Pupil size reveals preparatory processes in the generation of pro- and anti-saccades
Bibliographic record
Abstract
The ability to generate flexible behaviors to accommodate changing goals in response to identical sensory stimuli is a signature inherited in humans and higher-level animals. In the oculomotor system, this function has often been examined using the anti-saccade task in which subjects are instructed, prior to stimulus appearance, to either look at the peripheral stimulus automatically (pro-saccade) or to suppress the automatic response and voluntarily look in the opposite direction of the stimulus (anti-saccade). Distinct neural preparatory activity has been well-documented between the pro-saccade and anti-saccade conditions, particularly in the superior colliculus (SC) and the frontal eye field (FEF), showing higher inhibition-related fixation activity in preparation for anti- compared to pro-saccades. Moreover, the level of preparatory activity related to motor preparation negatively correlated with reaction times. Pupil size is widely used to index cognitive and neural processing, a link between the SC and the pupil control circuitry has been suggested recently, showing pupil dilation evoked transiently by weak microstimulation of the SC. We hypothesize that preparatory signals in the SC should be reflected on pupil size through this pathway. Here, we examined pupil dynamics in humans during saccade preparation prior to the execution of pro- and anti-saccades. Pupil size was larger in preparation for correct anti-saccades, compared to either correct pro-saccades or erroneous pro-saccades made in the anti-saccade condition. Furthermore, larger pupil size prior to stimulus appearance accompanied saccades with faster reaction times. Overall, our results demonstrated that pupil size was modulated by saccade preparation, providing unique insight into the neural substrate coordinating cognitive processing, saccade preparation, and pupil diameter. Meeting abstract presented at VSS 2015
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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) | 0.001 | 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".