Team flow is a unique brain state associated with enhanced information integration and neural synchrony
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Abstract
Team flow occurs when a group of people reaches high task engagement while sharing a common goal as in sports teams and music bands. While team flow is a superior enjoyable experience to individuals experiencing flow or regular socialization, the neural basis for such superiority is still unclear. Here, we addressed this question utilizing a music rhythm task and electroencephalogram hyper-scanning. Experimental manipulations held the motor task constant while disrupted the hedonic musical correspondence to blocking flow or occluded the partner’s body and task feedback to block social interaction. The manipulations’ effectiveness was confirmed using psychometric ratings and an objective measure for the depth of flow experience through the inhibition of the auditory-evoked potential to a task-irrelevant stimulus. Spectral power analysis revealed higher beta/gamma power specific to team flow at the left temporal cortex. Causal interaction analysis revealed that the left temporal cortex receives information from areas encoding individual flow or socialization. The left temporal cortex was also significantly involved in integrated information at both the intra- and inter-brains levels. Moreover, team flow resulted in enhanced global inter-brain integrated information and neural synchrony. Thus, our report presents neural evidence that team flow results in a distinct brain state and suggests a neurocognitive mechanism by which the brain creates this unique experience.
Data Availability
All data and analysis codes used in the preparation of this article are available at https://osf.io/3b4hp .
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###Reviewer #3:
This is an interesting paper that looks for neural markers of "team flow" experiences compared to individual flow or social interaction using EEG measured during a musical social app game. The approach and analyses are sophisticated, with the main findings being that in a combined beta-low gamma frequency range there was higher power in regions of left temporal cortex for team flow than the other conditions; that other brain regions responded to individual flow or social interaction; that directed analyses found greater information from these other brain regions to the left temporal cortex; and that the left temporal cortices of players engaged in team flow synchronized.
However, these findings are difficult to interpret as they depend on the behavioural manipulation of the experiment that is purported to separate team …
###Reviewer #3:
This is an interesting paper that looks for neural markers of "team flow" experiences compared to individual flow or social interaction using EEG measured during a musical social app game. The approach and analyses are sophisticated, with the main findings being that in a combined beta-low gamma frequency range there was higher power in regions of left temporal cortex for team flow than the other conditions; that other brain regions responded to individual flow or social interaction; that directed analyses found greater information from these other brain regions to the left temporal cortex; and that the left temporal cortices of players engaged in team flow synchronized.
However, these findings are difficult to interpret as they depend on the behavioural manipulation of the experiment that is purported to separate team flow, individual flow and social interaction, and I don't think these are clearly separated behaviourally. There were 3 conditions. In SyncA, players each tapped on a screen to control one stream of the music. In ScrA the music was scrambled and in Occl the game was as in SyncA, but the players were separated by a barrier. SyncA is supposed to measure team flow, ScrA individual flow but not team flow, and Occl is supposed to reduce social interaction. However, when one examines the ratings that players gave for team flow, individual flow and social interaction, they do not line up exactly with this theoretical manipulation. Specifically (Fig 1), individual flow ratings are higher in SyncA and Occl than ScrA, so SyncA and Occl don't differ in individual flow. Social interaction ratings are higher in SyncA than ScrA, and SrcA is higher than Occl, so Occl disrupts social interaction, but so does ScrA. And Team flow is disrupted by both ScrA and Occl. In other words, there is no clean mapping of the 3 experimental manipulations to the three ratings scales. Also very problematic is that for the rating questions, the three scales of individual flow, team flow and social interaction were not independent (Figure S2). Individual flow was taken as the average of questions 1-6, the social interaction as questions 7-9 and the team flow as questions 1-9! This makes it hard to interpret the findings because team flow is conceptually taken here as the combination of individual flow and social interaction, making the arguments appear circular.
The "depth of flow state" is a potentially interesting measure, consisting of the mean auditory evoked response (although I note that it is not clear how it was calculated: if it is the average of P1, N1, P2 and N2 or the power in theta) to unexpected task irrelevant beeps. Essentially it measures how distractible the person is from the task. So theoretically, it is not clear exactly how this relates to the complex concept of team flow. People were found to be most distractible for ScrA, not surprisingly, as the scrambled game is probably less fun and engaging, but across subjects, only SyncA was correlated with the individual flow index. Why? I also assume there was no correlation with team flow. Why not? So this is an interesting measure, but conceptually I'm not sure what it tells us about team flow.
