Humans are social animals who engage in a variety of collective activities requiring coordinated joint action. Collective goals can be achieved through spontaneously distributed workloads among group members, such that the emerging collective dynamics confer benefits to performance not available to individuals—the ‘wisdom of crowds’ (Galton, 1907; Surowiecki, 2005).

Principles of collective dynamics explain the adaptive value of collective behavior in some species (Couzin, 2018). For example, collective dynamics can overcome the limitations of individuals’ knowledge and ability to communicate (Goldstone and Roberts, 2006) by integrating information quickly during group decision making (Ispolatov, 2015; Miller et al., 2013; Rosenthal et al., 2015). Large coherent swarming and flocking in groups can arise from short-range interactions among proximal individuals in non-human animals including primates (Farine et al., 2017), insects (Jacobs et al., 2007), fish, and birds (Miller et al., 2013; Parrish et al., 2002). As such dynamics have been demonstrated in numerous species, theoretical models have been developed to provide additional quantitative explanatory support. In contrast, in spatial tasks such as navigation, aggregate group behavior, formalized as a mean field, can serve to stabilize feedback to individuals by virtue of being a group average (Berdahl et al., 2018; Sumpter, 2006; Torney et al., 2009). Here, we test whether mean field behavior accounts for stability of temporal group dynamics in humans during a drumming task.

Collective dynamics are observable in animal as well as in human group behavior. Crowds of walking individuals achieve globally coherent states based on local inter-individual interactions (Rio et al., 2018; Warren, 2018). In audiences, individual-group interactions and social contagion govern the spontaneous onset and offset of applause (Mann et al., 2013). Collective dynamics can also play a constitutive role in sports by permitting advantages not available to individuals alone (Vilar et al., 2013). Evidence of collective dynamics is seen even in the minimal group, a dyad: spontaneous synchronization between two individuals emerges from constraints such as weak coupling (Oullier et al., 2008; Schmidt et al., 1990; Schmidt and O’Brien, 1997).

Synchronized group action is an essential element of music making, a defining social behavior of human interaction (Honing et al., 2015; Patel and Iversen, 2014; Salimpoor et al., 2011; Savage et al., 2015; Trainor, 2015), and the value of precise synchronization may vary from culture to culture and across musical contexts (Benadon et al., 2018; Davies et al., 2013; Lucas et al., 2011).The archaeological evidence of musical instruments goes back 30,000 years, and singing and drumming are thought to be even older (Conard et al., 2009). The evolutionary origins of musical rhythmic actions may relate to social motor behaviors in non-human species, such as the synchronization and desynchronization of vocalizations between individuals in group chorusing, arising from pressures to either collaborate or compete (Gamba et al., 2016; Greenfield et al., 2017; Ravignani et al., 2014; Ravignani et al., 2019). Fundamental aspects of music are present in humans from early stages in development: infants show early musical preferences, social-emotional responses to music, and rate-sensitive motoric responses to musical rhythm (Cirelli et al., 2014; Cirelli et al., 2018; Trainor and Marsh-Rollo, 2019; Zentner and Eerola, 2010). Although musical behavior is found in individuals alone, it occurs mainly in groups ranging from duets to hundreds of participants. Yet, the role of collective dynamics, especially in groups larger than dyads, has largely remained untested. Here, we consider a musical task in which timing consistency and synchrony are crucial. We investigate in what ways the group average performs better than the individuals, a temporal version of the wisdom of the crowd phenomenon.

To account for group interaction, one popular strategy is to first develop a theory for single-person tasks and then extrapolate into dyadic contexts. For example, the idea that the brain is a prediction machine that enables timely motor control in an uncertain environment can be extrapolated as two brains mutually predicting each other (Friston and Frith, 2015; Wolpert et al., 2003). This means that each individual instantiates two processes: the first for controlling one’s own rhythm and timing and the second for predicting the rhythm and timing of the partner in the dyad (Heggli et al., 2019; Heggli et al., 2021; Keller et al., 2014; van der Steen and Keller, 2013). It is not clear, however, if this can serve as an adequate foundation for behavior in larger groups. Every individual would have to predict every other individual and potentially factor-in higher-order predictions. Furthermore, at the perceptual level, group size changes the auditory information available to an individual. A listener is likely to hear an entire choir as one, or a small number of, coherent sounds rather than perceive every singer’s voice within that choir.

