Decoding of Envelope vs. Fundamental Frequency During Complex Auditory Stream Segregation
Bibliographic record
Abstract
Hearing-in-noise perception is a challenging task that is critical to human function, but how the brain accomplishes it is not well understood. A candidate mechanism proposes that the neural representation of an attended auditory stream is enhanced relative to background sound via a combination of bottom-up and top-down mechanisms. To date, few studies have compared neural representation and its task-related enhancement across frequency bands that carry different auditory information, such as a sound's amplitude envelope (i.e., syllabic rate or rhythm; 1-9 Hz), and the fundamental frequency of periodic stimuli (i.e., pitch; >40 Hz). Furthermore, hearing-in-noise in the real world is frequently both messier and richer than the majority of tasks used in its study. In the present study, we use continuous sound excerpts that simultaneously offer predictive, visual, and spatial cues to help listeners separate the target from four acoustically similar simultaneously presented sound streams. We show that while both lower and higher frequency information about the entire sound stream is represented in the brain's response, the to-be-attended sound stream is strongly enhanced only in the slower, lower frequency sound representations. These results are consistent with the hypothesis that attended sound representations are strengthened progressively at higher level, later processing stages, and that the interaction of multiple brain systems can aid in this process. Our findings contribute to our understanding of auditory stream separation in difficult, naturalistic listening conditions and demonstrate that pitch and envelope information can be decoded from single-channel EEG data.
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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 | 0.003 |
| 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 | 0.000 |
| 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".