The Impact of Perceptual Organization on the Limits of Binocular Fusion
Bibliographic record
Abstract
As a consequence of the separation of the two eyes in the head, the images of objects projected onto the two retinas are in different positions, called binocular disparity. The brain uses this positional information to represent the 3D layout or depth in a scene, a process called stereopsis. The two monocular half-images of objects will be integrated and seen as single if the binocular disparity is within Panums fusional area. Objects with disparities beyond this region will be seen as double. There are likely many factors which influence the perception of diplopia, including cognition (i.e., attention or suppression) or low-level object features (i.e., size). This thesis evaluates the proposal that higher-order visual processing, grouping through uniform connectedness, diminishes the perception of diplopia. To this end, in a series of experiments I presented isolated single elements, pairs of isolated and connected elements, and elements with varying levels of connectedness. Taken together, the results show that connecting elements elevates diplopia thresholds. This result is consistent with both the impact of perceptual organization and the use of larger receptive fields to process the disparity of the object. These two possibilities are discussed along with future experiments designed to distinguish between these accounts.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".