The disparity gradient limit has limited relevance to natural environments
Bibliographic record
Abstract
Diplopia thresholds depend on disparity magnitude and the lateral separation of disparate elements. The disparity gradient limit is the ratio of these values; since Burt and Julesz (1980), a gradient limit of one has been commonly accepted as the limit for binocular fusion. This is true for isolated stimuli typically used in psychophysical studies. However, gradients greater than one are common in natural cluttered environments. Here we re-evaluate the relevance of the disparity gradient limit in more ecologically valid stimuli. We rendered realistic 3D branches viewed in a virtual reality headset. A low complexity condition comprised two isolated branches, presented on either side of a central reference branch. For high complexity, multiple overlapping branches were overlaid to create the equivalent of a ‘thicket’. The thicket comprised two identical but mirrored clusters of branches, centred on a reference branch. The two clusters were shifted in opposite directions in depth to vary the disparity range. Observers indicated the depth extent of the structure as a whole with a virtual ruler. We also measured diplopia thresholds for these stimuli. For the thickets, gradient limit violations are always present, so theoretically, diplopia should always be apparent. We found that diplopia was experienced at lower disparities in the thickets relative to the branches. However, there was a large range over which the thicket appeared fused. Further, there was a linear relationship between perceived and physical depth for the branches, but the functions obtained in the thicket condition exhibited a compressive non-linearity; following an inflection point, estimates plateaued. These data suggest that the disparity gradient limit has limited relevance to cluttered stimuli like those typically encountered in the natural environment. We propose that the resilience of fusion is achieved via coarse disparity mechanisms which have previously been shown to mediate depth percepts for diplopic targets.
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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 | 0.005 |
| 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 | 0.001 |
| 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".