Human Tonotopic Maps and their Rapid Task-Related Changes Studied by Magnetic Source Imaging
Bibliographic record
Abstract
A brief review of previous studies is presented on tonotopic organization of primary auditory cortex (AI) in humans. Based on the place theory for pitch perception, in which place information from the cochlea is used to derive pitch, a well-organized layout of tonotopic map is likely in human AI. The conventional view of tonotopy in human AI is a layout inwhich the medial-to-lateral portion of Heschl's gyrus represents high-to-low frequency tones. However, we have shown that the equivalent current dipole (ECD) in auditory evoked magnetic fields in the rising phase of N100m response dynamically moves along the long axis of Heschl's gyrus. Based on analyses of the current sources for high-pitched and low-pitched tones in the right and left hemispheres, we propose an alternative tonotopic map in human AI. In the right AI, isofrequency bands for each tone frequency are parallell to the first transverse sulcus; on the other hand, the layout for tonotopy in the left AI seems poorly organized. The validity of single dipole modelling in the calculation of a moving source and the discrepancy as to tonotopic maps in the results between auditory evoked fields or intracerebral recordings and neuroimaging studies also are discussed. The difference in the layout of isofrequency bands between the right and left auditory cortices may reflect distinct functional roles in auditory information processing such as pitch versus phonetic analysis.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".