How many objects can you track? Evidence for a flexible tracking resource
Bibliographic record
Abstract
The number of moving objects that can be tracked with attention is often reported to be 4, suggesting that there is a “structural limit” on tracking. We show that the tracking limit is not fixed, but depends systematically on the speed of the objects, such that at slow speeds observers can track 8 targets as well as a single target moving at a fast speed. Critically, the function relating the speed limit to the number of targets tracked is continuous, without any noticeable break in the 3-5 target range, suggesting that tracking accuracy is limited only by the amount of resources devoted to each target, not by a structural limitation. Method: Observers performed a multiple object tracking task in which they tracked 1–8 circles among a set of 16 identical moving circles. In session 1, observers adjusted the speed of the objects to find the maximum speed at which they could perfectly track the targets for 5 seconds. In session 2, we verified the accuracy of these settings by having observers track 1-8 targets moving at their “personal” speed limit for each number of targets. Results: With each increase in the number of targets, the speed limit decreased significantly. Moreover, the function was continuous, without any noticeable discontinuities in the 3–5 object range (r = .998 between speed and log of the number of targets). All speed settings were greater than zero indicating that on average observers estimated there was a speed at which they could perfectly track as many as 8 moving targets. It also appears that observers were able to accurately estimate their speed limits for tracking different numbers of targets, as tracking performance was near 100% for each number of targets in the second session and did not differ for different numbers of targets. Conclusion: These results are inconsistent with models that assume a fixed 4 object limit on tracking, and suggest that that tracking capacity is limited only by the amount of resources devoted to each target.
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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 | 0.004 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".