The Use of Task-Evoked Pupillary Response as an Objective Measure of Cognitive Load in Novices and Trained Physicians
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: Task-evoked pupillary responses (TEPRs), or changes in pupil size, correlate with changes in cognitive processing demands. The magnitude of this change is a reliable marker of cognitive load. The authors used TEPRs to compare cognitive load between novices and trained physicians as they answered clinical knowledge questions. METHOD: In 2013, 20 emergency medicine trainees were recruited and divided into novice (n = 10) and trained physician (n = 10) groups. The authors used mobile eye-tracking glasses to assess changes in pupil diameter as participants answered arithmetic questions, general knowledge questions, and clinical emergency medicine questions in a controlled setting. Questions were categorized by difficulty a priori. RESULTS: Difficult arithmetic questions caused greater changes in TEPRs than easy ones (P = .024). TEPRs were similar between groups when answering general knowledge questions (P = .383) but were significantly greater for novices than trained physicians when answering clinical questions (P < .001). TEPRs in trained physicians were significantly greater when answering difficult clinical questions than easy ones (P < .001), whereas TEPRs in novices were similar (P = .291). For those clinical questions answered correctly by both groups, TEPRs in novices were greater than those in trained physicians despite all participants answering correctly (P < .001). CONCLUSIONS: Novices require more mental effort to answer clinical questions than trained physicians, even when both respond correctly. Measuring TEPRs has the potential to be a valuable assessment tool by providing objective measures of expertise and is worthy of further study.
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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 | 0.002 | 0.008 |
| 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) | 0.000 | 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