Spatial Frequencies Mediating Music Reading
Bibliographic record
Abstract
The purpose of this study was to examine Spatial Frequencies (SFs) mediating music reading compared to text reading. The SFs Bubbles technique (Willenbockel et al., 2009), which consists in randomly sampling multiple SFs simultaneously on each trial, was used. A set of 70 piano excerpts selected from the unfamiliar piano repertoire was used for music reading and 50 sentences from MNRead Acuity Charts were used for text reading. The visual size of each letter and note was about 0.34°. Five pianists and five naïve observers took part in the experiments. The percentage of correctly produced pitches and ‘ascii code’ was used as a performance measure for music and text reading respectively. To find out which SFs drove the participants' correct responses for music and text reading, a multiple linear regression was performed. A statistical test (Chauvin et al., 2005) was then used to determine thresholds that selected the diagnostic SFs for accurate performance. The music reading results showed a significant SFs band (from 1 to 1.7 cycles per note (cpn)) peaking at 1.19 cpn, compared to two SF bands for text reading: the first SFs band (from 1.08 to 1.3 cycles per letter (cpl)) peaking at 1.2 cpl and the second SFs band (from 1.6 to 2.6 cpl) peaking at 1.8 cpl. In a control experiment, five new pianists were instructed to play the set of 70 excerpts, first sampled with the obtained diagnostic filter for music reading, and then without sampling. Pianist performances with the diagnostic filter (94%) were comparable (96%) to those without filtering (P > 0.05). The present findings show that music reading is mediated only partly by SF bands mediating text reading, which may explain why in some cases, difficulties in music reading are not necessarily accompanied by difficulties in text 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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".