The human dynamic clamp as a paradigm for social interaction

Predicting social experience from dyadic interaction dynamics: the BallGame, a novel paradigm to study social engagement

Theories of embodied cognition suggest that a shared environment and ongoing sensorimotor interaction are central for interpersonal learning and engagement. To investigate the embodied, distributed and hence dynamically unfolding nature of social cognitive capacities, we present a novel laboratory-based coordination task: the BallGame. Our paradigm requires continuous sensing and acting between two players who jointly steer a virtual ball around obstacles towards as many targets as possible. By analysing highly resolved measures of movement coordination and gaming behaviour, game-concurrent experience ratings, semi-structured interviews, and personality questionnaires, we reveal contributions from different levels of observation on social experience. In particular, successful coordination (number of targets collected) and intermittent periods of high versus low movement coordination (variability of relation) emerged as prominent predictors of social experience. Importantly, having the same (but incomplete) view on the game environment strengthened interpersonal coordination, whereas complementary views enhanced engagement and tended to generate more complex interactive behaviour. Overall, we find evidence for a critical balance between similarity and synchrony on the one hand, and variability and difference on the other, for successful engagement in social interactions. Finally, following participant reports, we highlight how interpersonal experience emerges from specific histories of coordination that are closely related to the interaction context in both space and time.

Online chasing action recruits both mirror neuron and mentalizing systems: A pilot fNIRS study

Engaging in chasing, where an actor actively pursues a target, is considered a crucial activity for the development of social skills. Previous studies have focused predominantly on understanding the neural correlates of chasing from an observer's perspective, but the neural mechanisms underlying the real-time implementation of chasing action remain poorly understood. To gain deeper insights into this phenomenon, the current study employed functional near-infrared spectroscopy (fNIRS) techniques and a novel interactive game. In this interactive game, participants (N = 29) were tasked to engage in chasing behavior by controlling an on-screen character using a gamepad, with the goal of catching a virtual partner. To specifically examine the brain activations associated with the interactive nature of chasing, we included two additional interactive actions: following action of following the path of a virtual partner and free action of moving without a specific pursuit goal. The results revealed that chasing and following actions elicited activation in a broad and overlapping network of brain regions, including the temporoparietal junction (TPJ), medial prefrontal cortex (mPFC), premotor cortex (PMC), primary somatosensory cortex (SI), and primary motor cortex (M1). Crucially, these regions were found to be modulated by the type of interaction, with greater activation and functional connectivity during the chasing interaction than during the following and free interactions. These findings suggested that both the MNS, encompassing regions such as the PMC, M1 and SI, and the mentalizing system (MS), involving the TPJ and mPFC, contribute to the execution of online chasing actions. Thus, the present study represents an initial step toward future investigations into the roles of MNS and MS in real-time chasing interactions.

Prospects for Augmenting Team Interactions with Real-Time Coordination-Based Measures in Human-Autonomy Teams

Complex work in teams requires coordination across team members and their technology as well as the ability to change and adapt over time to achieve effective performance. To support such complex interactions, recent efforts have worked toward the design of adaptive human-autonomy teaming systems that can provide feedback in or near real time to achieve the desired individual or team results. However, while significant advancements have been made to better model and understand the dynamics of team interaction and its relationship with task performance, appropriate measures of team coordination and computational methods to detect changes in coordination have not yet been widely investigated. Having the capacity to measure coordination in real time is quite promising as it provides the opportunity to provide adaptive feedback that may influence and regulate teams' coordination patterns and, ultimately, drive effective team performance. A critical requirement to reach this potential is having the theoretical and empirical foundation from which to do so. Therefore, the first goal of the paper is to review approaches to coordination dynamics, identify current research gaps, and draw insights from other areas, such as social interaction, relationship science, and psychotherapy. The second goal is to collate extant work on feedback and advance ideas for adaptive feedback systems that have potential to influence coordination in a way that can enhance the effectiveness of team interactions. In addressing these two goals, this work lays the foundation as well as plans for the future of human-autonomy teams that augment team interactions using coordination-based measures.

Revealing the mechanism and function underlying pairwise temporal coupling in collective motion

Coordinated motion in animal groups has predominantly been studied with a focus on spatial interactions, such as how individuals position and orient themselves relative to one another. Temporal aspects have, by contrast, received much less attention. Here, by studying pairwise interactions in juvenile zebrafish (Danio rerio)-including using immersive volumetric virtual reality (VR) with which we can directly test models of social interactions in situ-we reveal that there exists a rhythmic out-of-phase (i.e., an alternating) temporal coordination dynamic. We find that reciprocal (bi-directional) feedback is both necessary and sufficient to explain this emergent coupling. Beyond a mechanistic understanding, we find, both from VR experiments and analysis of freely swimming pairs, that temporal coordination considerably improves spatial responsiveness, such as to changes in the direction of motion of a partner. Our findings highlight the synergistic role of spatial and temporal coupling in facilitating effective communication between individuals on the move.

The Emerging Science of Interacting Minds

For over a century, psychology has focused on uncovering mental processes of a single individual. However, humans rarely navigate the world in isolation. The most important determinants of successful development, mental health, and our individual traits and preferences arise from interacting with other individuals. Social interaction underpins who we are, how we think, and how we behave. Here we discuss the key methodological challenges that have limited progress in establishing a robust science of how minds interact and the new tools that are beginning to overcome these challenges. A deep understanding of the human mind requires studying the context within which it originates and exists: social interaction.

Using a haptic dynamic clamp to reduce arousal: preference, arousal, and coordination stability are related

We have developed a haptic dynamic clamp dedicated to the regulation of arousal. It takes the form of a vibrating stress ball to be squeezed, called Viball, controlled by Righetti's nonlinear adaptive Hopf oscillator. Participants squeezed an adaptive Viball which adapts its frequency of vibration to the current frequency of human squeezing. The adaptive Viball was compared to three non-adaptive Viballs, parametrized to vibrate at a lower, equal, or higher frequency than the participants' preferred frequency. While squeezing the ball, participants looked at stressful or calming pictures and their electrodermal activity was recorded. Using the preference paradigm, we show that participants preferred to interact with the adaptive Viball rather than with the most slowly vibrating ball that most strongly reduced arousal. The stability of the human-ball coordination was the highest with the adaptive Viball. There was also a positive correlation between the stability of coordination and arousal. The data are discussed in light of the energy-based interpretation of coordination dynamics.

