Eyes on hold: motion task difficulty jointly delays microsaccade and pupil responses
Bibliographic record
Abstract
Microsaccades and pupil dynamics exhibit canonical temporal profiles, providing insights into perceptual and cognitive processes. Microsaccades are typically suppressed with respect to expected stimulus onset and followed by a rebound to baseline rates. Here, we investigated whether and how the temporal dynamics of microsaccades and pupil dilation vary with task difficulty for a motion perception task. We hypothesized that difficulty jointly delays the rebound of microsaccade rates and the time of peak pupil dilation when discriminating motion direction. Human observers discriminated motion direction (clockwise or counterclockwise) in a briefly presented perifoveal drifting stimulus, which varied according to two 'easy' vs 'hard' difficulty manipulations -cardinal vs oblique motion directions, and large vs small tilt offsets from the discriminated direction. We found that (1) increased task difficulty strengthened and prolonged microsaccade inhibition resulting in delayed rebounds, (2) peak pupillary responses were both larger in amplitude and delayed for more difficult conditions, (3) discrimination response time correlated with microsaccade rebounds and peak pupillary responses. We conclude that the delays in these microsaccade rebound and pupil responses are due to a prolonged period of sensory evidence accumulation, and that their correlated temporal dynamics support a shared neural mechanism underlying both pupil and microsaccade responses.
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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) | 0.003 | 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".