Multiple object tracking and attentional processing.
Bibliographic record
Abstract
How are attentional priorities set when multiple stimuli compete for access to the limited-capacity visual attention system? According to Pylyshyn (1989) and Yantis and Johnson (1990), a small number of visual objects can be preattentively indexed or tagged and thereby accessed more rapidly by a subsequent attentional process (e.g., the traditional "spotlight of attention"). In the present study, we used the multiple object tracking methodology of Pylyshyn and Storm (1988) to investigate the relation between what we call "visual indexing" and attentional processing. Participants visually tracked a subset of a set of identical, independently randomly moving objects in a display (the targets), and made a speeded identification response when they noticed a target or a nontarget (distractor) object undergo a subtle form transformation. We found that target form changes were identified more rapidly than nontarget form changes, and that the speed of responding to target form changes was unaffected by the number of nontargets in the display when the form-changing targets were successfully tracked. We also found that this enhanced processing only applied to the targets themselves and not to nearby nontarget distractors, showing that the allocation of a broadened region of visual attention (as in the zoom-lens model of attentional allocation) could not account for these findings. These results confirm that visual indexing bestows a processing priority to a number of objects in the visual field.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".