Learning from facial expressions in individuals with Williams syndrome
Bibliographic record
Abstract
BACKGROUND: Despite high levels of social engagement, the social competence of individuals with Williams syndrome (WS) is frequently compromised. This descriptive study explores the ability of young people with WS to learn from facial expressions when provided as a source of feedback for their actions. METHOD: Using a novel task, the ability to interpret facial expressions and adapt behaviour after receiving feedback in the form of happy or angry faces was assessed in 12 participants with WS aged between 10 and 28 years and with a mean nonverbal mental age of 6.5 years, and in typically developing (TD) children aged between 4 and 7 years. RESULTS: Individuals with WS were able to use facial expressions as feedback in a manner commensurate with their mental age, only when other cognitive demands were low. Their performance profile differed from that of the TD children matched for mental age and from the performance profile of 4 year olds. CONCLUSIONS: Possible explanations for the unique performance profile observed in the participants with WS are discussed. The results highlight the need to examine social competencies in the context of the cognitive demands characteristic of social environments.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".