Spatial bias in anti‐saccade endpoints following bilateral dorsal posterior parietal lesions
Bibliographic record
Abstract
Anti-saccades are eye movements in which the saccade is executed in the opposite direction of a visual target and are often hypometric. Because the visual target and saccade goal are decoupled, it has been suggested that competition between the two locations occurs and needs to be resolved. It has been hypothesized that the hypometria of anti-saccades reflects this spatial competition by revealing a bias towards the visual target. To confirm that this hypometria is not simply due to reduced gain, we tested 10 healthy subjects on three different anti-saccade spatial configuration tasks: 90° away across hemifields, 90° away within the same hemifield and 180° away (classic, diagonally opposite). Specifically, we examined whether saccade endpoints showed evidence for the visual target location's interference with anti-saccade programming and execution processes. Among other neural substrates involved in anti-saccades production, the dorsal posterior parietal cortex (PPC) has been implicated in the spatial inhibition of contralateral visual target. To gain insight into the neural processes involved in spatial competition during anti-saccades, we also tested one patient with a bilateral dorsal PPC lesion. In all spatial configurations, we observed that anti-saccade endpoints demonstrated a spatial bias towards the visual target for all participants, likely due to an incomplete inhibition of the visual target location. This spatial bias was exacerbated in our patient, which suggests that the dorsal PPC contributes to the amalgamation of the two competing spatial representations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".