Lexical entrenchment and cross-language activation: Two sides of the same coin for bilingual reading across the adult lifespan
Bibliographic record
Abstract
We used eye movement measures of paragraph reading to examine whether two consequences of bilingualism, namely, reduced lexical entrenchment (i.e., reduced lexical quality and accessibility arising from less absolute language experience) and cross-language activation (i.e., simultaneous co-activation of target- and non-target-language lexical representations) interact during word processing in bilingual younger and older adults. Specifically, we focused on the interaction between word frequency (a predictor of lexical entrenchment) and cross-language neighborhood density (a predictor of cross-language activation) during first- and second-language reading. Across both languages and both age groups, greater cross-language (and within-language) neighborhood density facilitated word processing, indexed by smaller word frequency effects. Moreover, word frequency effects and, to a lesser extent, cross-language neighborhood density effects were larger in older versus younger adults, potentially reflecting age-related changes in lexical accessibility and cognitive control. Thus, lexical entrenchment and cross-language activation multiplicatively influence bilingual word processing across the adult lifespan.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".