On the role of visual information about head motion in the interpretation of dynamic interaural cues for front/back sound localization
Bibliographic record
Abstract
The dynamic interaural cues generated by listener head rotation specify the front/back location of a sound source only when coupled with information about the head motion that produced them. Potential sources of this information are vestibular, proprioceptive, and visual inputs. To investigate the influence of these extra-auditory modalities on the interpretation of dynamic acoustic cues, we use real-time motion tracking and dynamic virtual auditory space synthesis in conjunction with an oscillating chair apparatus that permits dissociation of head-on-body and head-in-space motion. The results of a previous study conducted in darkness [Kim, Barnett-Cowan, & Macpherson, ICA 2013], suggest that vestibular (head-in-space) information is necessary and sufficient for accurate interpretation of dynamic cues, whereas proprioceptive (head-on-body) information is neither necessary nor sufficient. In the present study, active (providing vestibular and proprioceptive information) or passive (providing primarily vestibular information) head-in-space rotations at 50°/s were combined with congruent or incongruent visual motion information in a task requiring the identification of the front/back location of 200- or 400-ms low-pass noise targets. Experiments were conducted in a lighted room, and incongruent visual stimuli were produced with left/right-reversing prism glasses providing ~30° field of view. We found that this visual input had little or no influence on listeners’ interpretation of the dynamic auditory cues.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".