Lability and Interactive EDA Predict Leadership Emergence
Bibliographic record
Abstract
Leadership emergence (LE), the process by which individuals are recognized as leaders within their groups, has a significant impact on group performance. However, the physiological processes underlying LE remain largely unexplored. This study investigates the role of objective physiological measures, specifically electrodermal activity (EDA), in uncovering the mechanisms that contribute to LE. EDA reflects sympathetic nervous system activation and provides real-time insights into processes such as emotional arousal, attention, and vigilance—factors previously linked to LE. Participants (N = 144), nested in 48 three-person groups, completed a group decision-making task while their physiological data were continuously recorded. Two EDA components were analyzed: lability (baseline activity) and interactive EDA (dynamic responses during group interactions). LE was assessed using peer rankings conducted at the end of the group interaction. Multilevel Poisson regression analyses revealed that both mean baseline and interactive EDA positively predicted LE, even after controlling for variables such as gender, age, intelligence proxies, and other relevant factors. These findings suggest that processes associated with elevated EDA levels, such as emotional arousal, attention, and vigilance, promote the emergence of leaders in small groups. This study highlights the value of physiological measures like EDA in overcoming the limitations of traditional self-report tools, offering deeper and more accurate insights into the mechanisms driving LE.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".