Cortico-Cerebellar neurodynamics during social interaction in Autism Spectrum Disorders

BACKGROUND

Exploring neural network dynamics during social interaction could help to identify biomarkers of Autism Spectrum Disorders (ASD). A cerebellar involvement in autism has long been suspected and recent methodological advances now enable studying cerebellar functioning in a naturalistic setting. Here, we investigated the electrophysiological activity of the cerebro-cerebellar network during real-time social interaction in ASD. We focused our analysis on theta oscillations (3-8 Hz), which have been associated with large-scale coordination of distant brain areas and might contribute to interoception, motor control, and social event anticipation, all skills known to be altered in ASD.

METHODS

We combined the Human Dynamic Clamp, a paradigm for studying realistic social interactions using a virtual avatar, with high-density electroencephalography (HD-EEG). Using source reconstruction, we investigated power in the cortex and the cerebellum, along with coherence between the cerebellum and three cerebral-cortical areas, and compared our findings in a sample of participants with ASD (n = 107) and with typical development (TD) (n = 33). We developed an open-source pipeline to analyse neural dynamics at the source level from HD-EEG data.

RESULTS

Individuals with ASD showed a significant increase in theta band power over the cerebellum and the frontal and temporal cortices during social interaction compared to resting state, along with significant coherence increases between the cerebellum and the sensorimotor, frontal and parietal cortices. However, a phase-based connectivity measure did not support a strict activity increase in the cortico-cerebellar functional network. We did not find any significant differences between the ASD and the TD group.

CONCLUSIONS

This exploratory study uncovered increases in the theta band activity of participants with ASD during social interaction, pointing at the presence of neural interactions between the cerebellum and cerebral networks associated with social cognition. It also emphasizes the need for complementary functional connectivity measures to capture network-level alterations. Future work will focus on optimizing artifact correction to include more participants with TD and increase the statistical power of group-level contrasts.

Investigating the concept of representation in the neural and psychological sciences

The concept of representation is commonly treated as indispensable to research on brains, behavior, and cognition. Nevertheless, systematic evidence about the ways the concept is applied remains scarce. We present the results of an experiment aimed at elucidating what researchers mean by "representation." Participants were an international group of psychologists, neuroscientists, and philosophers (N = 736). Applying elicitation methodology, participants responded to a survey with experimental scenarios aimed at invoking applications of "representation" and five other ways of describing how the brain responds to stimuli. While we find little disciplinary variation in the application of "representation" and other expressions (e.g., "about" and "carry information"), the results suggest that researchers exhibit uncertainty about what sorts of brain activity involve representations or not; they also prefer non-representational, causal characterizations of the brain's response to stimuli. Potential consequences of these findings are explored, such as reforming or eliminating the concept of representation from use.

Intertwining the social and the cognitive loops: socially enactive cognition for human-compatible interactive systems

It is increasingly important for technical systems to be able to interact flexibly, robustly and fluently with humans in real-world scenarios. However, while current AI systems excel at narrow task competencies, they lack crucial interaction abilities for the adaptive and co-constructed social interactions that humans engage in. We argue that a possible avenue to tackle the corresponding computational modelling challenges is to embrace interactive theories of social understanding in humans. We propose the notion of socially enactive cognitive systems that do not rely solely on abstract and (quasi-)complete internal models for separate social perception, reasoning and action. By contrast, socially enactive cognitive agents are supposed to enable a close interlinking of the enactive socio-cognitive processing loops within each agent, and the social-communicative loop between them. We discuss theoretical foundations of this view, identify principles and requirements for according computational approaches, and highlight three examples of our own research that showcase the interaction abilities achievable in this way. This article is part of a discussion meeting issue 'Face2face: advancing the science of social interaction'.

Brain networks for temporal adaptation, anticipation, and sensory-motor integration in rhythmic human behavior

Human interaction often requires the precise yet flexible interpersonal coordination of rhythmic behavior, as in group music making. The present fMRI study investigates the functional brain networks that may facilitate such behavior by enabling temporal adaptation (error correction), prediction, and the monitoring and integration of information about 'self' and the external environment. Participants were required to synchronize finger taps with computer-controlled auditory sequences that were presented either at a globally steady tempo with local adaptations to the participants' tap timing (Virtual Partner task) or with gradual tempo accelerations and decelerations but without adaptation (Tempo Change task). Connectome-based predictive modelling was used to examine patterns of brain functional connectivity related to individual differences in behavioral performance and parameter estimates from the adaptation and anticipation model (ADAM) of sensorimotor synchronization for these two tasks under conditions of varying cognitive load. Results revealed distinct but overlapping brain networks associated with ADAM-derived estimates of temporal adaptation, anticipation, and the integration of self-controlled and externally controlled processes across task conditions. The partial overlap between ADAM networks suggests common hub regions that modulate functional connectivity within and between the brain's resting-state networks and additional sensory-motor regions and subcortical structures in a manner reflecting coordination skill. Such network reconfiguration might facilitate sensorimotor synchronization by enabling shifts in focus on internal and external information, and, in social contexts requiring interpersonal coordination, variations in the degree of simultaneous integration and segregation of these information sources in internal models that support self, other, and joint action planning and prediction.

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

Interpersonal motor interactions require the simultaneous monitoring of one's own and one's partner's actions. To characterize how the action monitoring system tracks self and other behavior during synchronous interactions, we combined electroencephalography recordings and immersive virtual reality in two tasks where participants were asked to synchronize 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 changes to which the participant needed to adapt. By extracting the neural activity locked to the detection of unexpected changes in the VP's action (other-monitoring) or to the participants' action-replanning (self-monitoring), we show that 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 changes were reflected both in early fronto-central and later posterior neural responses while trajectory deviations were reflected only in later posterior responses. Altogether, our results indicate that the monitoring system adopts an inherent social mode to handle interpersonal motor interactions.

