An application of eyegaze tracking for designing radiologists' workstations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The goal of this research is to use eyegaze tracking data to provide insights into designing radiology workstations. We designed a look-alike radiology task with artificial stimuli. The task involved a comparative visual search of two side-by-side images, using two different interaction techniques. We tracked the eyegaze of four radiologists while they performed the task and measured the duration of the fixations on the controls, the left and right images, and on the artificial targets. Response time differences between the two interaction techniques exceeded the differences of fixations on the controls. Fixations on the left-side images are longer than the right-side images, and the search for multifeatured targets occurs in two phases: first a regular scan path search phase for a likely target and then a confirmation phase of several fixations on the target in each side-by-side image. We conclude that eyegaze tracking shows that disruption of visual search leads to cognitive disruption; subjects use the left image as a reference image and multiple saccades between left and right side images are necessary, because of the limitations of the visual working memory.
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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 | 0.001 | 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 it