Exploring the perceptual spaces of faces, cars and birds in children and adults
Bibliographic record
Abstract
While much developmental research has focused on the strategies that children employ to recognize faces, less is known about the principles governing the organization of face exemplars in perceptual memory. In this study, we tested a novel, child-friendly paradigm for investigating the organization of face, bird and car exemplars. Children ages 3-4, 5-6, 7-8, 9-10, 11-12 and adults were presented with 50/50 morphs of typical and atypical face, bird and car parent images. Participants were asked to judge whether the 50/50 morph more strongly resembled the typical or the atypical parent image. Young and older children and adults showed a systematic bias to the atypical faces and birds, but no bias toward the atypical cars. Collectively, these findings argue that by the age of 3, children encode and organize faces, birds and cars in a perceptual space that is strikingly similar to that of adults. Category organization for both children and adults follows Krumhansl's (1978) distance-density principle in which the similarity between two exemplars is jointly determined by their physical appearance and the density of neighboring exemplars in the perceptual space.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".