A key emergent property of group social dynamic is synchrony–the coordination of actions, emotions, or physiological processes between group members. Despite this fact and the inherent nested structure of groups, little research has assessed physiological synchronization between group members from a multi-level perspective, thus limiting a full understanding of the dynamics between members. To address this gap of knowledge we re-analyzed a large dataset (N = 261) comprising physiological and psychological data that were collected in two laboratory studies that involved two different social group tasks. In both studies, following the group task, members reported their experience of group cohesion via questionnaires. We utilized a non-linear analysis method-multidimensional recurrence quantification analysis that allowed us to represent physiological synchronization in cardiological interbeat intervals between group members at the individual-level and at the group-level. We found that across studies and their conditions, the change in physiological synchrony from baseline to group interaction predicted a psychological sense of group cohesion. This result was evident both at the individual and the group levels and was not modified by the context of the interaction. The individual- and group-level effects were highly correlated. These results indicate that the relationship between synchrony and cohesion is a multilayered construct. We re-affirm the role of physiological synchrony for cohesion in groups. Future studies are needed to crystallize our understanding of the differences and similarities between synchrony at the individual-level and synchrony at the group level to illuminate under which conditions one of these levels has primacy, or how they interact.

The COVID-19 pandemic has a major impact on mental well-being and interpersonal relationships. Nonetheless, little is known about the complex interactions between one's overall perceived interpersonal closeness and physiological or psychological aspects of interpersonal functioning. This study aimed to understand the interaction between perceived interpersonal closeness during COVID-19 and interpersonal mechanisms in predicting well-being. We focused on two interpersonal mechanisms, one physiological and the other psychological: (a) prepandemic physiological synchrony, a physiological measure of interpersonal coupling, and (b) peripandemic emotional contagion, one's tendency to "catch" others' emotions. One hundred fifty-five participants took part in the study. Cardiological interbeat interval synchrony was collected 1.5 to 3 years prior to the beginning of the COVID pandemic in two previous lab studies. Participants were recontacted during the pandemic, this time to complete several questionnaires tapping into perceived interpersonal closeness, tendency for emotional contagion, and psychological well-being during COVID. As hypothesized, overall perceived interpersonal closeness was positively related to well-being. Moreover, this effect was moderated by one's tendency for emotional contagion or by physiological synchrony. Thus, individuals with higher emotional contagion scores or higher physiological synchrony had higher well-being if their interpersonal closeness was perceived as greater. Conversely, their well-being was lower if they perceived their interpersonal closeness as weaker. These results emphasize that individuals may be differentially susceptible to the effects of their relationships on their well-being. Future mental health interventions should consider both the quality of one's perceived interpersonal closeness and the extent to which one is sensitive to others' emotional experiences.

Dominant theoretical accounts of interpersonal synchrony, the temporal coordination of biobehavioral processes between several individuals, have employed a linear approach, generally considering synchrony as a positive state, and utilizing aggregate scores. However, synchrony is known to take on a dynamical form with continuous shifts in its timeline. Acting as one continuously, is not always the optimal state, due to an intrinsic tension between individualistic and synergistic forms of action that exist in many social situations. We propose an alternative theory of flexible multimodal synchrony which highlights context as a key component that defines “pulls” toward synchrony and “pulls” toward segregation inherent to the social situation. Trait-like individual differences then sensitize individuals to these contextual “pulls”. In this manner, context and individual differences provide the backdrop to the emergence of flexible and dynamical synchrony patterns, which we consider adaptive, in several modalities– behavioral, physiological, and neural. We point to two main interpersonal consequences of multimodal synchrony patterns: Social and task-oriented outcomes. We delineate hypotheses that emanate from the theory which have not been articulated by previous theories and provide two empirical proofs-of-concept: In the first, we show how individual differences modulate the effect of context on synchrony’s outcomes in a novel dyadic motor game. In the second, we re-analyze previously reported data, to show how a ‘flexibility’ data-analysis approach to synchrony improves predictive ability when testing for synchrony’s effects on social cohesion. We end this review with guidelines for future synchrony research in light of the flexible multimodal theory presented

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Fractal properties in time series of human behavior and physiology are quite ubiquitous, and several methods to capture such properties have been proposed in the past decades. Fractal properties are marked by similarities in statistical characteristics over time and space, and it has been suggested that such properties can be well-captured through recurrence quantification analysis. However, no methods to capture fractal fluctuations by means of recurrence-based methods have been developed yet. The present paper takes this suggestion as a point of departure to propose and test several approaches to quantifying fractal fluctuations in synthetic and empirical time-series data using recurrence-based analysis. We show that such measures can be extracted based on recurrence plots, and contrast the different approaches in terms of their accuracy and range of applicability.