PMC:6803479 / 1663-8718
Annnotations
{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/6803479","sourcedb":"PMC","sourceid":"6803479","source_url":"https://www.ncbi.nlm.nih.gov/pmc/6803479","text":"Introduction\nPeople frequently engaged in social interactions, the achievement of which are largely relied on our capacity to coordinate behaviors with others in space and time (Sebanz et al., 2006; Richardson et al., 2007; Nessler and Gilliland, 2009). In some coordination activities, we just perform movements at the same frequency (i.e., isofrequency pattern, IP), or mirroring, such as side-by-side walking or together singing/humming. In some other activities, however, we are required to perform non-mirroring pattern—acting at different frequencies (i.e., multifrequency pattern, MP), such as dancing or sports. Various interpersonal coordination tasks based on either MP or IP are developed and studied for different researching purposes separately. However, in real life people sometimes need to balance different coordination strategies to achieve the goal more effectively (Skewes et al., 2015). Thus, it would be important for us to understand how the interpersonal coordination pattern (IP vs. MP) modulates the coordination outcome and the related brain-to-brain connectivity.\nThe essential distinction between the IP and MP is the relative frequency with which the actions of interacting individuals occur—IP has the movement frequency ratio of 1:1 while MP has the movement frequency ratios of non 1:1. According to the frequency-locking dynamics model, higher order frequency ratios were more difficult for individuals to perform or maintain than lower order frequency ratios (Treffner and Turvey, 1993; Peper et al., 1995). Related studies have provided evidence that when the frequency differences increased, the coordination of the group became less stable (Zhang et al., 2018), and more challenging (Gorman et al., 2017), which suggested that the MP would harm the coordination performance. However, it was found that team performance could also benefit from the asymmetry interpersonal relations in some complex social interactions (Wallot et al., 2016). In order to complete interpersonal coordination, individuals would use available information to predict another person's actions and then adjust their own actions (Knoblich and Jordan, 2003; Vesper et al., 2013). During this process, individuals might spontaneously form specific coordination strategies, for example, one person led the task and the other one followed (“leader-follower” relationship) (Davidson and Good, 2002; Goebl and Palmer, 2009; Richardson et al., 2015), or two persons equally adapted their behavior to each other (“hyper-leaders/followers” relationship) (Konvalinka et al., 2010; Pecenka and Keller, 2011). In MP condition, individuals moved at different frequencies, so that the partner's action could serve as a reference for individuals to adjust their action, which could facilitate the development of specific strategies and improve the interpersonal coordination performance.\nIn order to better understand the neural activities during social interaction, researchers have proposed a “two-person neuroscience” approach to study the physiological basis of human social interaction (Hari and Kujala, 2009). By recording the brain activities of two or more interacting individuals (i.e., hyperscanning), related studies have demonstrated that social interactions are associated with enhanced interpersonal brain synchronization (IBS), as measured by EEG (e.g., Jahng et al., 2017; Hu et al., 2018), fNIRS (e.g., Cui et al., 2012; Cheng et al., 2015; Liu et al., 2015; Hu et al., 2017), or fMRI (e.g., Schippers et al., 2010; Stolk et al., 2014). Specifically, the IBSs were found in various interpersonal coordination tasks. For example, studies have found inter-brain coherence when individuals moved at the same frequency, such as together key pressing (Cui et al., 2012; Cheng et al., 2015; Pan et al., 2017), coordinated group walking (Ikeda et al., 2017), and cooperative singing/humming (Osaka et al., 2014, 2015). Also, a related study has revealed synchronous oscillatory activities across individuals in a more complicated task in which a captain and a co-pilot coordinately operated the flight mission of plane takeoff and landing (Astolfi et al., 2012). By using a neuroimaging approach, studies have further observed the emergence of specific coordination strategies (e.g., leader-follower relationship) in terms of brain activities during individuals' interaction, demonstrating the greater information flow from the leader/sender to the follower/receiver (Schippers et al., 2010; Holper et al., 2012). Note that in these studies individuals were assigned with the dominant or secondary roles in the interaction tasks. If there are no such assigned roles, will individuals gradually form specific strategies during a continuous coordination activity? Moreover, will the brain-to-brain connectivity be varied by different coordination patterns?\nHere, we explored how the coordination pattern (MP vs. IP) modulated the coordination outcome and the related brain-to-brain connectivity. We adopted a two-person complementary continuous joint drawing task to simulate the complex interpersonal coordination context in our real life. In the task, participants co-drew shapes of parallelograms on the computer by using a marker (one participant controlled the horizontal movement of the marker, while the other controlled the vertical movement of the marker), either at the same frequency (IP) or different frequencies (MP). Dyadic coordination performance in both IP and MP condition were calculated for comparison. Further, we recorded the brain activities from interacting individuals by simultaneously using the functional near-infrared spectroscopy (fNIRS) based hyperscanning technique. The prior region of interest was the frontopolar cortex, as it played an essential role in social interactions (especially in interpersonal coordination), where synchronous activities across brains were identified in previous fNIRS-based studies (Cheng et al., 2015; Nozawa et al., 2016; Ikeda et al., 2017). We would focus on the IBS during the interpersonal coordination. Further, Granger causality analysis (GCA) was used to provide a neurobiological suggestion of coupling directionality, i.e., which individual was more actively driving the other. In the study, we also collected participants' evaluations of self-control level, which was found to be related to the leadership (Fairhurst et al., 2014). Previous study has revealed that, compared to the followers, leaders would be generally associated with a stronger internal locus of control (self-control) and, therefore, a greater belief that outcomes were contingent on their behavior (Anderson and Schneier, 1978). Individuals' self-control bias could further modulate the degree of their adaptation to the partner when coordinating (Fairhurst et al., 2014). Collecting the participants' evaluations of self-control level and exploring their relationship with coordination performance and brain-to-brain connectivity would help us better understand the interpersonal coordination 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