Representation of concurrent acoustic objects in primary auditory cortex
Bibliographic record
Abstract
Auditory scene analysis involves the simultaneous grouping and parsing of acoustic data into separate mental representations (i.e., objects). Over two experiments, we examined the sequence of neural processes underlying concurrent sound segregation by means of recording of human middle latency auditory evoked responses. Participants were presented with complex sounds comprising several harmonics, one of which could be mistuned such that it was not an integer multiple of the fundamental frequency. In both experiments, Na (approximately 22 ms) and Pa (approximately 32 ms) waves were reliably generated for all classes of stimuli. For stimuli with a fundamental frequency of 200 Hz, the mean Pa amplitude was significantly larger when the third harmonic was mistuned by 16% of its original value, relative to when it was tuned. The enhanced Pa amplitude was related to an increased likelihood in reporting the presence of concurrent auditory objects. Our results are consistent with a low-level stage of auditory scene analysis in which acoustic properties such as mistuning act as preattentive segregation cues that can subsequently lead to the perception of multiple 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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".