Using Equivalence-Based Instruction to Improve Expression and Comprehension of Emotional Metaphors for Children on the Autism Spectrum
Bibliographic record
Abstract
Metaphors are frequently used in daily life. Many children on the autism spectrum have difficulties in comprehending and generating metaphors. The purpose of this study was to evaluate an equivalence-based instruction (EBI) procedure aimed at improving comprehension and expression of emotions in metaphors. Four Chinese boys on the spectrum participated in this multiple probe design across emotions (i.e., angry, sad, happy, anxious, and fear) study. In this procedure, first, the instructor presented a picture of an emotion (A) with its commonly understood features (B) (e.g., facial expression—smile, nonverbal gestures—bouncing up and down). The child was taught the A→B relation by labeling the emotion and its corresponding features. Next, the instructor presented a picture of the stimulus used in a metaphor (C) sharing a similar feature (B) with the corresponding emotion. The child was taught the C→B relation by labeling the stimulus and its corresponding feature. After completion of the training, the untaught A→C (metaphorical expression, e.g., “What is happy like?”) and C→A (metaphorical comprehension, e.g., “If I say, “She is like a bouncing ball. How does she feel?”) relations were tested. Results indicated that the EBI procedure was functionally related to improved performance for metaphorical expression in three children and for metaphorical comprehension in two children. The use of EBI to facilitate the acquisition of metaphorical expression and comprehension for children on the spectrum is discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".