Abstract
This study examined whether interpersonal synchrony could be extracted using spectrum analysis (i.e., wavelet transform) in an unstructured conversation. Sixty-two female undergraduates were randomly paired and they engaged in a 6-min unstructured conversation. Interpersonal synchrony was evaluated by calculating the cross-wavelet coherence of the time-series movement data, extracted using a video-image analysis software. The existence of synchrony was tested using a pseudo-synchrony paradigm. In addition, the frequency at which the synchrony occurred and the distribution of the relative phase was explored. The results showed that the value of cross-wavelet coherence was higher in the experimental participant pairs than in the pseudo pairs. Further, the coherence value was higher in the frequency band under 0.5 Hz. These results support the validity of evaluating interpersonal synchron Behavioral mimicry and interpersonal syyby using wavelet transform even in an unstructured conversation. However, the role of relative phase was not clear; there was no significant difference between each relative-phase region. The theoretical contribution of these findings to the area of interpersonal coordination is discussed.
Highlights
Interpersonal coordination has attracted the attention of social psychology and communication researchers
The result of separate t-tests indicated that the average coherence under 4 Hz throughout the time line was higher in the genuine pairs (M = 0.26, SD = 0.02) than in the pseudo pairs (M = 0.24, SD = 0.02), and this difference was significant [t(59.52) = 2.22, p = 0.030, d = 0.56]
This study examined whether the coordination represented in a time–frequency plane could be seen in an unstructured conversation
Summary
Interpersonal coordination has attracted the attention of social psychology and communication researchers. To evaluate synchrony in the frequency domain, a spectrum analysis that deconstructs a complex time-series into its rhythmic components, was employed (e.g., Schmidt and O’Brien, 1997; Schmidt et al, 2012). As with the Fourier transform, cross-spectrum analysis can be conducted using the wavelet transform, and cross-wavelet coherence represents the similarity between the two time series at each component frequency throughout the time line. Previous research (Fujiwara and Daibo, 2014), using a former version of this software (Dipp-Motion XD Ver. 3.20-2), demonstrated that gestures categorized by information on a coordinate point corresponded closely with a third person’s judgment (Spearman rank correlations: rs = 0.78) This finding indicates that this software can track and capture body movements with high resolution, even if the movement is not very large. The movements of the fingertips and nose were added together
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