Do They Really Mean It? Children’s Inference of Speaker Intentions and the Role of Age and Gender
Bibliographic record
Abstract
Interpreting other people’s intentions during communication represents a remarkable challenge for children. Although many studies have examined children’s understanding of, for example, sarcasm, less is known about their interpretation. Using realistic audiovisual scenes, we invited 124 children between 8 and 12 years old to watch video clips of young adults using different speaker intentions. After watching each video clip, children answered questions about the characters and their beliefs, and the perceived friendliness of the speaker. Children’s responses reveal age and gender differences in the ability to interpret speaker belief and social intentions, especially for scenarios conveying teasing and prosocial lies. We found that the ability to infer speaker belief of prosocial lies and to interpret social intentions increases with age. Our results suggest that children at the age of 8 years already show adult-like abilities to understand literal statements, whereas the ability to infer specific social intentions, such as teasing and prosocial lies, is still developing between the age of 8 and 12 years. Moreover, girls performed better in classifying prosocial lies and sarcasm as insincere than boys. The outcomes expand our understanding of how children observe speaker intentions and suggest further research into the development of teasing and prosocial lie interpretation.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".