A roadmap to computational social neuroscience

Tognoli, Emmanuelle; Dumas, Guillaume; Kelso, J. A. Scott

doi:10.1007/s11571-017-9462-0

Cited by 11 publications

(15 citation statements)

References 31 publications

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“…Further research work is necessary to go beyond the limits of the model of this paper. An interesting example of a valuable direction to follow to realize this extension is afforded by the recent work of Tognoli et al ( 2018 ). The authors of this illuminating paper adopt a multiscale neurocomputational model of social coordination that enables exploration of social self–organization at all levels, from neuronal patterns to people interacting with each other.…”

Section: Complexity Information and Conclusionmentioning

confidence: 99%

“…Adopting the distinction between neurophysiologic and sociologic level (Tognoli et al, 2018 ), interpreted as two distinct complex systems, we should be able to take into account the perception of task difficulty, going beyond the limitations of the model adopted herein. A remarkable example of a problem that would be settled using this extension of the model of the present paper is given by Tuladhar et al ( 2018 ).…”

Section: Complexity Information and Conclusionmentioning

confidence: 99%

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Complex Periodicity and Synchronization

2020

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A recent experiment proves the therapeutic effect of arm-in-arm walking, showing that if an aged participant walks in close synchrony with a young companion, the complexity matching effect results in the restoration of complexity in the former. A clear manifestation of complexity restoration is a perfect synchronization. The authors of this interesting experiment leave open two important problems. The first is the measure of complexity that is interpreted as a degree of multifractality. The second problem is the lack of a theoretical derivation of synchronization, which is experimentally observed with no theoretical derivation. The main goal of this paper is to establish a physiological foundation of these important results based on the recent advances on the dynamics of the brain, interpreted as a system at criticality. Criticality is a phenomenon requiring the cooperative interaction of units, the neurons of the brain, and is hypothesized as the main source of cognition. Using the criticality-induced intelligence, we define complexity as a property of crucial events, a form of temporal complexity, and we prove that the perfect synchronization is due to the interaction between the two systems, with the more complex system restoring the temporal complexity of the less complex system. The phenomenon of temporal complexity is characterized by ergodicity breaking that has made it difficult in the past to derive the perfect synchronization generated by complexity matching. For this reason, we supplement the main result of this paper with a comparison between complexity matching and complexity management.

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Section: Complexity Information and Conclusionmentioning

confidence: 99%

Section: Complexity Information and Conclusionmentioning

confidence: 99%

Complex Periodicity and Synchronization

2020

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“…Collective capability of producing patterned collective behaviors is one important field of research in work psychology (e.g., shared cognition approach, Fiore and Salas, 2004; interactive team cognition approach, Cooke et al, 2013), neurosciences (e.g., social neuromarkers, Tognoli et al, in press; neurological mirroring, Waldman et al, 2015), sociology (Miller, 2013), or human movement science (e.g., joint movement, Schmidt and Richardson, 2008; team behavior, Araujo and Bourbousson, 2016). Within this stream of research, one neglected topic has been to conceptualize how interactors regulate online their dynamic involvement in collective activity, which is the individual skillful activity of adjusting online to the needs of the collective behavior.…”

Section: Introductionmentioning

confidence: 99%

How do Co-agents Actively Regulate their Collective Behavior States?

Bourbousson

Fortes-Bourbousson

2016

Front. Psychol.

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“…A key concept of Coordination Dynamics -following along the lines of Synergetics (Haken, 1977) -is the collective variable or order parameter which has been demonstrated to cut across different kinds of parts and processes (and across levels) thereby dissolving traditional (and somewhat arbitrary) divisions (e.g., between "cognitive" and "motor") and enabling a novel, dynamic framework for understanding collective/social behavior (Kelso, 1995(Kelso, , 2009a(Kelso, ,b, 2012Tognoli, 2008;Coey et al, 2012;Richardson et al, 2014;Tognoli et al, 2018a; see also FIGURE 1 | Schematic of the experimental paradigm of social coordination dynamics in which two participants simultaneously perceive and produce behavior in view of each other. More specifically, in our canonical experiment, subjects move their fingers in continuous fashion while at the same time observing their partner doing the same.…”

Section: Order Parameter: Connecting Models and Experimentsmentioning

confidence: 99%

“…Quite a few modeling frameworks have been applied to social systems, including agent-based models (e.g., Axelrod, 1997;Gilbert and Terna, 2000;Bonabeau, 2002;Schweitzer, 2007), cellular automata (e.g., Hegselmann, 1998;Batty, 2007), Lotka-Volterra (e.g., Matsuda et al, 1992;Castellano et al, 2009), stochastic diffusion (e.g., Arató, 2003;López-Pintado, 2008;Kimura et al, 2010), Bayesian (e.g., Yang et al, 2011), Markov (e.g., Singer and Spilerman, 1976;Gintis, 2013), signal flow graphs and block diagrams (Liu and Ma, 2018), recurrent networks (Irsoy and Cardie, 2014), and, central to this review, the HKB model (after Haken et al, 1985; see also Schöner and Kelso, 1988b;Kelso, 1995;Tognoli et al, 2018a), itself based on the concepts of synergetics (Haken, 1977) and the mathematical tools of non-linearly coupled non-linear oscillators. One of the key strengths of the HKB model (and its numerous extensions) is its possession of intrinsic dynamics (Kelso, 1995, Ch 6).…”

Section: Introductionmentioning

confidence: 99%

Coordination Dynamics: A Foundation for Understanding Social Behavior

Tognoli

Zhang

Fuchs

et al. 2020

Front. Hum. Neurosci.

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Humans' interactions with each other or with socially competent machines exhibit lawful coordination patterns at multiple levels of description. According to Coordination Dynamics, such laws specify the flow of coordination states produced by functional synergies of elements (e.g., cells, body parts, brain areas, people.. .) that are temporarily organized as single, coherent units. These coordinative structures or synergies may be mathematically characterized as informationally coupled self-organizing dynamical systems (Coordination Dynamics). In this paper, we start from a simple foundation, an elemental model system for social interactions, whose behavior has been captured in the Haken-Kelso-Bunz (HKB) model. We follow a tried and tested scientific method that tightly interweaves experimental neurobehavioral studies and mathematical models. We use this method to further develop a body of empirical research that advances the theory toward more generalized forms. In concordance with this interdisciplinary spirit, the present paper is written both as an overview of relevant advances and as an introduction to its mathematical underpinnings. We demonstrate HKB's evolution in the context of social coordination along several directions, with its applicability growing to increasingly complex scenarios. In particular, we show that accommodating for symmetry breaking in intrinsic dynamics and coupling, multiscale generalization and adaptation are principal evolutions. We conclude that a general framework for social coordination dynamics is on the horizon, in which models support experiments with hypothesis generation and mechanistic insights.

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A roadmap to computational social neuroscience

Cited by 11 publications

References 31 publications

Complex Periodicity and Synchronization

Complex Periodicity and Synchronization

How do Co-agents Actively Regulate their Collective Behavior States?

Coordination Dynamics: A Foundation for Understanding Social Behavior

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