Comprehension of Null and Pronominal Object Sentences in Japanese-speaking Children
Bibliographic record
Abstract
In successful communication, it is critical to have the ability to identify what a speaker is referring to from previously mentioned information. This ability requires the identification of the topic initially introduced by lexical forms and its continuity in discourse expressed by anaphora such as null and pronominal forms in the subsequent sentences. While Japanese-speaking children are frequently provided with pronominal and null forms, especially the null form, in reference to previously mentioned topics, it remains unclear from what age they understand the anaphoric use of such referential forms. The current study investigated the age at which Japanese-speaking children are able to identify the presence of topic chains connecting null and pronoun anaphora to the topic referred to by a lexical form in the preceding sentence. We tested children’s comprehension of null and pronominal object sentences using an intermodal preferential-looking paradigm. The results demonstrated that the Japanese-speaking children aged 2;7 and 3;2 as a group looked at the target animation reliably longer after hearing the test sentences than before or during the test sentences. This finding provides evidence that Japanese-speaking children’s ability to track topic chains and understand anaphora in the discourse develop by 2;7 years of age. However, unlike the 3;2-year-old group, the 2;7-year-old group showed weaker performance in interpreting pronominal object sentences, suggesting a possibility that young children find the interpretation of null anaphora easier than that of pronoun anaphora.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".