Different luminance- and texture-defined contrast sensitivity profiles for school-aged children
Bibliographic record
Abstract
Our current understanding of how the visual brain develops is based largely on the study of luminance-defined information processing. This approach, however, is somewhat limiting, since everyday scenes are composed of complex images, consisting of information characterized by physical attributes relating to both luminance and texture. Few studies have explored how contrast sensitivity to texture-defined information develops, particularly throughout the school-aged years. The current study investigated how contrast sensitivity to luminance- (luminance-modulated noise) and texture-defined (contrast-modulated noise) static gratings develops in school-aged children. Contrast sensitivity functions identified distinct profiles for luminance- and texture-defined gratings across spatial frequencies (SFs) and age. Sensitivity to luminance-defined gratings reached maturity in childhood by the ages of 9-10 years for all SFs (0.5, 1, 2, 4 and 8 cycles/degree or cpd). Sensitivity to texture-defined gratings reached maturity at 5-6 years for low SFs and 7-8 years for high SFs (i.e., 4 cpd). These results establish that the processing of luminance- and texture-defined information develop differently as a function of SF and age.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".