The impact of object-based grouping on perceived depth magnitude: Virtual vs. physical targets
Bibliographic record
Abstract
The amount of depth perceived between two vertical lines is markedly reduced when those lines are connected to form the boundaries of a uniform closed object (Deas & Wilcox, 2014). Recently, we suggested that this degraded depth effect is contingent on perceptual grouping of elements to form an object and on disparity changes along the horizontal axis (Sudhama et al., 2015 VSS). In previous studies stimuli were presented virtually on LCD displays, using a mirror stereoscope. In this set of experiments, we ask whether the same distortions in perceived depth are observed when multiple, consistent 2D depth cues are present. Here, we replicated Deas and Wilcox's original paradigm using physical stimuli. Targets consisted of 3D-printed vertical posts (in isolation and connected to form rectangles), mounted on a customized computer-controlled motion platform. Stimuli were printed with a range of horizontal disparities between the vertical contours. The stimulus dimensions, viewing distance, and test manipulations closely matched the original paradigm. A set of four disparities was tested ten times apiece in random order, for isolated and connected stimuli. On each trial, observers judged the amount of depth between two vertical posts using a touch sensitive strip. We found that the resulting depth magnitude estimates were accurate over a large range of disparities. Moreover, there was no difference between estimates obtained in the isolated line vs. closed object configurations. In follow-up experiments with virtual targets we also found that the disruptive effects of perceptual grouping are modulated by the presence of multiple depth cues. We argue that this is not due to conflicts between 2D and stereoscopic depth cues. Instead, the absence of reliable additional depth cues makes stereoscopic depth estimates more susceptible to phenomena such as object-based grouping. These results have clear implications for creation and use of stereoscopic imagery in virtual environments. Meeting abstract presented at VSS 2017
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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 | 0.002 |
| 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 | 0.001 |
| 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".