Widespread but Limited in Context: Absolute Tuning Judgments are Disrupted by Relative Pitch Cues
Bibliographic record
Abstract
Most listeners exhibit absolute pitch memory for familiar pitched sounds, ranging from simple tones (e.g., the “bleep” used to censor broadcast media) to rich, polyphonic melodies (e.g., excerpts from popular songs). Given that relative pitch is the predominant means of processing music for most listeners, this observed absolute pitch memory suggests that most listeners have hybrid representations, containing both absolute and relative pitch cues. The present experiment assesses how relative pitch cues influence absolute pitch memory in a variety of listening contexts, varying in terms of ecological validity. Participants were asked to explicitly judge different musical sounds (isolated notes, chords, scales, and short excerpts), varying in complexity, in terms of absolute tuning (i.e., whether a musical sound adhered to conventional, A440 tuning). Listeners showed robust above-chance performance in judging absolute tuning when the to-be-judged sounds did not contain relative pitch changes (i.e., isolated notes and chords), replicating prior work. Critically, however, performance was significantly reduced and was not statistically different from chance when the musical sounds contained relative pitch changes (scales and short excerpts). Taken together, the results suggest that most listeners possess some long-term memory for absolute tuning, but this ability has limited generalizability to more ecologically valid musical contexts that contain relative pitch changes.
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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 | 0.009 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".