Crosslinguistic Developmental Consistency in the Composition of Toddlers’ Internal State Vocabulary: Evidence from Four Languages
Bibliographic record
Abstract
Mental state language, emerging in the second and third years of life in typically developing children, is one of the first signs of an explicit psychological understanding. While mental state vocabulary may serve a variety of conversational functions in discourse and thus might not always indicate psychological comprehension, there is evidence for genuine references to mental states (desires, knowledge, beliefs, and emotions) early in development across languages. This present study presents parental questionnaire data on the composition of 297 toddler-aged (30-to 32-month-olds) children’s internal state vocabulary in four languages: Italian, German, English, and French. The results demonstrated that across languages expressions for physiological states (e.g., hungry and tired) were among the most varied, while children’s vocabulary for cognitive entities (e.g., know and think) proved to be least varied. Further, consistent with studies on children’s comprehension of these concepts, across languages children’s mastery of volition terms (e.g., like to do and want) preceded their mastery of cognition terms. These findings confirm the cross-linguistic consistency of children’s emerging expression of abstract psychological concepts.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".