Noise‐induced increase in human auditory evoked neuromagnetic fields
Bibliographic record
Abstract
Noise is usually detrimental to auditory perception. However, recent psychophysical studies have shown that low levels of broadband noise may improve signal detection. Here, we measured auditory evoked fields (AEFs) while participants listened passively to low-pitched and high-pitched tones (Experiment 1) or complex sounds that included a tuned or a mistuned component that yielded the perception of concurrent sound objects (Experiment 2). In both experiments, stimuli were embedded in low or intermediate levels of Gaussian noise or presented without background noise. For each participant, the AEFs were modeled with a pair of dipoles in the superior temporal plane, and the effects of noise were examined on the resulting source waveforms. In both experiments, the N1m was larger when the stimuli were embedded in low background noise than in the no-noise control condition. Complex sounds with a mistuned component generated an object-related negativity that was larger in the low-noise condition. The results show that low-level background noise facilitates AEFs associated with sound onset and can be beneficial for sorting out concurrent sound objects. We suggest that noise-induced increases in transient evoked responses may be mediated via efferent feedback connections between the auditory cortex and lower auditory centers.
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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 | 0.000 | 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 | 0.000 |
| 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".