Different neural activities support auditory working memory in musicians and bilinguals
Bibliographic record
Abstract
Musical training and bilingualism benefit executive functioning and working memory (WM)-however, the brain networks supporting this advantage are not well specified. Here, we used functional magnetic resonance imaging and the n-back task to assess WM for spatial (sound location) and nonspatial (sound category) auditory information in musician monolingual (musicians), nonmusician bilinguals (bilinguals), and nonmusician monolinguals (controls). Musicians outperformed bilinguals and controls on the nonspatial WM task. Overall, spatial and nonspatial WM were associated with greater activity in dorsal and ventral brain regions, respectively. Increasing WM load yielded similar recruitment of the anterior-posterior attention network in all three groups. In both tasks and both levels of difficulty, musicians showed lower brain activity than controls in superior prefrontal frontal gyrus and dorsolateral prefrontal cortex (DLPFC) bilaterally, a finding that may reflect improved and more efficient use of neural resources. Bilinguals showed enhanced activity in language-related areas (i.e., left DLPFC and left supramarginal gyrus) relative to musicians and controls, which could be associated with the need to suppress interference associated with competing semantic activations from multiple languages. These findings indicate that the auditory WM advantage in musicians and bilinguals is mediated by different neural networks specific to each life experience.
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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) | 0.002 | 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".