Rhythmic and non-rhythmic auditory precues: Multiple mechanisms mediating movement performance
Bibliographic record
Abstract
Rhythmic auditory stimuli presented before a goal-directed movement have been found to improve temporal and spatial movement outcomes. However, little is known about the mechanisms mediating these benefits. The present experiment used three types of auditory stimuli to probe how improved scaling of movement parameters, temporal preparation and an external focus of attention may contribute to changes in movement performance. Three types of auditory stimuli were presented for 1200 ms before movement initiation; three metronome beats (RAS), a tone that stayed the same (tone-same), a tone that increased in pitch (tone-change) and a no sound control, were presented with and without visual feedback for a total of eight experimental conditions. The sound was presented before a visual go-signal, and participants were instructed to reach quickly and accurately to one of two targets randomly identified in left and right hemispace. Twenty-two young adults completed 24 trials per blocked condition in a counterbalanced order. Movements were captured with an Optotrak 3D Investigator, and a 4(sound) by 2(vision) repeated measures ANOVA was used to analyze dependant variables. All auditory conditions had shorter reaction times than no sound. Tone-same and tone-change conditions had shorter movement times and higher peak velocities, with no change in trajectory variability or endpoint error. Therefore, rhythmic and non-rhythmic auditory stimuli impacted movement performance differently. Based on the pattern of results we propose multiple mechanisms impact movement planning processes when rhythmic auditory stimuli are present.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".