{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T13:14:23Z","timestamp":1780060463757,"version":"3.54.0"},"reference-count":57,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T00:00:00Z","timestamp":1780012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004070","name":"Khalifa University of Science, Technology and Research","doi-asserted-by":"publisher","award":["RIG2024-046"],"award-info":[{"award-number":["RIG2024-046"]}],"id":[{"id":"10.13039\/501100004070","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Syst. Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Human conversation involves moment-to-moment reciprocal adjustments between interlocutors, expressed through both emotional cues and autonomic physiology.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To quantify how physiological synchrony continuously builds and subsides between debate partners during speaker-listener turn-taking, and to test whether the direction of this coupling (speaker-leading vs listener-leading) is associated with (i) self- versus partner-perceived arousal\/valence and (ii) autonomic and complexity-based heart rate variability (HRV) characteristics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Multimodal data from the K-EmoCon database were analyzed, comprising HRV-derived cardiac activity, speech timing, and multi-perspective emotion ratings from 32 individuals engaged in a structured dyadic debate. Interactions were segmented into speaking and listening phases, and a phase-based bidirectional coupling framework was applied to quantify both the strength and polarity of physiological synchrony. Associations between emotional states and HRV features were examined using correlation analysis across coupling segments, followed by principal component analysis (PCA), to reduce dimensionality and cluster emotions and features based on their shared variance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Positive coupling segments, corresponding to speaker-leading dynamics, were characterized by strong associations between partner-related emotional states and parasympathetic HRV indices, including RMSSD and SD1, with correlations reaching up to 0.63 (\n                      <jats:italic>p<\/jats:italic>\n                      &amp;lt; 0.001). In contrast, negative coupling segments, reflecting listener-leading dynamics, showed stronger associations with sample entropy, R\u00e9nyi entropy, and low-frequency power, with correlations reaching 0.71 (\n                      <jats:italic>p<\/jats:italic>\n                      &amp;lt; 0.001). Diffusion entropy exhibited a polarity-dependent pattern consisting of positively correlated self-reported emotions during positive coupling, whereas during negative coupling it was negatively correlated with partner-related emotions, with correlations reaching 0.71 (\n                      <jats:italic>p<\/jats:italic>\n                      &amp;lt; 0.001) for the complexity index \u03bc\n                      <jats:sub>\n                        <jats:italic>r<\/jats:italic>\n                      <\/jats:sub>\n                      at scale 1. PCA showed that positive coupling was characterized by a clear separation of arousal, with self-related emotions aligning with diffusion entropy features and partner-related emotions clustering with HRV and R\u00e9nyi entropy measures. In contrast, negative coupling exhibited a pattern in which partner-related emotions formed more compact clusters across power- and entropy-based features.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>These findings demonstrate that bidirectional physiological coupling provides a sensitive framework for disentangling leadership, responsiveness, and emotional exchange during conversation. By revealing distinct autonomic and complexity-based signatures of self- and partner-related affect, this work advances understanding of interpersonal emotional regulation. It has implications for therapeutic, educational, and collaborative communication contexts.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnsys.2026.1802891","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T13:00:55Z","timestamp":1780059655000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Continuous bidirectional coupling of heart rate variability and emotions in dyad speaker-listener dynamics reveals unique interpersonal synchronization"],"prefix":"10.3389","volume":"20","author":[{"given":"Mohanad","family":"Alkhodari","sequence":"first","affiliation":[{"name":"Department of Medical Sciences, Khalifa University","place":["Abu Dhabi, United Arab Emirates"]},{"name":"Cardiovascular Clinical Research Facility (CCRF), Radcliffe Department of Medicine, University of Oxford","place":["Oxford, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ioannis","family":"Ziogas","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering & Biotechnology, Khalifa University","place":["Abu Dhabi, United Arab Emirates"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaskeerat","family":"Singh","sequence":"additional","affiliation":[{"name":"Center for Nonlinear Science, University of North Texas","place":["Denton, TX, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paolo","family":"Grigolini","sequence":"additional","affiliation":[{"name":"Center for Nonlinear Science, University of North Texas","place":["Denton, TX, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Glenn W.","family":"Muschert","sequence":"additional","affiliation":[{"name":"Healthcare Engineering Innovation Group (HEIG), College of Medicine and Health Sciences, Khalifa University","place":["Abu Dhabi, United Arab Emirates"]},{"name":"Department of Public Health and Epidemiology, Khalifa University","place":["Abu Dhabi, United Arab Emirates"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fedor","family":"Kusmartsev","sequence":"additional","affiliation":[{"name":"Department of Physics, Khalifa University","place":["Abu Dhabi, United Arab Emirates"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Herbert F.","family":"Jelinek","sequence":"additional","affiliation":[{"name":"Department of Medical Sciences, Khalifa University","place":["Abu Dhabi, United Arab Emirates"]},{"name":"Healthcare Engineering Innovation Group (HEIG), College of Medicine and Health Sciences, Khalifa University","place":["Abu Dhabi, United Arab Emirates"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,29]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1915978","DOI":"10.1155\/hbe2\/1915978","article-title":"Machine learning identifies the emotion climate during naturalistic conversations using speech features and affect dynamics.","volume":"2025","author":"Alhussein","year":"2025","journal-title":"Hum. 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