Discrimination of amplitude spectrum slope of natural scenes during childhood
Bibliographic record
Abstract
Accumulating evidence suggests that the adult visual system is optimally tuned for processing the spatial properties of natural scenes (1/fαamplitude spectra). It is also documented that different aspects of spatial vision (e.g., acuity and spatial contrast sensitivity) develop at different rates and some do not become mature until late childhood. We compared natural scene perception in children aged 6, 8, and 10 years (n = 16 per age) and in adults (mean age = 23). A same-different task combined with a staircase procedure measured thresholds for discriminating change in the slope of the amplitude spectra of natural scene stimuli with reference α's of 0.7, 1.0, or 1.3. First, consistent with previous studies, adults were least sensitive for the shallowest α (i.e., 0.7) and most sensitive for the steepest α (i.e., 1.3). Second, a 4 (age group) X 3 (reference α's) repeated measures ANOVA revealed a significant interaction (p [[lt]] 0.01). Post-hoc analyses of the interaction indicated no difference in threshold among any of the age groups for the α of 0.7. However, the 6- and 8-year-olds had significantly higher discrimination thresholds compared to the 10-year-olds and adults for α's of 1.0 and 1.3. Finally, the 10-year-olds' thresholds did not differ significantly from those of adults for any of the α's tested. These data suggest that sensitivity for detecting change in the spatial characteristics of natural scenes during childhood may not be optimally tuned to the statistics of natural images until about 10 years of 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".