Bibliographic record
Abstract
Bottom-up attention is driven by stimulus features, which may be occurring at the level of the object or the feature itself. Most studies investigating stimulus-driven selection have used spatially separate stimuli and thus are confounded by spatial mechanisms. To investigate whether it is spatial or object-based, we superimposed two surfaces (random dot kinetograms, RDKs). We asked whether dot density, previously found to be involved in bottom-up attention, can also drive object-based selection in the absence of spatial mechanisms. Superimposing two surfaces of different densities places both densities at the same spatial location and thus, those densities are separated only by the surface on which they appear. We performed 2 experiments to investigate the effect of density on surface selection. In experiment 1, subjects fixated a central dot and an aperture with a single surface of dots moving left or right appeared in the periphery. After a random period of time, the fixation spot disappeared which was the cue for the subjects to saccade to the aperture. The density of the surface varied trial-by-trial from 0.24–30.6 dots/deg2. Saccading to the surface resulted in an automatic pursuit of that surface. We found that pursuit speed varied with surface density. In experiment 2, one surface again varied in density while a second surface was placed in the aperture, moving at the same speed in the opposite direction and of a constant density. We varied the opposing surface's density across sessions (range: 0.24–1.9 dots/deg2). As the relative difference in density between the two surfaces decreased, the gain of pursuit to the higher density surface decreased. At equal densities, no pursuit occurred. These findings are consistent with competitive circuitry between the two surfaces. Overall, these results suggest that density is a salient object feature that can drive automatic selection, regardless of location-based mechanisms.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".