Undetectable very-low frequency sound increases dancing at a live concert
Bibliographic record
Abstract
Does low frequency sound (bass) make people dance more? Music that makes people want to move tends to have more low frequency sound, and bass instruments typically provide the musical pulse that people dance to1. Low pitches confer advantages in perception and movement timing, and elicit stronger neural responses for timing compared to high pitches2, suggesting superior sensorimotor communication. Low frequency sound is processed via vibrotactile3 and vestibular4 (in addition to auditory) pathways, and stimulation of these non-auditory modalities in the context of music can increase ratings of groove (the pleasurable urge to move to music)3, and modulate musical rhythm perception4. Anecdotal accounts describe intense physical and psychological effects of low frequencies, especially in electronic dance music5, possibly reflecting effects on physiological arousal. However, we do not know if these associations extend to direct causal effects of low frequencies in complex, real-world, social contexts like dancing at concerts, or if low frequencies that are not consciously detectable can affect behaviour. We tested whether non-auditory low-frequency stimulation would increase audience dancing by turning very-low frequency (VLF) speakers on and off during a live electronic music concert and measuring audience members’ movements using motion-capture. Movement increased when VLFs were present, and because the VLFs were below or near auditory thresholds (and a subsequent experiment suggested they were undetectable), we believe this represents an unconscious effect on behaviour, possibly via vestibular and/or tactile processing.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".