Activation of L1 orthography in L2 word reading: Constraints from language and writing system
Why this work is in the frame
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Bibliographic record
Abstract
When reading in a second language, a reader’s first language may be involved. For word reading, the question is how and at what level: lexical, pre-lexical, or both. In three experiments, we employed an implicit reading task (color judgment) and an explicit reading task (word naming) to test whether a Chinese meaning equivalent character and its sub-character orthography are activated when first language (L1) Chinese speakers read second language (L2) English words. Because Chinese and English have different spoken and written forms, any cross language effects cannot arise from shared written and spoken forms. Importantly, the experiments provide a comparison with single language experiments within Chinese, which show cross-writing system activation when words are presented in alphabetic Pinyin, leading to activation of the corresponding character and also its sub-character (radical) components. In the present experiments, Chinese–English bilinguals first silently read or made a meaning judgment on an English word. Immediately following, they judged the color of a character (Experiments 1A and 1B) or named it (Experiment 2). Four conditions varied the relation between the character that is the meaning equivalent of the English word and the following character presented for naming or color judgment. The experiments provide evidence that the Chinese meaning equivalent character is activated during the reading of the L2 English. In contrast to the within-Chinese results, the activation of Chinese characters did not extend to the sub-character level. This pattern held for both implicit reading (color judgment) and explicit reading (naming) tasks, indicating that for unrelated languages with writing systems, L1 activation during L2 reading occurs for the specific orthographic L1 form (a single character), mediated by meaning. We conclude that differences in writing systems do not block cross-language co-activation, but that differences in languages limit co-activation to the lexical level.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it