4/4 and more, rhythmic complexity more strongly predicts groove in common meters
Bibliographic record
Abstract
The pleasurable urge to move to music, termed "groove," is thought to arise from the tension between top-down metric expectations or predictions and rhythmic complexity. Specifically, groove ratings are highest for moderately complex rhythms, balancing expectation and surprise. To test this, meter and rhythmic complexity need to be manipulated independently to assess their impact on groove. Thus, we compared Western listeners' ratings for musical clips of varying rhythmic complexity composed in either the most common Western meter (4/4) or less common meters (e.g., 7/8). In several behavioral studies (Experiment 1, N = 143; Experiment 2, N = 120; Experiment 3, N = 120), we used Bayesian regression to show that groove is greatest for moderately complex rhythms, but only in 4/4. In non-4/4 meters, simpler rhythms elicited the greatest groove. This provides support for the theory that bottom-up rhythmic features interact with meter in a way that shapes the pleasurable urge to move to music.
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 | 0.004 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".