Correction of saccadic decisions during free-viewing visual search in the monkey
Bibliographic record
Abstract
Humans and monkeys routinely make two to four saccadic eye-movements per second to foveate and analyze salient and/or relevant regions in the visual scene. Prior research has shown that during tasks with very salient saccadic targets, like popout visual-search and double-step saccade tasks in both humans and monkeys, two-saccade sequences have been shown to occur, where an initial saccade to a distractor is followed soon after by a short-latency second saccade to the popout target, with little time spent fixating the initially chosen distractor. This indicates that the short-latency second saccade uses visual information obtained before the first saccade and corrects the erroneous decision to make the first saccade to the distractor. Here, we demonstrate that such short-latency second saccades also occur frequently when monkeys perform free-viewing visual search without a popout visual target. Short-latency saccades are especially accurate at foveating the target, and almost exclusively occur after errors. We describe several properties of short-latency saccades that shed light on the computations related to search-related saccadic eye-movements in visual and oculomotor salience maps, with a specific focus on transsaccadic remapping and surround suppression. Our results expand our understanding of saccadic targeting and information transfer across saccades during goal-directed free-viewing and suggest that error-processing in different sensory and response modalities show similar patterns and may be accounted for by similar models.
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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) | 0.000 | 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".