Music-reading expertise associates with face but not Chinese character processing ability
Bibliographic record
Abstract
A growing number of behavioural and neuroimaging studies have investigated the cognitive mechanisms and neural substrates underlying various forms of visual expertise, such as face and word processing. However, it remains poorly understood whether and to what extent the acquisition of one form of expertise would be associated with that of another. The current study examined the relationship between music-reading expertise and face and Chinese character processing abilities. In a series of experiments, music experts and novices performed discrimination and recognition tasks of musical notations, faces, and words. Results consistently showed that musical experts responded more accurately to musical notations and faces, but not to words, than did musical novices. More intriguingly, the music expert's age of training onset could well predict their face but not word processing performance: the earlier musical experts began musical notation reading, the better their face processing performance. Taken together, our findings provide preliminary and converging evidence that music-reading expertise links with face, but not word, processing, and lend support to the notion that the development of different types of visual expertise may not be independent, but rather interact with each other during their acquisition.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".