Group interaction can also be addressed in terms of theoretical models explicitly developed for synchronization in systems made of many dynamic units (Alderisio et al., 2016; Oullier et al., 2008; Schmidt et al., 1990; Schmidt and O’Brien, 1997). The individual units in such models usually do not contain processes dedicated separately to self-timing and other-predicting. Instead, each unit has only a self-timing and a phase-correction coupling term. The propagation of phase adjustment across all units is sufficient for the collective to enter a group-synchronized state, despite that each unit is only trying to cancel a phase difference. An important benefit of this approach is that it is inherently collective, converging on the same formalisms used for small or large groups of animals (Kelso, 2021; Zhang et al., 2019). Here, we consider such a system of coupled oscillators to account for empirical data on individual and group variability in a drumming task.

Group music making constitutes an ecologically valid and convenient paradigm for studying group action and collective experience in the laboratory (e.g. D’Ausilio et al., 2015). We used a timing task performed by groups of different sizes. Larger group dynamics are less studied because measuring highly precise timing while collecting group data is difficult for both logistical and methodological reasons (cf. Alderisio et al., 2016; Chang et al., 2017; Chang et al., 2019; Chauvigné et al., 2019; Shahal et al., 2020). Participants completed a group synchronization-continuation task (SCT; Figure 1A). It required them to drum in synchrony with an isochronous auditory stimulus and continue drumming at the same rate after the stimulus stopped, while we collected the onset times of each drum hit (Figure 1B). Stimulus tempo was varied across trials. We tested groups of two (dyads), four (quartets), and eight (octets) participants (Figure 1C). The task tested participants’ ability to synchronize to an external reference as well as to other participants, while also minimizing temporal variability and maintaining the initial stimulus rate. This ensemble drumming was also compared against the solo condition. Specifically, participants tested in duets or quartets completed both solo and ensemble conditions. The octet group did not complete the solo conditions for logistical reasons, but they instead completed a control condition in which the synchronization phase continued for the whole trial (i.e. there was no continuation phase) as well as trials using a more complex and musically realistic rhythm. Analyses of the more complex rhythm will be reported separately.

Figure 1

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Inspired by theoretical models from the animal literature (Sumpter, 2006), we assumed that individual interactions average to overall group behavior, a mean field, which provides stabilizing feedback to individual group members (Figure 2). A key prediction was that the relative influence of the stabilizing feedback would increase with increasing ensemble size. To this end, we measured separately the variability of individual drummers and the group. We constructed a theoretical group-aggregate onset time as the center of clusters of individual onset times. We expected that larger groups would exhibit lower variability as measured using the coefficient of variation of inter-onset intervals. We also tested this idea formally by adapting a Kuramoto model of group synchronization.

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The (Kuramoto, 1975) dynamic system of coupled phase oscillators, Equation 1, was conceptualized as a large population of oscillators with different natural frequencies capable of spontaneously locking to a common frequency.

Here, θ_i is the phase of oscillator i, ω_i is its preferred frequency, i.e., how fast around the unit circle it likes to go, K is coupling strength, and N is the number of oscillators.

The model gives a mathematical account of group synchronization as dependent on a mean field, referred to here as group aggregate. A central feature of the model is that the feedback is positive: the amplitude of the mean field grows as a function of inter-individual synchronization, and reciprocally, the individual oscillators are affected more by the mean field if its amplitude is larger, see Figure 2. This is shown by using the definition of the mean of phases, Equation 2, to express Equation 1 equivalently (Strogatz, 2000) in terms of the coupling between individual oscillators and the mean field, Equation 3. ${\displaystyle \mathrm{\Psi }}$ is the mean field phase, and r is the mean field coherence, also called order parameter (Kuramoto, 1975).

