How lexical competition unfolds in the recognition of reduced and unreduced word-medial stops
Bibliographic record
Abstract
Listeners are able to derive the semantics of at intended message with a highly variable speech signal where speakers often hypoarticulate words and utterances. The present study investigates: how word recognition unfolds in cases where hypoarticulation occurs and thus increased competition. Thirty native English-speaking participants listened to individual reduced and unreduced words with a medial /d/ or /g/ while their eye-gaze was tracked. For each item, participants saw a visual display containing four words -- one matching the auditory target, a phonological or reduction competitor, and two distractors -- and they were asked to click on the word corresponding to what they heard. The phonological competitors overlapped with the target in the first 2-3 phonemes and letters ( puddle - pudgy ), and the the reduction competitors were words that would be confused with the target if the medial /d/ or /g/ were heavily reduced ( poodle - pool ). There is a general effect of reduction, with participants spending more time looking at the competitor when listening to reduced speech (Ernestus et al., 2002; Tucker, 2011). Results show a 2-way interaction between reduction and competitor type. For unreduced items, the phonological and reduction competitors distracted similarly from the target, while the reduction competitor elicited more looks than the phonological competitor for reduced items. Crucially, the eye-gaze data reveal a different time-course for items with medial /d/ versus /g/. There was a significant 3-way interaction between reduction, competitor type, and consonant from 300ms onwards. The 2-way interaction between reduction and competitor type emerged by 300-600ms for /g/ items, but not until 1200-1500ms for /d/ items. This study adds to previous research by identifying differences in processing between the two types of consonants. We will consider several perceptual explanations in accounting for this difference and broader explanations for the processing of reduced word forms.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".