The Human Cochlear Mechanical Nonlinearity Inferred via Psychometric Functions
Bibliographic record
Abstract
Schairer and colleagues hypothesized that the slope of any psychometric function for forward-masked probe-tone detection depends upon the standard deviation of an external Gaussian input distribution of probe-tone intensities, which in turn reflects the coupling of an internal Gaussian output distribution to the cochlear mechanical nonlinearity. The latter was postulated to have two conjoined branches, straight lines of differing slopes. Hence, just two possible standard deviations were predicted for the external input distributions, i.e., one for each branch of the nonlinearity – and therefore two different slopes of psychometric functions for forward-masked probe-tone detection. To confirm the latter, Schairer and colleagues obtained psychometric functions for the detection of forward-masked probe-tones. Such psychometric functions were already available, for detection thresholds of unprecedented precision which had been found as a function of either (1) time gap between probe-tone and same-frequency constant-intensity forward-masker (“recovery”), or (2) same-frequency forward-masker intensity at fixed masker-probe time gap (“growth”). Those psychometric functions were re-analyzed here because the model of Schairer and colleagues can be extended such that the hypothesized relation of a probe-tone’s psychometric-function slope to its detection threshold can be specified as an equation, through which psychometric-function slope becomes proportional to the cochlear nonlinearity’s own rate-of-change with intensity. The cochlear nonlinearity’s rate-of-change, for “recovery” data, follows an angle-shape, whereas for “growth” data it declines as a single power function; it does so also for re-analyzed probe-tone-detection thresholds of Schairer and colleagues. The equations for rates-of-change were integrated to give the cochlear nonlinearities themselves, each characterized by a single unknown parameter. The parameter’s possible values were implied by comparing the nonlinearity’s inferred rates- of-change in man to those measured in animals. Altogether, then, the human cochlear nonlinearities inferred from “recovery” have a distinct but smooth bend between two branches, a steep low-intensity branch and a shallow high-intensity branch, whereas those inferred from “growth” resemble the smoothly decelerating nonlinearities observed for animals. Extension of the model of Schairer and colleagues results in credible cochlear nonlinearities in man, suggesting that forward-masking provides a non-invasive way to infer the human mechanical cochlear nonlinearity.
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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 | 0.002 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".