The effects of word frequency and word predictability during first- and second-language paragraph reading in bilingual older and younger adults.
Bibliographic record
Abstract
We used eye movement measures of paragraph reading to examine how word frequency and word predictability impact first-language (L1) and second-language (L2) word processing in matched bilingual older and younger adults, varying in amount of current L2 experience. Our key findings were threefold. First, across both early- and late-stage reading, word frequency effects were generally larger in older than in younger adults, whereas word predictability effects were generally age-invariant. Second, across both age groups and both reading stages, word frequency effects were larger in the L2 than in the L1, whereas word predictability effects were language-invariant. Third, graded differences in current L2 experience modulated L1 and L2 word processing in younger adults, but had no impact in older adults. Specifically, greater current L2 experience facilitated L2 word processing, but impeded L1 word processing among younger adults only. Taken together, we draw 2 main conclusions. First, bilingual older adults experience changes in word-level processing that are language-non-specific, potentially because lexical accessibility decreases with age. Second, bilingual older adults experience changes in word-level processing that are insensitive to graded differences in current L2 experience, potentially because lexical representations reach a functional ceiling over time. (PsycINFO Database Record
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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 | 0.000 | 0.004 |
| 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) | 0.001 | 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".