Spatiotemporal cues for tracking multiple objects through occlusion
Bibliographic record
Abstract
As we move about the world, and objects in the world move relative to us, objects constantly move in and out of view as they are occluded by other objects. Given such disruptions, how does the visual system maintain attention on objects of interest? We used a multiple object tracking task (Pylyshyn & Storm, 1988) to explore the spatiotemporal cues used to track objects through occlusion. Observers tracked four target objects moving among four identical distractor objects, as all objects frequently passed behind static vertical occluders. Across three experiments, we manipulated the way that objects behaved under occlusion, and observed the effect on tracking performance. When an object passes behind an occluder, the observer must link the preocclusion object to the postocclusion object across a brief disappearance. There are at least two major spatiotemporal features that could be important for making this link. First, the location where the object first disappears might be critical. When an item becomes occluded, a ‘‘marker’ ’ could be placed at the location where the object disappeared (the marker might also be placed on the expected location of the object’s reappearance, based on an extrapolation of the object’s path). When an object disoccludes near this marker, it could signal the object’s link to the original object. Additionally, the history of the object’s motion could be an important feature that could be used to link the two views of the object across the disruption. We might expect a similar angle of disocclusion to be important for establishing this
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".