Contributions to the Cognitive Study of Facial Recognition on Down Syndrome: A New Approximation to Exploring Facial Emotion Processing Style
Bibliographic record
Abstract
Background: This paper aimed to explore the ability of people with Down syndrome (PWDS) in recognizing facial emotion by considering automatic cognitive processing levels of face recognition. Method: A sample of PWDS and participants with typical development (PWTD) participated in a set of two affective priming studies. In each study, participants had to categorize an emotional or neutral target face that was preceded by another emotional face. Stimuli presentation for each facial set (one face after another) was conducted by using an stimulus onset asynchrony (SOA) of 300 ms with the inter-stimulus interval (ISI) set at 50 ms. The first affective priming study manipulated emotion congruency between prime and target emotional faces to explore emotion classification abilities and to identify the cognitive mechanisms underlying automatic recognition of some emotional faces. The second study explored the effect that gender of a face has over categorization of facial emotion and difficulty in recognizing negative facial expressions. Results: The results strongly suggest that not all of the PWDS present difficulties in recognizing negative facial emotions. PWDS’ performance pattern in categorizing emotion was similar to that of PWTDs if they had to use broad classification categories (e.g., emotion vs. no emotion). However, differences between both samples occurred if PWDS had to use a specific category task (e.g., classification of happiness, sadness, etc.). Conclusions: At least two emotion information processing styles can be identified in PWDS. Methodological and theoretical implications for exploring the emotional capabilities of people with DS are 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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".