Such individual-collective positive feedback loops enable ant trails and other phenomena in swarming animals (Sumpter, 2006) as well as acoustic herding in chorusing animals (Ravignani et al., 2014). The system also serves as a model for neuronal collective synchronization (Breakspear et al., 2010; Frank et al., 2000; Noori et al., 2020). Theoretically, the principles embodied by this system should apply to group action in humans too (Zhang et al., 2019). It has found application in understanding inter-personal synchronization of dyads (Dotov et al., 2019; Heggli et al., 2019; Roman et al., 2019), individual rate preferences in the dyad (Bégel et al., 2022), and effects of coupling topology in larger groups (Alderisio et al., 2016). In the present context, the model predicts that ensembles will be more stable than individuals because of the feedback loop between the timing of individuals and the group aggregate of individuals.

The original Kuramoto model involves continuous dynamics and continuous coupling with constant gain and no delay. In a drumming task, participants are coupled by way of discrete acoustic onset times. Here, we propose a hybrid continuous-discrete system of oscillators with event-based feedback updated once per cycle, Equation 4. It utilizes a pulse function shaped like the acoustic envelope of a drum hit. To this end, Equation 5 specifies an asymmetric gamma distribution that rises sharply at time 0 and then decays slowly (Figure 2—figure supplement 1). In the model, an oscillator emits a pulse once per cycle when its phase angle crosses zero. Furthermore, we posit additive phase variability with a Gaussian distribution ${\displaystyle N\left(0,\sigma \right)}$.

Like the original system, this model involves intrinsic dynamics (ω_i) and interaction dynamics given by the whole coupling term. For comparison, we tested two additional models: the classic Kuramoto system with constant full coupling, Equation 1, as well as the hybrid continuous-discrete model, Equations 4; 5, with a sparse coupling matrix in a ring topology. Specifically, the individual units were only coupled to their two immediate neighbors, $j=i\pm 1$, with a periodic boundary condition, ${\theta }_{N+1}={\theta }_1$ and ${\theta }_0={\theta }_N$ .

In Performance, Dynamics and coordination in ensemble, SCT conditions, and Group dynamics, we present the analyses of the participants’ drumming behavior. In Variability in the mean-field model with discretely updated feedback, we present the results from our adapted hybrid model consisting of a continuous-discrete system of oscillators with pulse-based feedback updated once per cycle.

Performance

Variability

Does individual variability increase in ensemble compared to solo conditions?

To answer this question, we fitted a model including only individuals’ data in dyads and quartets (octets did not complete solo conditions). As Figure 3A shows, individuals’ variability increased when playing in an ensemble relative to playing solo (β=0.009, SE=0.001, t=6.65, and ω²=0.10).

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In the context of true ensemble playing, how does variability of the individuals and group aggregate change across dyads, quartets, and octets?

To specifically test the main hypothesis, we fitted a model to continuation (SCT) trials from all three group sizes in the ensemble playing condition. We found that individual variability increased with group size (β=0.001, SE=0.0005, t=2.20, and ω²=0.09), group-aggregate variability was lower than individual variability (β=−0.010, SE=0.003, t=−3.29, and ω²=0.01), and the difference between individual and group-aggregate variability increased for larger group sizes (β=−0.0025, SE=0.0005, t=−5.19, and ω²=0.03), see Figure 3A. As a sanity check, we verified that the procedure for group-aggregate onset times did not lead to spuriously periodic data. We observed very high variability for the pseudo-group-aggregate in solo condition where individuals did not hear each other, see Figure 3—figure supplement 1A.

How does variability compare between continuation and synchronization?

The octet group completed trials in conditions of both SCT (i.e. after the offset of the reference metronome) and synchronization-only (with a constant reference metronome). Surprisingly, there were no differences between these conditions either in individual (t<1) or in group-aggregate variability (t<1).

Speeding up

Do individuals speed up more when playing solo than in dyad or quartet ensembles?

