Predictive Saccade Target Selection in Superior Colliculus during Visual Search
Bibliographic record
Abstract
Searching for a visual object naturally involves sequences of gaze fixations, during which the current foveal image is analyzed and the next object to inspect is selected as a saccade target. Fixation durations during such sequences are short, suggesting that saccades may be concurrently processed. Therefore, the selection of the next saccade target may occur before the current saccade target is acquired. To test this hypothesis, we trained four female rhesus monkeys (Macaca mulatta) to perform a multiple-fixation visual conjunction search task. We simultaneously recorded the activity of sensorimotor neurons in the midbrain superior colliculus (SC) in two monkeys. In this task, monkeys made multiple fixations before foveating the target. Fixation durations were significantly shorter than the latency of the initial responses to the search display, with approximately one-quarter being shorter than the shortest response latencies. The time at which SC sensorimotor activity discriminated the target from distracters occurred significantly earlier for the selection of subsequent fixations than for the selection of the first fixation. Target selection during subsequent fixations occurred even before the visual afferent delay in more than half of the neuronal sample, suggesting that the process of selection can encompass at least two future saccade targets. This predictive selection was present even when differences in saccade latencies were taken into account. Altogether, these findings demonstrate how neural representations on the visual salience map are processed in parallel, 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 | 0.000 | 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".