Children's processing of written irony: An eye-tracking study
Bibliographic record
Abstract
Ironic language is challenging for many people to understand, and particularly for children. Comprehending irony is considered a major milestone in children's development, as it requires inferring the intentions of the person who is being ironic. However, the theories of irony comprehension generally do not address developmental changes, and there are limited data on children's processing of verbal irony. In the present pre-registered study, we examined, for the first time, how children process and comprehend written irony in comparison to adults. Seventy participants took part in the study (35 10-year-old children and 35 adults). In the experiment, participants read ironic and literal sentences embedded in story contexts while their eye movements were recorded. They also responded to a text memory question and an inference question after each story, and children's levels of reading skills were measured. Results showed that for both children and adults comprehending written irony was more difficult than for literal texts (the "irony effect") and was more challenging for children than for adults. Moreover, although children showed longer overall reading times than adults, processing of ironic stories was largely similar between children and adults. One group difference was that for children, more accurate irony comprehension was qualified by faster reading times whereas for adults more accurate irony comprehension involved slower reading times. Interestingly, both age groups were able to adapt to task context and improve their irony processing across trials. These results provide new insights about the costs of irony and development of the ability to overcome them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".