Transient brain networks underlying interpersonal strategies during synchronized action

Heggli, Ole Adrian; Konvalinka, Ivana; Cabral, Joana; Brattico, Elvira; Kringelbach, Morten L.; Vuust, Peter

doi:10.1093/scan/nsaa056

Cited by 30 publications

(25 citation statements)

References 68 publications

(55 reference statements)

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“…In previous work, we used a data-driven approach to identify a functional brain network selectively associated with mutual adaptation and leading–leading synchronization strategies [88]. Mutually adapting participants exhibited a higher occurrence of synchronized activity within this network, perhaps representing the proposed synchronization between self- and other representations in the brain.…”

Section: The Metastable Attractor Model Of Self–other Integration In the Brainmentioning

confidence: 99%

A metastable attractor model of self–other integration (MEAMSO) in rhythmic synchronization

Heggli

Konvalinka

Kringelbach

et al. 2021

Phil. Trans. R. Soc. B

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Human interaction is often accompanied by synchronized bodily rhythms. Such synchronization may emerge spontaneously as when a crowd's applause turns into a steady beat, be encouraged as in nursery rhymes, or be intentional as in the case of playing music together. The latter has been extensively studied using joint finger-tapping paradigms as a simplified version of rhythmic interpersonal synchronization. A key finding is that synchronization in such cases is multifaceted, with synchronized behaviour resting upon different synchronization strategies such as mutual adaptation, leading–following and leading–leading. However, there are multiple open questions regarding the mechanism behind these strategies and how they develop dynamically over time. Here, we propose a metastable attractor model of self–other integration (MEAMSO). This model conceptualizes dyadic rhythmic interpersonal synchronization as a process of integrating and segregating signals of self and other. Perceived sounds are continuously evaluated as either being attributed to self -produced or other -produced actions. The model entails a metastable system with two particular attractor states: one where an individual maintains two separate predictive models for self - and other -produced actions, and the other where these two predictive models integrate into one. The MEAMSO explains the three known synchronization strategies and makes testable predictions about the dynamics of interpersonal synchronization both in behaviour and the brain. This article is part of the theme issue ‘Synchrony and rhythm interaction: from the brain to behavioural ecology’.

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Section: The Metastable Attractor Model Of Self–other Integration In the Brainmentioning

confidence: 99%

A metastable attractor model of self–other integration (MEAMSO) in rhythmic synchronization

Heggli

Konvalinka

Kringelbach

et al. 2021

Phil. Trans. R. Soc. B

Self Cite

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“…Thus, our results may shed light only on follower-related monitoring processes. Future studies should aim at distinguishing processes involved in other dyadic synchronization strategies, such as mutual adaptation (55,56) or leading-leading interactions (57).…”

Section: Limitationsmentioning

confidence: 99%

The monitoring system is attuned to others’ actions during dyadic motor interactions

Moreau

Tieri

Aglioti

et al. 2021

Preprint

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Successful interpersonal motor interactions necessitate the simultaneous monitoring of our own and our partner's actions. To characterize the dynamics of the action monitoring system for tracking self and other behaviors during dyadic synchronous interactions, we combined EEG recordings and immersive Virtual Reality in two tasks where participants were asked to coordinate their actions with those of a Virtual Partner (VP). The two tasks differed in the features to be monitored: the Goal task required participants to predict and monitor the VP's reaching goal; the Spatial task required participants to predict and monitor the VP's reaching trajectory. In both tasks, the VP performed unexpected movement corrections to which the participant needed to adapt. By comparing the neural activity locked to the detection of unexpected changes in the VP action (other-monitoring) or to the participants' action-replanning (self-monitoring), we show that during interpersonal interactions the monitoring system is more attuned to others' than to one's own actions. Additionally, distinctive neural responses to VP's unexpected goals and trajectory corrections were found: goal corrections were reflected both in early fronto-central and later posterior neural responses while trajectory deviations from the expected movement were reflected only in later and posterior responses. Since these responses were locked to the partner's behavior and not to one's own, our results indicate that during interpersonal interactions the action monitoring system is dedicated to evaluating the partner's movements. Hence, our results reveal an eminently social role of the monitoring system during motor interactions.

