Within-hemifield mutual inteference and repulsion in the programming of antisaccades
Bibliographic record
Abstract
Background: A previous study of prior probability effects uncovered a mutual interference effect when the two high-probability antisaccade locations were located in the same hemifield, and thus processed by the same hemisphere. Objective: We hypothesized that a mutual interference effect would have a spatial distribution for targets at different locations within a single hemifield. Methods: We assessed prosaccades and antisaccades in 8 normal subjects, contrasting low-probability blocks with 8 possible target locations (0.125 probability for each target) and high-probability blocks with 2 possible target locations (0.50 probability for each target). There were four high-probability blocks that varied the angle of separation between the two targets, from 10°, 30°, 90° to 150°. We assessed latency, directional error and amplitude precision. Results: Effects on prosaccades were minimal. For antisaccades, increasing prior-probability reduced latency and improved accuracy. However, this benefit was less for targets in close proximity (10° or 30°). The patterns of directional errors and amplitude precision for these close-proximity targets showed that saccadic trajectories were deviated away from the location of the other potential target. Conclusion: The mutual interference effect is strongest when targets are closer than 90° apart, a directional separation that is similar in magnitude to the directional tuning of receptive fields of neurons in the frontal eye field and superior colliculus. Our results also show that this interference is more specifically a mutual repulsion, causing saccades to deviate away from the other location.
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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 | 0.002 |
| 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) | 0.002 | 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".