Preliminary Investigation of the Passively Evoked N400 as a Tool for Estimating Speech-in-Noise Thresholds
Bibliographic record
Abstract
PURPOSE: Speech-in-noise testing relies on a number of factors beyond the auditory system, such as cognitive function, compliance, and motor function. It may be possible to avoid these limitations by using electroencephalography. The present study explored this possibility using the N400. METHOD: Eleven adults with typical hearing heard high-constraint sentences with congruent and incongruent terminal words in the presence of speech-shaped noise. Participants ignored all auditory stimulation and watched a video. The signal-to-noise ratio (SNR) was varied around each participant's behavioral threshold during electroencephalography recording. Speech was also heard in quiet. RESULTS: The amplitude of the N400 effect exhibited a nonlinear relationship with SNR. In the presence of background noise, amplitude decreased from high (+4 dB) to low (+1 dB) SNR but increased dramatically at threshold before decreasing again at subthreshold SNR (-2 dB). CONCLUSIONS: The SNR of speech in noise modulates the amplitude of the N400 effect to semantic anomalies in a nonlinear fashion. These results are the first to demonstrate modulation of the passively evoked N400 by SNR in speech-shaped noise and represent a first step toward the end goal of developing an N400-based physiological metric for speech-in-noise testing.
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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 | 0.006 |
| 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) | 0.002 | 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".