Close TIES in relationships: A dynamic systems approach for modeling physiological linkage

Abstract

We explore complex dynamic patterns of autonomic physiological linkage (i.e., statistical interdependence of partner’s physiology; PL), within the sympathetic and parasympathetic nervous systems (SNS and PNS), as potential correlates of emotional and regulatory dynamics in close relationships. We include electrodermal activity (EDA) as an indicator of SNS activation and respiratory sinus arrhythmia (RSA) as an indicator of regulatory and/or homeostatic processes within the PNS. Measures of EDA and RSA were collected in 10-second increments from 53 heterosexual couples during a mixed-emotion conversation in the laboratory. We used the R statistical package, rties (Butler & Barnard, 2019), to model the dynamics of EDA and RSA with a coupled oscillator model and then categorized couples into qualitatively distinct profiles based on the set of parameters that emerged. We identified two patterns for EDA and three patterns for RSA. We then investigated associations between the PL patterns and self-report measures of relationship and conversation quality and emotional valence using Bayesian multilevel and logistic regression models. Overall, we found robust results indicating that PL profiles were credibly predicted by valence and relationship quality reported prior to the conversations. In contrast, we found very little evidence suggesting that PL patterns predict self-reported conversation quality or valence following the conversation. Together, these results suggest that PL across autonomic subsystems may reflect different processes and therefore have different implications when considering interpersonal dynamics.

Introduction to the special issue: Nonlinear effects and dynamics in close relationships

“…For example, nonlinearity can be defined as a function of patterns between variables (e.g., curvilinear effects, Chopik et al, 2022, Lafit et al, 2022; also see correlated intercepts, slopes or residuals, Dugan et al, 2022), within variables over time (e.g., within-person variation, Eller et al, 2022), and/or within variables between dyads (e.g., time-series analyses, Ogolsky et al, 2021). More complex models can reveal the dynamic ways in which partners’ emotions (e.g., change point detection, Sels et al, 2022), behaviors (e.g., sequence analysis, Solomon et al, 2022), or physiological responses (e.g., couple-oscillator model, Kuelz et al, 2022), unfold or shift abruptly across time. We recognize that most of these analyses are inherently linear in that they involve extracting variables that represent nonlinear effects or dynamics to include in linear-based models to predict outcomes.…”

“…Two papers in this special issue, however, illustrate how nonlinear modelling can identify additional dyadic processes involving patterns of synchrony between relationship partners. Kuelz et al, (2022), for example, use the R statistical package rties (Butler & Barnard, 2019) to model different patterns of physiological synchrony between dyad members using a coupled oscillator model. Using this method, they: (a) identify distinct patterns of physiological synchrony (such as when partners’ physiologies counterbalance, but never achieve homeostasis or stability), and (b) illustrate what partner and couple characteristics might predict distinct patterns of physiological synchrony (such as whether dissatisfied couples have more maladaptive patterns of physiological synchrony).…”

Considering the role of cardiac vagal tone in adolescents' behaviors within friendships

“…Recent advances in statistical approaches to examining coregulation permit the examination of both linkage and net change simultaneously. In addition, such approaches allow researchers to identify different patterns of coregulation within the sample (Butler & Barnard, 2019;Kuelz et al, 2022). It will be important in future work to both determine whether friendship support in response to problem disclosures contributes to adaptive (i.e., morphostatic) coregulation and whether there is variability in the types of coregulation friends demonstrate within the sample.…”