Cue weighting and vestibular mediation of temporal dynamics in sound localization via head rotation
Bibliographic record
Abstract
Our studies have quantified the salience and weighting of dynamic acoustic cues in sound localization via head rotation. Results support three key findings: 1) low-frequency interaural time-difference (ITD) is the dominant dynamic binaural difference cue; 2) dynamic cues dominate front/rear localization only when spectral cues are unavailable; and 3) the temporal dynamics of dynamic cue processing are particular to auditory-vestibular integration. ITD dominance is shown indirectly in findings that head movements are highly effective for localizing low-frequency targets but not narrow-band high-frequency targets. Direct evidence comes from manipulation of dynamic binaural cues in spherical-head simulations lacking spectral cues. If the stimulus provides access to dominant high-frequency spectral cues, location illusions involving head-coupled source motion fail. For low-frequency targets, localization performance improves with increasing head-turn angle, but decreases with increasing velocity such that performance depends primarily on stimulus duration; ~100 ms being required for accurate front/back localization. That duration threshold only applies in dynamic localization tasks, and not in auditory-only tasks involving similar stimuli. Correct spatial interpretation of dynamic acoustic cues appears to require vestibular information about head motion, thus the temporal threshold is likely a property of vestibular-auditory integration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| 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) | 0.000 | 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 teacher head, 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".