The Effect of Feature Changes on Multiple Object Tracking
Bibliographic record
Abstract
Multiple object tracking (MOT: Pylyshyn & Storm, 1988) is the ability to monitor the positions of a subset of identical items (targets) among identical non-target items (distractors). This ability is believed to be required for everyday activities such as keeping track of children in a crowd or driving a vehicle. However, most real-world situations involve items that differ in surface features (e.g., colour and shape). Our previous research investigated the effect of target similarity and item uniqueness by using displays of 16 items that varied on two feature dimensions: colour (red, blue, green, yellow) and shape (circle, triangle, square, cross), such that each item was a unique combination. Every trial had four targets and 12 distractors. Targets could have the same colour or shape (Colour-share and Shape-share conditions, respectively), or no common features (e.g., the No Share condition: e.g., red circle, blue triangle, green square, yellow cross). We found that performance was significantly better when targets shared a colour or shape than when they did not (target similarity effect), though even performance in the No-share condition was superior to that when items were identical (the uniqueness benefit). To determine whether these two effects were stable across featural change, we compared performance in the four conditions when the items retained their colours and shapes to when they adopted novel colours and shapes during item motion, manipulating whether the items preserved feature grouping (e.g., all red items become purple) or disrupted it (e.g., red items become purple, pink, aqua, brown). In the Colour and Shape Share conditions, performance was significantly impaired when the change disrupted the grouping but not when grouping was preserved. In contrast, colour and shape changes did not affect performance in the No-share or Identical item conditions. This suggests the uniqueness benefit and target similarity effects reflect different mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".