Cardiac-respiratory coordination during memory encoding predicts performance

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Authors: Xindan Zhang, Timo Lehmann Kvamme, Yoko Nagai, Juha Silvanto
Cited by: 0
References: 16

Abstract

Abstract Cardiorespiratory coupling (i.e., the phase synchronization between cardiac and respiratory rhythms) varies with physiological state, but whether coupling strength functionally predicts cognitive performance remains unknown. Here we demonstrate that cardiorespiratory coupling predicts trial-level accuracy during the initial formation of mental representations but not during their subsequent retention, revealing temporal specificity in the relationship between bodily rhythms and cognition. We experimentally manipulated coupling via paced breathing (slow: 6 bpm vs. fast: 20 bpm) while participants performed mental imagery and working memory tasks with continuous physiological monitoring. Slow breathing enhanced parasympathetic activity (respiratory sinus arrhythmia), which strengthened baseline cardiorespiratory coupling. This coordinated autonomic state persisted into subsequent cognitive trials, predicting trial-by-trial accuracy specifically during the initial encoding phase when internal representations were actively constructed, but not during subsequent maintenance when representations were sustained. This temporal dissociation indicates that cardiorespiratory coordination selectively predicts performance when the brain constructs internal representations under elevated processing demands. Experimental manipulation of breathing improved behavioral accuracy, validating coupling’s functional relevance. These findings establish cardiorespiratory coupling as a physiological state marker that reliably predicts encoding success, providing a measurable target for interventions targeting memory and mental representation.

Fields of Study & Subjects

Heart Rate Variability and Autonomic Control Neural dynamics and brain function Neuroscience of respiration and sleep

Publication details

Journal / source
bioRxiv (Cold Spring Harbor Laboratory)
Publication date
15 January 2026
Document type
Preprint