Changing Faces: Dynamic Emotional Face Processing in Autism Spectrum Disorder Across Childhood and Adulthood
Bibliographic record
Abstract
BACKGROUND: Autism spectrum disorder (ASD) is classically associated with poor emotional face processing. Few studies, however, have used more ecological dynamic stimuli. We contrasted functional magnetic resonance imaging measures of dynamic emotional face processing in ASD and typically developing (TD) cohorts across a wide age range to determine if the processing and age-related trajectories differed between participants with and without ASD. METHODS: Functional magnetic resonance imaging data collected from 200 participants (5-42 years old; 107 in ASD cohort, 93 in TD cohort) during the presentation of dynamic emotional faces (neutral-to-happy, neutral-to-angry) and dynamic flowers (closed-to-open) were analyzed. Group differences and group-by-age interactions in the faces versus flowers and between emotion contrasts were investigated. RESULTS: Differences in activation between dynamic faces and flowers in occipital regions, including the fusiform gyri, were reduced in the ASD group. Contrasting the two emotions, ASD compared with TD participants showed increased engagement of the precentral, postcentral, and superior temporal gyri to happy faces and increased activation to angry faces occipitally. Emotion processing regions, such as insula, temporal pole, and frontal regions, showed increased recruitment with age to happy faces compared with both angry faces and flowers in the TD group, but decreased recruitment with age in the ASD group. CONCLUSIONS: Using dynamic stimuli, we demonstrated that participants with ASD processed faces similarly to nonface stimuli, and age-related atypicalities were more pronounced to happy faces in participants with ASD. We demonstrated emotion-specific atypicalities in a large group of participants with ASD that underscore persistent difficulties from childhood into mid-adulthood.
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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".