7.2: <i>Invited Paper:</i> Quantification and Modulation of Stereopsis in Humans
Bibliographic record
Abstract
Binocular vision in general and stereopsis in particular are fundamental to human vision and visual actions. It is widely thought that about 5% of the population have a lazy eye and lack stereo vision, so it is often supposed that most of the population (95%) have good stereo abilities. We show that this is not the case; 68% have good to excellent stereo (the haves) and 32% have moderate to poor stereo (the have‐nots). Why so many people lack good 3‐D stereo vision is unclear but it is likely to be neural and reversible. Interocular suppression and stereopsis are two important binocular functions. To date, it still remains unclear about the relationship between them. Here, we use the binocular phase combination paradigm and a comparable paradigm to provide a normative dataset for perceptual eye dominance and stereo acuity in 142 normal‐sighted human adults and analyze their correlation. We show that our eye dominance and stereopsis are not strongly correlated in the normalsighted subjects. These results thus support the idea that the binocular phase combination and stereopsis may be limited by different factors in normal‐sighted adults. At present there is no good way of reliably measuring stereopsis in the clinic other than the standard book tests designed for children. These are convenient but coarsely quantized in disparity with no measure of variance. This makes it difficult to be able to assess the significance of differences from visit to visit contingent on therapy. We report an iPod app that has been developed from laboratory studies of normal stereopsis, to measure stereo in the clinic that is not limited by these factors. The application enables measurements over the wide disparity range and not just at the finest disparities. In addition, it allows changes in stereopsis of the order of 1.9 to be statistically distinguished. Luminance plays a modulating role in the processes of several visual tasks, which in turn provides significant information for the understanding of visual processing. To study the relationship between luminance and stereopsis, we manipulated the mean luminance seen by both eyes or the interocular difference in mean luminance by using ND filters placed in front of both eyes or just one eye respectively. We found that disparity processing was little affected by a binocular change in luminance, but was greatly affected by a luminance mismatch between the two eyes. To investigate its origin we manipulated two factors, the temporal synchrony between the two eyes and the interocular contrast. Both factors are implicated in the loss of stereo performance when the mean luminance is different between the eyes, suggesting an underlying explanation in terms of temporal low‐pass filtering resulting in the combination of a luminance‐dependent temporal delay and a luminance‐dependent change in contrast gain. These results add to our knowledge of stereopsis and have therapeutic potential in re‐establishing stereopsis in patients with binocular disorders.
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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 | 0.001 |
| 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".