Ultra-responsive, low-dimensional unfamiliar movement sonification guides unconstrained reaches to invisible targets in 3D space
Bibliographic record
Abstract
Recent studies suggest that artificial auditory feedback, called movement sonification, can function as sensory substitution in motor control, replacing or augmenting vision and proprioception. To a first approximation, studies either apply (1) natural sonifications (exploiting ecological spatial encodings or action-sound associations) of kinematic variables, e.g. velocity, to high (≥3) degree of freedom movements, or (2) nonnatural, i.e. unfamiliar, sonifications including spatial position to low degree of freedom movements. To the best of our knowledge, no one has shown that unfamiliar sonifications of spatial position can be used to guide high degree of freedom movements. We reasoned that an ultra-responsive (1-2ms latency, 1000Hz sampling) sonification compacting 3D spatial information into a low (<3) number of acoustic dimensions would enable spatial guidance of a high degree of freedom movement. We constructed such a movement sonification system using a bespoke digital sound synthesis technique (two-timer pulse-width modulation), real-time time-warping algorithm, and unaligned quaternion representations of body limb rotation. We used the system to sonify unconstrained reaches in space in a way that indicated invisible targets. We validated the system with hardware benchmarking and validated the approach by showing that users were able to reach for targets presented in sonification with approximately half the error of either (a) reaching while listening to qualitatively similar auditory feedback lacking spatial information, or (b) reaching randomly while listening to a constant tone.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".