Markers of musical expertise in a sight-reading task: An eye-tracking study.
Bibliographic record
Abstract
Classical music pianists of five different conservatory levels, from undergraduate to professional, were tested on a sight-reading task with eye-movement recording. They had to sight read both tonal classical scores that followed the rules specific to Western tonal music, and atonal contemporary scores, which do not follow these rules. This study aimed at determining the extent to which eye movements and musical performance metrics can account for the level of sight-reading expertise. First, the results indicated that with the acquisition of expertise, musicians process visual information more rapidly (increasing their played tempo while decreasing average fixation duration and their number of fixations), more structurally (tending to increase their eye-hand span), and more accurately (increasing their sight-reading accuracy). Second, when they sight read contemporary scores compared to classical scores, musicians decreased their played tempo, tended to be less accurate, increased their number of fixations, and tended to decrease their eye-hand span. Finally, expertise effects were moderated by the type of score. These results suggest (a) that visual perception is progressively shaped through music reading expertise and through domain-specific knowledge acquisition, (b) that tonal-specific cues play a significant role to use an efficient eye-movement behavior and (c) that the benefit conferred by expert prior music-specific knowledge seems to be even greater for sight-reading tonal rather than atonal scores. Our findings are discussed in the light of expert memory theories (long-term working memory theory; Ericsson & Kintsch, 1995; template theory, Gobet & Simon, 1996). (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".