The role of phonology-to-orthography consistency in predicting the degree of pupil dilation induced in processing reduced and unreduced speech
Bibliographic record
Abstract
Abstract The relationship between the ways in which words are pronounced and spelled has been shown to affect spoken word processing, and a consistent relationship between pronunciation and spelling has been reported as a possible cause of unreduced pronunciations being easier to process than reduced counterparts although reduced pronunciations occur more frequently. In the present study, we investigate the effect of pronunciation-to-spelling consistency for reduced and unreduced pronunciations in L1 and L2 listeners of a logographic language. More precisely, we compare L1 and L2 Japanese listeners to probe whether they use orthographic information differently when processing reduced and unreduced speech. Using pupillometry, the current study provides evidence that extends the hypothesis about the role of orthography in the processing of reduced speech. Orthographic realization matters in processing for L1 and L2 advanced listeners. More specifically, how consistent the orthographic realization is with its phonological form (phonology-to-orthography consistency) modulates the extent to which reduced pronunciation induces additional processing costs. The results are further discussed in terms of their implications for how listeners process reduced speech and the role of the orthographic form in speech processing.
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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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".