Masked orthographic neighbor priming effects in Chinese two-character words
Bibliographic record
Abstract
Abstract In masked priming lexical decision tasks in alphabetic or syllabic script languages, latencies are longer when a word target is primed by a higher frequency neighbor (e.g., blue–BLUR) than when primed by an unrelated word of equivalent frequency (e.g., care–BLUR) – an “inhibitory neighbor priming effect.” In contrast, Zhou et al. (1999) demonstrated facilitatory orthographic neighbor priming for two-character Chinese words (e.g., 华丽–华贵). However, Zhou et al. did not control for relative prime-target frequency, which has been shown to be important in experiments when examining languages with other scripts. In the present Experiment 1 word neighbor primes (e.g., 容易-容貌) produced an inhibitory neighbor priming effect when the prime was higher frequency than the target, paralleling effects in other script languages. In further experiments, paralleling those in other script languages, two-character targets primed by nonword neighbors (容待-容貌) or single constituent characters matching the target in either position (容-容貌) showed significant facilitation. These results suggest that lexical activation/competition processes for two-character Chinese words are reasonably similar to those for words in alphabetic/syllabic script languages.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".