Can You Hear That Peak? Utilization of Auditory and Visual Feedback at Peak Limb Velocity
Bibliographic record
Abstract
PURPOSE: At rest, the central nervous system combines and integrates multisensory cues to yield an optimal percept. When engaging in action, the relative weighing of sensory modalities has been shown to be altered. Because the timing of peak velocity is the critical moment in some goal-directed movements (e.g., overarm throwing), the current study sought to test whether visual and auditory cues are optimally integrated at that specific kinematic marker when it is the critical part of the trajectory. METHODS: Participants performed an upper-limb movement in which they were required to reach their peak limb velocity when the right index finger intersected a virtual target (i.e., a flinging movement). Brief auditory, visual, or audiovisual feedback (i.e., 20 ms in duration) was provided to participants at peak limb velocity. Performance was assessed primarily through the resultant position of peak limb velocity and the variability of that position. RESULTS: Relative to when no feedback was provided, auditory feedback significantly reduced the resultant endpoint variability of the finger position at peak limb velocity. However, no such reductions were found for the visual or audiovisual feedback conditions. Further, providing both auditory and visual cues concurrently also failed to yield the theoretically predicted improvements in endpoint variability. CONCLUSIONS: Overall, the central nervous system can make significant use of an auditory cue but may not optimally integrate a visual and auditory cue at peak limb velocity, when peak velocity is the critical part of the trajectory.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".