Object attraction and the role of structural hierarchy: Evidence from Persian
Bibliographic record
Abstract
Research on subject-verb agreement production in SVO languages has shown that objects moved pre-verbally sometimes trigger attraction, i.e., erroneous agreement of the verb with the object rather than the subject. Moreover, objects c-commanding one of the agreement positions in the hierarchical structure were found to generate stronger attraction than those linearly preceding them. Evidence for the role of c-command comes from the observation that the accusative clitic in French triggers stronger attraction than the preverbal dative pronoun and the PP modifier (Franck et al. 2006; 2010). In this study, we report the results of an experiment in Persian (an SOV language) in which subject–verb agreement was elicited by presenting sentences in Rapid Serial Visual Presentation procedure (RSVP) followed by verb selection (Staub 2009; 2010). We compared attraction errors induced by pre-verbal accusatives versus datives in the canonical SOV word order as well as the OSV word order. Corroborating Franck et al. (2006; 2010), we found stronger attraction when the pre-verbal object occupies a c-commanding position in the hierarchical structure than when it simply precedes one of the agreement positions in the linear string. We also found stronger attraction in the OSV word order as compared to the canonical SOV word order. This finding is attributed to the real-time processes of erroneous structure building and/or erroneous controller selection during subject-verb agreement computation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".