Phonological-orthographic consistency for Japanese words and its impact on visual and auditory word recognition.
Bibliographic record
Abstract
In most models of word processing, the degrees of consistency in the mappings between orthographic, phonological, and semantic representations are hypothesized to affect reading time. Following Hino, Miyamura, and Lupker's (2011) examination of the orthographic-phonological (O-P) and orthographic-semantic (O-S) consistency for 1,114 Japanese words (339 katakana and 775 kanji words), in the present research, we initially attempted to measure the phonological-orthographic (P-O) consistency for those same words. In contrast to the O-P and O-S consistencies, which were equivalent for kanji and katakana words, the P-O relationships were much more inconsistent for the kanji words than for the katakana words. The impact of kanji words' P-O consistency was then examined in both visual and auditory word recognition tasks. Although there was no effect of P-O consistency in the standard visual lexical-decision task, significant effects were detected in a lexical-decision task with auditory stimuli, in a perceptual identification task using masked visual stimuli, and in a lexical-decision task with degraded visual stimuli. The implications of these results are discussed in terms of the impact of P-O consistency in auditory and visual word recognition. (PsycINFO Database Record
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".