The eyes know it: Toddlers' visual scanning of sad faces is predicted by their theory of mind skills
Bibliographic record
Abstract
The current research explored toddlers' gaze fixation during a scene showing a person expressing sadness after a ball is stolen from her. The relation between the duration of gaze fixation on different parts of the person's sad face (e.g., eyes, mouth) and theory of mind skills was examined. Eye tracking data indicated that before the actor experienced the negative event, toddlers divided their fixation equally between the actor's happy face and other distracting objects, but looked longer at the face after the ball was stolen and she expressed sadness. The strongest predictor of increased focus on the sad face versus other elements of the scene was toddlers' ability to predict others' emotional reactions when outcomes fulfilled (happiness) or failed to fulfill (sadness) desires, whereas toddlers' visual perspective-taking skills predicted their more specific focusing on the actor's eyes and, for boys only, mouth. Furthermore, gender differences emerged in toddlers' fixation on parts of the scene. Taken together, these findings suggest that top-down processes are involved in the scanning of emotional facial expressions in toddlers.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| 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".