Towards a Vocal Constraints Model of Melodic Expectancy: Evidence from Two Listening Experiments
Bibliographic record
Abstract
Where does a listener's anticipation of the next note in an unfamiliar melody come from? One view is that expectancies reflect innate grouping biases; another is that expectancies reflect statistical learning through previous musical exposure. Listening experiments support both views but in limited contexts, e.g., using only instrumental renditions of melodies. Here we report replications of two previous experiments, but with additional manipulations of timbre (instrumental vs. sung renditions) and register (modal vs. upper). Following a proposal that melodic expectancy is vocally constrained, we predicted that sung renditions would encourage an expectation that the next tone will be a “singable” one, operationalized here as one having an absolute pitch height that falls within the modal register. Listeners heard melodic fragments and gave goodness-of-fit ratings on the final tone (Experiment 1) or rated how certain they were about what the next note would be (Experiment 2). Ratings in the instrumental conditions were consistent with the original findings, but differed significantly from ratings in the sung conditions, which were more consistent with the vocal constraints model. We discuss how a vocal constraints model could be extended to include expectations about duration and tonality.
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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 | 0.005 | 0.033 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".