What Are the Modulators of Cross-Language Syntactic Activation During Natural Reading?
Bibliographic record
Abstract
Bilinguals juggle knowledge of multiple languages, including syntactic constructions that can mismatch (e.g., the red car, la voiture rouge; Mary sees it, Mary le voit). We used eye-tracking to examine whether French-English ( n = 23) and English-French ( n = 21) bilingual adults activate non-target language syntax during English L2 (Experiment 1) and L1 (Experiment 2) reading, and whether this differed from functionally monolingual English reading (Experiment 3, n = 26). People read English sentences containing syntactic constructions that were either partially shared across languages (adjective-noun constructions) or completely unshared (object-pronoun constructions). These constructions were presented in an intact form, or in a violated form that was French-consistent or French-inconsistent. For both L2 and L1 reading, bilinguals read French-consistent adjective-noun violations relatively quickly, suggesting cross-language activation. This did not occur when the same people read object-pronoun constructions manipulated in the same manner. Surprisingly, English readers exposed to French in their lifetime but functionally monolingual, also read French-consistent violations for adjective-noun constructions faster, particularly for some items. However, when we controlled for item differences in the L2 and L1 reading data, cross-language effects observed were similar to the original data pattern. Moreover, individual differences in L2 experience modulated both L2 and L1 reading for adjective-noun constructions, consistent with a cross-language activation interpretation of the data. These findings are consistent with the idea of syntactic cross-language activation during reading for some constructions. However, for several reasons, cross-language syntactic activation during comprehension may be overall more variable and challenging to investigate methodologically compared to past work on other forms of cross-language activation (i.e., single words).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".