What you see is what you get: contextual modulation of face scanning in typical and atypical development
Bibliographic record
Abstract
Infants' visual scanning of social scenes is influenced by both exogenously and endogenously driven shifts of attention. We manipulate these factors by contrasting individual infants' distribution of visual attention to the eyes relative to the mouth when viewing complex dynamic scenes with multiple communicative signals (e.g. peek-a-boo), relative to the same infant viewing simpler scenes where only single features move (moving eyes, mouth and hands). We explore the relationship between context-dependent scanning patterns and later social and communication outcomes in two groups of infants, with and without familial risk for autism. Our findings suggest that in complex scenes requiring more endogenous control of attention, increased scanning of the mouth region relative to the eyes at 7 months is associated with superior expressive language (EL) at 36 months. This relationship holds even after controlling for outcome group. In contrast, in simple scenes where only the mouth is moving, those infants, irrespective of their group membership, who direct their attention to the repetitive moving feature, i.e. the mouth, have poorer EL at 36 months. Taken together, our findings suggest that scanning of complex social scenes does not begin as strikingly different in those infants later diagnosed with autism.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".