Multilevel development of cognitive abilities in an artificial neural network

Several neuronal mechanisms have been proposed to account for the formation of cognitive abilities through postnatal interactions with the physical and sociocultural environment. Here, we introduce a three-level computational model of information processing and acquisition of cognitive abilities. We propose minimal architectural requirements to build these levels, and how the parameters affect their performance and relationships. The first sensorimotor level handles local nonconscious processing, here during a visual classification task. The second level or cognitive level globally integrates the information from multiple local processors via long-ranged connections and synthesizes it in a global, but still nonconscious, manner. The third and cognitively highest level handles the information globally and consciously. It is based on the global neuronal workspace (GNW) theory and is referred to as the conscious level. We use the trace and delay conditioning tasks to, respectively, challenge the second and third levels. Results first highlight the necessity of epigenesis through the selection and stabilization of synapses at both local and global scales to allow the network to solve the first two tasks. At the global scale, dopamine appears necessary to properly provide credit assignment despite the temporal delay between perception and reward. At the third level, the presence of interneurons becomes necessary to maintain a self-sustained representation within the GNW in the absence of sensory input. Finally, while balanced spontaneous intrinsic activity facilitates epigenesis at both local and global scales, the balanced excitatory/inhibitory ratio increases performance. We discuss the plausibility of the model in both neurodevelopmental and artificial intelligence terms.

Artificial Partners to Understand Joint Action: Representing Others to Develop Effective Coordination

In the last years, artificial partners have been proposed as tools to study joint action, as they would allow to address joint behaviors in more controlled experimental conditions. Here we present an artificial partner architecture which is capable of integrating all the available information about its human counterpart and to develop efficient and natural forms of coordination. The model uses an extended state observer which combines prior information, motor commands and sensory observations to infer the partner's ongoing actions (partner model). Over trials, these estimates are gradually incorporated into action selection. Using a joint planar task in which the partners are required to perform reaching movements while mechanically coupled, we demonstrate that the artificial partner develops an internal representation of its human counterpart, whose accuracy depends on the degree of mechanical coupling and on the reliability of the sensory information. We also show that human-artificial dyads develop coordination strategies which closely resemble those observed in human-human dyads and can be interpreted as Nash equilibria. The proposed approach may provide insights for the understanding of the mechanisms underlying human-human interaction. Further, it may inform the development of novel neuro-rehabilitative solutions and more efficient human-machine interfaces.

Social Neuro AI: Social Interaction as the “Dark Matter” of AI

This article introduces a three-axis framework indicating how AI can be informed by biological examples of social learning mechanisms. We argue that the complex human cognitive architecture owes a large portion of its expressive power to its ability to engage in social and cultural learning. However, the field of AI has mostly embraced a solipsistic perspective on intelligence. We thus argue that social interactions not only are largely unexplored in this field but also are an essential element of advanced cognitive ability, and therefore constitute metaphorically the “dark matter” of AI. In the first section, we discuss how social learning plays a key role in the development of intelligence. We do so by discussing social and cultural learning theories and empirical findings from social neuroscience. Then, we discuss three lines of research that fall under the umbrella of Social NeuroAI and can contribute to developing socially intelligent embodied agents in complex environments. First, neuroscientific theories of cognitive architecture, such as the global workspace theory and the attention schema theory, can enhance biological plausibility and help us understand how we could bridge individual and social theories of intelligence. Second, intelligence occurs in time as opposed to over time, and this is naturally incorporated by dynamical systems. Third, embodiment has been demonstrated to provide more sophisticated array of communicative signals. To conclude, we discuss the example of active inference, which offers powerful insights for developing agents that possess biological realism, can self-organize in time, and are socially embodied.

Human unintentional and intentional interpersonal coordination in interaction with a humanoid robot

In order to establish natural social synchrony between two humans, two requirements need to be fulfilled. First, the coupling must be bi-directional. The two humans react to each other's actions. Second, natural social bodily synchronization has to be intentional or unintentional. Assuming that these essential aspects of human-human interactions are present, the present paper investigates whether similar bodily synchrony emerges between an interacting human and an artificial agent such as a robot. More precisely, we investigate whether the same human unintentional rhythmic entrainment and synchronization is present in Human Robot Interaction (HRI). We also evaluate which model (e.g., an adaptive vs non adaptive robot) better reproduces such unintentional entrainment. And finally, we compare interagent coordination stability of the HRI under 1) unidirectional (robot with fixed frequency) versus bidirectional (robot with adaptive frequency) rhythmic entrainment and 2) human intentional versus unintentional coupling. Fifteen young adults made vertical arm movements in front of the NAO robot under five different conditions of intentional/unintentional and unidirectional/bidirectional interactions. Consistent with prior research investigating human-human interpersonal coordination, when humans interact with our robot, (i) unintentional entrainment was present, (ii) bi-directional coupling produced more stable in-phase un-intentional and intentional coordination, (iii) and intentional coordination was more stable than unintentional coordination. To conclude, this study provides a foundation for modeling future social robots involving unintentional and bidirectional synchronization-aspects which seem to enhance humans' willingness to interact with robots.

Integration and Causality in Enactive Approaches to Psychiatry

In this paper I address what has been called the integration problem in psychiatry. This problem is tied to conceptions of causality and explanatory levels in our understanding of mind. I take an interdisciplinary enactive perspective to develop a 3-fold method for exploring the dynamics of integration, based on a concept of dynamical causation and a non-hierarchical (level-free) notion of gestalt. I also consider Autism Spectrum Disorder (ASD) as a test case.

Movement markers of schizophrenia: a detailed analysis of patients' gait patterns

Motor abnormalities occur in the majority of persons with schizophrenia but are generally neglected in clinical care. Psychiatric diagnostics fail to include quantifiable motor variables and few assessment tools examine full-body movement. We assessed full-body movement during gait of 20 patients and 20 controls with motion capture technology, symptom load (PANSS, BPRS) and Neurological Soft Signs (NSS). In a data-driven analysis, participants' motion patterns were quantified and compared between groups. Resulting movement markers (MM) were correlated with the clinical assessment. We identified 16 quantifiable MM of schizophrenia. While walking, patients and controls display significant differences in movement patterns related to posture, velocity, regularity of gait as well as sway, flexibility and integration of body parts. Specifically, the adjustment of body sides, limbs and movement direction were affected. The MM remain significant when controlling for medication load. They are systematically related to NSS. Results add assessment tools, analysis methods as well as theory-independent MM to the growing body of research on motor abnormalities in schizophrenia.

