Eye movement patterns when playing from memory: Examining consistency across repeated performances and the relationship between eyes and audio
Bibliographic record
Abstract
While the eyes serve an obvious function in the context of music reading, their role during memorized music performance (i.e., when there is no score) is currently unknown. Given previous work showing relationships between eye movements and body movements and eye movements and memory retrieval, here I ask 1) whether eye movements become a stable aspect of the memorized music (motor) performance, and 2) whether the structure of the music is reflected in eye movement patterns. In this case study, three pianists chose two pieces to play from memory. They came into the lab on four different days, separated by at least 12hrs, and played their two pieces three times each. To answer 1), I compared dynamic time warping cost within vs. between pieces, and found significantly lower warping costs within piece, for both horizontal and vertical eye movement time series, providing a first proof-of-concept that eye movement patterns are conserved across repeated memorized music performances. To answer 2), I used the Matrix Profiles of the eye movement time series to automatically detect motifs (repeated patterns). By then analyzing participants’ recorded audio at moments of detected ocular motifs, repeated sections of music could be identified (confirmed auditorily and with inspection of the extracted pitch and amplitude envelopes of the indexed audio snippets). Overall, the current methods provide a promising approach for future studies of music performance, enabling exploration of the relationship between body movements, eye movements, and musical processing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".