Guidance of Eye Movements During Visual Conjunction Search: Local and Global Contextual Effects on Target Discriminability
Bibliographic record
Abstract
The composition of a visual scene influences the ability of humans to select specific details within that scene for discrimination or foveation with saccadic eye movements. With the goal of establishing an animal model to investigate the neural mechanisms underlying the deployment of visual attention and the guidance of saccades during visual search, we studied the visual behavior of three monkeys while they performed a conjunction (color + form) search task similar to those used in human studies. We found that search performance declined when distractors adjacent to the target shared its color, thereby revealing that color was more discriminable than form in these displays and suggesting that monkeys perceptually grouped stimuli by proximity and similarity. Search performance also varied with the overall composition of the display. Most importantly, saccades were biased toward distractors sharing the target color when there were few of them within the display and away from those distractors when they were numerous. Last, the monkeys initiated saccades with a fixed latency, suggesting that their responses to the display were automatic and that search strategies did not involve attentional resources beyond those recruited for regulating saccades. We conclude that monkeys adapt their visual strategies, largely via bottom-up processes, to both the local and the global context of the search. These findings suggest that the visual behavior of monkeys is guided by strategies similar to those observed in humans.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".