Exploring the perceptual spaces of faces, cars, and birds in children and adults
Bibliographic record
Abstract
To date, much of the developmental research concerning age-related changes in face processing has focused on the type of information and the specific strategies utilized by children during face recognition. Other aspects of facial recognition, such as the principles governing organization of individual face exemplars and other objects in perceptual memory, have been less extensively investigated. The present study explores the organization of face, bird, and car objects in perceptual memory using a morphing paradigm. Children ages five-six, seven-eight, nine-ten, and eleven-twelve, and adults were shown a series of morphs created with equal contributions from typical and atypical face, bird, and car parent images. Participants were asked to judge whether each 50/50 morph more strongly resembled the typical or the atypical parent image from which it was created. Children in all age groups and adults demonstrated a systematic atypicality bias for faces and birds: the 50/50 face (bird) morph was judged as appearing more similar to the atypical parent face (bird) than the typical parent face (bird). Interestingly, the magnitude of the atypicality bias remained robust and stable across all age groups, indicating an absence of age-related differences. No reliable atypicality bias emerged for the car category. Collectively, these findings establish that by the age of five, children are sensitive to the structure and density of face and bird probes, and are capable of encoding and organizing face, bird, and car exemplars into a perceptual space that is strikingly similar to that of an adult's. These results suggest that category organization, for both children and adults, follows a distance-density principle (Krumhansl, 1978) where the perceived similarity between any two category exemplars is attributed to both their relative distance and the density of neighboring exemplars in the perceptual space.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".