Behavioural Synchrony

# Group entrainment experiment: "Rhythm Battle"

## Key

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Using the autocorrelation method to estimate periodicities, we see that different limbs move in slightly different tempi. In the left panel on figure 45, the average periodicities for the three phases, for the sensors of one participant are plotted. These are the period lengths associated with the first peaks in the autocorrelation function, or the fastest rate for each limb. We can see that e.g. for the right hand, the average tempo gets slightly faster in each phase, same goes for chest. One option for From the right panel, where the autocorrelation functions themselves are plotted, we can see that the "clearest" periodicities (highest peaks) for this participant are mostly at double the metronome tempo, or at the 1.4 second area. The feet give the clearest periodical signals overall, and as they move in an alternating pattern (see figure 34), together they form a rhythm with one step / one beat.

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First of all, as we concluded above, the chest markers seem to capture the different limb patterns and sum them up into a periodic signal that is quite stable and at the frequency of the metronome. Figure 5 6 shows the chest accelerations of all participants, divided to the three phases, and this time also the participants are divided into the two teams or groups.

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If we only have two systems (two dancers in this case), we could take the acceleration of their chests, for example, and use Hilbert transform to obtain their instantaneous phases, and then get their phase difference through subtraction and then see whether it stays stable or not. If the two are synchronised, they should maintain a stable phase difference (which does not have to be zero). But, as we have ten players in two teams, we need a different method. We can use the Kuramoto model, which describes collective synchronisation of a large number of independent oscillators, spontaneously synchronising their period and phase. The model explains how the oscillators sync, and we can also use it to quantify the level at which a group of oscillators have synchronised. This can be summarised in an order parameter, an index that ranges from 0 to 1, with 0 indicating disarray and 1 perfect sync. We calculated the order parameter for team 1, team 2 (in figure legend "group 1, " and "group 2"), and everyone together. Figure 6 7 shows the average OP's for each of the three phases.

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###### Figure 7. Average order parameters for the different phases of the game

It seems that both groups teams reached a similar sync level in the first phase, but group team 1 somehow lost it when the groups teams started communicating with each other. In the third stage, the groups teams get closer in terms of sync, but both are weaker than in the first stage. But, let's look at how the OP evolves in time.

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###### Figure 8. Order parameter evolution

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The data is smoothed with a long averaging window, as the actual parameter is quite noisy, as the participants' slightly different tempi make the phase relationships somewhat unstable. The peak values at the ends of each graph are a result of the smoothing. But, these smoothed curves show some interesting features. In the first phase, the fact that participants are moving very little means that the chest signals are small, this might contribute to the jittery order parameters. In the second phase, groups both teams seem to have a more steady state, although as we already saw in the bar graph, group team 1 seems to have a difficult time, while group team 2 has a very good run. In the third phase, things evolve more gradually. While group team 1 improves in the middle, it seems that group team 2 then gets better and better, overtaking the group team 1. And as the overall order also grows towards the end, following the path of group team 2, it might be that group team 2 have managed to pull at least some in group team 1 to sync with them. Definitely some group team 1 members seem to correlate highly with group team 2 especially in phase 3.

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For this analysis example, we use data from another pilot. Another way to look at what goes on in the game is to visualise the teams as networks. Here, the protocol was more refined, but due to a technical

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#### Conclusions

In this example, we have walked through a possible group synchronisation analysis with some accelerometer data recorded from two groups playing a sync - resist sync game. Using the Kuramoto model and the order parameter as a group synchronisation metric yields interesting information about the evolution of group synchrony. The example analysis looks at just three short windows of the original data, and is just from one group, one game. However, the whole performance could be analysed using similar methods, and for example the average OP's could be compared across different games or different groups, to compare performance within (as in pre- and post-intervention) or between participant groupswe visualise the average tempi of each participant, and the tempo differences between each pair of participants (both within and between teams) to form our graph. First, lets take a look at the overview of how this game progressed. In Figure 9 we have plotted the mean tempi in two teams for the different phases of the game. Based on these, we can say that the fast tempo team "won" the game–they kept their average tempo close to where they started, whereas the slow tempo team shifted to the fast tempo already after the metronomes were switched off, even before the teams started moving (figure 9).

Figure 9. Average tempi in the different phases of the game. These means are calculated from 20-second segments from each phase. Error bars represent the means of individual standard deviations of inter-beat intervals in these segments.

