Teaching Lexical Humor to Children with Autism
Bibliographic record
Abstract
Comprehension and production of linguistic humor is an important social, linguistic, and cognitive skill for all children, but it often fails to develop in children with autism. One form of linguistic humor that is based on multiple meaning words and can take the form of riddles or jokes has been identified as “lexical humor.” This study investigated the effects of the Lexical Humor Treatment Program (Gill, White, & Reyes, 2010) on six high-functioning children with autism. All of the children increased their understanding of multiple-meaning words and their ability to answer related riddles on which they had been trained. More importantly, the children were able to transfer their skill in practiced riddles to novel riddles on which they had not been trained. These results suggest that the teaching of humor in the form of lexical riddles might be an important treatment consideration for children with autism. Key words : Lexical Humor; Linguistic Humor; Autism; Asperger Syndrome; Riddles; Multiple Meaning Words; Humor Instruction
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".