Bibliographic record
Abstract
Mental rotation refers to the process of rotating the image of an object to determine if it is identical to another object presented at a different orientation (Shepard & Metzler, 1971). This ability is thought to involve regions of the cortical visual system involved in processing real motion (e.g. Zacks, 2008). Because children born preterm often show damage to these regions (e.g. Back et al., 2001) and compromised motion processing (MacKay et al., 2005; Taylor et al., submitted), we predicted that they would experience more difficulty than full-term controls with mental rotation. We assessed mental rotation ability (using identical and mirror-image objects) in 15, 5–9 year-old children born at [[lt]]32 weeks gestation, and in 16 full-term controls. The two groups were matched in age and SES and had a similar gender distribution. We observed a classic mental rotation function; thus, matching accuracy decreased as the angular disparity between stimuli increased (F = 13.3, p [[lt]].001). Both groups showed a similar function, suggesting that preterm children can mentally rotate unfamiliar figures in the picture plane (although, as response time data were not collected, it is not clear if they are as efficient as controls in this regard). Despite showing a typical mental rotation function, preterm children performed more poorly than their full-term counterparts overall (F = 10.6, p [[lt]].001), even on trials involving the 0° disparity, mirror-normal discrimination (t = 2.1, p [[lt]]0.05). This suggests that preterm children may have a relative deficit in their ability to mentally transform objects out of the picture plane, a skill thought to be required to make accurate mirror-normal discriminations (cf. Hamm et al., 2004). This finding is consistent with other evidence suggesting the dissociability of (planar) mental rotation and mirror-normal discrimination ability (e.g. Davidoff & Warrington, 2001; Lawson et al., 2000).
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".