In our network, each participant is a node. The edges (connections between the nodes) come from We calculated next the tempo differences for each pair of participants, and then converted these into edge weights for the graph - the closer two participants are in tempo, the stronger their connection. Also, if the tempo difference gets too large, the two are not connected at all. This way we get a more interesting graph than the fully connected one that we would get if everyone was connected to everyone else by default. Figure 10 shows the networks for this group, in phase 1 and phase 3. (We did have two teams of equal size in the game, however due to technical reasons we only have data from 5 participants in one team and 3 in the other. We used chest accelerometer data for this analysis.) We visualised the networks in Gephi, using a force-algorithm that pushes weakly connected nodes away from each other and pulls strongly connected nodes closer.

###### Figure 10. Networks of the game in the beginning (left) and the end (right) of the game. Node colours indicate team membership. The thickness of the edges reflect how close the two are in tempo. The font size of the participant number reflects the "authority" of the participant, or how influential they are in the network (font sizes are not compareable across the left and the right graphs, only within each).

In the beginning (figure 10, left) the participants are in two groups that are relatively weakly interconnected, apart from participant 613 who seems to be an outlier here. Perhaps their pattern was such that the chest accelerometer did not pick it up very clearly, or they were just not in the tempo the rest of their team was. Apart from this one outlier, the graph looks exactly as we'd expect for this first phase: two groups, each highly coherent within, and with a clear difference to the other group.

Figure 10 right shows the network at the end of the game, when they have already been moving for a while. Our original outlier is still an outlier, but now has gained another, from the slow team. These two are somewhat close to each other, but almost not connected to anyone else. The rest of the fast team is still together, and now crucially they've managed to attract one of the other slow team participants (619) to be with them. 619 was also the most authoritative member of the slow team in the beginning, so that had a big effect on the slow team. 618 has also drifted closer to the fast group. Also the visual analysis of the graph supports the view from looking at the mean tempi: the fast team was stronger.

### Rhythm battle group and subjective assessment of closeness

In this pilot, we also asked participants to rate how close they felt to the other participants. These ratings were collected at the very end of the day, and the ratings were not specifically focused on the group synchronisation task, but generally to the whole day, that consisted of a number of experiments, an improv glass and various rhythm games alone and in groups.

We used a version of the Inclusion of Others in the Self -measure (Aron, Aron & Smollan, 1992). Pooling together ratings from three groups of participants (three games), we could compare whether on average, members in the same team were rated closer than members in the opposing team. Previous research points out to the social benefits of synchronisation (e.g. Hove & Risen, 2009; Rabinowitch, et al. 2013), and thus our hypothesis would be that team membership in the game would have an effect here although the participants experience together a longer program of various activities.

Indeed, our analysis indicates that participants rated those participants closer that they shared a team with in the rhythm battle. This difference was highly significant (figure 11).

#### Conclusions

In this example, we have walked through a possible group synchronisation analysis with some accelerometer data recorded from two groups playing a sync - resist sync game. Using the Kuramoto model and the order parameter as a group synchronisation metric yields interesting information about the evolution of group synchrony. The example analysis looks at just three short windows of the original data, and is just from one group, one game. However, the whole performance could be analysed using similar methods, and for example the average OP's could be compared across different games or different groups, to compare performance within (as in pre- and post-intervention) or between participant groups.

We also presented a network-based analysis of the game. Constructing a graph with participants as nodes and their tempo differences as edges produces a visualisation that allows for analysis of the dynamics during the game and how the "winning" team won.

The team membership in the rhythm battle also was found to have an effect on how close participants felt at the end of the day. To see that the IOS measure seems sensitive to synchronisation is very interesting, it would be in line with previous studies that have used different measures, and usually dyadic or at least shorter interventions.

We are currently pursuing the network analyses further, looking e.g. dynamic models where we could visualise the evolving tempo differences more continuously. Also, the protocol is constantly being refined. Furthermore, we can use the other rhythm games (solo and triad finger tapping) as background measures, to e.g. identify if outliers in the game are ones that have lowest coherence also in solo tapping or whether their unsynchrony is just related to the group context. Also, we might be able to group people based on their tempo preferences (those with faster spontaneous tempi make up the faster team), instead of the grouping being (pseudo)random as it is currently

## References:

Acebrón JA, Bonilla LL, Vicente CJP, Ritort F, Spigler R. (2005). The Kuramoto model: A simple paradigm for synchronization phenomena. Reviews of modern physics. 77(1):137–185.

Aron, A., Aron, E. N., & Smollan, D. (1992). Inclusion of Other in the Self Scale and the structure of interpersonal closeness. Journal of Personality and Social Psychology, 63(4), 596.

Lucas, G., Clayton, M., & Leante, L. (2011). Inter-group entrainment in Afro-Brazilian Congado ritual. Empirical Musicology Review, 6(2), 75–102.

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