For the analysis of beta-gamma, power at the electrode level at left temporal regions was higher for SyncA - but it was also higher for ScrA than for Occl (Fig 3), so what does that mean? From Fig 1e, team flow ratings were actually lower for ScrA than Occl (although maybe not significantly, but this is in the opposite direction). Also, this difference became exaggerated with high gamma, so why was this not analyzed? And how is this interpreted within the team flow concept?
For the cluster analysis, some clusters were found with higher beta-gamma power for SyncA, other clusters for ScrA and yet other clusters where power was lower for Occl. However, given as I describe above, that it is not clear exactly how these conditions relate to the concepts of individual flow, team flow and social interaction, I don't think the authors can say as they do that clusters where power is highest for SyncA represent team flow. Clusters where power is lowest for Occl were said to represent social interaction, but this cannot be said because Occl also had high ratings for individual flow (Fig 1) so could be either or both high individual flow and/or low social interaction. Clusters where power is highest for ScrA are interpreted as "flow suppression", but not clear why and whether this refers to individual flow or team flow as both are suppressed behaviourally (Fig 1)?
The directed connectivity analyses are interesting, but again difficult to interpret in terms of the individual flow, group flow, social interaction model. The regions need to be named more descriptively than GP1, etc. At the very least a table in the main text saying what these regions are would be helpful.
For the analyses of inter-brain effects, why did they authors go to a new measure, information, rather than using a directed measure as in the previous analysis?
I am also concerned about the very large number of statistical tests done here - probably experiment-wise error rate control is necessary. The more significant tests will survive this in any case.
I am also questioning the very detailed brain regions used in the source analysis. It would be difficult I think for EEG to be able to independently separate signals coming from nearby regions so precisely.
It also seems problematic that many participants were eliminated because they did not prefer to play the game in an interpersonal way over a solo or occlusion setup. Thus it seems that a very selected type of participant was used and I'm not sure if this can generalize. Also, some of the participants were friends, and this may have also influenced how they responded. At least some discussion of these issues is necessary.
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###Reviewer #2:
In the present manuscript, the authors introduce a novel task to measure 'team flow'. They test if alignment of brain activity is indicative of a shared experience, similar to mutual understanding (see e.g. work by Stolk et al. TiCS). They utilize a hyperscanning procedure where EEG recordings were obtained for two participants, while they were engaged in a task that requires cooperation.
While the approach is interesting and the topic timely; all the results rest on a methodological assumption, which has not been accounted for.
Both participants are presented with the same visual and auditory stimuli, which, when presented simultaneously, elicit the very same evoked response. When applying spectral analysis techniques to these simultaneously evoked responses, one can easily observe 'synchronization', which however, is …
###Reviewer #2:
In the present manuscript, the authors introduce a novel task to measure 'team flow'. They test if alignment of brain activity is indicative of a shared experience, similar to mutual understanding (see e.g. work by Stolk et al. TiCS). They utilize a hyperscanning procedure where EEG recordings were obtained for two participants, while they were engaged in a task that requires cooperation.
While the approach is interesting and the topic timely; all the results rest on a methodological assumption, which has not been accounted for.
Both participants are presented with the same visual and auditory stimuli, which, when presented simultaneously, elicit the very same evoked response. When applying spectral analysis techniques to these simultaneously evoked responses, one can easily observe 'synchronization', which however, is completely driven by the simultaneous presentation of the external stimuli. This problem is aggravated when rhythmic visual stimuli are presented.
In addition, several statistical comparisons do not explicitly test the interactions, which are implied by the authors (this problem has been discussed in detail here: https://www.nature.com/articles/nn.2886)
In addition, several queries apply:
The Flow index needs to be defined earlier in the manuscript (at least prior to Figure 1)
a. Per Fig. 2c: The authors state 'As expected, the mean AEP response was significantly higher in the Inter-ScrA condition more than the other two conditions.' - Why was this expected? This statement is not trivial, why should the violation introduce a stronger response?
b. Furthermore, it is difficult to reconcile it with the next statement 'Thus, this weaker AEP for the task-irrelevant stimulus in the Inter-SyncA and Occl-SyncA conditions provides neural evidence that the brain has reached a distinct selective-attentional state marking the flow experience.'
- This is a far stretch from the ERP data
- Fig. 2d - The authors need to test for differences in interactions and they cannot claim differences when one test is significant and the other is not. See e.g. https://garstats.wordpress.com/2017/03/01/comp2dcorr/
This again pertains to Figure 4c
Testing different frequency bands independently is again not valid, since, power values across bands are strongly correlated, see e.g. see work by Donoghue and Voytek (2020) biorxiv or Haller et al. (2018) biorxiv. Fig 3c makes this even more likely that some of the effects are broadband and not band-limited 'oscillations'.
