Strangers, Friends, and Lovers Show Different Physiological Synchrony in Different Emotional States

Bizzego, Andrea; Azhari, Atiqah; Campostrini, Nicola; Truzzi, Anna; Ng, Li Ying; Gabrieli, Giulio; Bornstein, Marc H.; Setoh, Peipei; Esposito, Gianluca

doi:10.3390/bs10010011

Cited by 57 publications

(41 citation statements)

References 55 publications

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“…Moreover, due to its putative role for adaptive dyadic functioning, interpersonal synchrony has been studied in human dyads such as mother and infant (Davis et al., 2018; Feldman et al., 2011), therapist‐patient (Tschacher & Meier, 2019), romantic partners (Cacioppo et al., 2014; Coutinho et al., 2018; Reed et al., 2013), and even strangers (Bizzego et al., 2020; Tschacher et al., 2014).…”

Section: Introductionmentioning

confidence: 99%

When our hearts beat together: Cardiac synchrony as an entry point to understand dyadic co‐regulation in couples

et al. 2020

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The degree to which romantic partners' autonomic responses are coordinated, represented by their pattern of physiological synchrony, seems to capture important aspects of the reciprocal influence and co‐regulation between spouses. In this study, we analyzed couple's cardiac synchrony as measured by heart rate (HR) and heart rate variability (HRV). A sample of 27 couples (N = 54) performed a structured interaction task in the lab where they discussed positive and negative aspects of the relationship. During the interaction, their cardiac measures (HR and HRV) were recorded using the BIOPAC System. Additional assessment, prior to the lab interaction task, included self‐report measures of empathy (Interpersonal Reactivity Index and Interpersonal Reactivity Index for Couples) and relationship satisfaction (Revised Dyadic Adjustment Scale). Synchrony computation was based on the windowed cross‐correlation of both partner's HR and HRV time series. In order to control for random synchrony, surrogate controls were created using segment‐wise shuffling. Our results confirmed the presence of cardiac synchrony during the couple's interaction when compared to surrogate testing. Specifically, we found evidence for negative (antiphase) synchrony of couple's HRV and positive (in‐phase) synchrony of HR. Further, both HRV and HR synchronies were associated with several dimensions of self‐report data. This study suggests that cardiac synchrony, particularly, the direction of the covariation in the partners' physiological time series, may have an important relational meaning in the context of marital interactions.

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Section: Introductionmentioning

confidence: 99%

When our hearts beat together: Cardiac synchrony as an entry point to understand dyadic co‐regulation in couples

et al. 2020

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“…Figure 4 top-left visualizes an example pair of time-normalized IBI series. This effectively resamples the IBI series at 2 Hz as has been done in prior research ( Golland et al , 2015 ; Bizzego et al , 2020 ) in which linear interpolation was shown to have better performance for time-domain and high-frequency variability compared with other interpolation measures ( Choi and Shin, 2018 ). Heart rate variability measures, typically extracted from the IBI series, are parsed into the following frequency bands: ultra-low frequency (≤ 0.003 Hz), very low frequency (0.0033–0.04 Hz), low frequency (0.04–0.15 Hz), and high frequency (0.15–0.4 Hz) ( Shaffer and Ginsberg, 2017 ).…”

Section: Physiological Data Set For Demonstrating Methodsmentioning

confidence: 99%

Windowed multiscale synchrony: modeling time-varying and scale-localized interpersonal coordination dynamics

Likens

Wiltshire

2020

Social Cognitive and Affective Neuroscience

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Social interactions are pervasive in human life with varying forms of interpersonal coordination emerging and spanning different modalities (e.g., behaviors, speech/language, and neurophysiology). However, during social interactions, as in any dynamical system, patterns of coordination form and dissipate at different scales. Historically, researchers have used aggregate measures to capture coordination over time. While those measures (e.g., mean relative phase, cross-correlation, coherence) have provided a wealth of information about coordination in social settings, some evidence suggests that multiscale coordination may change over the time course of a typical empirical observation. To address this gap, we demonstrate an underutilized method, windowed multiscale synchrony, that moves beyond quantifying aggregate measures of coordination by focusing on how the relative strength of coordination changes over time and the scales that comprise social interaction. This method involves using a wavelet transform to decompose time series into component frequencies (i.e., scales), preserving temporal information and then quantifying phase synchronization at each of these scales. We apply this method to both simulated and empirical interpersonal physiological and neuromechanical data. We anticipate that demonstrating this method will stimulate new insights on the mechanisms and functions of synchrony in interpersonal contexts using neurophysiological and behavioral measures.

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“…F 1 is associated with the components in the signals that convey the information about the heart beats (the QRS complexes in the ECG and the pulses in the BVP); F 2 is the band which contains all the main frequency components of the signals. For ECG signals: F 1 = [5, 14] Hz, F 2 = [5, 50] Hz , [29]; for BVP signals: ( F 1 = [1, 2.25] Hz, F 2 = [0, 8] Hz [18]). ψ is expected to have values 0.5 ≤ ψ ≤ 0.8 [29].…”

Section: Methodsmentioning

confidence: 99%

“…Many scientific fields would benefit from the adoption of WDs for long-term, unobtrusive recording of physiological signals. In particular, scientific use of WDs is explored in affective computing [37, 35], research on autism [46, 26], interpersonal coupling [3, 8] and psychology in general.…”

Section: Introductionmentioning

confidence: 99%

Comparison of wearable and clinical devices for acquisition of peripheral nervous system signals

Bizzego

Gabrieli

Furlanello

et al. 2020

Preprint

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A key access point to the functioning of the Autonomic Nervous System is the investigation of peripheral signals. Wearable Devices (WDs) enable the acquisition and quantification of peripheral signals in a wide range of contexts, from personal uses to scientific research. WDs have lower costs and higher portability than medical-grade devices. But achievable data quality can be lower, subject to artifacts due to body movements and data losses. It is therefore crucial to evaluate the reliability and validity of WDs before their use in research. In this study we introduce methods and scientific software for the assessment of WDs for multivariate physiological signals. The quality of cardiac and Electrodermal Activity signals is validated with a standard set of Signal Quality Indicators, available as open source in the pyphysio Python package. We apply the indicators for the analysis of signal quality on data simultaneously recorded from a clinical-grade device and two WDs. The dataset provides signals of 6 different physiological measures collected from 18 subjects with WDs. This study indicates the need of validating the use of WD in experimental settings for research and the importance of both technological and signal processing aspects to obtain reliable signals and reproducibility of results.

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Strangers, Friends, and Lovers Show Different Physiological Synchrony in Different Emotional States

Cited by 57 publications

References 55 publications

When our hearts beat together: Cardiac synchrony as an entry point to understand dyadic co‐regulation in couples

When our hearts beat together: Cardiac synchrony as an entry point to understand dyadic co‐regulation in couples

Windowed multiscale synchrony: modeling time-varying and scale-localized interpersonal coordination dynamics

Comparison of wearable and clinical devices for acquisition of peripheral nervous system signals

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