Comparison of depth percepts created by binocular disparity, Panum's limiting case, and monoptic depth
Bibliographic record
Abstract
Sensations of depth can be produced by diplopic images with horizontal disparity beyond the fusion limit (conventional stereopsis), a monocular image flanking a binocular image (Panum's limiting case), and an eccentric monocular image (monoptic depth, Kaye 1978; Wilcox et al. 2007). Conceivably, depth perception in Panum's limiting case could be explained by stereopsis (double-duty matching, Hering 1879), monoptic depth or another mechanism entirely. Our goal is to determine which of these options is valid. Subjects judged the magnitude of perceived depth of a target stimulus viewed for 67 ms relative to a prior fixation point. The target was (1) a monocular vertical line with variable horizontal offset relative to a midline monocular line seen by the other eye (stereoscopic), (2) a monocular line with variable offset relative to a midline binocular line (Panum's limiting case), and (3) a monocular line with variable offset relative to the prior fixation point (monoptic). For Panum's limiting case, apparent depth at first increased with increasing lateral offset of the monocular line. However, this occurred only for offsets of up to 15 and 45 arcmin on the temporal and nasal side of retina, respectively. At larger offsets, depth was similar to that perceived from monoptic targets. In contrast, perceived depth from stereopsis increased with increasing disparity of up to 1° and remained constant up to a disparity of at least 2° (stimuli became diplopic at 30 arcmin). The magnitude of perceived depth was much smaller in monoptic compared with stereoscopic conditions, at all offsets. The distinct properties of depth perceived with these three types of stimuli suggest that they have different physiological substrates, and that depth from Panum's limiting case is not simply due to stereoscopic matching.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".