow, the team members exhibited higher inte-

gration and neural synchrony between their brains. This en-

hancement in information integration and neural synchrony is

consistent with the phenomenological experience during

team flow, and it might be the neurocognitive basis for the

superior subjective experience of team flow.

Discussion

In summary, we established a new objective neural

measure of flow, consistent with subjective reports. We

Figure 5.

PLVs show enhanced interbrain synchrony during team flow.

, The mean PLV connectivity matrix for the brain regions

(RG1

–

RG7). Normalized PLV is calculated by subtracting the mean per condition from the average PLV across conditions for each

RG-RG connection across conditions.

“

Paired

”

indicates the actual experimental pair,

“

random

”

indicates randomly selected pairs.

, The mean global normalized PLV averaged across all RG-RG connections showing significantly higher interbrains during the

team flow condition. Two-way repeated measures ANOVA for interbrain comparison with Bonferroni

post hoc

test; *

0.05. Error

bars represent mean

SEM;

= 15. ns, not significant.

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