Measuring Spatial Contrast Sensitivity in Adults and Children by Combining Sine Waves with Landolt Cs.
Bibliographic record
Abstract
Purpose In the quest to develop a contrast sensitivity test that combines experimental rigor with clinical ease of use and interpretability, we (VSS 2009) reported on a new prototype test which consists of classic Landolt C optotypes that are created with luminance modulated sine-waves. In the present work, we have improved the technical aspects of this test and now provide some normative data from larger samples of children and adults. Methods: The modified test consists of 6 charts, each containing rows of Landolt Cs which, from the outside to the inside edge of each C, modulate sinusoidally at 1 of 6 spatial frequencies (0.37, 0.75, 1.5, 3, 6 and 12 cy/deg). With each successive row, optotypes decrease in contrast from 40% (2.5 CS units) to 0.5% (200 CS units) in equal log steps. 110 preschoolers (3-5 yr) and 65 adults were tested monocularly at 3m with all 6 charts. For comparison, adults were also tested with commercially available FACT and Vector Vision sine-wave CS tests. Results: All adults and 95% of preschoolers completed the entire test (M = 4 min) and each subject generated an interpretable contrast sensitivity function (CSF). Distributions of CS at each spatial frequency were distributed normally with mean performance (across SF) increasing slightly from 38 CS units at 3 years to 51 units at 5 years to 66 units in adulthood, a value very consistent with both the commercial FACT and Vector Vision CS tests. Conclusions: The new sine-wave Landolt C test of contrast sensitivity appears to be a valid and time-efficient tool for measuring full-spectrum contrast sensitivity in young children and adults. The ease of responding to the Landolt target yields both definitive responses and clear estimates of contrast thresholds. Moreover, the software that we have developed to produce Landolt targets is easily modified to allow additional estimates of high and low contrast visual acuity. Meeting abstract presented at VSS 2012
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".