Tap, Move, or Dance? How Groove Ratings Differ Across Movement Descriptors
Bibliographic record
Abstract
Groove, defined as the pleasurable urge to move to music, is affected by properties of the music as well as factors that differ among individuals, such as music training. Ratings of the desire to produce movement (e.g., tapping and dancing) have been used to quantify groove, but we do not know whether the specific type of movement that is rated affects scores. Further, few studies have considered the effect of dance training on groove perception. Therefore, the current paper investigates whether rating different types of movements (i.e., tapping vs. dancing) affects groove ratings, and how dance experience may alter these ratings. The first study used a within-subject design, with participants rating forty unfamiliar songs on their elicited desire to tap, desire to move, and desire to dance. To test whether joint rating affected responses, a between-subjects study had each group rate only one movement descriptor. In both studies, ratings of groove differed based on the type of movement rated with desire to dance ratings lower than move or tap ratings across both studies. In the first study, dance training influenced desire to move and desire to dance ratings, while music training influenced desire to tap ratings. However, these findings were not replicated in Study 2. Overall, the findings suggest that groove ratings differ based on the type of movement rated, that within- versus between-subject designs affect these ratings, and that dance and music training differentially affect different groove responses.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".