Spectro-temporal modulation transfer function of single voxels in the human auditory cortex measured with high-resolution fMRI
Bibliographic record
Abstract
Are visual and auditory stimuli processed by similar mechanisms in the human cerebral cortex? Images can be thought of as light energy modulations over two spatial dimensions, and low-level visual areas analyze images by decomposition into spatial frequencies. Similarly, sounds are energy modulations over time and frequency, and they can be identified and discriminated by the content of such modulations. An obvious question is therefore whether human auditory areas, in direct analogy to visual areas, represent the spectro-temporal modulation content of acoustic stimuli. To answer this question, we measured spectro-temporal modulation transfer functions of single voxels in the human auditory cortex with functional magnetic resonance imaging. We presented dynamic ripples, complex broadband stimuli with a drifting sinusoidal spectral envelope. Dynamic ripples are the auditory equivalent of the gratings often used in studies of the visual system. We demonstrate selective tuning to combined spectro-temporal modulations in the primary and secondary auditory cortex. We describe several types of modulation transfer functions, extracting different spectro-temporal features, with a high degree of interaction between spectral and temporal parameters. The overall low-pass modulation rate preference of the cortex matches the modulation content of natural sounds. These results demonstrate that combined spectro-temporal modulations are represented in the human auditory cortex, and suggest that complex signals are decomposed and processed according to their modulation content, the same transformation used by the visual system.
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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 | 0.002 |
| 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".