Collective music listening: Movement energy is enhanced by groove and visual social cues
Bibliographic record
Abstract
; the higher the groove, the more it induces the desire to move. We investigated questions related to collective music-listening among 33 participants in an experiment conducted in a naturalistic yet acoustically controlled setting of a research concert hall with motion tracking. First, does higher groove music induce (1) movement with more energy and (2) higher interpersonal movement coordination? Second, does visual social information manipulated by having eyes open or eyes closed also affect energy and coordination? Participants listened to pieces from four categories formed by crossing groove (high, low) with tempo (higher, lower). Their upper body movement was recorded via head markers. Self-reported ratings of grooviness, emotional valence, emotional intensity, and familiarity were collected after each song. A biomechanically motivated measure of movement energy increased with high-groove songs and was positively correlated with grooviness ratings, confirming the theoretically implied but less tested motor response to groove. Participants' ratings of emotional valence and emotional intensity correlated positively with movement energy, suggesting that movement energy relates to emotional engagement with music. Movement energy was higher in eyes-open trials, suggesting that seeing each other enhanced participants' responses, consistent with social facilitation or contagion. Furthermore, interpersonal coordination was higher both for the high-groove and eyes-open conditions, indicating that the social situation of collective music listening affects how music is experienced.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".