Assessing Music Perception in Young Children: Evidence for and Psychometric Features of the M-Factor
Bibliographic record
Abstract
Given the relationship between language acquisition and music processing, musical perception (MP) skills have been proposed as a tool for early diagnosis of speech and language difficulties; therefore, a psychometric instrument is needed to assess music perception in children under 10 years of age, a crucial period in neurodevelopment. We created a set of 80 musical stimuli encompassing seven domains of music perception to inform perception of tonal, atonal, and modal stimuli, in a random sample of 1006 children, 6-13 years of age, equally distributed from first to fifth grades, from 14 schools (38% private schools) in So Paulo State. The underlying model was tested using confirmatory factor analysis. A model encompassing seven orthogonal specific domains (contour, loudness, scale, timbre, duration, pitch, and meter) and one general music perception factor, the "m-factor," showed excellent fit indices. The m-factor, previously hypothesized in the literature but never formally tested, explains 93% of the reliable variance in measurement, while only 3.9% of the reliable variance could be attributed to the multidimensionality caused by the specific domains. The 80 items showed no differential item functioning based on sex, age, or enrolment in public vs. private school, demonstrating the important psychometric feature of invariance. Like Charles Spearman's g-factor of intelligence, the m-factor is robust and reliable. It provides a convenient measure of auditory stimulus apprehension that does not rely on verbal information, offering a new opportunity to probe biological and psychological relationships with music perception phenomena and the etiologies of speech and language disorders.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".