Investigating Arousal, Saccade Preparation, and Global Luminance Effects on Microsaccade Behavior
Bibliographic record
Abstract
Microsaccades, small saccadic eye movements occurring during fixation, have been suggested to be modulated by various sensory, cognitive, and affective processes relating to arousal. Although the modulation of fatigue-related arousal on microsaccade behavior has previously been characterized, the influence of other aspects of arousal, such as emotional arousal, is less understood. Moreover, microsaccades are modulated by cognitive processes (e.g., voluntary saccade preparation) that could also be linked to arousal. To investigate the influence of emotional arousal, saccade preparation, and global luminance levels on microsaccade behavior, emotional auditory stimuli were presented prior to the onset of a fixation cue whose color indicated to look either at the peripheral stimulus (pro-saccade) or in the opposite direction of the stimulus (anti-saccade). Microsaccade behavior was found to be significantly modulated by saccade preparation and global luminance level, but not emotional arousal. In the pro- and anti-saccade task, microsaccade rate was lower during anti-saccade preparation as compared to pro-saccade preparation, though microsaccade dynamics were comparable during both trial types. Our results reveal a differential role of arousal linked to emotion, fatigue, saccade preparation, and global luminance level on microsaccade behavior.
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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 | 0.000 |
| 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".