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

Moulder, Robert; Boker, Steven M.; Ramseyer, Fabian; Tschacher, Wolfgang

doi:10.1037/met0000172

Cited by 98 publications

(91 citation statements)

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“…A final, quite important step in our analysis was to rule out that the detected synchrony could have occurred solely by chance: Comparing synchrony with so‐called pseudosynchrony provides an estimate of the strength of the synchrony phenomenon (Moulder, Boker, Ramseyer, & Tschacher, ; Ramseyer & Tschacher, ). We therefore included a procedure that generated N = 5,000 surrogate datasets (out of a possible N = 7,320) by permuting the allocation of interviewers and subjects across all available dyads ( N = 31) in a yoked control design.…”

Section: Methodsmentioning

confidence: 99%

“…We therefore included a procedure that generated N = 5,000 surrogate datasets (out of a possible N = 7,320) by permuting the allocation of interviewers and subjects across all available dyads ( N = 31) in a yoked control design. This strategy is different from another approach (Ramseyer & Tschacher, ), but provides more randomized pairings in situations with few patients and short observations (see Moulder et al , ). Our choice of lags up to ±3 s was based on the empirical comparison of synchrony versus pseudosynchrony, namely the crossing points of genuine synchrony and pseudosynchrony (Tschacher, Ramseyer, & Koole, ).…”

Section: Methodsmentioning

confidence: 99%

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Exploring nonverbal synchrony in borderline personality disorder: A double‐blind placebo‐controlled study using oxytocin

Ramseyer

Ebert

Roser

et al. 2019

British J Clinic Psychol

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Objectives. Interpersonal dysfunction is a central feature of borderline personality disorder (BPD), and the neuropeptide oxytocin (OT) has been shown to impact patients' behaviour in numerous ways. Nonverbal signals such as the coordination of body movement (nonverbal synchrony) are associated with the success of interpersonal exchanges and could thus be influenced by features of BPD and by the administration of OT.Design. We explored the effect of intranasal OT (inOT) on nonverbal synchrony in sixteen patients with BPD and fifteen healthy controls (CTL) randomly assigned to two double-blind clinical interviews under inOT and placebo (PL).Methods. Nonverbal synchrony was assessed by automated video-analyses of subject's and interviewer's body movement. Lagged cross-correlations were used to objectively quantify coordination in dyads.Results. Synchrony was higher than pseudosynchrony (= synchrony expected by chance), and there was a differential effect of inOT between groups: While healthy controls displayed increased synchrony under inOT, patients with BPD showed low levels of synchrony under inOT. Additionally, patient's synchrony was negatively associated with self-reported childhood trauma.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Exploring nonverbal synchrony in borderline personality disorder: A double‐blind placebo‐controlled study using oxytocin

Ramseyer

Ebert

Roser

et al. 2019

British J Clinic Psychol

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“…Different methods exist by which spurious correlations can be controlled for. One possibility is the use of surrogate/virtual pairs-that is, time series pairs that are split and randomly recombined (Louwerse et al, 2012;Moulder, Boker, Ramseyer, & Tschacher, 2018). Synchrony determined within these surrogate pairs can be used as a baseline to evaluate the meaningfulness of genuine synchrony.…”

Section: Strengths and Limitationsmentioning

confidence: 99%

Quantification of nonverbal synchrony using linear time series analysis methods: Lack of convergent validity and evidence for facets of synchrony

