Measuring Children’s Harmonic Knowledge with Implicit and Explicit Tests
Bibliographic record
Abstract
We used implicit and explicit tasks to measure knowledge of Western harmony in musically trained and untrained Canadian children. Younger children were 6–7 years of age; older children were 10–11. On each trial, participants heard a sequence of five piano chords. The first four chords established a major-key context. The final chord was the standard, expected tonic of the context or one of two deviant endings: the highly unexpected flat supertonic or the moderately unexpected subdominant. In the implicit task, children identified the timbre of the final chord (guitar or piano) as quickly as possible. Response times were faster for the tonic ending than for either deviant ending, but the magnitude of the priming effect was similar for the two deviants, and the effect did not vary as a function of age or music training. In the explicit task, children rated how good each chord sequence sounded. Ratings were highest for sequences with the tonic ending, intermediate for the subdominant, and lowest for the flat supertonic. Moreover, the difference between the tonic and deviant sequences was larger for older children with music training. Thus, the explicit task provided a more nuanced picture of musical knowledge than did the implicit task.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".