Effects of Chinese word structure on object categorization in Chinese–English bilinguals
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 issunflower), whereas in Chinese, some nouns provide category information morphologically (e.g., 鸵鸟-ostrichand 知更鸟-robinhave the morpheme鸟-birdin their Chinese names), while some nouns do not (e.g., 企鹅-penguinand 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".