Frequency-specific modulation of population-level frequency tuning in human auditory cortex
Bibliographic record
Abstract
BACKGROUND: Under natural circumstances, attention plays an important role in extracting relevant auditory signals from simultaneously present, irrelevant noises. Excitatory and inhibitory neural activity, enhanced by attentional processes, seems to sharpen frequency tuning, contributing to improved auditory performance especially in noisy environments. In the present study, we investigated auditory magnetic fields in humans that were evoked by pure tones embedded in band-eliminated noises during two different stimulus sequencing conditions (constant vs. random) under auditory focused attention by means of magnetoencephalography (MEG). RESULTS: In total, we used identical auditory stimuli between conditions, but presented them in a different order, thereby manipulating the neural processing and the auditory performance of the listeners. Constant stimulus sequencing blocks were characterized by the simultaneous presentation of pure tones of identical frequency with band-eliminated noises, whereas random sequencing blocks were characterized by the simultaneous presentation of pure tones of random frequencies and band-eliminated noises. We demonstrated that auditory evoked neural responses were larger in the constant sequencing compared to the random sequencing condition, particularly when the simultaneously presented noises contained narrow stop-bands. CONCLUSION: The present study confirmed that population-level frequency tuning in human auditory cortex can be sharpened in a frequency-specific manner. This frequency-specific sharpening may contribute to improved auditory performance during detection and processing of relevant sound inputs characterized by specific frequency distributions in noisy environments.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".