How children attend to events before speaking: crosslinguistic evidence from the motion domain
Bibliographic record
Abstract
How do children talk about the dynamic world around them? In this eyetracking study, we demonstrate language-specific patterns in the way 3- and 4-year-old speakers of English and Greek inspect motion events prior to speaking and describe such events in their native language. Across age and language groups, children were more likely to mention manners of motion than paths, but English-speaking children were more likely to provide manner information than Greek-speaking children were. Comparison of eyegaze patterns from the linguistic (description) task to eyegaze patterns observed during a nonlinguistic (memory) task with a different group of English- and Greek-speaking 3- and 4-year-olds revealed effects of language background on event inspection. These effects suggest that by the age of 3 years, children exhibit sensitivities to language-specific patterns of motion event encoding that influence the way they gather information from the visual world during the process of language production.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".