Shared mental models and intra-team psychophysiological patterns: a test of the juggling paradigm
Bibliographic record
Abstract
We explored implicit coordination mechanisms underlying the conceptual notion of "shared mental models" (SMM) through physiological (i.e., breathing and heart rates) and affective-cognitive (i.e., arousal, pleasantness, attention, self-efficacy, other's efficacy) monitoring of two professional jugglers performing a real-time interactive task of increasing difficulty. There were two experimental conditions: "individual" (i.e., solo task) and "interactive" (i.e., two jugglers established a cooperative interaction by juggling sets of balls with each other). In both conditions, there were two task difficulties: "easy" and "hard." Descriptive analyses revealed that engaging in a dyadic cooperative motor task (interactive condition) required greater physiological effort (Median Cohen's d = 2.13) than performing a solo motor task (individual condition) of similar difficulty. Our results indicated a strong positive correlation between the jugglers' heart rate for the easy (r = .87) and hard tasks (r = .77). The relationship between the jugglers' breathing rate was significant for the easy task (r = .73) but non-significant for the hard task. The findings are interpreted based on research on SMM and Theory of Mind. Practitioners should advance the notion of "shared-regulation" in the context of team coordination through the use of biofeedback training.
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 | 0.003 | 0.015 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".