Adaptation reveals multiple levels of representation in auditory stream segregation.
Bibliographic record
Abstract
When presented with alternating low and high tones, listeners are more likely to perceive 2 separate streams of tones ("streaming") than a single coherent stream when the frequency separation (Deltaf) between tones is greater and the number of tone presentations is greater ("buildup"). However, the same large-Deltaf sequence reduces streaming for subsequent patterns presented after a gap of up to several seconds. Buildup occurs at a level of neural representation with sharp frequency tuning. The authors used adaptation to demonstrate that the contextual effect of prior Deltaf arose from a representation with broad frequency tuning, unlike buildup. Separate adaptation did not occur in a representation of Deltaf independent of frequency range, suggesting that any frequency-shift detectors undergoing adaptation are also frequency specific. A separate effect of prior perception was observed, dissociating stimulus-related (i.e., Deltaf) and perception-related (i.e., 1 stream vs. 2 streams) adaptation. Viewing a visual analogue to auditory streaming had no effect on subsequent perception of streaming, suggesting adaptation in auditory-specific brain circuits. These results, along with previous findings on buildup, suggest that processing in at least 3 levels of auditory neural representation underlies segregation and formation of auditory streams.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".