Speeding up was defined as the linear increase in tempo (i.e. decrease of IOIs) over the course of a trial. As expected, individuals sped up even when playing solo (β=0.018, SE=0.007, t=2.67, and ω²=0.10), and greater speeding up was observed in ensemble than in solo conditions (β=0.022, SE=0.007, t=3.29, and ω²=0.16). The effect of group size was not significant (t<1).

Do dyad, quartet, and octet ensembles speed up to different extents?

To test the effect of group size across the duets, quartets, and octets, a second model was fitted with group size as a continuous predictor, but only including trials in SCT ensemble playing. As suggested by Figure 4, there was a trend for speeding up to decrease as group size increased. The best linear model included an effect of tempo (β=−0.001, SE=0.00005, t=−1.98, and ω²=0.005) and an interaction between group size and tempo (β=−.00004, SE=0.000007, t=−6.20, and ω²=0.11), reflecting that at higher tempos larger ensembles sped up less than smaller ensembles, see Figure 4—figure supplement 1.

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Dynamics and coordination in ensemble, SCT conditions

Individual and group-aggregate dynamics (autocorrelations)

Individuals

Figure 5A shows that individuals’ autocorrelations at lag 1 were negative across all group sizes and tempos. This is typical for the alternating long-short IOIs seen in synchronization tasks, reflecting alternating fast-slow synchronization errors when adapting to the previous interval. There was also a trend across all group sizes and tempo conditions for positive peaks at even lags (2, 4, 6, and 8) and negative peaks at odd lags (3, 5, and 7), see Figure 5—figure supplement 1. However, this general pattern became smoother with increasing group size. To test this statistically, in each trial, we took the average absolute difference between successive lags, thus measuring the average range of the autocorrelation function up to lag 8 and applied the same linear modeling approach as in Performance. The effect of group size was significant (β=−0.0054, SE=0.0023, t=−2.35, and ω²=0.13). There was also a decrease of range with increasing tempo (β=−0.00039, SE=0.0001, t=−4.92, and ω²=0.04). Additionally, we verified the signs, relative magnitudes, and the effects of group size and tempo on the autocorrelations by fitting separate linear models for each lag, see Supplementary file 1a.

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Group-aggregate

Interestingly, group-aggregate IOIs revealed the same overall pattern of results even though by definition the group-aggregate timing was smoother and less variable (see Group-aggregate (mean field) measure for ensembles and pseudo-ensembles), see Figure 5B and Supplementary file 1b. There was a trend across all group sizes for relative positive peaks at even lags 2, 4, 6, and 8 and relative negative peaks at odd numbered lags 1, 3, 5, and 7. The similarity of the dynamics of individuals and the group aggregate is not trivial because it implies that even groups of up to eight participants spontaneously acquire the alternating long-short interval dynamics characteristic of when individual participants synchronize with a stimulus.

Inter-personal coordination (cross-correlations)

Cross-correlation applied to the pre-processed IOIs assessed inter-personal coordination between pairs of participants drumming together in the continuation phase of ensemble performances. On rare occasions, individuals produced diverging beat times by, for example, missing the drum or hitting it two times. The resulting outlier IOIs were removed, and the remaining were re-aligned relative to the other participants by temporal adjacency. The last pre-processing step consisted of whitening each time-series of IOIs (see From beat onset times to IOIs, outlier removal, and clustering–Pre-whitening).

We analyzed lags in the range 0–4 because cross-correlation coefficients tended to be symmetric between positive and negative lags, see Figure 6. As expected, cross-correlations at lag 0 were negative in duets (β=−0.039, SE=0.011, t=−3.60, and ω²=0.04), consistent with the pattern of results for autocorrelations, and inter-personal coordination dynamics were smoother in larger group sizes. Furthermore, lag 0 cross-correlations became less negative with increasing group size (β=0.012, SE=0.002, t=5.93, and ω²=0.13). Lag 1 cross-correlations were positive (β=0.14, SE=0.012, t=11.80, and ω²=0.32), but their magnitude decreased with group size (β=−0.016, SE=0.002, t=−7.14, and ω²=0.20). There were also effects of tempo, see Figure 6—figure supplement 1.

