Sensitivity to the geometric variability of faces in infants
Bibliographic record
Abstract
Purpose. Face perception research has suggested that 2-month-olds can perceive face-like stimuli as a unique configuration of features. The parameters of the face configuration necessary for infants to discriminate between faces, however, has not been examined. To investigate the configurational parameters of face stimuli that support discrimination, synthetic face stimuli (Wilson et al., 2002), both frontal view and 20-degree side view, that equate faces on all parameters except geometric variability were used. More 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 picture sequences in which mean face cues predicted plus sign targets appearing on one side of the screen, and face cues that geometrically varied from the mean by either 5 or 10% predicted plus sign targets appearing on the other side. Eye movements were analyzed for correct anticipatory eye movements to the targets in response to which face cue had been presented. Results. When seen in frontal view, infants exhibited above chance correct anticipations for all face comparisons. When seen in the side view, infants exhibited above chance correct anticipations only for the mean vs. 10% variability comparison. Thus, infants discriminate as little as a 5% change in the overall configuration of a face when seen from the front, but need 10% change to discriminate faces seen from the side. Conclusions. These results indicate that by 6–7 months of age infants rely on the overall geometric variability in configuration to discriminate between individual faces. Moreover, these findings are consistent with those obtained with adults (Wilson et al., 2002) in both the amount of variability necessary for discrimination and that less variability is needed to discriminate frontal than side view faces, perhaps suggesting the recruitment of the same neural mechanisms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".