Fine-grained Implicit Memory for Key and Tempo
Bibliographic record
Abstract
Listeners remember the pitch level (key) and tempo of musical recordings they have heard multiple times. They also have long-term implicit memory for the key and tempo of novel melodies heard for the first time in the laboratory. In previous research, however, the stimulus melodies were simple and repetitive and the changes in key or tempo were large. Here, we tested the limits of implicit memory for the key and tempo of more complex stimulus melodies. Musically trained and untrained listeners heard 12 novel melodies during an exposure phase and 24 (12 old, 12 new) during a subsequent test (recognition) phase. From exposure to test, half of the melodies were transposed up or down (changed in key) (Experiment 1), or sped up or slowed down (Experiment 2), but to varying degrees. Musically trained listeners displayed enhanced recognition, but transposing or changing the tempo of the melodies reduced performance similarly for all listeners. The effect of the key change did not wane as the transposition was reduced from 6 semitones to 1, but recognition in general was worse as the pitch range of the stimulus melodies increased. The magnitude of the tempo change had a very small effect on response patterns, but Bayesian analyses indicated that the observed data were more likely without considering magnitude. The results suggest that musically trained and untrained listeners have implicit memory for key and tempo that is remarkably fine-grained, even for melodies that are heard for the first time in the laboratory, such that small changes in either feature make a melody less recognizable.
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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 | 0.003 |
| 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) | 0.003 | 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".