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Group dynamics

Network analysis

We used network analysis in the continuation phase of trials of ensemble playing to describe group coordination at an even higher level of organization than interpersonal coordination. First, we obtained the directed graphs of functional connectivity among participant drummers. In each trial, the graph consisted of the real-valued directed links between pairs of drummers estimated by way of the transfer entropy (TE), see Figure 1C. Then, network properties of these graphs were computed, see Network analysis. There was a significant effect of group size on causal density (β=−0.0006, SE=0.0001, t=−5.88, and ω²=0.47), see Figure 7A. Causal density increased with tempo (β=0.008, SE=0.0019, t=4.01, and ω²=0.12). As Figure 7B shows, mean node strength increased with group size (β=0.0032, SE=0.00032, t=10.13, and ω²=0.73). Node strength was affected by tempo (β=0.016, SE=0.005, t=2.96, and ω²=0.07). For better model conditioning, tempos values were reduced and compressed by transforming them with a logistic function. Comparing between SCT trials and synchronization-only trials in the octet group found no difference for causal density (t<1) or mean node strength (t<1), suggesting group dynamics were similar regardless of whether a pacing stimulus was present or not.

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Network properties and task performance

The relation between group dynamics and group performance was evaluated by regressing the network properties separately with respect to speeding up and IOI variability in ensemble playing. Speeding up decreased with increasing causal density (β=−0.005, SE=0.002, t=−2.22, and ω²=0.02) and was not associated with node strength (β=−0.01, SE=0.007, and t=−1.66), see Figure 7—figure supplement 1A–B. Variability was not associated with causal density (β=−.003, SE=0.0017, and t=−1.97) but decreased with increasing node strength (β=−0.007, SE=0.0019, t=−4.01, and ω²=0.09), see Figure 7—figure supplement 1C–D. Interestingly, we also observed that TE tended to be higher in trials that deviated less from the stimulus tempo, see Figure 7—figure supplement 2.

The present study examined how group performance in a synchronization timing task depends on group size and interactions among group members. Our overall hypothesis was that the mean field would influence individuals in a group, thereby stabilizing group performance. We expected that larger groups would exhibit more stable performance due to a more stable mean field. This hypothesis was motivated by the collective dynamics that allow animals with limited capacity for interacting with each other to achieve mutual goals by exploiting processes such as positive collective feedback and wisdom of crowds (Sumpter, 2006). As expected, we observed that temporal variability in group drumming decreased with group size. This occurred only at the level of the ensemble or mean field when the group was taken as a single entity. The effect was reversed for individual participants; their variability increased from solo to group playing conditions and with group size. We showed that this result agrees with a collective dynamics framework based on an adapted Kuramoto model. The model is consistent with the idea from theoretical biology that self-organizing systems can successfully coordinate with an external constraint by reducing their disorder at the macro-level at the expense of increasing their micro-level disorder (Kugler and Turvey, 1987; Prokopenko et al., 2014) and that medium-sized groups exhibit commonalities with principles of organization found both in two-unit and large collectives (Kelso, 2021; Zhang et al., 2019). Our theory is related to the Vicsek model, an influential account of collective animal behavior which explains the spontaneous coherent heading direction arising in collective animal movement, also in human crowds, in terms of local interactions of attraction and repulsion (Silverberg et al., 2013; Vicsek et al., 1995). The Vicsek model also relies on individual coupling with the average of a group, with the important difference that only a small radius of neighbors defines the interaction field, called a local order parameter (Chaté et al., 2008).

The continuous-discrete Kuramoto system introduced here included fluctuating intrinsic frequencies and event-based feedback. Consistent with the discrete nature of time asynchronies in a drumming task, the model used a pulse function instead of continuous coupling. The implications of this idea reach beyond the topic of the present study. The combination of continuous movement and discrete sensory sampling of the environment is a frequently observed scenario; in the present study, participants used the discrete auditory feedback of drum taps to inform their continuous movements leading up to future drum taps. Our task reduced the opportunity of visual coupling but did not eliminate it completely, leaving the possibility that group drumming involved both discrete and continuous feedback such as visual information about continuous arm, hand, and drumstick movements related to the timing of individual drumming sounds. Future studies should address whether visual feedback during group drumming is informative, as well as explore how that visual information is captured within the individual and collective dynamics of the system.

