The contribution of binocular and monocular texture elements to depth ordering
Bibliographic record
Abstract
While once considered simply a source of noise in binocular images, recent experiments show that monocularly visible elements that are consistent with the sign of a depth discontinuity improve depth perception (Gillam & Borsting, Perception,1988; Nakayama & Shimojo, VR,1989). This improvement is evident in simple (Pianta & Gillam, VR, 2002) and complex (Wilcox et al.JOV suppl, 2003) stereoscopic displays. However, we do not know how this monocular signal is combined with other cues. To this end, the experiments described here evaluate the relative contribution of monocular elements and disparity to depth perception. We used random dot stereograms and a 2AFC paradigm to assess the contribution of monocular elements and disparity to ordinal depth judgments. Experiments 1 and 2 used suprathreshold stimuli and demonstrated that when monocular elements alone signalled a discontinuity depth perception was poorer than in conditions where disparity was presented alone or conflicted with the monocular cue. We posited that the monocular signal is used when disparity is unreliable. In Experiment 3 we measured the minimum amount of contrast needed to see depth via disparity and then measured percent correct in a depth ordering task at threshold and at 1.5 times threshold. At threshold, performance was the same in the monocular and the disparity alone conditions. When contrast was increased slightly, performance improved in the monocular conditions (with or without disparity) relative to the disparity only condition. We conclude that if a reliable disparity signal is present it will be used to make depth ordering judgments; the presence or absence of a valid monocular signal does not influence performance. However, if the disparity signal is weak, then the monocular information is exploited to make depth judgments. Significantly, we have found no evidence of summation of disparity and monocular signals suggesting that this process cannot be modeled as a weighted average of the two cues.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".