Task dynamics define the contextual emergence of human corralling behaviors

Social animals have the remarkable ability to organize into collectives to achieve goals unobtainable to individual members. Equally striking is the observation that despite differences in perceptual-motor capabilities, different animals often exhibit qualitatively similar collective states of organization and coordination. Such qualitative similarities can be seen in corralling behaviors involving the encirclement of prey that are observed, for example, during collaborative hunting amongst several apex predator species living in disparate environments. Similar encirclement behaviors are also displayed by human participants in a collaborative problem-solving task involving the herding and containment of evasive artificial agents. Inspired by the functional similarities in this behavior across humans and non-human systems, this paper investigated whether the containment strategies displayed by humans emerge as a function of the task's underlying dynamics, which shape patterns of goal-directed corralling more generally. This hypothesis was tested by comparing the strategies naïve human dyads adopt during the containment of a set of evasive artificial agents across two disparate task contexts. Despite the different movement types (manual manipulation or locomotion) required in the different task contexts, the behaviors that humans display can be predicted as emergent properties of the same underlying task-dynamic model.

Navigational Behavior of Humans and Deep Reinforcement Learning Agents

Rapid advances in the field of Deep Reinforcement Learning (DRL) over the past several years have led to artificial agents (AAs) capable of producing behavior that meets or exceeds human-level performance in a wide variety of tasks. However, research on DRL frequently lacks adequate discussion of the low-level dynamics of the behavior itself and instead focuses on meta-level or global-level performance metrics. In doing so, the current literature lacks perspective on the qualitative nature of AA behavior, leaving questions regarding the spatiotemporal patterning of their behavior largely unanswered. The current study explored the degree to which the navigation and route selection trajectories of DRL agents (i.e., AAs trained using DRL) through simple obstacle ridden virtual environments were equivalent (and/or different) from those produced by human agents. The second and related aim was to determine whether a task-dynamical model of human route navigation could not only be used to capture both human and DRL navigational behavior, but also to help identify whether any observed differences in the navigational trajectories of humans and DRL agents were a function of differences in the dynamical environmental couplings.

Exploring the "Dark Matter" of Social Interaction: Systematic Review of a Decade of Research in Spontaneous Interpersonal Coordination

Interpersonal coordination is a research topic that has attracted considerable attention this last decade both due to a theoretical shift from intra-individual to inter-individual processes and due to the development of new methods for recording and analyzing movements in ecological settings. Encompassing spatiotemporal behavioral matching, interpersonal coordination is considered as "social glue" due to its capacity to foster social bonding. However, the mechanisms underlying this effect are still unclear and recent findings suggest a complex picture. Goal-oriented joint action and spontaneous coordination are often conflated, making it difficult to disentangle the role of joint commitment from unconscious mutual attunement. Consequently, the goals of the present article are twofold: (1) to illustrate the rapid expansion of interpersonal coordination as a research topic and (2) to conduct a systematic review of spontaneous interpersonal coordination, summarizing its latest developments and current challenges this last decade. By applying Rapid Automatic Keyword Extraction and Latent Dirichlet Allocation algorithms, keywords were extracted from PubMed and Scopus databases revealing the large diversity of research topics associated with spontaneous interpersonal coordination. Using the same databases and the keywords "behavioral matching," "interactional synchrony," and "interpersonal coordination," 1,213 articles were identified, extracted, and screened following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol. A total of 19 articles were selected using the following inclusion criteria: (1) dynamic and spontaneous interactions between two unacquainted individuals (2) kinematic analyses, and (3) non-clinical and non-expert adult populations. The results of this systematic review stress the proliferation of various definitions and experimental paradigms that study perceptual and/or social influences on the emergence of spontaneous interpersonal coordination. As methods and indices used to quantify interpersonal coordination differ from one study to another, it becomes difficult to establish a coherent picture. This review highlights the need to reconsider interpersonal coordination not as the pinnacle of social interactions but as a complex dynamical process that requires cautious interpretation. An interdisciplinary approach is necessary for building bridges across scattered research fields through opening a dialogue between different theoretical frameworks and consequently provides a more ecological and holistic understanding of human social cognition.

Probing associations between interbrain synchronization and interpersonal action coordination during guitar playing

Playing music in an ensemble is a highly coordinated human action involving musicians' brain-body interactions at different levels of physical and cortical organization in time and space. It has been suggested that interbrain phase synchronization plays an essential role in musical interaction. In this study, we aimed to explore associations between interbrain synchronization and interpersonal action coordination, using electroencephalographic recordings of the brain activity of guitarists playing in a duet as well as the acoustic recordings of their music. By applying phase synchronization algorithms to the musicians' brain activities and the sounds produced during guitar playing, we show that synchronous brain activity is strongly related to instrument sounds and behavioral play-onset synchrony, as indicated by phase alignment in relation to the time differences in play onsets and an angular-linear correlation between phase and time differences across trials and guitarist pairs. Interestingly, this correlation was especially strong in the first part of the music piece, when the guitarists seem to adjust their coordinated play onsets and brain rhythms actively. This suggests that the methods capturing intra- and interbrain synchronization and its relations to coordinated playing provide crucial information about the underlying mechanisms of interpersonal action coordination.

Two dimensionless parameters and a mechanical analogue for the HKB model of motor coordination

The widely cited Haken-Kelso-Bunz (HKB) model of motor coordination is used in an enormous range of applications. In this paper, we show analytically that the weakly damped, weakly coupled HKB model of two oscillators depends on only two dimensionless parameters; the ratio of the linear damping coefficient and the linear coupling coefficient and the ratio of the combined nonlinear damping coefficients and the combined nonlinear coupling coefficients. We illustrate our results with a mechanical analogue. We use our analytic results to predict behaviours in arbitrary parameter regimes and show how this led us to explain and extend recent numerical continuation results of the full HKB model. The key finding is that the HKB model contains a significant amount of behaviour in biologically relevant parameter regimes not yet observed in experiments or numerical simulations. This observation has implications for the development of virtual partner interaction and the human dynamic clamp, and potentially for the HKB model itself.

The Haken-Kelso-Bunz (HKB) model: from matter to movement to mind

This article presents a brief retrospective on the Haken-Kelso-Bunz (HKB) model of certain dynamical properties of human movement. Though unanticipated, HKB introduced, and demonstrated the power of, a new vocabulary for understanding behavior, cognition and the brain, revealed through a visually compelling mathematical picture that accommodated highly reproducible experimental facts and predicted new ones. HKB stands as a harbinger of paradigm change in several scientific fields, the effects of which are still being felt. In particular, HKB constitutes the foundation of a mechanistic science of coordination called Coordination Dynamics that extends from matter to movement to mind, and beyond.

