The Effect of Item Uniqueness on Multiple Object Tracking
Bibliographic record
Abstract
Multiple object tracking is the ability to keep track of the positions of a subset of identical items (targets) among equally identical distractors (MOT; Pylyshyn & Storm, 1988). MOT is thought to be an important ability for many real-world activities including playing a sport or driving a vehicle. However, these situations typically involve objects with distinct properties (such as different colours and shapes) rather than identical properties. Previous research has found inconsistent effects of item uniqueness on MOT. One explanation is that item uniqueness is only beneficial when there are sufficient working memory resources. Alternatively, the uniqueness effect may be dependent on perceptual differences between items. The goal of this study was to determine what types of features produce a uniqueness benefit. To investigate this question, we designed an MOT task with 16 unique items - every combination of four colours and four shapes (Experiment1: basic geometric shapes; Experiment 2: digits; Experiment 3: line orientations) - or 16 identical items. Each trial had four targets and 12 distractors. In the colour-share condition, targets shared the same colour but had different shapes. In the shape-share condition, targets shared the same shape but had different colours. In the no-share condition, each target had a different colour and shape. All items were identical in the identical condition. Across all experiments, there was an advantage for unique items over identical items. Furthermore, there was a consistent large benefit of the colour-share condition over the no-share condition. However, there was only a benefit of the shape-share condition over the no-share condition in Experiment 1 (basic shapes) and Experiment 2 (digits). Given that line orientation is a basic feature that requires little working memory resources, these results suggest that the inconsistent effect of shape is driven by perceptual differences between stimuli.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".