et al. 2018

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Nonverbal synchrony describes coordination of the nonverbal behavior of two interacting partners. Additionally, it seems to be important in human interactions, such as during psychotherapy. Currently, there are several options for the automated determination of synchrony based on linear time series analysis methods (TSAMs). However, investigations into whether the different methods measure the same construct have been missing. In this study, N = 84 patient-therapist dyads were videotaped during psychotherapy sessions. Motion energy analysis was used to assess body movements. We applied seven different TSAMs and recorded multiple output scores (average synchrony, maximum synchrony, and frequency of synchrony; in total, N = 16 scores). Convergent validity was examined using correlations of the output scores and exploratory factor analysis. Additionally, two criterion-based validations were conducted: investigations of concordant validity with a more generalized nonlinear method, and of the predictive validity of the synchrony scores for improvement in interpersonal problems at the end of therapy. We found that the synchrony measures only partially correlated with each other. The factor analysis did not support a common-factor model. A three-factor model with a second-order synchrony variable showed the best fit for eight of the selected synchrony scores. Only some synchrony scores were able to predict improvement at the end of therapy. We concluded that the considered TSAMs do not measure the same synchrony construct, but different facets of synchrony: the strength of synchrony of the total interaction, the strength of synchrony during synchronization intervals, and the frequency of synchrony. Keywords Nonverbal behavior. Movement synchrony. Motion energy analysis. Time series analysis. Convergent validity Currently, body movements can be assessed fully automatically and with high time resolution (e.g., 25 times per second) using either motion-tracking, motion capture devices, or video-based algorithms (Delaherche et al., 2012). Motion energy analysis (MEA) is a method that quantifies the intensity of videotaped movements frame-wise (Grammer, Honda, Juette, & Schmitt, 1999). By determining a region of interest (ROI) for each of two videotaped individuals (e.g., a patient and therapist during a psychotherapy session), two time series can be generated displaying the time course of the individuals' body movements. This technique has several advantages: (1) it is less timeconsuming than collecting human ratings; (2) it is highly objective, reliable, and valid; and (3) in comparison to motion capture devices, no high-resolution camera equipment is necessary, and no sensors are attached to the patient's body (Altmann, 2010; Ramseyer & Tschacher, 2011). Therefore, during the past few years, the use of MEA has become enormously widespread. In behavioral and social science, MEA has been used to assess movements in mother-child interactions (

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“…SuSy was implemented in R version 3.0.2, in strict analogy to previous projects (Ramseyer & Tschacher, 2011;Tschacher et al, 2014). Surrogate tests (Moulder, Boker, Ramseyer, & Tschacher, 2018) are especially important because times series are (by definition) serially dependent data sets, which means they are often autocorrelated. Autocorrelation may, e.g., result from trend-like behavior of EDA values.…”

Section: Surrogate Synchronymentioning

confidence: 99%

“…Finally, "synchrony" in one study may not equal "synchrony", "attunement", "coupling", or like phenomena in another study. There are various options for synchrony computation as well as for surrogate testing (Moulder et al, 2018;Palumbo et al, 2017;Ramseyer & Tschacher, 2010). Different methodological choices can entail a marked impact on the results of a study.…”

Section: / 727mentioning

confidence: 99%

Psychophysiological Synchrony During Verbal Interaction in Romantic Relationships

et al. 2018

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Previous studies about romantic relationships have shown that the reciprocal influence between partners occurs not only at the behavioral and socio-emotional levels, but also at the psychophysiological level. This reciprocal influence is expressed in a pattern of physiological synchrony between partners (i.e., coordinated dynamics of the physiological time series). The main aim of the present study was to explore the presence of a pattern of physiological synchrony in electrodermal activity (EDA) during a couple interaction task. A second objective was to compare the synchrony levels during a negative interaction condition versus a positive interaction condition. Finally, we analyzed the association between synchrony and self-perception of empathy, dyadic empathy, and relationship satisfaction. Thirty-two couples (64 individuals) participated in this study. Each couple performed a structured interaction task while the EDA of both partners was being registered. The quantification of synchrony was based on the cross-correlation of both members' EDA time-series. In order to control for coincidental synchrony, surrogate datasets were created by repeatedly shuffling the original data of spouses X and Y of a dyad and computing synchronies on the basis of the shuffled data (pseudosynchrony values). Our results confirmed the presence of significant EDA synchrony during the interaction. We also found that synchrony was higher during the negative interactions relative to the positive interactions. Additionally, physiological synchrony during positive interaction was higher for those couples in which males scored higher in dyadic empathy. The clinical implications of these findings are discussed.

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

Cited by 98 publications

References 50 publications

Exploring nonverbal synchrony in borderline personality disorder: A double‐blind placebo‐controlled study using oxytocin

Exploring nonverbal synchrony in borderline personality disorder: A double‐blind placebo‐controlled study using oxytocin

Quantification of nonverbal synchrony using linear time series analysis methods: Lack of convergent validity and evidence for facets of synchrony

Psychophysiological Synchrony During Verbal Interaction in Romantic Relationships

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