Face recognition is similarly affected by viewpoint in school-aged children and adults
Bibliographic record
Abstract
There is an ongoing debate on the question when face processing abilities mature. One aspect that has been part of this debate is the ability to recognize faces in and across different viewpoints. Here, we tested 128 participants consisting of school-age children (ages, 5–10 years) and adults (ages, 19–37 years) in two experiments to investigate the effects of different viewpoints (including front, three-quarter, profile view) on face recognition during development. Furthermore, we compared recognition performance for faces to that of another object category (cars). In the first experiment ( n = 88) we tested if the pattern of performance for faces presented in different viewpoints is similar in school-aged children and adults. Participants completed a two-alternative-forced-choice (2AFC) memory task comprising images of both faces and cars in front, three-quarter and profile view, which were presented in the same viewpoint during learning and testing. In the second experiment ( n = 40) we tested if face recognition is similarly affected by viewpoint changes in children and adults. In this experiment the 2AFC memory task included a change of viewpoint between learning and testing. While in both experiments we found higher recognition performance for faces with increasing age, the overall pattern of both viewpoint and viewpoint-change-effects and also the difference between view-change- and no-change-conditions was similar across age groups. In contrast to faces, no viewpoint effects were observed in cars (experiment 1), viewpoint change effects, however, were similar for cars and faces (experiment 2). In sum, our results suggest early maturity of the ability to recognize faces in and across different viewpoints.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
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 | 0.000 | 0.001 |
| 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 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".