Attentional color hierarchy for pursuit target selection
Bibliographic record
Abstract
We performed 2 experiments to investigate the effect of color on object selection. In Experiment 1, subjects fixated on a central dot and an aperture with a single surface of colored dots, either red, green, blue or yellow, moving left or right at a constant speed of 6.0 deg/sec 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. Saccading to the surface resulted in an automatic pursuit of that surface. Experiment 1 showed that color modulates motion processing as measured in smooth pursuit velocity for single surfaces. Next, we investigated whether this color modulation was equivalent to a modulation of salience, by seeing whether target selection would be biased towards the color that produced a higher pursuit speed over a color that produced less pursuit speed. In Experiment 2, a second surface was placed in the aperture, moving at the same speed in the opposite direction and differing in color, and pursuit was again measured. If task-irrelevant color has no effect on salience and target selection, then pursuit would not be biased towards either surface of equal speed and contrast. In contrast, we found evidence of a selection hierarchy determining which surface was pursued: red [[gt]] green [[gt]] yellow [[gt]] blue. Furthermore, the strength of selection (pursuit speed) was strongly correlated with the distance in color space between the two colors. These results suggest a bottom-up attentional hierarchy based on color processing, similar to the bottom-up salience effects of contrast. This attentional color hierarchy intrinsically modulated other features of the object; more specifically the motion processing that drives smooth pursuit. Thus, color and motion are likely bound at or below the level of areas MT and MST, and color modulates bottom-up salience.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".