Interestingly, in the octet group, individual variability was similar in both the synchronization-only condition, where the metronome pacing stimulus was present, and in the self-paced continuation stage of the SCT trials, where no pacing stimulus was present. This result implies that, although tempo accuracy and drift were superior when the pacing stimulus was present, the stability of group timing in a group of eight participants may hit a ceiling, where performance becomes as stable as with a pacing stimulus.

Individuals’ tempos tended to increase in solo trials, and as expected, this effect was amplified (i.e. speeding up was greater) when trying to stay synchronized with other participants (Okano et al., 2017). Surprisingly, the amount of speeding up was not higher for larger groups of drummers, and it was even reduced for larger groups, at least at faster tempos. In contrast, accounts based on mutual prediction imply more speeding up for larger groups (Thomson et al., 2018; Wolf et al., 2019). This discrepancy highlights the importance of testing tempo drift with specific task constraints and avoiding hasty generalizations from smaller to larger groups. Indeed, tempo can also slowdown in some circumstances. This was observed, for example, in a group synchronization task when participants synchronized their oscillations using swung pendulums, a task involving continuous visual coupling rather than discrete acoustic event coupling (Bardy et al., 2020).

Having summarized task performance in terms of consistency and accuracy, we next considered drumming dynamics. Individuals’ autocorrelations exhibited negative coefficient peaks at odd lags and positive coefficient peaks at even lags. This is the typical error-correction process demonstrated when individuals synchronize their tapping with a stimulus or with each other in pairs (Konvalinka et al., 2010; Repp, 2005). With increasing group size, however, the magnitudes of the autocorrelation coefficient fluctuations with lag decreased; individuals’ alternating patterns became smoother. This suggests that as group size increases, drumming dynamics are dominated less by tap-by-tap error-correction and more by a group dynamic. Interestingly, the group-aggregate onsets exhibited the same autocorrelation pattern as individuals, even in octets. This implies that the collective mean field behavior spontaneously acquired some of the dynamic properties typically associated with individual performance.

Inter-personal coordination in duets exhibited the negative cross-correlation at lag 0 and positive cross-correlation at lag 1 expected from past studies (Repp, 2005). The results for speeding up, variability, and cross-correlations in the dyads were consistent with the literature on dyadic interaction with tapping tasks. Yet, as group size increased in the present study, the coefficient magnitudes for cross-correlations decreased. In fact, they all but disappeared for groups of eight, suggestive of a leader-less group coordination scenario (Heggli et al., 2019). In a small number of trials, we even observed positive lag 0 cross-correlations in groups of eight, a signature of anticipation emerging from dynamic interaction. This result is consistent with the idea that group members coordinated less with each other and more with the mean field. Along with a more slowly decaying autocorrelation function, this suggests the emergence of a larger-scale group dynamic in the octet. As we hypothesized, mutual prediction is not necessarily the only basis for coordination in larger groups. The present model would be more like a predictive account had we included a new variable and an additional equation in the model, ${\dot{\theta }}_{j,i}=f({\theta }_i,{\theta }_j,{\theta }_{j,i})$, corresponding to a separate internal oscillatory process ${\theta }_{j,i}$ that individual i dedicates to the prediction of an external signal j. Yet, there is no reason to assume that collective dynamics and mutual prediction are exclusive alternatives, only that collective dynamics are a mode of social interaction that emerges under certain constraints such as large groups of interacting agents.

