Neural signatures of stream segregation: From childhood to adulthood
Bibliographic record
Abstract
ABSTRACT When faced with noisy environments, listeners perform auditory scene analysis, which allows them to parse the auditory target from concurrent interferences. Stream segregation involves organizing similar sound waves into a coherent stream, while distinguishing dissimilar acoustic components and attributing them to distinct sources. Two event-related potential components have been identified as “neural signatures” of stream segregation: the Object-Related Negativity (ORN) and the P400. Our study aims to examine (i) the maturation of neural and behavioural correlates of stream segregation and (ii) the development of the relationship between stream segregation and speech perception in noise. ORN/P400 were recorded while 8-23 year-olds performed an active stream segregation task based on temporal coherence. Participants also performed speech identification in noise tasks (behaviourally). Behavioral results indicate an improvement in both stream segregation and speech perception in noise from childhood to adulthood. Amplitude of the ORN (but not P400) decreases, and latency of both ORN and P400 decreases throughout development. Critically, P400 amplitude significantly predicts stream segregation performance. Overall, our results suggest that the neural mechanisms underlying stream segregation follow a prolonged maturation trajectory, and support the progressive maturation of auditory scene analysis and speech perception in noise. RESEARCH HIGHLIGHTS - Neurophysiological indices of stream segregation mature from childhood to adulthood - Neural indices of top-down attentive processing of complex auditory sequences predict stream segregation, irrespective of the listeners age - In adults (but not in children or adolescents), stream segregation predicts speech perception in noise - Stream segregation may thus operate as a bottleneck to speech in noise difficulties in children and adolescents
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".