Pupil Dilation and Eye Movements Can Reveal Upcoming Choice in Dynamic Decision-Making
Bibliographic record
Abstract
In dynamic environments such as air-traffic control, emergency response and security surveillance, there are severe constraints to information processing and decision-making. Human operators must constantly monitor, assess, and integrate incoming information in order to make optimal decisions in such complex environments. In order to maximize operators’ performance, there is a need for effective technological support for dynamic decision-making. Eye tracking is one promising avenue that can provide online, non-obtrusive indices of cognitive functioning. Using a simulated maritime decision-making environment, we evaluated whether oculometry may be exploited to foretell the decision made by the operator beforehand. Our results showed that pupil dilation and fixation transitions can reveal the upcoming judgment of the human operator by about half a second before the decision. This finding can be useful to design adaptive support tools for dynamic decision-making by integrating the operator’s cognitive state.
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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 | 0.001 | 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".