Sentivitity to the spacing of features in novel objects after learning individuals vs. categories
Bibliographic record
Abstract
Adults appear to be more sensitive to configural information, including the spacing of features, in faces than in other objects (reviewed in Maurer, Le Grand, & Mondloch, 2002). This difference arises even when adults are simply primed to perceive 4 blobs (placed in the position of two eyes, nose, and mouth) as facial features rather than points of the letter Y (Nishimura, Maurer, & Mondloch, 2004). The difference may arise because spacing information plays a greater role in learning to identify individual exemplars of an object category (e.g. faces: Bob vs. John) than in learning to identify objects at the basic level of categorization (e.g. table vs. chair; Gauthier & Tarr, 1997). We simulated this learning difference by having two groups view the same stimuli but learn to label them only at the categorical level or at both the categorical and individual levels. One group (n=9) was trained to label three categories of ambiguous stimuli: bobos formed from 4 blobs, tikas formed from 6 blobs, and pelis formed from 7 blobs. The other group (n=9) was trained, in addition, to use different labels for the three individual bobos, each of which has a slightly different spacing of its constituent blobs. The two groups were matched based on a pre-test of sensitivity to spacing differences in a different set of bobos. On a post-test with novel bobos, the group trained to label individual bobos was significantly more accurate (M=71.1%) in detecting changes in the spacing of the constituent blobs than the group that learned only the category labels (M=64.8%; p = .03, one-tailed). The spacing changes were of the magnitude that naturally exists among human faces. The findings are consistent with the hypothesis that we become more sensitive to the spacing of features in faces than in other objects because we have more experience identifying individual faces than identifying individual members of non-face categories.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".