Auditory Working Memory Mediates the Relationship Between Musicianship and Auditory Stream Segregation
Bibliographic record
Abstract
Abstract This study investigates the interactions between musicianship and two auditory cognitive mechanisms: auditory working memory (AWM) and stream segregation. The primary hypothesis is that AWM mediates the relationship between musical training and enhanced stream segregation capabilities. Two groups of listeners were tested, the first to establish the relationship between the two variables and the second to replicate the effect in an independent sample. Music history and behavioural data were collected from a total of 145 healthy young adults with normal binaural hearing. They performed a task that requires manipulation of tonal patterns in working memory, and the Music-in-Noise Task (MINT), which measures stream segregation abilities in a musical context. The MINT task expands measurements beyond traditional Speech-in-Noise (SIN) assessments by capturing auditory subskills (e.g., rhythm, visual, spatial, prediction) relevant to stream segregation. Our results showed that musical training is associated with enhanced AWM and MINT task performance, and that this effect is replicable across independent samples. Moreover, we found in both samples that the enhancement of stream segregation was largely mediated by AWM capacity. The results suggest that musical training and/or aptitude enhances music-in-noise perception by way of improved AWM capacity.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".