Elucidating Interpersonal Cardiac Synchrony During a Naturalistic Social Interaction Using Dyadic Poincaré Plot Analysis
Bibliographic record
Abstract
Interpersonal synchrony refers to the physiological or behavioral alignment between individuals within specific social contexts. While a variety of physiological signals are used to investigate synchrony, heartrate variability (HRV) has emerged as a valuable indicator of autonomic coordination between interacting participants. Among the most popular methods for measuring HRV synchrony is linear cross-correlation. However, given the complexity of cardiac signals, nonlinear analytical approaches may be more effective in uncovering synchrony. We introduce dyadic Poincaré plot analysis (dPPA) as tool for both visualizing and quantifying interpersonal cardiac synchrony over time. The method is based on simultaneously plotting time-delayed embeddings of interbeat intervals of two interacting individuals on a single graph and deriving inter-centroid distances between the dyadic point clouds over time. We demonstrate dPPA with cardiac data from acquainted (for at least 1 year) and unacquainted dyads interacting during a 30-minute unstructured conversation. dPPA findings were compared to those of conventional cross-correlation analyses. dPPA analysis uniquely revealed that acquainted and unacquainted dyads experienced, respectively, a significant increase and decrease in cardiac synchrony over time. Furthermore, dPPA indicated that all dyads experienced heightened sympathetic nervous system activity during conversation. dPPA affords both a simple visualization and sensitive quantitative characterization of time-evolving cardiac synchrony.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".