Visual configural processing in adults born at extremely low birth weight
Bibliographic record
Abstract
Abstract Being born at extremely low birth weight (ELBW; ≤1,000 g) is associated with enduring visual impairments. We tested for long‐term, higher order visual processing problems in the oldest known prospectively followed cohort of ELBW survivors. Configural processing (spacing among features of an object) was examined in 62 adults born at ELBW ( M age = 31.9 years) and 82 adults born at normal birth weight (NBW; ≥2,500 g: M age = 32.5 years). Pairs of human faces, monkey faces, or houses were presented in a delayed match‐to‐sample task, where non‐matching stimuli differed only in the spacing of their features. Discrimination accuracy for each stimulus type was compared between birth weight groups, adjusting for neurosensory impairment, visual acuity, binocular fusion ability, IQ, and sex. Both groups were better able to discriminate human faces than monkey faces ( p < .001). However, the ELBW group discriminated between human faces ( p < . 001), between monkey faces ( p < . 001), and to some degree, between houses ( p < .06), more poorly than NBW control participants, suggesting a general deficit in perceptual processing. Human face discrimination was related to performance IQ (PIQ) across groups, but especially among ELBW survivors. Coding (a PIQ subtest) also predicted human face discrimination in ELBW survivors, consistent with previously reported links between visuo‐perceptive difficulties and regional slowing of cortical activity in individuals born preterm. Correlations with Coding suggested ELBW survivors may have used a feature‐matching approach to processing human faces. Future studies could examine brain‐based anatomical and functional evidence for altered face processing, as well as the social and memory consequences of face‐processing deficits in ELBW survivors.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".