Spontaneous otocoherence of the active ear
Bibliographic record
Abstract
Abstract Spontaneous otoacoustic emission (SOAE) provides compelling evidence of active force generation inside the inner ear, although there is significant debate about the underlying generation mechanism(s). SOAE is commonly characterized by peaks in a spectral domain representation (as derived from a discrete Fourier transform), occurring at idiosyncratic frequencies unique to a given ear. Such is typically computed as an averaged magnitude spectrum that discards phase information. Here, we explore the hypothesis that SOAE phase, readily extracted from pre-existing recordings, contains complementary information. We propose several measures to use this information to quantify otocoherence (a form of autocoherence referring to a phenomenon of the ear), primarily by measuring the consistency in SOAE phase accumulation over a particular timescale. We present results based on recordings from different species with disparate inner ear morphologies (humans, barn owls, lizards). For regions of SOAE activity we extract time constants representing the timescale over which otocoherence is maintained. We demonstrate that these vary significantly across species and (for the barn owl and Tokay gecko where this data is available) appear to correlate with measures of auditory nerve fiber tuning. Additionally, we adapted the method to identify regions of weak SOAE activity among fluctuations in the noise floor. These methods can readily be employed to re-analyze SOAE waveforms previously collected from a variety of species, making them of broader comparative utility to reveal information about SOAE generation and thereby active cochlear mechanics. Significance Statement We examined spontaneous otoacoustic emission, a widely accepted hallmark of active auditory biomechanics, and describe a novel approach to extract measures of its self-coherence. This was explored comparatively across different species of disparate inner ear morphology to provide insight into how active amplification operates in the presence of noise. Our approach elucidates the notion that what has been traditionally referred to as “spontaneous activity” is not unstructured noise, but fluctuations that reveal internal system dynamics. The methodology described here is straightforward to employ and can be applied to a variety of data types, both within and beyond auditory neuroscience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".