Windowed multiscale synchrony: modeling time-varying and scale-localized interpersonal coordination dynamics

Social interactions are pervasive in human life with varying forms of interpersonal coordination emerging and spanning different modalities (e.g. behaviors, speech/language, and neurophysiology). However, during social interactions, as in any dynamical system, patterns of coordination form and dissipate at different scales. Historically, researchers have used aggregate measures to capture coordination over time. While those measures (e.g. mean relative phase, cross-correlation, coherence) have provided a wealth of information about coordination in social settings, some evidence suggests that multiscale coordination may change over the time course of a typical empirical observation. To address this gap, we demonstrate an underutilized method, windowed multiscale synchrony, that moves beyond quantifying aggregate measures of coordination by focusing on how the relative strength of coordination changes over time and the scales that comprise social interaction. This method involves using a wavelet transform to decompose time series into component frequencies (i.e. scales), preserving temporal information and then quantifying phase synchronization at each of these scales. We apply this method to both simulated and empirical interpersonal physiological and neuromechanical data. We anticipate that demonstrating this method will stimulate new insights on the mechanisms and functions of synchrony in interpersonal contexts using neurophysiological and behavioral measures.

Reciprocity and alignment: quantifying coupling in dynamic interactions

Recent accounts of social cognition focus on how we do things together, suggesting that becoming aligned relies on a reciprocal exchange of information. The next step is to develop richer computational methods that quantify the degree of coupling and describe the nature of the information exchange. We put forward a definition of coupling, comparing it to related terminology and detail, available computational methods and the level of organization to which they pertain, presenting them as a hierarchy from weakest to richest forms of coupling. The rationale is that a temporally coherent link between two dynamical systems at the lowest level of organization sustains mutual adaptation and alignment at the highest level. Postulating that when we do things together, we do so dynamically over time and we argue that to determine and measure instances of true reciprocity in social exchanges is key. Along with this computationally rich definition of coupling, we present challenges for the field to be tackled by a diverse community working towards a dynamic account of social cognition.

The effect of inherent and incidental constraints on bimanual and social coordination

The current investigation was designed to examine the influence of inherent and incidental constraints on the stability characteristics associated with bimanual and social coordination. Individual participants (N = 9) and pairs of participants (N = 18, 9 pairs) were required to rhythmically coordinate patterns of isometric forces in 1:1 in-phase and 1:2 multi-frequency patterns by exerting force with their right and left limbs. Lissajous information was provided to guide performance. Participants performed 13 practice trials and 1 test trial per pattern. On the test trial, muscle activity from the triceps brachii muscles of each arm was recorded. EMG-EMG coherence between the two EMG signals was calculated using wavelet coherence. The behavioral data indicated that individual participants performed the 1:1 in-phase pattern more accurately and with less variability than paired participants. The EMG coherence analysis indicated significantly higher coherence for individual participants than for the paired participants during the 1:1 in-phase pattern, whereas no differences were observed between groups for the 1:2 coordination pattern. The results of the current investigation support the notion that neural crosstalk can stabilize 1:1 in-phase coordination when contralateral and ipsilateral signals are integrated via the neuromuscular linkage between two effectors.

Beyond Dyadic Coordination: Multimodal Behavioral Irregularity in Triads Predicts Facets of Collaborative Problem Solving

We hypothesize that effective collaboration is facilitated when individuals and environmental components form a synergy where they work together and regulate one another to produce stable patterns of behavior, or regularity, as well as adaptively reorganize to form new behaviors, or irregularity. We tested this hypothesis in a study with 32 triads who collaboratively solved a challenging visual computer programming task for 20 min following an introductory warm-up phase. Multidimensional recurrence quantification analysis was used to examine fine-grained (i.e., every 10 s) collective patterns of regularity across team members' speech rate, body movement, and team interaction with the shared user interface. We found that teams exhibited significant patterns of regularity as compared to shuffled baselines, but there were no systematic trends in regularity across time. We also found that periods of regularity were associated with a reduction in overall behavior. Notably, the production of irregular behavior predicted expert-coded metrics of collaborative activity, such as teams' ability to construct shared knowledge and effectively negotiate and coordinate execution of solutions, net of overall behavioral production and behavioral self-similarity. Our findings support the theory that groups can interact to form interpersonal synergies and indicate that information about system-level dynamics is a viable way to understand and predict effective collaborative processes.

Making oneself predictable in linguistic interactions

While language production is a highly demanding task, conversational partners are known to coordinate their turns with striking precision. Among the mechanisms that allow them to do so is listeners' ability to predict what the speaker will say, and thus to prepare their response in advance. But do speakers also play a role in facilitating coordination? We hypothesized that speakers contribute by using coordination smoothers - in particular by making their turns easier to predict. To test this, we asked participants to type definitions for common English words, either on their own (n = 26 individuals) or interacting with a partner (n = 18 pairs), and we measured the timing with which they produced the definitions. In a post-test, additional participants (n = 55) attempted to predict the final word of these definitions and rated them for quality. We found that interacting speakers initiated their turns with less variable delays than solo individuals. In contrast, our post-test measures suggested that jointly produced definitions were in fact of lower predictability and quality than those produced by individuals, but the analysis revealed these findings were likely confounded by task difficulty. We propose that the reduction in temporal variability observed for interacting speakers may facilitate prediction and thus act as a coordination smoother in linguistic interactions.

Coordination Dynamics: A Foundation for Understanding Social Behavior

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.

Hopf Bifurcations in Complex Multiagent Activity: The Signature of Discrete to Rhythmic Behavioral Transitions

Most human actions are composed of two fundamental movement types, discrete and rhythmic movements. These movement types, or primitives, are analogous to the two elemental behaviors of nonlinear dynamical systems, namely, fixed-point and limit cycle behavior, respectively. Furthermore, there is now a growing body of research demonstrating how various human actions and behaviors can be effectively modeled and understood using a small set of low-dimensional, fixed-point and limit cycle dynamical systems (differential equations). Here, we provide an overview of these dynamical motorprimitives and detail recent research demonstrating how these dynamical primitives can be used to model the task dynamics of complex multiagent behavior. More specifically, we review how a task-dynamic model of multiagent shepherding behavior, composed of rudimentary fixed-point and limit cycle dynamical primitives, can not only effectively model the behavior of cooperating human co-actors, but also reveals how the discovery and intentional use of optimal behavioral coordination during task learning is marked by a spontaneous, self-organized transition between fixed-point and limit cycle dynamics (i.e., via a Hopf bifurcation).

