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 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 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".