Can auditory brainstem and midbrain processing of interaural level difference cues really explain perceptual performance?
Bibliographic record
Abstract
Neurons in the lateral superior olive (LSO) are believed to be involved in processing of interaural level difference (ILD) cues for sound localization. However, the ILD-tuning of LSO neurons varies with the absolute sound pressure levels (SPLs) at the two ears, in contrast to the relatively robust perceptual processing of ILD cues at different absolute SPLs. Tsai et al. (J. Neurophysiol. 2010) proposed that if some inferior colliculus (IC) neurons compute the difference between the contralateral and ipsilateral LSO outputs, the dependencies on absolute SPLs could be canceled out. However, they only considered pure-tone stimulation of single neurons at their characteristic frequencies and at relatively low SPLs. In this study, we evaluated the hypothesis of Tsai and colleagues using computational models of populations of auditory nerve, LSO and IC neurons consistent with Tsai and colleagues’ proposal. Predictions from two different neural decoder algorithms applied to LSO and IC model outputs indicate that discharge-rate saturation and spread of excitation in the auditory nerve lead to even greater SPL-dependence of ILD coding across a population of LSO neurons than is apparent in single neurons, and the IC differencing operation proposed by Tsai and colleagues does relatively little to ameliorate this problem.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".