Availability of envelope interaural time-difference cues does not improve front/back localization of narrow-band high-frequency targets via head movement
Bibliographic record
Abstract
Information about the front/rear location of a sound source is available in the relationship between the direction of head rotation and the direction of changes in interaural time and level differences (ITD and ILD). Our previous results show that such dynamic cues are highly effective for low-frequency stimuli, but minimally effective for narrowband high-frequency stimuli, in which, respectively, ITD and ILD cues are primarily available. In this study, we assessed the possible benefit for dynamic localization of providing more robust envelope ITD cues in high-frequency stimuli. Listeners judged the front/rear location of anechoic free-field stimuli presented over the central portion of a slow (~0.25 Hz), continual, 90-degree head oscillation. Stimuli were bursts of wideband (0.5–16 kHz), low-frequency (0.5–1 kHz), or high-frequency (6–6.5 kHz) random-phase noise or of raised-sine stimuli with exponent 2, modulation frequency 125 Hz, and bandwidth 6–6.5 kHz. Localization accuracy was high for wideband and lowpass stimuli but poor (and similar) for high-frequency noise and raised-sine stimuli, despite listeners' measured ITD JNDs for raised-sine stimuli being significantly lower than for high-frequency noise. The results suggest that neither veridical dynamic ILD nor ITD cues can overcome the erroneous spectral cue for front/back created by narrowband high-frequency stimuli.
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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".