Children With Autism Spectrum Disorder Can Attribute False Beliefs in a Spontaneous-Response Preferential-Looking Task
Bibliographic record
Abstract
An established body of literature indicates that children with autism spectrum disorder (ASD) have difficulty understanding figurative language due to a deficit in theory of mind, or the ability to consider the beliefs of other people. Children with ASD tend to similarly fail traditional theory of mind tasks, which assess their ability to represent false beliefs. Our claim is, however, that these tasks involve cognitive processing demands that might mask false belief understanding because they require elicited responses. We examined whether children with ASD demonstrate false-belief understanding when tested with a spontaneous-response false belief task that measures children’s eye gaze durations. The two child participant groups were composed of 20 males with ASD (aged 3–9 years) and 20 typically developing males (aged 2–5 years) who were individually matched according to verbal mental age. Children with ASD and typically developing children listened to a change-of-location story accompanied by a book with matching and non-matching pictures. The final page showed the character searching for her object in a location that was either consistent or inconsistent with her belief. Both groups of children looked reliably longer at the belief-consistent picture, regardless of whether the character’s belief was true or false, though children with ASD were slower to do so. We suggest that a spontaneous-response assessment technique can potentially reveal figurative language comprehension in children with ASD in future research.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".