Bibliographic record
Abstract
We examined pitch-error detection in well-known songs sung with or without meaningful lyrics. In Experiment 1, adults heard the initial phrase of familiar songs sung with lyrics or repeating syllables ( la) and judged whether they heard an out-of-tune note. Half of the renditions had a single pitch error (50 or 100 cents); half were in tune. Listeners were poorer at pitch-error detection in songs with lyrics. In Experiment 2, within-note pitch fluctuations in the same performances were eliminated by auto-tuning. Again, pitch-error detection was worse for renditions with lyrics (50 cents), suggesting adverse effects of semantic processing. In Experiment 3, songs were sung with repeating syllables or scat syllables to ascertain the role of phonetic variability. Performance was poorer for scat than for repeating syllables, indicating adverse effects of phonetic variability, but overall performance exceeded Experiment 1. In Experiment 4, listeners evaluated songs in all styles (repeating syllables, scat, lyrics) within the same session. Performance was best with repeating syllables (50 cents) and did not differ between scat or lyric versions. In short, tracking the pitches of highly familiar songs was impaired by the presence of words, an impairment stemming primarily from phonetic variability rather than interference from semantic processing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".