Stereoscopy benefits processing of dynamic visual scenes by disambiguating object occlusions
Bibliographic record
Abstract
How and to what extent stereoscopic cues help process dynamic visual scenes is still unknown. As we navigate in crowds or when we play sports we are often obliged to make rapid decisions in complex motion environments while attending to multiple elements. Stereoscopy can improve speed thresholds for a multiple object tracking (MOT) (Faubert & Sidebottom, 2012), but the nature of this advantage remains undetermined. From an attention perspective, there are at least two possible hypotheses to explain this advantage. One possibility is that stereoscopy reduces the attentional bottleneck as seen in 2-dimensional MOT environments (Intrilligator & Canavagh, 2001) by distributing attention uniformly in 3D space. Another hypothesis is that the stereoscopy helps attentional tracking by segregating the target and the non-target objects during occlusions. We have addressed these hypotheses by testing subjects with and without stereoscopic cues for different stimulus configurations in which pairs of spheres were rotating in orbit with one another in a 3D virtual space. In one set of conditions, object pairs rotated without ever occluding each other. In the second set of conditions, the objects occasionally occluded each other as they were rotating around an axis perpendicular to the observer. All conditions consisted of 4 pairs of spheres presented in each quadrant of the visual field at 20 degrees of eccentricity. Stereoscopy improved MOT speed threshold by a factor of about 3 when objects occluded each other, but slightly, yet significantly, impaired speed threshold by about 18% when objects did not occlude each other. We conclude that the overall benefit of stereoscopy for processing dynamic scenes comes from improved attention tracking by disambiguating targets from non-targets during occlusions. Conversely, stereoscopic cues were a disadvantage in absence of occlusions. Meeting abstract presented at VSS 2013
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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 | 0.000 |
| 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".