The effects of recursive communication dynamics on belief updating

Many social interactions are characterized by dynamic interplay, such that individuals exert reciprocal influence over each other's behaviours and beliefs. The present study investigated how the dynamics of reciprocal influence affect individual beliefs in a social context, over and above the information communicated in an interaction. To this end, we developed a simple social decision-making paradigm in which two people are asked to make perceptual judgments while receiving information about each other's decisions. In a Static condition, information about the partner only conveyed their initial, independent judgment. However, in a Dynamic condition, each individual saw the evolving belief of their partner as they learnt about and responded to the individual's own judgment. The results indicated that in both conditions, the majority of confidence adjustments were characterized by an abrupt change followed by smaller adjustments around an equilibrium, and that participants' confidence was used to arbitrate conflict (although deviating from Bayesian norm). Crucially, recursive interaction had systematic effects on belief change relative to the static baseline, magnifying confidence change when partners agreed and reducing confidence change when they disagreed. These findings indicate that during dynamic interactions-often a characteristic of real-life and online social contexts-information is collectively transformed rather than acted upon by individuals in isolation. Consequently, the output of social events is not only influenced by what the dyad knows but also by predictable recursive and self-reinforcing dynamics.

Attributed social context and emotional content recruit frontal and limbic brain regions during virtual feedback processing

In communication, who is communicating can be just as important as what is said. However, sender identity in virtual communication is often inferred rather than perceived. Therefore, the present research investigates the brain structures activated by sender identity attributions and evaluative feedback processing during virtual communication. In a functional magnetic resonance imaging (fMRI) study, 32 participants were told that they would receive personality feedback, either sent from another human participant or from a randomly acting computer. In reality, both conditions contained random but counterbalanced feedback, automatically delivered by approving or denying negative, neutral, or positive adjectives. Although physically identical, feedback attributed to the "human" sender activated multiple regions within a "social brain" network, including the superior frontal, medial prefrontal, and orbitofrontal cortex, anterior and posterior parts of the cingulate cortex, and the bilateral insula. Regardless of attributed sender, positive feedback increased responses in the striatum and bilateral amygdalae, while negative compared to neutral feedback elicited stronger insula and somatosensory responses. These results reveal the recruitment of an extensive mentalizing and social brain network by mere sender attributions and the activation of brain structures related to reward and punishment by verbal feedback, demonstrating its embodied processing.

The Human Dynamic Clamp Reveals the Fronto-Parietal Network Linking Real-Time Social Coordination and Cognition

How does the brain allow us to interact with others? Social neuroscience has already provided some answers to these questions but has tended to treat high-level, cognitive interpretations of social behavior separately from the sensorimotor mechanisms upon which they rely. The goal here is to identify the underlying neural processes and mechanisms linking sensorimotor coordination and intention attribution. We combine the human dynamic clamp, a novel paradigm for studyingrealistic social behavior, with high-resolution electroencephalography. The collection of humanness and intention attribution reports, kinematics, and neural data affords an opportunity to relate brain activity to the ongoing social behavior. Behavioral results demonstrate that sensorimotor coordination influences the judgments of cooperativeness and humanness. Analysis of brain dynamics reveals two distinct networks related to the integration of visuo-motor information from self and other which overlap over the right parietal region. Furthermore, judgment of humanness and cooperation of others modulate the functional connectivity between this right parietal hub and the prefrontal cortex. These results reveal how distributed neural dynamics integrates information from "low-level" sensorimotor mechanisms and "high-level" social cognition to support the realistic social behaviors that play out in real time during interactive scenarios.

Autism spectrum disorder

Autism spectrum disorder is a construct used to describe individuals with a specific combination of impairments in social communication and repetitive behaviours, highly restricted interests and/or sensory behaviours beginning early in life. The worldwide prevalence of autism is just under 1%, but estimates are higher in high-income countries. Although gross brain pathology is not characteristic of autism, subtle anatomical and functional differences have been observed in post-mortem, neuroimaging and electrophysiological studies. Initially, it was hoped that accurate measurement of behavioural phenotypes would lead to specific genetic subtypes, but genetic findings have mainly applied to heterogeneous groups that are not specific to autism. Psychosocial interventions in children can improve specific behaviours, such as joint attention, language and social engagement, that may affect further development and could reduce symptom severity. However, further research is necessary to identify the long-term needs of people with autism, and treatments and the mechanisms behind them that could result in improved independence and quality of life over time. Families are often the major source of support for people with autism throughout much of life and need to be considered, along with the perspectives of autistic individuals, in both research and practice.

Neurologically Motivated Coupling Functions in Models of Motor Coordination

We present an analysis of two Haken-Kelso-Bunz (HKB) oscillators coupled by a neurologically motivated function. We study the effect of time delay and weighted self-feedback and mutual feedback on the synchronization behavior of the model. We focus on identifying parameter regimes supporting experimentally observed decrease in oscillation amplitude and loss of anti-phase stability that has inspired the development of the HKB model. We show that a combination of cross-talk and nonlinearity in the coupling, along with physiologically relevant time delay, is able to quantitatively account for both drop in oscillation amplitude and loss of anti-phase stability in a frequency dependent manner. Furthermore, we demonstrate that the transition between discrete and rhythmic movements could be captured by this model. To this end, we carry out theoretical and numerical analysis of the emergence of in-phase and anti-phase oscillations.

