Attraction Effects for Verbal Gender and Number Are Similar but Not Identical: Self-Paced Reading Evidence From Modern Standard Arabic
Bibliographic record
Abstract
Previous work on the comprehension of agreement has shown that incorrectly inflected verbs do not trigger responses typically seen with fully ungrammatical verbs when the preceding sentential context furnishes a possibly matching distractor noun (i.e., agreement attraction). We report eight studies, three being direct replications, designed to assess the degree of similarity of these errors in the comprehension of subject-verb agreement along the dimensions of grammatical gender and number in Modern Standard Arabic. A meta-analysis of the results demonstrate the presence of agreement attraction effects in reading comprehension for gender and number on verbs. Moreover, the meta-analysis demonstrates that these two features do not behave identically: gender effects are larger and occur later relative to number attraction effects. These results challenge models of agreement that predict agreement features to be equipotent and show that real-time models of agreement require modifications in the form of cue-weighting in order to account for these differential results.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".