Cognitive Load Modulates Microsaccade Rate and Pupil Size
Bibliographic record
Abstract
Microsaccade (MS) is the largest and fastest component of fixational eye movements. Recently, several behavioral studies have attempted to establish links between microsaccade and various cognitive activities. In this study, we examined the relationship between cognitive load and MS rate. While studies linking MS rate with cognitive activities typically employed visual tasks, in this study, we examined MS rate in a mental arithmetic task, where, after initial visual presentation of the calculation required, visual processing was no longer required during the actual calculation. Cognitive load was manipulated by varying number of digits involved in the calculation. We used the time it took to finish the task (response time, RT) as the indicator for cognitive load. We also recorded pupil size which has been linked to cognitive load. After a display of a fixation spot, two numbers were presented sequentially on the center of the screen. Before and after the display of these two numbers, an operational sign ("+"for addition; "-"for subtraction) was also displayed on the center of the screen. Participants made a verbal response as soon as they finished the calculation. We found that MS rates immediately following calculation increased to and maintained for a period of time at a level about twice of that during calculation. During calculation, MS rates were higher for trials with longer RTs (linear regression r = 0.57, p=0.016), while after calculation MS rate were much less affected by RTs. Moreover, during calculation, pupil size increased. Following calculation, pupil size continued to increase for a short period of time then decreased. The peak pupil sizes were higher for longer RTs (linear regression r = 0.42, p=0.0001). The results of this study, for the first time, demonstrated that that both MS rate and pupil size are affected by the cognitive load during the arithmetic task. Meeting abstract presented at VSS 2013
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".