Category interaction and stimulus effects in the perception of synthetic liquid+stop clusters.
Bibliographic record
Abstract
There are numerous studies of the perception of English stops in the syllables /arga, alga, arda, alda/ [e.g., A. Lotto and K. Kluender, Percept. Psychophys. 60, 602–619 (1998)]. Listeners give more /-da/ responses after /ar-/ syllables. There is controversy over the degree to which this effect involves general auditory contrast rather than phonetic context. The present study uses a two-dimensional continuum of 49 stimuli composed by crossing a seven-step /al-/ to /ar-/ series with a seven-step /-da/ to /-ga/ series. The variation in stimulus properties is localized to F3 only for both VC and CV stimuli. Listeners (n = 34) responded with all four categories. Results show clear phoneme level context effects. Ambiguous VC stimuli near the /al/-/arr/ boundary show significantly more /da/ responses in cases where the VC is heard as /ar/. This pattern is consistent with what has been called a diphone bias effect [T. Nearey, J. Acoust. Soc. Am. 101, 3241–3256 (1997)]. Surprisingly, for these stimuli, evidence for more continuous tuning of the /da/-/ga/ boundary by preceding l/r is quite weak. Thus, in this experiment, phonetic effects appear to dominate auditory contrast effects.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".