Determining synchrony between behavioral time series: An application of surrogate data generation for establishing falsifiable null-hypotheses

• Main Authors: Boker, S.M (Author), Moulder, R.G (Author), Ramseyer, F. (Author), Tschacher, W. (Author)
• Format: Article
• Language: English
• Published: American Psychological Association Inc. 2018
• Subjects: adult; article; Behavioral time series; case report; clinical article; computer simulation; Computer Simulation; cooperation; Cooperative Behavior; Data Interpretation, Statistical; female; human; human experiment; Humans; male; Models, Psychological; Models, Statistical; motion; Nonlinear dynamics; nonlinear system; nonverbal communication; Nonverbal Communication; Nonverbal synchrony; null hypothesis; Null-hypothesis testing; physiology; procedures; psychological model; psychology; Psychology; statistical analysis; statistical model; Surrogate data; time factor; Time Factors; time series analysis
• Online Access: View Fulltext in Publisher

 Synchrony between interacting systems is an important area of nonlinear dynamics in physical systems. Recently psychological researchers from multiple areas of psychology have become interested in nonverbal synchrony (i.e., coordinated motion between two individuals engaged in dyadic information exchange such as communication or dance) as a predictor and outcome of psychological processes. An important step in studying nonverbal synchrony is systematically and validly differentiating synchronous systems from nonsynchronous systems. However, many current methods of testing and quantifying nonverbal synchrony will show some level of observed synchrony even when research participants have not interacted with one another. In this article we demonstrate the use of surrogate data generation methodology as a means of testing new null-hypotheses for synchrony between bivariate time series such as those derived from modern motion tracking methods. Hypotheses generated by surrogate data generation methods are more nuanced and meaningful than hypotheses from standard null-hypothesis testing. We review four surrogate data generation methods for testing for significant nonverbal synchrony within a windowed cross-correlation (WCC) framework. We also interpret the null-hypotheses generated by these surrogate data generation methods with respect to nonverbal synchrony as a specific use of surrogate data generation, which can then be generalized for hypothesis testing of other psychological time series. © 2018 American Psychological Association.