Do You Like Me? Differences in Learning Social Cues in Adolescents with Developmental Language Disorder (DLD)
Bibliographic record
Abstract
Abstract The pathways to the documented increased social and emotional difficulties in individuals with Developmental Language Disorder (DLD) are unclear. We explored whether differences in social evaluation could account for social and emotional difficulties in adolescents with DLD using a computerized social evaluation task. Twenty-four adolescents with DLD were matched with twenty-six adolescents with typical language development (TLD) (M age = 13.5 years, SE = 2.38; n = 18 female). They completed the Social Evaluation Learning Task (SELT; Button et al., 2015) which measures how quickly people learn the computer likes or dislikes either them or someone else. Adolescents and parents reported social and emotional functioning. Adolescents with DLD had poorer social understanding, in that they took longer to learn that the computer disliked them. They learned similarly to their TLD when the computer liked them and someone else. Adolescents with DLD also had higher self-reported anxiety and more parent reported emotional and peer problems; however, there was no mediational effect of social evaluation on socioemotional difficulties. This study demonstrates that adolescent with DLD have specific difficulties in interpreting cues that they are disliked by others but are just as good at understanding when they are liked. The differences seen in their social evaluation skills did not account for their increased socioemotional difficulties. This social evaluation bias might explain previous findings of good self-rated social competence while other ratings indicate social difficulties. Future research is necessary to investigate the implications of this finding further.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".