Attention allocation to facial expressions of emotion among persons with Williams and Down syndromes
Bibliographic record
Abstract
Individuals with Williams syndrome and those with Down syndrome are both characterized by heightened social interest, although the manifestation is not always similar. Using a dot-probe task, we examined one possible source of difference: allocation of attention to facial expressions of emotion. Thirteen individuals with Williams syndrome (mean age = 19.2 years, range = 10-28.6), 20 with Down syndrome (mean age = 18.8 years, range = 12.1-26.3), and 19 typically developing children participated. The groups were matched for mental age (mean = 5.8 years). None of the groups displayed a bias to angry faces. The participants with Williams syndrome showed a selective bias toward happy faces, whereas the participants with Down syndrome behaved similarly to the typically developing participants with no such bias. Homogeneity in the direction of bias was markedly highest in the Williams syndrome group whose bias appeared to result from enhanced attention capture. They appeared to rapidly and selectively allocate attention toward positive facial expressions. The complexity of social approach behavior and the need to explore other aspects of cognition that may be implicated in this behavior in both syndromes is discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".