Musicians at the Cocktail Party: Neural Substrates of Musical Training During Selective Listening in Multispeaker Situations
Bibliographic record
Abstract
Musical training has been demonstrated to benefit speech-in-noise perception. It is however unknown whether this effect translates to selective listening in cocktail party situations, and if so what its neural basis might be. We investigated this question using magnetoencephalography-based speech envelope reconstruction and a sustained selective listening task, in which participants with varying amounts of musical training attended to 1 of 2 speech streams while detecting rare target words. Cortical frequency-following responses (FFR) and auditory working memory were additionally measured to dissociate musical training-related effects on low-level auditory processing versus higher cognitive function. Results show that the duration of musical training is associated with a reduced distracting effect of competing speech on target detection accuracy. Remarkably, more musical training was related to a robust neural tracking of both the to-be-attended and the to-be-ignored speech stream, up until late cortical processing stages. Musical training-related increases in FFR power were associated with a robust speech tracking in auditory sensory areas, whereas training-related differences in auditory working memory were linked to an increased representation of the to-be-ignored stream beyond auditory cortex. Our findings suggest that musically trained persons can use additional information about the distracting stream to limit interference by competing speech.
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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".