Bibliographic record
Abstract
Past studies of object recognition in vision and language have shown that (1) identification of the larger structure of an object is possible even if its component units are ambiguous or missing, and (2) contexts often influence the perception of the component units. The present study asked whether a similar case could be found in audition, investigating (1) whether melody recognition would be possible with uncertain pitch cues, and (2) whether adding contextual information would enhance pitch perception. Sixteen musically trained listeners attempted to identify, on a piano keyboard, pitches of tones in three different context conditions: (1) single tones, (2) pairs of tones, and (3) familiar melodies. The pitch cues were weakened using bandpass filtered noises of varying bandwidths. With increasing bandwidth, listeners were less able to identify the pitches of the tones. However, they were able to name the melodies despite their inability to identify the individual notes. There was no effect of context; whether or not listeners heard single tones, pairs of tones, or melodies did not influence their pitch identification of the tones. Several possible explanations were discussed regarding types of information that listeners had access to, since they could not have relied on detailed features of the melodies.
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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) | 0.001 | 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 | 0.001 | 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".