A statistical summary representation in oculomotor control: (Some) evidence from the antisaccade task
Bibliographic record
Abstract
Prosaccades are stimulus-driven eye movements that bring the fovea onto an area of interest. In contrast, antisaccades involve inhibiting a stimulus-driven prosaccade (i.e., response suppression) and inverting a target's spatial location (i.e., vector inversion) to mirror-symmetrical space. Previous work indicates that prosaccades are mediated via absolute visual information; however, it is possible that the top-down nature of antisaccades renders responses supported via a statistical summary representation (SSR) akin to that supporting perceptual judgments (Gillen and Heath, 2014: Vis Res). To that end, we examined whether pro- and antisaccades are differentially influenced by the weighting of target eccentricities within a stimulus-set. Participants completed pro- and antisaccades to visual targets (eccentricities of 10.5°, 15.5° and 20.5°) with and without a delay (i.e. 2,000 ms). Importantly, responses were completed in blocks wherein target eccentricities were presented with equal frequency (i.e., the control condition), and when the 10.5° (proximal weighting condition) and 20.5° (distal weighting) targets were presented five times as often as the other eccentricities. If a SSR is used to support motor output then saccade amplitude should be biased in the direction of the most frequently presented target within a stimulus-set. Results showed that prosaccades were refractory to the weighting condition manipulations, whereas no-delay antisaccades (but not their delay counterparts) showed amplitudes biased in the direction of the most frequently presented target. Accordingly, our results provide some evidence that the top-down nature of antisaccade renders endpoints specified via a SSR – visual information that is functionally distinct from prosaccades. Acknowledgments: Supported by a Natural Sciences and Engineering Research Council of Canada Discovery Grant
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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 | 0.001 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".