On Measuring and Modeling Physiological Synchrony in Dyads

ABSTRACT

Physiological synchrony within a dyad, or the degree of temporal correspondence between two individuals' physiological systems, has become a focal area of psychological research. Multiple methods have been used for measuring and modeling physiological synchrony. Each method extracts and analyzes different types of physiological synchrony, where ‘type’ refers to a specific manner through which two different physiological signals may correlate. Yet, to our knowledge, there is no documentation of the different methods, how each method corresponds to a specific type of synchrony, and the statistical assumptions embedded within each method. Hence, this article outlines several approaches for measuring and modeling physiological synchrony, connects each type of synchrony to a specific method, and identifies the assumptions that need to be satisfied for each method to appropriately extract each type of synchrony. Furthermore, this article demonstrates how to test for between-dyad differences of synchrony via inclusion of dyad-level (i.e., time-invariant) covariates. Finally, we complement each method with an empirical demonstration, as well as online supplemental material that contains Mplus code.

KEYWORDS:

• Physiological synchrony
• multivariate growth models

Article information

Conflict of Interest disclosures: Each author signed a form for disclosure of potential conflicts of interest. No authors reported any financial or other conflicts of interest in relation to the work described.

Ethical principles: The authors affirm having followed professional ethical guidelines in preparing this work. These guidelines include obtaining informed consent from human participants, maintaining ethical treatment and respect for the rights of human or animal participants, and ensuring the privacy of participants and their data, such as ensuring that individual participants cannot be identified in reported results or from publicly available original or archival data.

Funding: This work was supported by Grant 2347.07 from the Fetzer Institute.

Role of the funders/sponsors: None of the funders or sponsors of this research had any role in the design and conduct of the study; collection, management, analysis, and interpretation of data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Acknowledgments: The ideas and opinions expressed herein are those of the authors alone, and endorsement by the authors' institutions, or the Fetzer Institute, is not intended and should not be inferred.

Notes

1 As an aside, the growth curve models often contain an estimate of residual covariance (i.e.,). This residual covariance can be interpreted as the average level of concurrent synchrony across dyads, but does not vary across dyads (at least not in most structural equation modeling packages).Therefore, it is not the focal method for estimating concurrent synchrony.

2 Estimation using the original metric of 30 s epochs produced highly unstable parameter estimates and poor model fit for the multiple group model (across different signals and tasks); whereas estimation following aggregation within each minute produced reliable parameter estimates with good fit. Hence, the aggregation performed here was done to enable a demonstration of trend synchrony rather than a suggestion of data aggregation.

3 It may seem surprising that a significant prediction occurred for a variable with non-significant variability. However, non-significant between-dyad differences (i.e., with p > .05) are not the same as nil between-dyad differences (i.e., = 0). Therefore, it is possible, albeit rare, to have significant covariation with a variable that has small variation, and this is what occurred in the investigation of between-dyad differences for lagged synchrony.