On the quantitative nature of depth percepts from fused and diplopic stimuli
Bibliographic record
Abstract
Introduction. Psychophysical and physiological studies of stereopsis have demonstrated the existence of luminance based (1st-order) and contrast-based (2nd-order) processing. The 2nd-order mechanism is thought to provide depth information when the luminance information is unreliable or unavailable. A number of investigators have suggested that 2nd-order stereopsis provides only qualitative depth information, but this proposal has not been tested empirically. The aim of this set of experiments is to evaluate this claim and in doing so clarify the contribution of 2nd-order processing to human depth perception. Methodology. We have used a novel two-temporal alternative forced-choice procedure and a method of constant stimuli. This task was designed to avoid the separation confound inherent in discrimination tasks using diplopic stimuli. Observers were required to judge which of two intervals contained the largest difference in depth between a reference + disparity pedestal and a target + disparity pedestal. We assessed performance across a large range of fused and diplopic disparities, and measured diplopia for each observer. In addition we used stimuli designed to favour 1st-order, or isolate 2nd-order processing, to permit comparison of the relative contributions of these two mechanisms. Results. All observers were able to perform the 2IFC depth interval judgment using both 1st and 2nd-order stimuli, though there were large and consistent differences between these conditions. The most notable result is that observers perceived quantitative depth from targets that isolated 2nd-order processing. Results from the 1st-order condition show a clear transition from high-resolution performance in the fused range to coarser low-resolution depth perception in the diplopic range, which is likely mediated by 2nd-order processing. This study provides the first definitive evidence that quantitative depth can be provided by both 1st- and 2nd-order mechanisms in the fused range, but only the 2nd-order signal is used when stimuli are diplopic.
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".