Phonological Overlap Connects Semantically-Unrelated Concepts: Evidence from Neural Correlates of Language Co-activation
Bibliographic record
Abstract
Language co-activation can strengthen associations between unrelated concepts. We tested whether cross-linguistic phonological overlap impacts semantic processing of non-overlapping written inputs. English monolinguals and Korean-English bilinguals were presented with an interlingual homophone (e.g., “moon”) and a word that is either semantically related (e.g., “lock” – “moon,” the sound /mu:n/ means "door" in Korean, which is semantically related with “lock”) or unrelated across languages (e.g., “fork” – “moon”). While their EEG was recorded, participants had to judge whether the word pairs were semantically related. A smaller N400 effect (difference in ERP amplitude between related and unrelated word pairs) was found in bilinguals than monolinguals, especially for word pairs related in meaning across languages. We conclude that phonological links across languages can connect unrelated concepts, reshaping the lexico-semantic network.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".