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Dyadic physiological responses are correlated with the quality of interpersonal processes – for example, the degree of “connectedness” in education and mental health counseling. Pattern recognition algorithms could be applied to such dyadic responses to identify the states of specific dyads, but such pattern recognition has primarily focused on classification. This paper instead uses regression algorithms to estimate three conversation aspects (valence, arousal, balance) from heart rate, skin conductance, respiration, and skin temperature. Data were collected from 35 dyads who engaged in 20 minutes of conversation, divided into 10 two-minute intervals. Each interval was rated with regard to conversation valence, arousal, and balance by an observer. When regression algorithms (support vector machines and Gaussian process regression) were trained on other data from the same dyad, they were able to estimate valence, arousal and balance with lower errors than a simple baseline estimator. However, when algorithms were trained on data from other dyads, errors were not lower than those of the baseline estimator. Overall, results indicate that, as long as training data from the same dyad are available, autonomic nervous system responses can be combined with regression algorithms to estimate multiple dyadic conversation aspects with some accuracy. This has applications in education and mental health counseling, though fundamental issues remain to be addressed before the technology is used in practice.
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Close TIES in relationships: A dynamic systems approach for modeling physiological linkage
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.
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- Award ID(s):
- 1941868
- PAR ID:
- 10407334
- Date Published:
- Journal Name:
- Journal of Social and Personal Relationships
- Volume:
- 39
- Issue:
- 10
- ISSN:
- 0265-4075
- Page Range / eLocation ID:
- 3059 to 3084
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/02654075221082594
