Does masking alter the mismatch negativity response to gaps?
Bibliographic record
Abstract
OBJECTIVE: Difficulties perceiving speech in noise can be studied using temporal resolution measures. This study investigates the processing of silent gaps in the central auditory system in various noise conditions. METHODS: Event-related potentials and psychoacoustic thresholds were measured in 14 normal hearing adult subjects. A multi-deviant paradigm was used to present an 80 dB SPL standard stimulus and a series of gapped deviants with gap durations ranging from 2 to 40 ms. The stimuli were presented in three noise conditions in which the noise (i.e. the masker) was either absent, presented at a low intensity (60 dB SPL) or high intensity (80 dB SPL). A composite N1 + mismatch negativity called a deviant-related negativity (DRN) and the following positive component (P2) measured peak-to-peak were compared to behavioral accuracy rates. RESULTS: The amplitude of the DRN-P2 increased as gap duration increased in the no and low masking conditions. However, this was not the case in the high masking condition. Behavioral gap detection correlated with all masking conditions. However, with high intensity masking, the gap detection accuracy was reduced, and the peak-to-peak measurement was no longer able to reliably code the temporal aspects of the signal. CONCLUSIONS: This study demonstrates that high masking noise make the detection of gaps very difficult as demonstrated by using electrophysiology and behavioural measures. However, low levels of masking have little effect on the presence of the gap and the auditory system appears to withstand some level of noise with results similar to no masking at all.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".