The role of working memory in the temporal control of discrete and continuous movements
Bibliographic record
Abstract
Music performance requires precise control of limb movements in order to achieve temporal precision of performed tone onsets. Previous findings suggest that processes recruited for the temporal control of rhythmic body movements, such as those required in music performance, depend on the movement type (discrete vs. continuous) and the rate of the produced interonset intervals (sub-second vs. supra-second). Using a dual-task paradigm, the current study addressed these factors in the temporal control of cellists' bowing movements. Cellists performed melodies in a synchronization-continuation timing task at a specified fast (intertone interval = 700 ms) or slow (intertone interval = 1,100 ms) tempo with either discrete (staccato) or continuous (legato) bowing movements. A secondary working memory task involved a concurrent digit-switch counting task. Analyses of the produced tone durations showed that the working memory load significantly impaired temporal regularity when the melodies were performed with discrete bowing movements at the slower tempo. In addition, discrete movements led to more errors on the working memory task. These findings suggest that continuous body movements provide temporal control information to performers under high cognitive load conditions.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".