Investigating the interaction between low and intermediate levels of spatial vision at different periods of development
Bibliographic record
Abstract
Although much research has investigated the visual development of lower (local) and higher levels (global) of processing in isolation, less is known about the developmental interactions between mechanisms mediating early- and intermediate-level vision. The objective of this study was to evaluate the development of intermediate-level vision by assessing the ability to discriminate circular shapes (global) whose contour was defined by different local attributes: luminance and texture. School-aged children, adolescents, and adults were asked to discriminate a deformed circle (radial frequency patterns or RFP) from a circle. RFPs varied as a function of (a) number of bumps or curvatures (radial frequency of three, five, and 10) and (b) the physical attribute (luminance or texture) that defined the contour. Deformation thresholds were measured for each radial frequency and attribute condition. In general, results indicated that when compared to adolescents and adults children performed worse only when luminance-defined shapes had fewer curvatures (i.e., three and five), but for texture-defined shapes, children performed worse across all types of radial frequencies (three, five, and 10). This suggests that sensitivity to global shapes mediated by intermediate level vision is differentially affected by the type of local information defining the global shape at different periods of development.
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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 | 0.003 |
| 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".