Characterizing inter-personal interaction on the basis of bivariate measures is typical in the context of dyadic paradigms. Larger groups, however, create the possibility for higher-order interactions. Network analysis was applied to the connectivity graph of TEs among drummers. Larger groups had lower causal density but higher node strength. This is possible because individual nodes in larger groups project to a larger number of other individual nodes and suggests that in larger groups, each individual has a decreased influence on the group, both less potential to destabilize the group and lower requirements to keep the group stable. We also found that higher node strength was associated with better overall performance, both in terms of variability and tempo accuracy.

Group timing tasks address some of the foundations of human social interaction. This area of research has been labeled the ‘dark matter’ of social neuroscience (Dumas et al., 2014; Insel, 2010; Schilbach et al., 2013) because it has not received enough attention despite the fact that large parts of the human cortex respond to social stimuli (Frith and Frith, 2001). The temporal regularities in music make it an ideal stimulus to promote synchronous and cooperative movement, providing evidence as to why music is often present at emotional social gatherings such as weddings, funerals, parties, and political rallies. Feelings of affiliation and cooperation increase between people who have experienced synchronous movement with each other, even during infancy (Cirelli et al., 2014; Cirelli et al., 2016; Hove and Risen, 2009; Mogan et al., 2017; Trainor and Cirelli, 2015; Valdesolo et al., 2010). The ability of music to strengthen social bonds within a group may have evolutionary roots that enhance survival capabilities of the group (Huron, 2001; Savage et al., 2020; Trainor, 2015). Arguably, the presence of a convergent regular rhythm defined over the ensemble allows individuals of varying skill level to perform together without having to predict each note they play, an idea that remained largely untested in the present study because of the relative simplicity of the isochronous stimulus.

Participants in group activities sometimes report a so-called state of flow characterized by absorption, loss of sense of control, loss of self-consciousness, togetherness, and effortless action toward a shared goal (Csikszentmihalyi, 1990; Gaggioli et al., 2017; Hart et al., 2014). Little is known about the underlying group processes that enable such experiences. Some of the required conditions have been studied in the context of group musical performance (Bishop et al., 2019; D’Amario et al., 2018). Collective intentionality is often seen as an extrapolation of single-person cognitive processes in that individuals predict other individuals’ goals and actions (Tomasello, 2000, Tomasello, 2014). Yet, the neural and cognitive mechanisms historically identified in single-person paradigms do not always map onto the ontology of social behavior (Przyrembel et al., 2012). Collective intentionality can stand for an independent mode of social cognition (Satne and Salice, 2018; Zahavi and Satne, 2015). Social cognition theory can even be turned upside-down by observing cases where individual agency arises from social interaction (De Jaegher and Froese, 2009; Froese et al., 2014). The present work shows how collective dynamics can absorb individual action.

The current manuscript is a computational study, so no data have been generated for this manuscript.

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Author details

1. Dobromir Dotov

   1. LIVELab, McMaster University, Hamilton, Canada
   2. Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Canada
   3. Department of Biomechanics, University of Nebraska Omaha, Omaha, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   ddotov@unomaha.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5543-360X

2. Lana Delasanta

   1. Center for the Ecological Study of Perception and Action, University of Connecticut, Storrs, United States
   2. Department of Psychological Sciences, University of Connecticut, Storrs, United States

   Contribution

   Formal analysis, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

3. Daniel J Cameron

   1. LIVELab, McMaster University, Hamilton, Canada
   2. Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Canada

   Contribution

   Conceptualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5543-9836

4. Edward W Large

   1. Center for the Ecological Study of Perception and Action, University of Connecticut, Storrs, United States
   2. Department of Psychological Sciences, University of Connecticut, Storrs, United States
   3. Department of Physics, University of Connecticut, Storrs, United States
   4. CT Institute for Brain and Cognitive Sciences, University of Connecticut, Storrs, United States

   Contribution

   Conceptualization, Software, Formal analysis, Supervision, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3909-3518

5. Laurel Trainor

   1. LIVELab, McMaster University, Hamilton, Canada
   2. Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Canada
   3. Rotman Research Institute, Toronto, Canada

   Contribution

   Conceptualization, Formal analysis, Supervision, Funding acquisition, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3397-2079

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