Multiple-object tracking (MOT) and visually guided actions: comparing change detection and localized touch to targets vs. distractors in MOT
Bibliographic record
Abstract
Multiple-object tracking (MOT) involves tracking the positions of several targets as they move among identical distractors. When we consider situations in everyday life that require MOT such as team sports or driving, they often require performing coordinated actions towards specific items in these dynamic environments (e.g. pointing, touching). Further, MOT is thought to employ cognitive mechanisms that are necessary for performing coordinated actions towards tracked items (Pylyshyn, 2001). In support of this, visually guided touch was found to interfere with the MOT task, especially when the touched item was a distractor in MOT as compared to a target (Terry & Trick, 2021). In the present study we sought out to investigate if this advantage for touching targets vs. distractors was simply due to a processing benefit for targets (i.e. faster to process change on tracked items) or if it was driven by action preparation (i.e. target tracking facilitating creation of action plans for targets but not distractors). We investigated this using a modified MOT task where participants performed two tasks at once: 1) track targets in MOT and 2) respond when any item in MOT changes colour. Participants respond to colour changes by pressing a button or touching the item that changed colour as fast as they can, depending on the condition. Critically, the time to touch or button press for targets vs. distractors that change colour inform the mechanism responsible for the touch target benefit. Participants were always faster to respond to changes on targets vs. distractors, however the difference between targets and distractors was much larger when the response involved touching vs. pressing a button. These results support the contention that tracking targets in MOT facilitates action preparation, providing evidence of a shared mechanism employed in tracking and visually guided actions.
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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 | 0.001 | 0.015 |
| 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".