Can Musical Ability be Tested Online?
Bibliographic record
Abstract
We sought to determine whether an objective test of musical ability could be administered online successfully. A sample of 754 participants was tested with an online version of the Musical Ear Test (MET), which had Melody and Rhythm subtests. Both subtests had 52 trials, each of which required participants to determine whether standard and comparison auditory sequences were identical. The testing session also included the Goldsmiths Musical Sophistication Index (Gold-MSI), a test of general cognitive ability, and self-report questionnaires that measured basic demographics (age, education, gender), mind wandering, and personality. Approximately 20% of the participants were excluded for incomplete responding or failing to finish the testing session. For the final sample (N = 608), findings were similar to those from in-person testing in many respects: (1) the internal reliability of the MET was maintained, (2) construct validity was confirmed by strong associations with Gold-MSI scores, (3) correlations with other measures (e.g., openness-to-experience, cognitive ability, mind wandering) were as predicted, (4) for individuals with no music training, mean levels of performance were similar, and (5) musical sophistication was a better predictor of performance on the Melody compared to the Rhythm subtest. In sum, online administration of the MET proved to be a reliable and valid way to measure musical ability.
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".