Contrast Sensitivity Across the Nasal and Temporal Peripheral Visual Fields: Measurements with Gabor Sinusoids
Bibliographic record
Abstract
Purpose: It is now well accepted that contrast sensitivity (CS) is the most comprehensive measure of human spatial vision. Previously (VSS 2015), we reported the first data for CS measurement in both the near and far periphery, using Gaussian filtered sine waves (Gabor patches). However, in order to better understand underlying neural mechanisms and to provide normative data for those interested in assessing peripheral eye disease (e.g. glaucoma, RP, retinal degeneration), here we measured CS across both the nasal and temporal retinal fields. Methods: Using a conventional staircase procedure, right eyes from 10 healthy emmetropic adults (16 to 55 yr.) were tested 10 times repeatedly with vertically-oriented sinusoidal Gabor patches that ranged logarithmically in spatial frequency (SF) from 0.375 to 18 cy/deg and in contrast from 0.001 to 0.30. Contrast thresholds at each SF were obtained foveally and from 100 to 800 within the temporal and nasal visual fields. Results: As expected, with increasing eccentricity, the drop-off in peripheral CS from foveal viewing (Mfoveal across SF = 145.9 CS units) is substantial. However, the pattern differs between the two fields: Nasal sensitivity drops off rapidly over the first 200 (to 10.5 units) but then completely stabilizes until 600 (10.6 units). In contrast, temporal sensitivity decreases more steadily but eventually yielding a lower CS level at 600 (5.6 units, p < 0.05). Conclusions: Human CS can be measured reliably up to 600 in both the nasal and temporal visual fields. Overall, CS decreases by about 0.24 log units per 10o eccentricity, but the rate of reduction is at first rapid and then plateaus in the nasal field, and is much more steady in the temporal field. These behavioural data are consistent with anatomical data describing the relative ratios of P ganglion cells in the peripheral retina. Meeting abstract presented at VSS 2016
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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 | 0.000 | 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) | 0.001 | 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".