The impact of individual differences on cross-language activation of meaning by phonology
Bibliographic record
Abstract
We investigated how individual differences in language proficiency and executive control impact cross-language meaning activation through phonology. Ninety-six university students read English sentences that contained French target words. Target words were high- and low-frequency French interlingual homophones (i.e., words that share pronunciation, but not meaning across langauges; mot means ‘word’ in French and sounds like ‘mow’ in English) and matched French control words (e.g., mois – ‘month’ in French). Readers could use the homophones’ shared phonology to activate their English meanings and, ultimately, make sense of the sentence (e.g., Tony was too lazy to mot/mois the grass on Sunday). Shorter reading times were observed on interlingual homophones than control words, suggesting that phonological representations in one language activate cross-language semantic representations. Importantly, the magnitude of the effect was modulated by word frequency, and several participant-level characteristics, including French proficiency, English word knowledge, and executive control ability.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".