Why is lexical retrieval slower for bilinguals? Evidence from picture naming
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Proficient bilinguals demonstrate slower lexical retrieval than comparable monolinguals. The present study tested predictions from two main accounts of this effect, the frequency-lag and competition hypotheses. Both make the same prediction for bilinguals but differ for trilinguals and for age differences. 200 younger or older adults who were monolingual, bilingual, or trilingual performed a picture naming task in English that included high and low frequency words. Naming times were faster for high than for low frequency words and, in line with frequency lag, group differences were larger for low than high frequency items. However, on all other measures, bilinguals and trilinguals performed equivalently, and lexical retrieval differences between language groups did not attenuate with age, consistent with the competition view.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it