An Exploratory Study of Physiological Linkage Among Strangers
Bibliographic record
Abstract
The present study explores physiological linkage (i.e., any form of statistical interdependence between the physiological signals of interacting partners; PL) using data from 65 same-sex, same ethnicity stranger dyads. Participants completed a knot-tying task with either a cooperative or competitive framing while either talking or remaining silent. Autonomic nervous system activity was measured continuously by electrocardiograph for both individuals during the interaction. Using a recently developed R statistical package (i.e., rties), we modeled different oscillatory patterns of coordination between partner's interbeat interval (i.e., the time between consecutive heart beats) over the course of the task. Three patterns of PL emerged, characterized by differences in frequency of oscillation, phase, and damping or amplification. To address gaps in the literature, we explored (a) PL patterns as predictors of affiliation and (b) the interaction between individual differences and experimental condition as predictors of PL patterns. In contrast to prior analyses using this dataset for PL operationalized as covariation, the present analyses showed that oscillatory PL patterns did not predict affiliation, but the interaction of individual differences and condition differentially predicted PL patterns. This study represents a next step toward understanding the roles of individual differences, context, and PL among strangers.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".