The Use of Task-Evoked Pupillary Response as an Objective Measure of Cognitive Load in Novices and Trained Physicians
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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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".