Fusing sine waves with optotypes: A new test of human spatial contrast sensitivity
Bibliographic record
Abstract
Purpose: Current tests of spatial contrast sensitivity (CS) fall into two categories, those that employ luminance-modulated sine-wave gratings (e.g., the FACT chart), or those that use recognizable Snellen-like optotypes (e.g., the Pelli Robson chart). Both forms possess distinct advantages but an ideal test would combine the desired characteristics of each format. Here, we report on a novel prototype for one such test. Methods: Five wall charts were constructed using custom software and a high quality PostScript printer. The optotypes on each row of a chart were Landolt Cs which, from the outside edge to the inside edge of each C, modulated sinusoidally in luminance. The average luminance within each C matched the chart's background. Each chart contained sine-wave Cs representing 1 of 5 spatial frequencies (0.75, 1.5, 3.0, 6.0 and 12.0 c/deg), with contrast on each successive row decreasing from 40% to 1% in equal log steps. 25 adults were tested monocularly at 3m, and for comparison and validation, were also tested with standard commercial CS tests: the FACT, Rabin, Pelli-Robson, and low contrast Sloan tests. To examine applicability with children, 25 4-and 5-year-olds were also tested. Results: Adults easily completed the test in an average of only 2.3 min. Results showed that each subject generated an interpretable contrast sensitivity function (CSF), with individual performance on the sine-wave Cs predicting very well, the results on the standard CS tests. Children required more time (5.7 min) but most (94%) were capable of successful completion. Conclusions: The new sine-wave C test of contrast sensitivity appears very successful. Both adults and children show definitive responses and clear estimates of threshold. Thus, the test holds promise as a hybrid tool for assessing simultaneously, both optotype CS and full spectrum contrast sensitivity, a feature that should have both experimental and clinical value.
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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 | 0.001 | 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".