Editorial: Interpersonal synchrony and network dynamics in social interaction
Bibliographic record
Abstract
Editorial on the Research Topic Interpersonal synchrony and network dynamics in social interactionThis Research Topic was launched with the aim of highlighting and exploring the mechanisms and functions of interpersonal interaction, and thereby deepening our understanding of these highly interesting and complex phenomena and their downstream effects on real-life social interaction.The collection of contributions includes a Hypothesis and Theory article, a Review, two Brief Research Reports, and 11 Original Research articles written by leading researchers in the field.They showcase the breadth of research studies, going from hyper-brain cell assembly hypothesis and theory of mind hyperscanning to ensemble singing and soccer playing to healthcare teams, music therapy and psychotherapy concepts based on the inter-brain plasticity model.This range exemplifies the promise of this field in being able to span multiple facets of life.Figure 1 illustrates the diversity yet thematic relatedness of the contributions.It displays the joint forward model for interpersonal action coordination with three representation levels (i.e., representation of individual, other's, and joint forward models with corresponding sensorimotor feedbacks) and the impact of a joint goal as well as external influences (cf.Müller et al., 2021).This model is a construct integrating and reflecting a variety of influences and interactions of human social behavior.Figure 1 also highlights different interaction situations described in the Research Topic.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.006 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.020 | 0.019 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".