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 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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 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".