The effect of grouping by common fate on stereoscopic depth estimates
Bibliographic record
Abstract
When two vertical lines are perceived to form the boundaries of a common object, observers underestimate their separation in depth (Deas & Wilcox 2014, 2015). This disruption in perceived depth magnitude depends directly on the perceived grouping via closure of the resultant figure. It has been proposed that this phenomenon is due to constraints on disparity-smoothing operations by high-level object representations. In previous experiments, perceptual grouping was manipulated by varying the spatial layout of figural elements. However, if the reported disruption in perceived depth is a general outcome of perceptual grouping then it should also occur when elements are grouped via other spatio-temporal properties. Here we tested this prediction by varying the relative motion of figural elements to introduce the Gestalt cue 'common fate'. In all experiments, participants viewed the stimuli on a mirror stereoscope and used an on screen ruler to estimate the separation in depth between two vertical lines. In Experiment 1 we found that depth estimates were accurate over a range of suprathreshold disparities, for both static and moving stimuli. In a subsequent series of experiments, we progressively strengthened the grouping cues, but found no impact on depth magnitude estimates. This was true even when we used a more complex biological motion stimulus, and asked observers to judge the amount of depth between two joints. Despite the compelling motion-based figural grouping, there was no corresponding impact on suprathreshold depth percepts. Taken together, our results show that previously reported reductions in perceived depth from disparity are not generalizable to grouping via common motion. Instead, it appears that this phenomenon only occurs when the spatial layout suggests they belong to a common object. Meeting abstract presented at VSS 2016
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".