Team Neurophysiological Synchrony: Evolutionary Foundations of Team Dynamics
Bibliographic record
Abstract
There are moments in life when we feel as one. When we share a laugh, high-five after a win, clap in unison with a huge crowd in a concert, dance, and even when watching an emotional moment on TV. These are times we feel together. Times we are in sync. Such moments and feelings have sprung the curiosity of academics and gave rise to the research field of interpersonal synchrony - the temporal coupling of relationship-relevant events between partners. Interpersonal synchrony is essential for bonding since it functions as an evolutionary ‘social glue’ – a natural, organizing mechanism, that coordinates the ongoing exchanges of sensory, hormonal, and physiological stimuli. In its infancy, synchrony research focused on dyadic relationships - parent-child, romantic couples, etc. Understandably, teams and organizational researchers have gradually taken an interest in studying the function and antecedents of neurophysiological synchrony in teams in a variety of contexts and with many issues in mind (e.g., team dynamics and outcomes). In this symposium, a diverse group of scholars would share insight from cutting-edge, large-scale scientific endeavors on teams, in and out of the lab. We will discuss synchrony in multiple physiological modalities, such as heart rate and specific brain activity patterns, and what each could add to our understating of team processes while focusing on issues such as team dynamics, leadership, composition, emotion regulation, and performance. Thus, we hope to contribute both to our current understanding of teams, as well as share hands-on experience in how team scholars and professionals could use similar practices in their respective fields and environments. Leading The Rhythm: Investigating Need Supportive Leadership Author: Florence Jauvin; U. du Québec à Montréal Author: Sebastiano Massaro; Surrey Business School Author: Jacques Forest; École des sciences de la gestion (ESG UQAM) Charismatic Signaling Stirs The Hearts and Brains of Followers: A Neurophysiological Inquiry Author: Alon Burns; Bar Ilan U., Department of Psychology, Israel Author: Ilanit Gordon; Bar Ilan U. Author: Laurel Trainor; McMaster U. Members' Emotional Regulation Impacts Team Performance, as Synchrony Drives Regulated Teams' Success Author: Chen Erez; Bar-Ilan U. Author: Ilanit Gordon; Bar Ilan U. On The ‘Same Wavelength’: Exploring Neural Predictors of Team Cognition and Performance Author: Imogen Weigall; U. of South Australia Author: Ruchi Sinha; UniSA Author: Ina Bornkessel-Schlesewsky; U. of South Australia Author: Matthias Schlesewsky; U. of South Australia Author: Zachariah Cross; Dynamic Brain Lab, Northwestern Feinberg School of Medicine
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".