A Model of Saccade Initiation Based on the Competitive Integration of Exogenous and Endogenous Signals in the Superior Colliculus
Bibliographic record
Abstract
Significant advances in cognitive neuroscience can be achieved by combining techniques used to measure behavior and brain activity with neural modeling. Here we apply this approach to the initiation of rapid eye movements (saccades), which are used to redirect the visual axis to targets of interest. It is well known that the superior colliculus (SC) in the midbrain plays a major role in generating saccadic eye movements, and physiological studies have provided important knowledge of the activity pattern of neurons in this structure. Based on the observation that the SC receives localized sensory (exogenous) and voluntary (endogenous) inputs, our model assumes that this information is integrated by dynamic competition across local collicular interactions. The model accounts well for the effects upon saccadic reaction time (SRT) due to removal of fixation, the presence of distractors, execution of pro- versus antisaccades, and variation in target probability, and suggests a possible mechanism for the generation of express saccades. In each of these cases, the activity patterns of "neurons" within the model closely resemble actual cell behavior in the intermediate layer of the SC. The interaction structure we employ is instrumental for producing a physiologically faithful model and results in new insights and hypotheses regarding the neural mechanisms underlying saccade initiation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".