The three-channel model of sound localization mechanisms: Interaural level differences
Bibliographic record
Abstract
The current understanding of mammalian sound localization is that azimuthal (horizontal) position assignments are dependent upon the relative activation of two populations of broadly-tuned hemifield neurons with overlapping medial borders. Recent psychophysical work has provided evidence for a third channel of low-frequency interaural time difference (ITD)-sensitive neurons tuned to the azimuthal midline. However, the neurophysiological data on free-field azimuth receptive fields, especially of cortical neurons, has primarily studied high-frequency cells whose receptive fields are more likely to have been shaped by interaural level differences (ILDs) than ITDs. In four experiments, a selective adaptation paradigm was used to probe for the existence of a midline channel in the domain of ILDs. If no midline channel exists, symmetrical adaptation of the lateral channels should not result in a shift in the perceived intracranial location of subsequent test tones away from the adaptors because the relative activation of the two channels will remain unchanged. Instead, results indicate a shift in perceived test tone location away from the adaptors, which supports the existence of a midline channel in the domain of ILDs. Interestingly, this shift occurs not only at high frequencies, traditionally associated with ILDs in natural settings, but at low frequencies as well.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".