Saccadic and visuo-motor flexibility towards local parafoveal complexity as a hallmark of expert knowledge-driven processing during sight-reading of music
Bibliographic record
Abstract
Expertise is associated with a knowledge-driven information-processing approach. Experts benefit from long-term knowledge structures-chunks and retrieval structures/templates-leading them to formulate expectations about local stimulus characteristics and to extract information projected onto distant areas from the fixation location. In an attempt to shed light on the way knowledge-driven processing impacts eye movements during music reading, this study aimed to determine how expert musicians deal with local complexity in a sight-reading task. Thirty musicians from two expertise levels had to sight read 4 bar score excerpts. Local analyses were conducted to investigate how the gaze behaves prior to and during the sight reading of different score characteristics, such as alteration, location of the notes on the staff, note count, and heterogeneity of notes. The more experts (1) were less affected by the foveal load induced by local complexity, showing a lower increase in fixation durations between noncomplex features and local complexity compared to the less experts; (2) presented a saccadic flexibility towards the local complexity projected onto the parafoveal area, being the only group to exhibit shorter progressive incoming saccade sizes on accidentals and larger progressive incoming saccade sizes on new notes compared to noncomplex features; and (3) presented a visuo-motor flexibility depending on the played complexity, being the only group to exhibit a shorter eye-hand span when playing accidentals or distant notes compared to noncomplex features. Overall, this study highlights the usefulness of local analyses as a relevant tool to investigate foveal and parafoveal processing skills during music reading.
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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 | 0.002 |
| 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) | 0.002 | 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".