Interactive Psychometrics for Autism With the Human Dynamic Clamp: Interpersonal Synchrony From Sensorimotor to Sociocognitive Domains

The human dynamic clamp (HDC) is a human-machine interface designed on the basis of coordination dynamics for studying realistic social interaction under controlled and reproducible conditions. Here, we propose to probe the validity of the HDC as a psychometric instrument for quantifying social abilities in children with autism spectrum disorder (ASD) and neurotypical development. To study interpersonal synchrony with the HDC, we derived five standardized scores following a gradient from sensorimotor and motor to higher sociocognitive skills in a sample of 155 individuals (113 participants with ASD, 42 typically developing participants; aged 5 to 25 years; IQ > 70). Regression analyses were performed using normative modeling on global scores according to four subconditions (HDC behavior "cooperative/competitive," human task "in-phase/anti-phase," diagnosis, and age at inclusion). Children with ASD had lower scores than controls for motor skills. HDC motor coordination scores were the best candidates for stratification and diagnostic biomarkers according to exploratory analyses of hierarchical clustering and multivariate classification. Independently of phenotype, sociocognitive skills increased with developmental age while being affected by the ongoing task and HDC behavior. Weaker performance in ASD for motor skills suggests the convergent validity of the HDC for evaluating social interaction. Results provided additional evidence of a relationship between sensorimotor and sociocognitive skills. HDC may also be used as a marker of maturation of sociocognitive skills during real-time social interaction. Through its standardized and objective evaluation, the HDC not only represents a valid paradigm for the study of interpersonal synchrony but also offers a promising, clinically relevant psychometric instrument for the evaluation and stratification of sociomotor dysfunctions.

Delayed feedback embedded in perception-action coordination cycles results in anticipation behavior during synchronized rhythmic action: A dynamical systems approach

Dancing and playing music require people to coordinate actions with auditory rhythms. In laboratory perception-action coordination tasks, people are asked to synchronize taps with a metronome. When synchronizing with a metronome, people tend to anticipate stimulus onsets, tapping slightly before the stimulus. The anticipation tendency increases with longer stimulus periods of up to 3500ms, but is less pronounced in trained individuals like musicians compared to non-musicians. Furthermore, external factors influence the timing of tapping. These factors include the presence of auditory feedback from one’s own taps, the presence of a partner performing coordinated joint tapping, and transmission latencies (TLs) between coordinating partners. Phenomena like the anticipation tendency can be explained by delay-coupled systems, which may be inherent to the sensorimotor system during perception-action coordination. Here we tested whether a dynamical systems model based on this hypothesis reproduces observed patterns of human synchronization. We simulated behavior with a model consisting of an oscillator receiving its own delayed activity as input. Three simulation experiments were conducted using previously-published behavioral data from 1) simple tapping, 2) two-person alternating beat-tapping, and 3) two-person alternating rhythm-clapping in the presence of a range of constant auditory TLs. In Experiment 1, our model replicated the larger anticipation observed for longer stimulus intervals and adjusting the amplitude of the delayed feedback reproduced the difference between musicians and non-musicians. In Experiment 2, by connecting two models we replicated the smaller anticipation observed in human joint tapping with bi-directional auditory feedback compared to joint tapping without feedback. In Experiment 3, we varied TLs between two models alternately receiving signals from one another. Results showed reciprocal lags at points of alternation, consistent with behavioral patterns. Overall, our model explains various anticipatory behaviors, and has potential to inform theories of adaptive human synchronization.

When navigating a busy sidewalk, people coordinate their behavior in an orderly manner. Other activities require people to carefully synchronize periodic actions, as in a group rowing or marching. When individuals tap in synchrony with a metronome, their taps tend to anticipate the metronome. Experiments have revealed that factors like musical expertise, the presence of a synchronizing partner, auditory feedback, and the sound travel time, all systematically affect the tendency to anticipate. While researchers have hypothesized a number of potential mechanisms for such anticipatory behavior, none have successfully accounted for all of the effects. Previous research on coupled physical systems has shown that when one system receives input from a second system, plus its own delayed signal as input, this causes system 1 to anticipate system 2. We hypothesize that the tendency to anticipate is the result of delayed communication between neurons. Our work demonstrates the ability of delay-coupled physical systems to capture human anticipation and the effect of external factors in the anticipation tendency. Our model supports the theory that delayed communication within the nervous system is crucial to understanding anticipatory coordinative behavior.

Dynamic Orchestration of Brains and Instruments During Free Guitar Improvisation

Playing music in ensemble requires enhanced sensorimotor coordination and the non-verbal communication of musicians that need to coordinate their actions precisely with those of others. As shown in our previous studies on guitar duets, and also on a guitar quartet, intra- and inter-brain synchronization plays an essential role during such interaction. At the same time, sensorimotor coordination as an essential part of this interaction requires being in sync with the auditory signals coming from the played instruments. In this study, using acoustic recordings of guitar playing and electroencephalographic (EEG) recordings of brain activity from guitarists playing in duet, we aimed to explore whether the musicians' brain activity synchronized with instrument sounds produced during guitar playing. To do so, we established an analytical method based on phase synchronization between time-frequency transformed guitar signals and raw EEG signals. Given phase synchronization, or coupling between guitar and brain signals, we constructed so-called extended hyper-brain networks comprising all possible interactions between two guitars and two brains. Applying a graph-theoretical approach to these networks assessed across time, we present dynamic changes of coupling strengths or dynamic orchestration of brains and instruments during free guitar improvisation for the first time. We also show that these dynamic network topology changes are oscillatory in nature and are characterized by specific spectral peaks, indicating the temporal structure in the synchronization patterns between guitars and brains. Moreover, extended hyper-brain networks exhibit specific modular organization varying in time, and binding each time, different parts of the network into the modules, which were mostly heterogeneous (i.e., comprising signals from different instruments and brains or parts of them). This suggests that the method capturing synchronization between instruments and brains when playing music provides crucial information about the underlying mechanisms. We conclude that this method may be an indispensable tool in the investigation of social interaction, music therapy, and rehabilitation dynamics.

Connecting empirical phenomena and theoretical models of biological coordination across scales

Coordination in living systems-from cells to people-must be understood at multiple levels of description. Analyses and modelling of empirically observed patterns of biological coordination often focus either on ensemble-level statistics in large-scale systems with many components, or on detailed dynamics in small-scale systems with few components. The two approaches have proceeded largely independent of each other. To bridge this gap between levels and scales, we have recently conducted a human experiment of mid-scale social coordination specifically designed to reveal coordination at multiple levels (ensemble, subgroups and dyads) simultaneously. Based on this experiment, the present work shows that, surprisingly, a single system of equations captures key observations at all relevant levels. It also connects empirically validated models of large- and small-scale biological coordination-the Kuramoto and extended Haken-Kelso-Bunz (HKB) models-and the hallmark phenomena that each is known to capture. For example, it exhibits both multistability and metastability observed in small-scale empirical research (via the second-order coupling and symmetry breaking in extended HKB) and the growth of biological complexity as a function of scale (via the scalability of the Kuramoto model). Only by incorporating both of these features simultaneously can we reproduce the essential coordination behaviour observed in our experiment.

