Neural Correlates of Saccade Target Selection and Programming in Superior Colliculus
Bibliographic record
Abstract
Decision-making process involves sequential stages of sensory processing, perceptual selection of a target for the behavior, preparation of an action, and execution of that action. Superior colliculus is at the center of a network which integrates the sensory information into evidence towards decisions about whether, to where and when a saccade should be made, and is therefore a suitable model to explore different aspects of these stages. This work explores the dynamics of interactions of SC populations during a decision that involves selection of a particular stimulus among the alternative options as the target for a future saccade. I show that these interactions evolve in time along with the requirements of the task, and the patterns of this evolution are different among neurons from the same or different SC populations. I further explore the impact of correlations within the activity rates of these neurons on the efficiency of coding this decision variable. I show that while the variability in pairs of neurons seems to increase the available information about the encoded target for the saccade, they decrease the performance of two biologically plausible decoders over simulated large populations. Finally, I explore the role of neuros in intermediate layers of SC in saccade production in a task where the subjects anticipate the possibility of withholding a programmed saccade. I show that while an accumulation of evidence towards a decision to execute a saccade appears to happen in SC, this activity pattern cannot fully account for the behavior of the subject. SC should be mainly considered as the threshold unit that transfers this decision variable from an accumulation unit elsewhere in the brain, such as the frontal eye fields, to the execution unit in the brainstem. This work confirms previous findings on the role of SC in two perceptual and executive stages of a decision process and provides additional insights on the details of how these roles are implemented.
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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 | 0.003 |
| 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".