Does the loss of sensory fusion demarcate fine vs coarse processing?
Bibliographic record
Abstract
In recent experiments we have found that a coarse stereoscopic mechanism is available to support depth perception at large disparities in individuals with amblyopia (VSS 2012). These subjects all performed poorly on conventional tests of stereopsis, but were similar to controls when the stimuli were presented at very large disparities and appeared diplopic. While the data are suggestive of distinct underlying (fine and coarse) mechanisms which map onto 1[sup]st[/sup] and 2[sup]nd[/sup] –order disparity processing, further study is required to make this link. The experiments reported here use visually normal observers and stimuli with different interocular contrast ratios presented at a large range of disparities. The goal is to determine whether coarse processing is responsible for depth percepts under conditions where the stimuli are diplopic and/or poorly matched in the two eyes. Luminance patches with a diameter of 30 or 21 min were presented stereoscopically at five interocular contrast ratios ranging from 20:100 to 100:100. In Experiment 1 we assessed diplopia thresholds to categorize disparities as fine (fused) or coarse (diplopic). In Experiment 2 observers performed a depth discrimination task for disparities ranging from 3 to 72 min. Regardless of size, depth discrimination was unaffected by interocular contrast differences when the test disparities were outside Panum’s fusional area. Within the fused range, performance declined gradually at each contrast ratio with steeper slopes corresponding to larger ratios. These data suggest that the disparity at which sensory fusion is lost may mark a boundary beyond which only coarse (2[sup]nd[/sup]-order) disparity processing is available to support stereopsis. Meeting abstract presented at VSS 2013
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".