The effect of spatial and temporal information on saccades and neural activity in oculomotor structures
Bibliographic record
Abstract
It has been argued that saccade generation is supported by two systems, a'where' system that decides the direction and extent of an impending saccade, and a 'when' system that is involved in the timing of the release of fixation. We evaluated the contributions of these systems to saccade latencies, and used functional MRI to identify the neural substrates of these systems. We found that advance knowledge of the direction and the timing of an impending target movement had both overlapping and discrete effects on saccade latencies and on neural activation. Knowledge of either factor decreased regular saccade latencies. However, knowledge of target direction increased the number of predictive and express saccades while knowledge of target timing did not. The brain activation data showed that advance knowledge of the direction or the timing of the target movement activated primarily overlapping structures. The precentral gyrus, in the region of the frontal eye fields, was more active in conditions in which some aspect of the target movement was predictable than in saccade control and fixation conditions. In the basal ganglia, activation discriminated between advance knowledge of target timing and target direction. The lenticular nuclei were more active when only target timing was known in advance, while the caudate was more active when only target direction was known in advance. These data suggest that the neural structures supporting the 'where' and 'when' systems are highly overlapping, although there is some dissociation sub-cortically. Knowledge of target timing and target direction converge in precentral gyrus, a region where there is strong evidence of context-dependent modulation of neural activity.
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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 | 0.001 |
| 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".