Which stripes are fatter? The development of spatial frequency discrimination
Bibliographic record
Abstract
Adults can discriminate a 2 – 11% change in spatial frequency (Hirsch & Hylton, 1982; Mayer & Kim, 1986). Purpose. To provide the first measurement of the development of spatial frequency discrimination. Methods. Participants were adults (range: 17–20 yrs, M = 18.9 yrs) and children aged 5, 7, and 9 years (all +/− 3 months; n = 20 per age). Participants saw sequential presentations of a baseline sine-wave grating of 1 or 3 cpd and a comparison sine-wave of higher spatial frequency. The task was to indicate whether the wider stripes occurred in interval 1 or 2. The spatial frequency of the comparison was varied over trials according to a ML-PEST staircase (Harvey, 1986) to measure the minimum spatial frequency discriminable from baseline at 82% correct. Results. An ANOVA showed no significant differences between thresholds at the two baseline spatial frequencies (p [[gt]] .20), significant improvement with age (p p [[gt]] .60). The minimum change from baseline necessary to discriminate spatial frequency decreased from 30.1% in 5-year-olds to 11.6% in 7-year-olds (p p [[gt]] .20). The data were best fit by an exponential function reflecting the rapid improvement in thresholds between 5 and 7 year of age and more gradual improvement thereafter until adulthood (r2 = .046, p Conclusions. The pattern of development for sensitivity to spatial frequency (this study) resembles those for the development of sensitivity to orientation (Lewis et al., 2009) and contrast (Ellemberg et al, 1999). These similar patterns are consistent with theories of common underlying mechanisms (Vincent & Regan, 1995; Zhu et al., 2008). The immaturities at 5 years of age may be caused by higher internal noise.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".