Infants' sensitivity to variability in face configuration
Bibliographic record
Abstract
PURPOSE. Research has suggested that 2-month-old infants can perceive face-like stimuli as a unique configuration of features. The face configuration parameters necessary for infants to discriminate between faces, however, have not been examined. To investigate the configurational parameters that support discrimination, synthetic face stimuli (Wilson et al., 2002), both frontal and 20-degree side views, which equate faces on all parameters except geometric variability were used. Specifically, this study was designed to determine how much geometric variation between faces is necessary for infants to discriminate them. METHODS. A cueing paradigm was used in which 6- to 7-month-olds saw mean face cues that predicted the appearance of targets on one side and face cues that geometrically varied by either 3, 5, 7 or 10% predicted targets that appeared on the other side. Eye movements were analyzed for correct anticipation of the targets in response to which face cue had been presented. RESULTS. When seen in frontal view, infants exhibited above chance correct anticipations for mean vs. 5 and 10% face comparisons but not mean vs. 3%. When seen in the side view, infants exhibited above chance correct anticipations only for the mean vs. 10% variability comparison. CONCLUSIONS. Infants thus rely on the overall variability in configuration to discriminate between individual faces. Moreover, infants' discrimination is consistent with adults' (Wilson et al., 2002) in the amount of variability necessary and that less variability is needed for frontal than side views, suggesting the recruitment of the same neural mechanisms.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".