Tonal priming is resistant to changes in pitch height
Bibliographic record
Abstract
Research on tonal priming has consistently shown that tonally expected events are processed more efficiently and has confirmed that the locus of the effect is cognitive rather than sensory. However, it is also important to investigate the role of pitch height, because models of tonal priming collapse across octaves, yet it is possible that pitch height may modulate the effectiveness of tonal priming. We systematically tested this issue by varying the pitch heights of a related (tonic) or a less-related (subdominant) target chord following a tonal context. Musically untrained participants (N = 30) made speeded consonant/dissonant judgments of the final chord of an eight-chord sequence. The effects of tonal priming emerged in accuracy and reaction time measures for all octaves, except for a ceiling effect on accuracy in the matching (original pitch height) condition. In a second experiment, we increased the shift to two octaves and compressed the chords to eliminate overlap between the target and context chords; again, tonal priming emerged. These findings have implications for the behavioral study of tonal priming and support the assumption of octave equivalence in computational models.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".