Phonological and auditory context effects in the perception of synthetic liquid-plus-stop clusters
Bibliographic record
Abstract
In experiments with English synthetic stops in the /VCCV/ disyllables /arga, alga, arda, alda/, listeners give more /-da/ responses after /ar-/ syllables [see, e.g., A. Lotto and K. Kluender, Percept. Psychophys. 60, 602–619 (1998)]. The importance of general auditory contrast compared to more specific phonetic mechanisms is a key issue in the literature. In three experiments that used factorially crossed F3 continua for both the liquid /r-l/ and for the stop /g-d/, we recorded responses to all four disyllables (4 alternative forced choice). Experiments 1 and 2 both showed that categorizing the liquid as /r/ biases stop responses toward /d/. However, Experiment 1, which involved a 4-step extreme-/r/ to extreme-/l/ continuum, showed effects consistent with auditory contrast of the F3 values across the stop. Experiment 2 focused on the ambiguous region of the /l-r/ continuum [sampled in 7 steps]. While a clear effect of the [-rd-] phonological bias remained, there was little reliable evidence of contrast effects in F3 across the stop gap. Here, we report analyses of a new larger (n > 60) Experiment 3 with more stimuli (70 compared to less than 50 each) that subsumes the ranges of the previous two experiments. We evaluate the hypothesis (among others) that the auditory-contrast-like effect is confined largely to stimuli with relatively low F3 at the offset of the /VC-/ syllable.
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".