The Impact of Musicianship on the Cortical Mechanisms Related to Separating Speech from Background Noise
Bibliographic record
Abstract
Musicians have enhanced auditory processing abilities. In some studies, these abilities are paralleled by an improved understanding of speech in noisy environments, partially due to more robust encoding of speech signals in noise at the level of the brainstem. Little is known about the impact of musicianship on attention-dependent cortical activity related to lexical access during a speech-in-noise task. To address this issue, we presented musicians and nonmusicians with single words mixed with three levels of background noise, across two conditions, while monitoring electrical brain activity. In the active condition, listeners repeated the words aloud, and in the passive condition, they ignored the words and watched a silent film. When background noise was most intense, musicians repeated more words correctly compared with nonmusicians. Auditory evoked responses were attenuated and delayed with the addition of background noise. In musicians, P1 amplitude was marginally enhanced during active listening and was related to task performance in the most difficult listening condition. By comparing ERPs from the active and passive conditions, we isolated an N400 related to lexical access. The amplitude of the N400 was not influenced by the level of background noise in musicians, whereas N400 amplitude increased with the level of background noise in nonmusicians. In nonmusicians, the increase in N400 amplitude was related to a reduction in task performance. In musicians only, there was a rightward shift of the sources contributing to the N400 as the level of background noise increased. This pattern of results supports the hypothesis that encoding of speech in noise is more robust in musicians and suggests that this facilitates lexical access. Moreover, the shift in sources suggests that musicians, to a greater extent than nonmusicians, may increasingly rely on acoustic cues to understand speech in noise.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".