Increasing Pupil Size Is Associated with Increasing Cognitive Processing Demands: A Pilot Study Using a Mobile Eye-Tracking Device
Bibliographic record
Abstract
Previous studies have shown that increases in pupil size are correlated with increasing cognitive processing demands. Our aim was to confirm whether these findings could be replicated with new portable and less obtrusive eye-tracking technology. We assessed the percentage change of pupillary diameter from baseline as eight subjects completed a series of randomly ordered arithmetic problems of varying difficulty. The mean peak pupil diameter expressed as a percentage change from baseline was significantly greater when answering difficult questions compared to easier questions. Moreover, the time to reach peak pupillary diameter occurred significantly faster when participants answered easier questions compared to more difficult questions. Finally, there was a significant difference when all groups were compared to control. This experiment confirms findings of previous studies that show that pupillary size is related to cognitive processing demands. It also demonstrates that mobile eye-trackers can be used to reliably gather this type of data. Furthermore, this experiment provides the basis for future studies using eye-tracking technology in new environments, for example in the study of expertise and performance in medical crisis situations.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".