Cortical processing of distracting speech in noisy auditory scenes depends on perceptual demand
Bibliographic record
Abstract
Selective attention is essential for the processing of multi-speaker auditory scenes because they require the perceptual segregation of the relevant speech ("target") from irrelevant speech ("distractors"). For simple sounds, it has been suggested that the processing of multiple distractor sounds depends on bottom-up factors affecting task performance. However, it remains unclear whether such dependency applies to naturalistic multi-speaker auditory scenes. In this study, we tested the hypothesis that increased perceptual demand (the processing requirement posed by the scene to separate the target speech) reduces the cortical processing of distractor speech thus decreasing their perceptual segregation. Human participants were presented with auditory scenes including three speakers and asked to selectively attend to one speaker while their EEG was acquired. The perceptual demand of this selective listening task was varied by introducing an auditory cue (interaural time differences, ITDs) for segregating the target from the distractor speakers, while acoustic differences between the distractors were matched in ITD and loudness. We obtained a quantitative measure of the cortical segregation of distractor speakers by assessing the difference in how accurately speech-envelope following EEG responses could be predicted by models of averaged distractor speech versus models of individual distractor speech. In agreement with our hypothesis, results show that interaural segregation cues led to improved behavioral word-recognition performance and stronger cortical segregation of the distractor speakers. The neural effect was strongest in the δ-band and at early delays (0 - 200 ms). Our results indicate that during low perceptual demand, the human cortex represents individual distractor speech signals as more segregated. This suggests that, in addition to purely acoustical properties, the cortical processing of distractor speakers depends on factors like perceptual demand.
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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 | 0.000 | 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".