Social-information processing and experiences with victimization in children and adolescents with autism spectrum disorder
Bibliographic record
Abstract
Several researchers have found that peer victimization is more frequent in those with ASD, yet few studies have examined the underlying social cognitive mechanisms involved. Crick and Dodge's (1994) social-information processing (SIP) model has been used to examine bullying in aggressive populations and has more recently been applied to difficulties with social interaction more broadly. The model consists of six steps that operate in a circular manner, from the encoding of social cues, to the behavioural enactment of a selected response. Only two studies have examined the SIP model with those with ASD and have found differences in encoding and assertive response selection in those with ASD. The current study is the first to examine victimization and SIP together in those with ASD. Further, this study is the first to include eye-tracking methodology to gain further insight into the encoding stage of processing. \n\nTwenty-four children with typical development aged 6-17 were compared with 24 children with high-functioning ASD. Those with ASD are more likely to experience victimization, according to parent report, across all types of bullying. The ASD group had a lower proportion of looking time to faces, during the ambiguous social exclusion video. Several social-information processing differences were also noted: those with ASD made more encoding errors, fewer assertive responses, and more passive responses (for a hostile social exclusion video only). Encoding errors were significantly correlated with victimization, but only in the ASD group. Some areas of similarity were also found across groups. Few participants in either group generated aggressive solutions. The groups were generally equivalent in their propensity to make hostile attributions and in the number of responses generated. The proportion of looking time was significantly related to victimization, assertive and passive responses across the entire sample for some of the videos. These potential processing differences may have cascading effects on the ability of those with ASD to effectively manage social interactions. Theoretical and clinical implications are examined and future research directions are suggested.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".