DECRUITMENT OF THE PERCEPTION OF CHANGING SOUND INTENSITY FOR SIMULATED SELF-MOTION
Bibliographic record
Abstract
Of the many cues that could be used to gauge self-motion, auditory cues seem to be the least studied. Lis-teners could potentially use either a sweep of rising sound intensity to judge their self-motion towards an object or con-versely use a sweep of falling sound intensity to judge their motion away from an object. Whether the sweep is rising or falling the listener must judge both the change in inten-sity across the sweep, and the temporal span of the sweep. Studies indicate that sweeping intensities are misperceived so that the sound intensity at the end of the sweep is judged differently than when the final sound intensity is presented alone. Although there is ongoing discussion as to whether the induced fading is greater for rising sound intensity as op-posed to falling sound intensity, both phenomena affect the perception of self-motion. This paper presents a series of experiments that examined self-motion perception with au-ditory cues. Results confirm the finding of decruitment for a sweeping broadband sound source that decreases at vari-ous rates of acceleration. Furthermore, the phenomenon of decruitment was greatly diminished at higher accelerations indicating that this phenomenon is likely correlated to the lowest rate at which listeners can perceive a change in in-tensity. 1.
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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 | 0.000 |
| 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".