Size matters: Perceived depth magnitude varies with stimulus height
Bibliographic record
Abstract
Stereoscopic acuity is known to vary with the overall size and width of the target. Recently, Tsirlin et al. (2012) suggested that perceived depth magnitude from stereopsis might also depend on the vertical extent of the stimulus. To test this hypothesis we compared perceived depth using small discs versus long bars with equivalent width and disparity. We used three estimation techniques. The first two, a virtual ruler and a touch-sensor (for haptic estimates), required that observers make quantitative judgements of depth differences between objects. The third method was a conventional disparity probe. This last technique, while often used for depth estimation, is a measure of disparity matching rather than quantitative depth perception. We found that depth estimates collected using the virtual ruler and the touch-sensor were significantly larger for the bar stimuli than for the disc stimuli. The disparity probe method yielded the same disparity estimates for both types of stimulus; which was not surprising given that they had the same relative disparity. In a second experiment, we measured perceived depth, using the virtual ruler, as a function of the height of a thin bar. In agreement with the first experiment, we found that perceived depth increased with increasing bar height. The dependence of perceived depth on the height of the stimulus is likely the result of the integration of disparity along the vertical edges, which enhances the reliability of depth estimation. The observed reduction in the magnitude of depth estimates for less reliable disparity signals may reflect a reweighting of depth cues or the expression of a bias towards small-disparities. Our results also underscore the often-overlooked difference between measurements of depth and disparity, as the effect of target height was obscured when the disparity probe was used. Meeting abstract presented at VSS 2014
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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 | 0.010 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".