Could social robots facilitate children with autism spectrum disorders in learning distrust and deception?
Bibliographic record
Abstract
Social robots have been increasingly involved in our daily lives and provide a new environment for children's growth. The current study aimed to examine how children with and without Autism Spectrum Disorders (ASD) learned complex social rules from a social robot through distrust and deception games. Twenty children with ASD between the ages of 5–8 and 20 typically-developing (TD) peers whose age and IQ were matched participated in distrust and deception tasks along with an interview about their perception of the human-likeness of the robot. The results demonstrated that: 1) children with ASD were slower to learn to and less likely to distrust and deceive a social robot than TD children and 2) children with ASD who perceived the robot to appear more human-like had more difficulty in learning to distrust the robot. Besides, by comparing to a previous study the results showed that children with ASD appeared to have more difficulty in learning to distrust a human compared to a robot, particularly in the early phase of learning. Overall, our study verified that social robots could facilitate children with ASD's learning of some social rules and showed that children's perception of the robot plays an important role in their social learning, which provides insights on robot design and its clinical applications in ASD intervention. • We examined how children with autism distrusted and deceived a social robot. • Children with autism show poorer distrust and deception performance than controls. • Perceived human-likeness of robot correlates with children's trust to robot. • Children with autism learn better to distrust a robot than a person.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".