A method for measuring dynamic respiratory sinus arrhythmia (RSA) in infants and mothers |
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| Institution: | 1. Department of Psychology, University of Georgia, United States;2. Division of Science, Indiana University – Purdue University Columbus, United States;3. Luddy School of Informatics, Computing, and Engineering, Indiana University, United States;4. Kinsey Institute, Indiana University, United States;5. Department of Psychological and Brain Sciences, Indiana University, United States;1. Institute of Linguistics, Shanghai International Studies University, Shanghai, China;2. School of Psychology and Cognitive Science, East China Normal University, Shanghai, China;1. Department of Psychology, Faculty of Philosophy, Sciences and Letters, University of São Paulo, Brazil;2. Department of Neurosciences and Behavior, Ribeirão Preto Medical School, University of São Paulo, Brazil;1. 0-3 Center for the at-Risk Infant, Scientific Institute IRCCS “Eugenio Medea”, Bosisio Parini, Lecco, Italy;2. Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA), National Research Council of Italy (CNR), Milan, Italy;3. Bioengineering Laboratory, Scientific Institute, IRCCS “Eugenio Medea”, Bosisio Parini, Lecco, Italy |
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| Abstract: | The measurement of respiratory sinus arrythmia (RSA) in infants, children and adults is critical to the study of physiological regulation, and more recently, interpersonal physiological covariation, but it has been impeded by methods that limit its resolution to 30 s or longer. Recent analytical developments have suggested methods for studying dynamic RSA in adults, and we have extended this work to the study of infants and mothers. In the current paper, we describe a new analytical strategy for estimating RSA time series for infants and adults. Our new method provides a means for studying physiological synchrony in infant-mother dyads that offers some important advantages relative to existing methods that use inter-beat-intervals (e.g. Feldman, Magori-Cohen, Galili, Singer, & Louzoun, 2011). In the middle sections of this paper, we offer a brief tutorial on calculating RSA continuously with a sliding window and review the empirical evidence for determining the optimal window size. In order to confirm the reliability of our results, we briefly discuss testing synchrony by randomly shuffling the dyads to control for spurious correlations, and also by using a bootstrapping technique for calculating confidence intervals in the cross-correlation function. One important implication that emerges from applying this method is that it is possible to measure both positive and negative physiological synchrony and that these categorical measures are differentially predictive of future outcomes. |
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| Keywords: | RSA physiological synchrony time series emotion regulation biobehavioral development Still Face paradigm |
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