Neural Responses to Repeated Noise Structure in Sounds Are Invariant to Temporal Interruptions
Bibliographic record
Abstract
The ability to extract meaning from acoustic environments requires sensitivity to repeating sound structures. Yet, how events that repeat are encoded and maintained in the brain and how the brain responds to events that reoccur at later points in time is not well understood. In two electroencephalography experiments, participants listened to a longer, ongoing white-noise sound which comprised shorter, frozen noise snippets that repeated at a regular 2-Hz rate. In several conditions, the snippet repetition discontinued for a brief period after which the noise snippet reoccurred. The experiments aimed to answer whether neural activity becomes entrained by the regular repetition of noise snippets, whether entrained neural activity self-sustains during the discontinuation period, and how the brain responds to a reoccurring noise snippet. Results show that neural activity is entrained by the snippet repetition, but there was no evidence for self-sustained neural activity during the discontinuation period. However, the auditory cortex responded with similar magnitude to a noise snippet reoccurring after a brief discontinuation as it responded to a noise snippet for which the snippet repetition had not been discontinued. This response invariance was observed for different onset times of the reoccurring noise snippet relative to the previously established regularity. The results thus demonstrate that the auditory cortex sensitively responds to, and thus maintains a memory trace of, previously learned acoustic noise independent of temporal interruptions.
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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 | 0.000 |
| 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".