Pronoun Interpretation in the Second Language: Effects of Computational Complexity
Bibliographic record
Abstract
Children acquiring their native language (L1) have been reported to have greater difficulty in interpreting pronouns than reflexives. In addition, they are less accurate when pronouns refer to referential antecedents than to quantified antecedents, and when they hear full pronouns as opposed to reduced pronouns. We hypothesize that similar difficulties of interpretation will occur for (non-advanced) second language (L2) learners, due to an elevated computational burden, as argued for L1 acquisition by Reinhart (2006, 2011). We report on an experiment with adult learners of English (L1s French and Spanish), using a truth-value judgment task. Participants interpreted reduced and full pronouns bound by referential and quantified antecedents in aurally presented test sentences. The learners' performance is affected by type of pronoun and antecedent. When a referential antecedent is combined with a full pronoun, learners' accuracy is significantly lower. These results are in line with Reinhart's analysis of reference set computation in processing pronouns.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.002 | 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".