Sensitivity to Auditory Object Features in Human Temporal Neocortex
Bibliographic record
Abstract
This positron emission tomography study examined the hemodynamic response of the human brain to auditory object feature processing. A continuum of object feature variation was created by combining different numbers of stimuli drawn from a diverse sample of 45 environmental sounds. In each 60 sec scan condition, subjects heard either a distinct individual sound on each trial or simultaneous combinations of sounds that varied systematically in their similarity or distinctiveness across conditions. As more stimuli are combined they become more similar and less distinct from one another; the limiting case is when all 45 are added together to form a noise that is repeated on each trial. Analysis of covariation of cerebral blood flow elicited by this parametric manipulation revealed a response in the upper bank of the right anterior superior temporal sulcus (STS): when sounds were identical across trials (i.e., a noise made up of 45 sounds), activity was at a minimum; when stimuli were different from one another, activity was maximal. A right inferior frontal area was also revealed. The results are interpreted as reflecting sensitivity of this region of temporal neocortex to auditory object features, as predicted by neurophysiological and anatomical models implicating an anteroventral functional stream in object processing. The findings also fit with evidence that voice processing may involve regions within the anterior STS. The data are discussed in light of these models and are related to the concept that this functional stream is sensitive to invariant sound features that characterize individual auditory objects.
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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".