Investigating the role of the superior colliculus in active vision with the visual search paradigm
Bibliographic record
Abstract
We review here both the evidence that the functional visuomotor organization of the optic tectum is conserved in the primate superior colliculus (SC) and the evidence for the linking proposition that SC discriminating activity instantiates saccade target selection. We also present new data in response to questions that arose from recent SC visual search studies. First, we observed that SC discriminating activity predicts saccade initiation when monkeys perform an unconstrained search for a target defined by either a single visual feature or a conjunction of two features. Quantitative differences between the results in these two search tasks suggest, however, that SC discriminating activity does not only reflect saccade programming. This finding concurs with visual search studies conducted in posterior parietal cortex and the idea that, during natural active vision, visual attention is shifted concomitantly with saccade programming. Second, the analysis of a large neuronal sample recorded during feature search revealed that visual neurons in the superficial layers do possess discriminating activity. In addition, the hypotheses that there are distinct types of SC neurons in the deeper layers and that they are differently involved in saccade target selection were not substantiated. Third, we found that the discriminating quality of single-neuron activity substantially surpasses the ability of the monkeys to discriminate the target from distracters, raising the possibility that saccade target selection is a noisy process. We discuss these new findings in light of the visual search literature and the view that the SC is a visual salience map for orienting eye movements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".