Expecting the end: Continuous expectancy ratings for tonal cadences
Bibliographic record
Abstract
Cognitive accounts for the formation of expectations during music listening have largely centered around mental representations of scales using both melodic and harmonic stimuli. This study extends these findings to the most recurrent cadence patterns associated with tonal music using a real-time, continuous-rating paradigm. Musicians and nonmusicians heard cadential excerpts selected from Mozart’s keyboard sonatas (perfect authentic cadence [PAC], imperfect authentic cadence [IAC], half cadence [HC], deceptive cadence [DC], and evaded cadence [EV]), and continuously rated the strength of their expectations that the end of each excerpt is imminent. As predicted, expectations for closure increased over the course of each excerpt and then peaked at or near the target melodic tone and chord. Cadence categories for which tonic harmony was the expected goal (PAC, IAC, DC, EV) received the highest and earliest expectancy ratings, whereas cadence categories ending with dominant harmony (HC) received the lowest and latest ratings, suggesting that dominant harmony elicits weaker expectations in anticipation of its occurrence in cadential contexts. A regression analysis also revealed that longer excerpts featuring dense textures and a cadential six-four harmony received the highest ratings overall.
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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 | 0.001 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".