Differences in the temporal course of interaural time difference sensitivity between acoustic and electric hearing in amplitude modulated stimuli
Bibliographic record
Abstract
Previous studies have shown that normal-hearing (NH) listeners' spatial perception of non-stationary interaural time differences (ITDs) is dominated by the carrier ITD during rising amplitude segments. Here, ITD sensitivity throughout the amplitude-modulation cycle in NH listeners and bilateral cochlear implant (CI) subjects is compared, the latter by means of direct stimulation of a single electrode pair. The data indicate that, while NH listeners are most sensitive to ITDs applied toward the beginning of a modulation cycle at 600 Hz, NH listeners at 200 Hz and especially bilateral CI subjects at 200 pulses per second (pps) are more sensitive to ITDs applied to the modulation maximum. This has implications for spatial-hearing in complex environments: NH listeners' dominant 600-Hz ITD information from the rising amplitude segments comprises direct sound information. The 200-pps low rate required to get ITD sensitivity in CI users results in a higher weight of pulses later in the modulation cycle where the source ITDs are more likely corrupted by reflections. This indirectly indicates that even if future binaural CI processors are able to provide perceptually exploitable ITD information, CI users will likely not get the full benefit from such pulse-based ITD cues in reverberant and other complex environments.
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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 | 0.003 |
| 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) | 0.003 | 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".