Interpersonal movement coordination is an indispensable aspect of daily life, and often entails a relationship between individuals trying to achieve a common goal. When people walk together and talk together, they show this kind of coordinated action. Well-trained athletes (e.g. synchronized divers, basketball players) show more refined coordination.
Behavioral studies on intra- and inter-individual coordination have found that rhythmic bimanual coordination of body movements is mainly influenced by two factors: one is the spatio-temporal relationship (symmetric or alternating), and the other is frequency. Complex interactions also exist between these two factors. Alternate movement mode involves the alternating activity of homologous muscle groups; symmetric mode involves the simultaneous activity of homologous muscle groups, and is generally more accurate and more stable in performing actions(Swinnen, Jardin, Meulenbroek, Dounskaia, & Den Brandt, 1997). Behavioral performance of Parkinson's patients and related research findings using transcranial magnetic stimulation (TMS) have demonstrated the instability of alternate mode. Parkinson's patients often show more errors and variations in performing alternate movement(Quincy, Laurie, & Timothy, 2002). Alternate mode can be disrupted by TMS, but not symmetric mode(Chen et al., 2005). As frequency increases, the stability of both symmetric and alternating modes decreases. Increasing frequency eventually leads to a phase transition of alternate to symmetric mode, but phase transitions in the opposite direction rarely occur(Kelso, 1984).
There are two major theories for explaining intra-individual movement coordination from a biological perspective. General motor program theory proposes that there is a common action plan for the control of both hands(R. A. Schmidt, 1975). Inter-manual crosstalk theory proposes that the two hands are governed by two different action plans, and crosstalk can occur between those one-handed action plans(Marteniuk & MacKenzie, 1980). From the perspective of action planning, these theories can partly explain the difference in stability between alternate and symmetric modes. Self-organization theory further employs a non-linear dynamic self-organizing system to model movement coordination in different modes(Haken, Kelso, & Bunz, 1985; Schöner & Kelso, 1988). Self-organization theory can be used to explain both intra- and inter-individual movement coordination. However, at the biological level it remains unclear whether inter-individual coordination uses a common action plan or crosstalk between individual action plans.
Due to the cooperative nature of humans, two individuals can be considered as a single organism (Newtson, Hairfield, Bloomingdale, & Cutino, 1987). Although intra- and inter-individual movement coordination follow some common rules, there are still essential differences between them. For example, inter-individual coordination relies on the connection of two independent nervous systems through vision(R. C. Schmidt, Carello, & Turvey, 1990; R. C. Schmidt & Richardson, 2008a). Thus the question arises as to how low-level (frequency) and high-level (spatiotemporal) factors interact to affect action coordination at the neural level. The answer may be fundamentally different from that of intra-individual coordination, which is controlled by a single nervous system.
Functional imaging studies have shown that hand movement coordination in different modes (symmetric, alternating) involves the supplementary motor area (SMA). In particular, SMA is integral to maintaining alternating movements and phase transitions(Debaere, Wenderoth, Sunaert, Van Hecke, & Swinnen, 2003; Jäncke et al., 2000; Weerd et al., 2003). The unique requirements of linking two independent nervous systems through vision makes the mirror nervous system a key mechanism of inter-individual movement coordination(Niu, Yu, Li, & Liu, 2019). Various neuroimaging studies have localized mirror neurons to the regions (anterior frontal gyrus, anterior premotor cortex, posterior inferior parietal lobule) (Buccino et al., 2010; Fadiga, Fogassi, Pavesi, & Rizzolatti, 1995; Fogassi, 2011; Grèzes, Armony, Rowe, & Passingham, 2003; Iacoboni, Woods, & Brass, 1999; Tai, Scherfler, Brooks, Sawamoto, & Castiello, 2004). These regions have been associated with phase transitions under frequency pressure (Aramaki, Honda, Okada, & Sadato, 2006) Our hemodynamic analysis targeted frontal and parietal areas because previous studies implicated these regions in rhythmic movement coordination.
Interpersonal interaction is often accompanied by head movement. Therefore, the present study used functional near-infrared spectroscopy (fNIRS) to track cortical activity because it is mobile and resistant to motion artifacts (Cui, Bryant, & Reiss, 2012; Ferrari & Quaresima, 2012; Scholkmann, Holper, Wolf, & Wolf, 2013). Pairs of subjects performed hand movements under conditions of different spatio-temporal relationships (symmetric, alternating) and different frequencies (1.8, 2.1, 2.4, 2.7, 3.0, and 3.3 Hz, based on previous studies)(Kelso, 1984; Kelso, Scholz, & Schoner, 1986). The present study adopts factor design to explore the neural basis of movement coordination under different spatio-temporal relationships and different frequencies, and their complicated interaction.