Cross-Correlation- and Entropy-Based Measures of Movement Synchrony: Non-Convergence of Measures Leads to Different Associations with Depressive Symptoms

Abstract

Background: Several algorithms have been proposed to quantify synchronization. However, little is known about their convergent and predictive validity. Methods: The sample included 30 persons who completed a manualized interview focusing on psychosomatic symptoms. The intensity of body motions was measured using motion-energy analysis. We computed several measures of movement synchrony based on the time series of the interviewer and participant: mutual information, windowed cross-recurrence analysis, cross-correlation, rMEA, SUSY, SUCO, WCLC–PP and WCLR–PP. Depressive symptoms were assessed with the Patient Health Questionnaire (PHQ9). Results: According to the explorative factor analyses, all the variants of cross-correlation and all the measures of SUSY, SUCO and rMEA–WCC led to similar synchrony measures and could be assigned to the same factor. All the mutual-information measures, rMEA–WCLC, WCLC–PP–F, WCLC–PP–R2, WCLR–PP–F, and WinCRQA–DET loaded on the second factor. Depressive symptoms correlated negatively with WCLC–PP–F and WCLR–PP–F and positively with rMEA–WCC, SUCO–ES–CO, and MI–Z. Conclusion: More standardization efforts are needed because different synchrony measures have little convergent validity, which can lead to contradictory conclusions concerning associations between depressive symptoms and movement synchrony using the same dataset.

Keywords

• nonverbal communication
• movement synchrony
• time-series analysis
• validity
• depression