Into the Hive-Mind: Shared Absorption and Cardiac Interrelations in Expert and Student String Quartets
Bibliographic record
Abstract
Expert musicians portray awe-inspiring precision, timing, and phrasing and may be thought to partake in a “hive-mind.” Such a shared musical absorption is characterized by a heightened empathic relation, mutual trust, and a sense that the music “takes over,” thus uniting the performers’ musical intentions. Previous studies have found correlations between empathic concern or shared experience and cardiac synchrony (CS). We aimed to investigate shared musical absorption in terms of CS by analyzing CS in two quartets: a student quartet, the Borealis String Quartet (BSQ), and an expert quartet, the Danish String Quartet (DSQ), world-renowned for their interpretations and cohesion. These two quartets performed the same Haydn excerpt in seven conditions, some of which were designed to disrupt their absorption. Using multidimensional recurrence quantification analysis (MdRQA), we found that: (1) performing resulted in significantly increased CS in both quartets compared with resting; (2) across all conditions, the DSQ had a significantly higher CS than the BSQ; (3) the BSQ's CS was inversely correlated with the degree of disruption; 4) for the DSQ, the CS remained constant across all levels of disruption, besides one added extreme disruption—a sight-reading condition. These findings tentatively support the claim that a sense of shared musical absorption, as well as group expertise, is correlated with CS.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".