Music training and inhibitory control: a multidimensional model
Bibliographic record
Abstract
Training programs aimed to improve cognitive skills have either yielded mixed results or remain to be validated. The limited benefits of such regimens are largely attributable to weak understanding of (1) how (and which) interventions provide the most cognitive improvements; and (2) how brain networks and neural mechanisms that underlie specific cognitive abilities can be modified selectively. Studies indicate that music training leads to robust and long-lasting benefits to behavior. Importantly, behavioral advantages conferred by music extend beyond perceptual abilities to even nonauditory functions, such as inhibitory control (IC) and its neural correlates. Alternative forms of arts engagement or brain training do not appear to yield such enhancements, which suggests that music uniquely taps into brain networks subserving a variety of auditory as well as domain-general mechanisms such as IC. To account for such widespread benefits of music training, we propose a framework of transfer effects characterized by three dimensions: level of processing, nature of the transfer, and involvement of executive functions. We suggest that transfer of skills is mediated through modulation of general cognitive processes, in particular IC. We believe that this model offers a viable framework to test the extent and limitations of music-related changes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".