The time course of recognition of reduced disyllabic Japanese words: Evidence from pupillometry with a Go-NoGo task
Bibliographic record
Abstract
While much attention has been paid to the importance of reduction in spoken word recognition, fewer studies have investigated the effect of reduction over time. Thirty-eight participants’ pupillary responses were measured during the perception of Japanese disyllabic words as they performed a Go-NoGo task. We used 226 lexical items, each of which contained both reduced and citation forms of the words. All stimuli consisted of a word-medial nasal or voiced stop. Results demonstrate that the overall amount of cognitive effort required to process reduced forms was higher than that of canonical forms. That is, greater pupil dilation was observed for reduced forms than for citation forms. This result is in line with previous research (e.g., Tucker, 2011). Specifically, pupil dilation was greater with reduced forms in the time window of 436 ms to 2000 ms after the onset of stimuli. Our results also indicate that the pattern of pupil dilation over time with reduced forms differs from citation forms, indicating that reduced forms show a later onset and offset of peak dilation and more gradual constriction of pupil compared to citation 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 | 0.002 |
| 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 | 0.000 | 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".