Effects of Chinese word structure on object perception in Chinese–English bilinguals: Evidence from an ERP visual oddball paradigm
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Lupyan's (2012) label-feedback hypothesis proposes that linguistic labels affect our conceptual and perceptual representations through top-down feedback. We investigated whether such representations in bilinguals are influenced by labels from both of their languages by examining the effect of Chinese word structure on picture perception in Chinese–English bilinguals. A visual-oddball task with ERP was used. Pictures of four birds were used as standards and deviants. The robin-ostrich pair shared a category cue in their Chinese names (like blackbird in English), and the pigeon-penguin pair did not. In Chinese–English bilinguals who were new to Canada, the visual mismatch negativity (vMMN) elicited by deviant stimuli was significantly larger for pairs without category cues than pairs with cues, but, in long-stay bilinguals and English monolinguals, the vMMN was similar for the two pairs. These results demonstrate that object perception is influenced by the labels in both of a bilingual's languages.
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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 | 0.001 |
| 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