Feedback delays can enhance anticipatory synchronization in human-machine interaction

Research investigating the dynamics of coupled physical systems has demonstrated that small feedback delays can allow a dynamic response system to anticipate chaotic behavior. This counterintuitive phenomenon, termed anticipatory synchronization, has been observed in coupled electrical circuits, laser semi-conductors, and artificial neurons. Recent research indicates that the same process might also support the ability of humans to anticipate the occurrence of chaotic behavior in other individuals. Motivated by this latter work, the current study examined whether the process of feedback delay induced anticipatory synchronization could be employed to develop an interactive artificial agent capable of anticipating chaotic human movement. Results revealed that incorporating such delays within the movement-control dynamics of an artificial agent not only enhances an artificial agent’s ability to anticipate chaotic human behavior, but to synchronize with such behavior in a manner similar to natural human-human anticipatory synchronization. The implication of these findings for the development of human-machine interaction systems is discussed.

Tutorial and simulations with ADAM: an adaptation and anticipation model of sensorimotor synchronization

Interpersonal coordination of movements often involves precise synchronization of action timing, particularly in expert domains such as ensemble music performance. According to the adaptation and anticipation model (ADAM) of sensorimotor synchronization, precise yet flexible interpersonal coordination is supported by reactive error correction mechanisms and anticipatory mechanisms that exploit systematic patterns in stimulus timing to plan future actions. Here, we provide a tutorial introduction to the computational architecture of ADAM and present a series of single- and dual-virtual agent simulations that examine the model parameters that produce ideal synchronization performance in different tempo conditions. In the single-agent simulations, a virtual agent synchronized responses to steady tempo sequence or a sequence containing gradual tempo changes. Parameters controlling basic reactive error (phase) correction were sufficient for producing ideal synchronization performance at the steady tempo, whereas parameters controlling anticipatory mechanisms were necessary for ideal performance with a tempo-changing sequence. In the dual-agent simulations, two interacting virtual agents produced temporal sequences from either congruent or incongruent internal performance templates specifying a steady tempo or tempo changes. Ideal performance was achieved with reactive error correction alone when both agents implemented the same performance template (either steady tempo or tempo change). In contrast, anticipatory mechanisms played a key role when one agent implemented a steady tempo template and the other agent implemented a tempo change template. These findings have implications for understanding the interplay between reactive and anticipatory mechanisms when agents possess compatible versus incompatible representations of task goals during human-human and human-machine interaction.

Embodied Dyadic Interaction Increases Complexity of Neural Dynamics: A Minimal Agent-Based Simulation Model

The concept of social interaction is at the core of embodied and enactive approaches to social cognitive processes, yet scientifically it remains poorly understood. Traditionally, cognitive science had relegated all behavior to being the end result of internal neural activity. However, the role of feedback from the interactions between agent and their environment has become increasingly important to understanding behavior. We focus on the role that social interaction plays in the behavioral and neural activity of the individuals taking part in it. Is social interaction merely a source of complex inputs to the individual, or can social interaction increase the individuals' own complexity? Here we provide a proof of concept of the latter possibility by artificially evolving pairs of simulated mobile robots to increase their neural complexity, which consistently gave rise to strategies that take advantage of their capacity for interaction. We found that during social interaction, the neural controllers exhibited dynamics of higher-dimensionality than were possible in social isolation. Moreover, by testing evolved strategies against unresponsive ghost partners, we demonstrated that under some conditions this effect was dependent on mutually responsive co-regulation, rather than on the mere presence of another agent's behavior as such. Our findings provide an illustration of how social interaction can augment the internal degrees of freedom of individuals who are actively engaged in participation.

Human social motor solutions for human-machine interaction in dynamical task contexts

Multiagent activity is commonplace in everyday life and can improve the behavioral efficiency of task performance and learning. Thus, augmenting social contexts with the use of interactive virtual and robotic agents is of great interest across health, sport, and industry domains. However, the effectiveness of human-machine interaction (HMI) to effectively train humans for future social encounters depends on the ability of artificial agents to respond to human coactors in a natural, human-like manner. One way to achieve effective HMI is by developing dynamical models utilizing dynamical motor primitives (DMPs) of human multiagent coordination that not only capture the behavioral dynamics of successful human performance but also, provide a tractable control architecture for computerized agents. Previous research has demonstrated how DMPs can successfully capture human-like dynamics of simple nonsocial, single-actor movements. However, it is unclear whether DMPs can be used to model more complex multiagent task scenarios. This study tested this human-centered approach to HMI using a complex dyadic shepherding task, in which pairs of coacting agents had to work together to corral and contain small herds of virtual sheep. Human-human and human-artificial agent dyads were tested across two different task contexts. The results revealed (i) that the performance of human-human dyads was equivalent to those composed of a human and the artificial agent and (ii) that, using a "Turing-like" methodology, most participants in the HMI condition were unaware that they were working alongside an artificial agent, further validating the isomorphism of human and artificial agent behavior.

Rowing together: Interpersonal coordination dynamics with and without mechanical coupling

Although most research on interpersonal coordination focuses on perceptual forms of interaction, many interpersonal actions also involve interactions of mechanical nature. We examined the effect of mechanical coupling in a rowing task from a coupled oscillator perspective: 16 pairs of rowers rowed on ergometers that were physically connected through slides (mechanical coupling condition) or on separate ergometers (no mechanical coupling condition). They rowed in two patterns (in- and antiphase) and at two movement frequencies (20 and 30 strokes per minute). Seven out of sixteen pairs showed one or more coordinative breakdowns, which only occurred in the antiphase condition. The occurrence of these breakdowns was not affected by mechanical coupling, nor by movement frequency. For the other nine pairs, variability of steady state coordination was substantially lower in the mechanical coupling condition. Together, these results show that the increase in coupling strength through mechanical coupling stabilizes coordination, even more so for antiphase coordination.