Effect of distributional shape on learning a target sound
Bibliographic record
Abstract
Vowel pronunciations (measured in Hz) form distributions that vary from normally distributed to quite skewed. Labov (2001) noted that vowels undergoing change tend to be skewed, while stable ones are not. We ask if the different distributions are causally related to change. We exposed participants (n = 238) to a positively skewed, negatively skewed, or normal distribution of pure tones varying in pitch (to mimic how vowels vary in quality). Participants were told they were listening to notes played by amateur musicians who’d been aiming for the same note. In each trial, participants listened to 20 tones then played the note they thought was the target. Output pitches were compared to the input. The question was whether learners would play a tone corresponding to the mean or the mode of the set or would instead “shift” the note. In the two conditions analyzed so far (normal and positively skewed), participants output pitches that were slightly higher than the mean of the input set, indicating shift in both conditions; however, the difference between conditions is not significant. There is also an effect of the final note in the set, evoking the well-established recency effect in memory. Analysis of the third condition is ongoing.
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 | 0.009 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".