Auditory Coding of Reaching Space
Bibliographic record
Abstract
Reaching movements are usually initiated by visual events and controlled visually and kinesthetically. Lately, studies have focused on the possible benefit of auditory information for localization tasks, and also for movement control. This explorative study aimed to investigate if it is possible to code reaching space purely by auditory information. Therefore, the precision of reaching movements to merely acoustically coded target positions was analyzed. We studied the efficacy of acoustically effect-based and of additional acoustically performance-based instruction and feedback and the role of visual movement control. Twenty-four participants executed reaching movements to merely acoustically presented, invisible target positions in three mutually perpendicular planes in front of them. Effector-endpoint trajectories were tracked using inertial sensors. Kinematic data regarding the three spatial dimensions and the movement velocity were sonified. Thus, acoustic instruction and real-time feedback of the movement trajectories and the target position of the hand were provided. The subjects were able to align their reaching movements to the merely acoustically instructed targets. Reaching space can be coded merely acoustically, additional visual movement control does not enhance reaching performance. On the basis of these results, a remarkable benefit of kinematic movement acoustics for the neuromotor rehabilitation of everyday motor skills can be assumed.
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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 | 0.004 |
| 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 | 0.001 |
| 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".