Perception of place of articulation of assimilated nasal and oral stops: What do response times and eye fixations tell us?
Bibliographic record
Abstract
Two forced-choice identification and two visual world eyetracking experiments examined perception of the place of articulation (PoA) of word-final nasal and oral stops either in canonical (coronal) or assimilated form (coronal-to-labial place assimilation, e.g., ‘phonem /batp button’). Listeners’ response times (RT) and eye fixations were measured as they heard and, using a screen-based paradigm, identified assimilated or unassimilated words presented auditorily either in isolation (excised from recorded sentences) or with the assimilation-triggering context present (next word began with a labial consonant). Listeners were slower to identify isolated words ending in nasals, especially when words were assimilated. When the triggering context was present, RTs were overall faster and no longer different between nasal and oral stops. The eye fixation data further showed an early sensitivity to PoA cues carried by vowel transitions for assimilated oral stops. For words ending in assimilated nasal stops, however, fixation patterns only showed sensitivity to the PoA cues at a later point where the assimilation-triggering context was heard. These findings indicate a distinction in terms of perceptual uptake of acoustic cues between nasal and oral stops. The results also suggest the precise mechanisms involved in compensation for assimilation may vary across sound classes (nasal vs. oral stops).
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".