Lexical competition in a non-Roman, syllabic script: An inhibitory neighbour priming effect in Japanese Katakana
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Previous masked priming studies have reported that lexical decision latencies are slower when a word target is primed by a higher-frequency neighbour (e.g., blue-BLUR) than when it is primed by an unrelated word of equivalent frequency (e.g., care-BLUR). These results suggest that lexical competition plays an important role in visual word identification in Indo-European languages such as English, French, and Dutch, consistent with activation-based accounts of lexical processing. The present research, using Japanese Katakana script, a syllabic script, demonstrates that lexical decision latencies were slower when targets were primed by word neighbour primes but not when targets were primed by nonword neighbour primes. Both results have clear parallels with previous research using Indo-European languages and therefore suggest that lexical competition is also an important component of word recognition processes in languages that do not employ the Roman alphabet.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it