Spatio-temporal Brain Dynamics Underlying Saccade Execution, Suppression, and Error-related Feedback
Bibliographic record
Abstract
Human and nonhuman animal research has outlined the neural regions that support saccadic eye movements. The aim of the current work was to outline the sequence by which distinct neural regions come on-line to support goal-directed saccade execution and error-related feedback. To achieve this, we obtained behavioral responses via eye movement recordings and neural responses via magnetoencephalography (MEG), concurrently, while participants performed an antisaccade task. Neural responses were examined with respect to the onset of the saccadic eye movements. Frontal eye field and visual cortex activity distinguished subsequently successful goal-directed saccades from (correct and erroneous) reflexive saccades prior to the deployment of the eye movement. Activity in the same neural regions following the saccadic movement distinguished correct from incorrect saccadic responses. Error-related activity in the frontal eye fields preceded that from visual regions, suggesting a potential feedback network that may drive corrective eye movements. This work provides the first empirical demonstration of simultaneous remote eyetracking and MEG recording. The coupling of behavioral and neuroimaging technologies, used here to characterize dynamic brain networks underlying saccade execution and error-related feedback, demonstrates a novel within-paradigm converging evidence approach by which to outline the neural underpinnings of cognition.
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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) | 0.001 | 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".