Tonotopic representation of missing fundamental complex sounds in the human auditory cortex
Bibliographic record
Abstract
The N1m component of the auditory evoked magnetic field in response to tones and complex sounds was examined in order to clarify whether the tonotopic representation in the human secondary auditory cortex is based on perceived pitch or the physical frequency spectrum of the sound. The investigated stimulus parameters were the fundamental frequencies (F0 = 250, 500 and 1000 Hz), the spectral composition of the higher harmonics of the missing fundamental sounds (2nd to 5th, 6th to 9th and 10th to 13th harmonic) and the frequencies of pure tones corresponding to F0 and to the lowest component of each complex sound. Tonotopic gradients showed that high frequencies were more medially located than low frequencies for the pure tones and for the centre frequency of the complex tones. Furthermore, in the superior-inferior direction, the tonotopic gradients were different between pure tones and complex sounds. The results were interpreted as reflecting different processing in the auditory cortex for pure tones and complex sounds. This hypothesis was supported by the result of evoked responses to complex sounds having longer latencies. A more pronounced tonotopic representation in the right hemisphere gave evidence for right hemispheric dominance in spectral processing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".