Does phonological overlap of cognate words modulate cognate acquisition and processing in developing and skilled readers?
Bibliographic record
Abstract
Very few studies exist on the role of cross-language similarities in cognate word acquisition. Here we sought to explore, for the first time, the interplay of orthography (O) and phonology (P) during the early stages of cognate word acquisition, looking at children and adults with the same level of foreign language proficiency and by using two variants of the word-association learning paradigm (auditory learning method vs. auditory + written method). Eighty participants (40 children and 40 adults, native speakers of European Portuguese [EP]), learned a set of EP-Catalan cognate words and noncognate words. Among the cognate words, the degree of orthographic and phonological similarity was manipulated. Half of the children and adult participants learned the new words via an L2 auditory and written-L1 word association method, while the other half learned the same words only through an L2 auditory-L1 word association method. Both groups were tested in an auditory recognition task and a go/no-go lexical decision task. Results revealed a disadvantage for children in comparison to adults, which was reduced in the auditory learning method. Furthermore, there was an advantage for cognates relative to noncognates regardless of the age of participants. Importantly, there were modulations in cognate word processing as a function of the degree of O and P overlap that were restricted to children. The findings are discussed in light of the most relevant bilingual models of word recognition.
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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 | 0.002 |
| 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 | 0.001 |
| 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".