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“…Paradigms differ in whether they elicit intentional or unintentional synchronization. Music making or tapping are examples for the former approach ( Heggli et al , this issue ; Zamm et al , this issue ) and passively observing others ( Kragness and Cirelli, this issue ) or engaging in conversation ( Nguyen et al , this issue ; Thorson et al , this issue ) are examples for the latter approach. As mentioned above, some authors even attempted to induce synchrony through brain stimulation ( Pan et al , this issue ).…”

Section: Why This Special Issue?mentioning

confidence: 99%

“…The study of internal parameters might focus on the peripheral nervous system with measures such as skin conductance ( Kragness and Cirelli, this issue ) or heart rate ( Thorson et al , this issue ) or on brain changes with neuroimaging measures. Interestingly, the latter is pursued most frequently not with functional magnetic resonance imaging, which many consider the gold standard of neuroimaging, but with other less popular techniques including the electroencephalography ( Heggli et al , this issue ; Schirmer et al , this issue ; Zamm et al , this issue ), magnetoencephalography ( Levy et al , 2017 ) and functional near-infrared spectroscopy ( Dieffenbach et al , this issue ; Kruppa et al , this issue ; Nguyen et al , this issue ; Pinti et al , this issue ). This choice nicely reflects the need to capture the fast temporal dynamics characterizing interactional synchrony and/or the need for fairly unconstrained face-to-face interactions (but see Misaki et al , this issue ).…”

Section: Why This Special Issue?mentioning

confidence: 99%

“…To this end, many mathematical approaches have been developed and applied. Some of these approaches, featuring in the current issue, include simple cross-correlations of original or wavelet-transformed time series ( Kruppa et al , this issue ; Pan et al , this issue ) as well as indices of phase locking ( Heggli et al , this issue ) or shared changes in the power of certain frequencies characterizing the time series ( Schirmer et al , this issue ; Zamm et al , this issue ). These as well as other analytical strategies including Granger causality ( Sciaraffa et al , this issue ) differently address the problem of how to define synchrony and whether and how delays between corresponding changes in the time series of interest (e.g.…”

Section: Why This Special Issue?mentioning

confidence: 99%

See 1 more Smart Citation

Being ‘in sync’—is interactional synchrony the key to understanding the social brain?

Schirmer

Fairhurst

Hoehl

2020

Social Cognitive and Affective Neuroscience

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The past couple of decades produced a surge of interest in interaction synchrony. Moving from the study of behavioral coordination to investigating the coordination of psychophysiological and brain activity, relevant research has tackled a broad range of interactional settings with a multitude of measurement and analysis tools. This method diversity produced a host of interesting results converging on the fact that individuals engaged in social exchange tend to temporally align external as well as internal processes. Moreover, there appears to be a reciprocal relationship between the individuals’ affective bond and the extent of synchronization, which together benefit interaction outcomes. Notably, however, the current breadth of study approaches creates challenges for the field including how to compare findings and how to develop a theoretical framework that unites and directs ongoing research efforts. More concerted efforts are called for to achieve the conceptual and methodological clarity needed to answer core questions and enabling a balanced pursuit of both synchronous and asynchronous processes.

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Transient brain networks underlying interpersonal strategies during synchronized action

Cited by 30 publications

References 68 publications

A metastable attractor model of self–other integration (MEAMSO) in rhythmic synchronization

A metastable attractor model of self–other integration (MEAMSO) in rhythmic synchronization

The monitoring system is attuned to others’ actions during dyadic motor interactions

Being ‘in sync’—is interactional synchrony the key to understanding the social brain?

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