HIIT the Road Jack: An Exploratory Study on the Effects of an Acute Bout of Cardiovascular High-Intensity Interval Training on Piano Learning
Bibliographic record
Abstract
Pairing high-intensity interval training (HIIT) with motor skill acquisition may improve learning of some implicit motor sequences (albeit with some variability), but it is unclear if HIIT enhances explicit learning of motor sequences. We asked whether a single bout of HIIT after non-musicians learned to play a piano melody promoted better retention of the melody than low-intensity interval training (LIIT). Further, we investigated whether HIIT facilitated transfer of learning to a new melody. We generated individualized exercise protocols by having participants (n=25) with little musical training undergo a graded maximal exercise test to determine their cardiorespiratory fitness (VO2peak) and maximal power output. In a subsequent session, participants practiced a piano melody (skill acquisition) and were randomly assigned to a single bout of HIIT or LIIT. Retention of the piano melody was tested one hour, one day, and one week after skill acquisition. We also evaluated transfer to learning a new melody one week after acquisition. Pitch and rhythm accuracy were analyzed with linear mixed-effects modeling. HIIT did not enhance sequence-specific retention of pitch or rhythmic elements of the piano melody, but there was modest evidence that HIIT facilitated transfer to learning a new melody. We tentatively conclude that HIIT enhances explicit, task-general motor consolidation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".