Focus effect unveils children’s local processing of pronouns and reflexives
Bibliographic record
Abstract
Studies on young children’s comprehension have shown that children can experience problems interpreting object pronouns, even when reflexive interpretation is already adult-like. Compared to resolving reflexives, linking pronouns to a referent is considered a more “intensive” process, because it also involves non-syntactic factors like discourse context. This could explain why children experience more difficulties with pronouns than with reflexives. Using eye-tracking and a truth value judgement task, we investigated the effect of focus via it-clefts on the processing of reflexives and pronouns in German-speaking children and adults. We analyzed gaze data of two time segments: before and during the mention of the pronoun/reflexive. The cleft segment revealed similar processing of it-clefts in children and adults. In the subsequent reflexive/pronoun segment, clefts caused adults to pay overall more attention to the local referent, while children fixated the clefted non-local referent more. The difference in focus effect, that is, children attend the clefted referent more, while adults pay more attention to the non-clefted referent, helped uncover processing differences between children and adults. That is, unlike adults, children consider only the local discourse context during referential processing. We argue that these processing differences cause children’s interpretation difficulties. However, the offline data showed no effect of information structure, suggesting that whether the processing differences transfer to the final interpretation depends on the language-specific function of the pronoun system, which may aid in restricting referential links.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".