Response suppression delays the planning of subsequent stimulus-driven saccades
Bibliographic record
Abstract
The completion of an antisaccade selectively increases the reaction time (RT) of a subsequent prosaccade: a result that has been interpreted to reflect the residual inhibition of stimulus-driven saccade networks (Weiler and Heath, 2012: Exp Brain Res; Weiler and Heath, 2012: Neurosci Lett). In the present investigation we sought to determine whether the increase in prosaccade RT is contingent on the constituent antisaccade planning processes of response suppression and vector inversion or limited to response suppression. To that end, in one block participants alternated between pro- and antisaccades after every second trial (task-switching block), and in another block participants completed a series of prosaccades that were randomly (and infrequently) interspersed with no-go catch-trials (go/no-go block). Notably, such a design provides a framework for disentangling whether response suppression and/or vector inversion delays the planning of subsequent prosaccades. As expected, results for the task-switching block showed that antisaccades selectively increased the RTs of subsequent prosaccades. In turn, results for the go/no-go block showed that prosaccade RTs were increased when preceded by a no-go catch-trial. Moreover, the magnitude of the RT ‘cost’ was equivalent across the task-switching and go/no-go blocks. That prosaccades preceded by an antisaccade or a no-go catch-trial produced equivalent RT costs indicates that the conjoint processes of response suppression and vector inversion do not drive the inhibition of saccade planning mechanisms. Rather, the present findings indicate that a general consequence of response suppression is a residual inhibition of stimulus-driven saccade networks.Acknowledgments: Supported by a grant from the Natural Sciences and Engineering Research Council of Canada and a Major Academic Development and a Faculty Scholar Award from the University of Western Ontario. JW was supported by an Ontario Graduate Scholarship.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".