By applying multiple wavelet coherence (MWC) to data from human body movements in triadic interaction, this study quantified triadic synchrony, rhythmic similarity among three interactants. Thirty-nine Japanese undergraduates were randomly assigned in a triad, and engaged in a brain-storming task. Triadic synchrony was quantified by calculating MWC to the time-series movement data collected by Kinect v2 sensor. The existence of synchrony was statistically tested by using a pseudo-synchrony paradigm. Results showed that the averaged value of MWC was higher in the experimental participant trio than in those of the pseudo trio in the frequency band of 0.5 - 1 Hz. The result supports the possible utility of applying multiple wavelet coherence to evaluate triadic synchrony in a small group interaction.
Synchrony has attracted the attention of psychology and communication researchers. Past work has revealed that individuals got synchronized or unsynchronized in their body posture and/or movement through the interaction. In previous studies, synchrony tends to be seemed as the subtype of interpersonal coordination. Interpersonal coordination, by definition, is when two or more individuals coordinate their behavior in a time series. The relationship between each time series can be analyzed in either time- or frequency-domains [
Over time, synchrony research, employing the analysis method from physics [
In the early stage of nonverbal research, microanalysis analyzing films of social interactions frame by frame was employed to generate time-series movement data [
After obtaining time-series data, a spectrum analysis is conducted. Many of the previous studies employed Fourier transform and calculated coherence by using two time-series [
As a potent alternative to the Fourier transform, the wavelet approach; it does not require stationarity in each time series, receives attention [
Considering our daily interaction, some are dyadic, others include more than three individuals. However, previous studies employing the wavelet approach [
The CWT of a time series (xn, n = 1, ∙∙∙, N) with uniform time steps δt, is defined as the convolution of xn with the scaled and normalized wavelet. Also, following [
Then, following [
To test the existence of synchrony, [
All procedures performed in studies involving human participants were in accordance with the ethical standards of the Japanese Society of Social Psychology. Before data collection, all the participants completed a consent form and approved data usage.
Thirty-nine Japanese undergraduates (13 females, Mean age = 20.34, SD = 1.10) participated in exchange for extra course credit. Each participant was randomly assigned to a mixed-gender trio. Their familiarity among participants was not identical; some participants knew each other before the interaction task, others did not know at all, which seemed to have a potent influence on the strength of synchrony, after their conversation they were asked to complete questionnaires regarding their familiarity with one another. Whether the familiarity was correlated to the extent of synchrony, i.e., MWC, was examined.
First, participants were seated to each desk at the four corners of a room, and completed a consent form, then they moved to another seat that was placed in a fanwise formation (
Time-series movement data was extracted with Brekel Pro Body v2 (Brekel). This software, activated together with Kinect v2, provided coordinate information saved as local coordinates, or in other words relative to their parent; therefore there is no need to conduct calibration for each participant. Sampling rate was 10 Hz. For each participant, coordinate points for the hands and head were captured in chronological order; the coordinate point changes between frames were calculated to compose the movement of each body part. The movement of hands and nose were added together.
To evaluate the significance of the extent of triadic synchrony in the genuine trio, a baseline is needed. Following the pseudo-synchrony experimental paradigm [
To evaluate triadic synchrony via MWC, Matlab 2015a (Mathworks), the wavelet toolbox [
We used a MWC value under 4 Hz (over 0.25 period) because our participants’ unstructured conversation was not so active or fast [
First, whether familiarity among group members was correlated to the MWC was examined. Each participant answered familiarity toward other two participant (e.g., participant A provided the familiarity score toward participant B and C), which was averaged to calculate the individual score of familiarity. Then the familiarity score from the three members in a group was averaged with respect to each group (n = 13) to calculate the group score of familiarity. The analysis of correlation indicated that the group score of familiarity was not significantly correlated to the average MWC under 4 Hz, 0.2 - 0.5 Hz, and 0.2 - 0.5 Hz (r = −0.11, −0.15, and −0.14, respectively; all ps > 0.63). Therefore, the influence of familiarity on MWC was not considered in subsequent analyses.
We compared the average value of MWC between the genuine and pseudo trios. The result of separate t-tests indicated that there was no significant difference between the genuine trios (M = 0.405, SD = 0.026) and the pseudo trios (M = 0.400, SD = 0.029) in the average MWC under 4 Hz throughout the time line (t(23.56) = 0.43, p = 0.67, d = 0.17). Also, the average MWC of 0.2 - 0.5 Hz in the genuine trios (M = 0.382, SD = 0.029) and the pseudo trios (M = 0.381, SD = 0.031) was not significantly different (t(23.78) = 0.05, p = 0.96, d = 0.01). On the other hand, the average MWC of 0.5 - 1 Hz was higher in the genuine trios (M = 0.394, SD = 0.015) than in the pseudo trios (M = 0.373, SD = 0.027), and this difference was statistically significant (t(19.04) = 2.48, p = 0.02, d = 0.97).
This study focused on a triadic interaction, and body movement data of each member was extracted by employing an automated technique, using Kinect v2 (Microsoft) and Brekel Pro Body v2 software (Brekel). Application of multiple wavelet coherence to the triadic movement data indicated that the genuine trio who were engaged in a brainstorming task was more synchronized in the frequency band of 0.5 - 1 Hz than the pseudo trio consisting of virtual data, which supported the hypothesis and demonstrated that triadic synchrony could be captured by employing multiple wavelet approach. However, the statistically significant difference was seen in the frequency band of 0.5 - 1 Hz; there was no significant difference in overall frequency band under 4 Hz, and slower frequency band of 0.2 - 0.5 Hz. These results provide three implications.
First, findings of this study could extend the field of synchrony and/or interpersonal coordination research. Interpersonal coordination was observed at various levels of communication behavior [
Second, this study supported the validity of automated techniques to extract body movement data in social interaction situation that was studied by nonverbal and communication researchers. In the early stage of nonverbal research, generating time-series movement data or coding behaviors has been resource intensive, or even painstaking, which hindered the theoretical and/or practical development of (nonverbal) behavioral research on a group interaction. Some recent studies began to utilize automated techniques to generate time-series movement data [
Finally, as a limitation of this study, I need to note that only the specific frequency band (i.e., 0.5 - 1 Hz) was significantly different from the virtual data. The previous study illustrated that synchrony in an unstructured conversation was characterized around 0.5 Hz; the value of cross wavelet coherence was increased around 0.5 Hz. This study also indicated that the group members were more synchronized in the frequency band of 0.5 - 1 Hz. However, in the overall (i.e., under 4 Hz) nor the slower frequency band (i.e., 0.2 - 0.5 Hz), there was no significant difference between the genuine and pseudo trios. These may be caused by the experimental setting; this study conducted a brain storming task, which results in requiring participants to behave actively. Although participants, in a three-person group, could not interact too fast, they could not talk together slowly; they were required to generate ideas as many as they possible. Thus, the frequency band of 0.5 - 1 Hz became discriminative. However, this interpretation remains a matter of speculation because it is not clear what the frequency band around 0.5 Hz exactly represent. To develop synchrony research, further examination employing the (multiple) wavelet approach would be needed.
This research was supported by Japan Society for the Promotion of Science (Grant-in-Aid for Young Scientists (B); 15K17259).
Ken Fujiwara, (2016) Triadic Synchrony: Application of Multiple Wavelet Coherence to a Small Group Conversation. Applied Mathematics,07,1477-1483. doi: 10.4236/am.2016.714126