All the differences localize to auditory areas, which makes one very suspicious that we are looking at evoked and therefore synchronized activity, and not alignment of endogenous oscillations, see e.g. a recent commentary: https://doi.org/10.1080/23273798.2020.1758335 The current paradigm basically would show synchrony (mistaken as team flow), when simultaneous spurious 'entrainment' (simultaneous evoked activity) is present in both participants; this confound needs to be accounted for since it confounds subsequent metrics of phase synchrony
Statistics in Fig. 4b, these tests and ROIs are not independent, a data-driven cluster approach could be utilized instead (see Maris and Oostenveld 2007).
Bar plots are deprecated, see Weissgerber et al PLOS Biol 2015.
Analysis for Figure 5a needs a depiction on what is actually analyzed. The hierarchical clustering approach is introduced with clear rationale and explanation.
Overall, this is an interesting approach. It is a methodological challenge to record EEG data from two interacting participants, but given that this is a relatively young field, some methodological prerequisites need to be established first. Critically, the authors need to present convincing evidence that we are not just facing the results of simultaneously evoked auditory and visual evoked responses.
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###Reviewer #1:
In this EEG study, the authors aimed to identify neural correlates of the subjective feeling of "team flow", i.e., a particular feeling of ease, task-related attention and control while doing a task together with someone else. This is a clearly interesting question and with a recent surge of hyperscanning research a timely study. The authors seem to have carefully selected pairs of participants who have similarly good performance in the game and similar music taste to be able to induce feelings of flow in their participants. Unfortunately, there seem to be quite serious problems in their statistical analyses which should be corrected first before the work can be assessed.
- Participants:
a. The methods state that there are 15 participants, of which five were paired twice (p.13). In the Statistical analysis section, the …
###Reviewer #1:
In this EEG study, the authors aimed to identify neural correlates of the subjective feeling of "team flow", i.e., a particular feeling of ease, task-related attention and control while doing a task together with someone else. This is a clearly interesting question and with a recent surge of hyperscanning research a timely study. The authors seem to have carefully selected pairs of participants who have similarly good performance in the game and similar music taste to be able to induce feelings of flow in their participants. Unfortunately, there seem to be quite serious problems in their statistical analyses which should be corrected first before the work can be assessed.
- Participants:
a. The methods state that there are 15 participants, of which five were paired twice (p.13). In the Statistical analysis section, the authors state that "the unit of analysis" was participation, i.e., n = 20 (p. 17). This means apparently that five participants took part twice but were considered as independent measures in the statistical analyses. However, these are obviously dependent measures (or, repeated measures). The authors should include 20 (independent) participants in their analyses or need to take into account that five of the recorded 20 participants are identical.
b. The supplementary material explains in detail the selection of participants. Based on the selection criteria, 38 participants were identified (suppl mat p. 3), but it is not explained what happened to the 23 participants which are not part of the current manuscript. (Also, only the supplementary materials state that preferably friends were selected as pairs and that only those were selected (and called "prosocial") who considered doing the task together more pleasurable than doing the task alone. This should be mentioned in the main text and it seems to bias the subjective evaluation of the conditions presented in Fig 1?)
- Statistical analyses:
Several of the analyses compare the neural data in the three different conditions with one-way ANOVAs. As these are dependent measures from the same participants, this should be analyzed with repeated measures ANOVA. Also, I didn't quite understand the statistics presented on p.8 (on information flow, with two-way ANOVAs with the impressive df of 26 and 494) and on p.9 (F(26,10133) = ... ?), but again the different measures within one subject seem to be considered as independent measures?
- At several points of the analyses, it seemed like the analyses were biased. For instance, for the AEP analyses (which I generally considered a nice way to establish an "objective" measure of flow) only those channels were considered which in each resting trial robustly showed an AEP (p.14/15). Does that mean that different channels were considered for each trial and condition? I would suggest selecting the same set of central electrodes and then take these for all AEP analyses. Another case is the clustering analyses in which the number of cluster was selected such that condition differences were significant. Maybe I misunderstood this point but I guess the clustering should be done first and in the second (and independent) step, the condition differences can be assessed.
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##Preprint Review
This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.
###Summary:
Your manuscript reports on a sophisticated experimental study in human participants. The study looks for neural markers of "team flow" experiences compared to individual flow or social interaction using EEG measured during a musical social app game. While the approach and analyses are sophisticated, all reviewers individually raised a series of substantial concerns with respect to EEG and statistical analysis. The editors and reviewers hence are unable to share the conclusions the authors would like to draw.
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