Infants' visual exploration strategies for adult and child faces
Bibliographic record
Abstract
By the end of the first year of life, infants' discrimination abilities tune to frequently experienced face groups. Little is known about the exploration strategies adopted to efficiently discriminate frequent, familiar face types. The present eye-tracking study examined the distribution of visual fixations produced by 10-month-old and 4-month-old singletons while learning adult (i.e., familiar) and child (i.e., unfamiliar) White faces. Infants were tested in an infant-controlled visual habituation task, in which post-habituation preference measured successful discrimination. Results confirmed earlier evidence that, without sibling experience, 10-month-olds discriminate only among adult faces. Analyses of gaze movements during habituation showed that infants' fixations were centered in the upper part of the stimuli. The mouth was sampled longer in adult faces than in child faces, while the child eyes were sampled longer and more frequently than the adult eyes. At 10 months, but not at 4 months, global measures of scanning behavior on the whole face also varied according to face age, as the spatiotemporal distribution of scan paths showed larger within- and between-participants similarity for adult faces than for child faces. Results are discussed with reference to the perceptual narrowing literature, and the influence of age-appropriate developmental tasks on infants' face processing abilities.
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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 | 0.000 |
| 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".