Toward a biologically grounded understanding of autonomic function in developmental science.

Authors

Valerie P Bambha, Brooke E Franklin, Manash Sahoo, Giang X Le, Bryce E Dubois, Diana Martinez, Laura A Greenwald, Soumya Gupta, Lydia J Borjon, Jeremy I Borjon

Published in

Neuroscience and biobehavioral reviews. Pages 106984. Sep 16, 2026. Epub Sep 16, 2026.

Abstract

For over half a century, developmental scientists have used autonomic measures to draw inferences about the physiological mechanisms underlying infant attention, self-regulation, and social behavior. These interpretations rest on a binary model of the autonomic nervous system that divides it into opposing sympathetic and parasympathetic branches, a model established more than a century ago. The current review traces the historical description of the autonomic nervous system and summarizes three challenges to the traditional model with evidence from contemporary biology and neuroscience suggesting that the classical model is incomplete. Spanning anatomy, molecular profiling, neurotransmitter phenotypes, and modes of autonomic control, we argue that the autonomic nervous system is more integrated than typically thought. These findings have practical consequences for how developmental scientists interpret physiological signals, including the commonly used measure of respiratory sinus arrhythmia (RSA). We conducted a survey of 316 peer-reviewed publications published in the past six and a half years that use RSA in developmental populations, showing that interpretations linking RSA to psychological outcomes relies heavily on the traditional autonomic model. We therefore ask what it would look like to reassess the autonomic nervous system free of the assumptions of the traditional model, providing considerations, guidelines, and recommendations for measurement, analysis, interpretation, and transparency. We support a modern, integrated, and multidimensional account of autonomic function grounded in contemporary biology and neuroscience, with data-driven models and interpretations of physiological signals in developmental science.

PMID:
42749237
Bibliographic data and abstract were imported from PubMed on 17 Sep 2026.

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