Multiple-object tracking across various fields of view
Bibliographic record
Abstract
Multiple-object tracking involves monitoring the locations of a number of targets as they move among identical distractors. Previous work on multiple-object tracking was restricted to smaller fields of view (ͬ4;20°). This study explored the effects of increasing the size of the field of view on multiple-object tracking. Twenty participants were required to track 1, 3, or 5 targets among 10 identical items across three fields of view (20°, 80°, and 120°) for an 8 second tracking interval. Field of view was blocked, though the number of targets varied randomly from trial to trial. As is usually seen in multiple-object tracking studies, tracking accuracy dropped with increases in the number of targets (p <.001) but it increased with the size of the visual field (p <.001). This result suggests that the visual indexing mechanism may be more attuned to tracking in fields of view more akin to what might be the case in daily life and not artificially small (and dense). With dense displays, it may be easier to confuse targets and distractors, and that may explain the differences seen. Differences between attentional mechanisms as used for making fine discriminations and attentional mechanisms as used for visual-motor coordination in tasks such as driving and team sports are discussed. Meeting abstract presented at VSS 2013
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".