Effects of Chinese word structure on object categorization in Chinese–English bilinguals
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT We investigated how verbal labels affect object categorization in bilinguals. In English, most nouns do not provide linguistic clues to their categories (an exception is sunflower ), whereas in Chinese, some nouns provide category information morphologically (e.g., 鸵鸟- ostrich and 知更鸟- robin have the morpheme鸟- bird in their Chinese names), while some nouns do not (e.g., 企鹅- penguin and 鸽子- pigeon ). We examined the effect of Chinese word structure on bilinguals’ categorization processes in two ERP experiments. Chinese–English bilinguals and English monolinguals judged the membership of atypical (e.g., ostrich , penguin ) vs. typical (e.g., robin , pigeon ) pictorial (Experiment 1) and English word (Experiment 2) exemplars of categories (e.g., bird ). English monolinguals showed typicality effects in RT data, and in the N300 and N400 of ERP data, regardless of whether the object name had a category cue in Chinese. In contrast, Chinese–English bilinguals showed a larger typicality effect for objects without category cues in their name than objects with cues, even when they were tested in English. These results demonstrate that linguistic information in bilinguals’ L1 has an effect on their L2 categorization processes. The findings are explained using the label-feedback hypothesis.
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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 | 0.001 |
| 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) | 0.001 | 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