Mechanisms of saccade suppression revealed in the anti-saccade task
Bibliographic record
Abstract
The anti-saccade task has emerged as an important tool for investigating the complex nature of voluntary behaviour. In this task, participants are instructed to suppress the natural response to look at a peripheral visual stimulus and look in the opposite direction instead. Analysis of saccadic reaction times (SRT: the time from stimulus appearance to the first saccade) and the frequency of direction errors (i.e. looking toward the stimulus) provide insight into saccade suppression mechanisms in the brain. Some direction errors are reflexive responses with very short SRTs (express latency saccades), while other direction errors are driven by automated responses and have longer SRTs. These different types of errors reveal that the anti-saccade task requires different forms of suppression, and neurophysiological experiments in macaques have revealed several potential mechanisms. At the start of an anti-saccade trial, pre-emptive top-down inhibition of saccade generating neurons in the frontal eye fields and superior colliculus must be present before the stimulus appears to prevent express latency direction errors. After the stimulus appears, voluntary anti-saccade commands must compete with, and override, automated visually initiated saccade commands to prevent longer latency direction errors. The frequencies of these types of direction errors, as well as SRTs, change throughout the lifespan and reveal time courses for development, maturation, and ageing. Additionally, patients diagnosed with a variety of neurological and/or psychiatric disorders affecting the frontal lobes and/or basal ganglia produce markedly different SRT distributions and types of direction errors, which highlight specific deficits in saccade suppression and inhibitory control. The anti-saccade task therefore provides valuable insight into the neural mechanisms of saccade suppression and is a valuable tool in a clinical setting.This article is part of the themed issue 'Movement suppression: brain mechanisms for stopping and stillness'.
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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 | 0.000 | 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) | 0.001 | 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".