Bibliographic record
Abstract
Current models of selective attention and visual search incorporate two processes believed to be crucial in searching for an item in a visual scene: the selection of locations to be attended and the temporary prevention of re-selecting previously attended locations. In natural situations, the deployment of visual attention is accomplished by sequences of gaze fixations, and the active suppression of recently visited locations can be examined by analyzing the distribution of gaze fixations as a function of time and location. We trained four monkeys to perform a visual search task, in which they could freely search for a target stimulus with a unique conjunction of features. Monkeys made multiple fixations on distracters before foveating the target (mean: 3.1; range: 1-14) and their probability of foveating the target with a single fixation was only 0.25. Performance in this difficult task, however, was generally efficient as monkeys rarely re-fixated previously inspected stimuli. The probability of a re-fixation increased with time and approximated chance levels after 5-6 fixations, suggesting that foveated information is retained across fixations but completely degraded within about 1000 ms of fixation. To investigate the neural mechanisms underlying this behavior, we recorded the activity of superior colliculus (SC) neurons while two animals performed the task. SC sensory-motor activity was sufficient to guide this behavior: activity associated with previously fixated stimuli was significantly lower than that of stimuli not yet fixated. More than two-thirds of neurons retained these differences up to 100 ms following fixation. These results suggest a neural mechanism for suppressing the re-fixation of stimuli temporarily maintained in memory. These findings demonstrate how neural representations on the visual salience map are dynamically updated from fixation to fixation, thus facilitating visual search.
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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 | 0.001 |
| 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 | 0.000 |
| 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".