Measuring cognitive load: performance, mental effort and simulation task complexity
Bibliographic record
Abstract
CONTEXT: Interest in applying cognitive load theory in health care simulation is growing. This line of inquiry requires measures that are sensitive to changes in cognitive load arising from different instructional designs. Recently, mental effort ratings and secondary task performance have shown promise as measures of cognitive load in health care simulation. OBJECTIVES: We investigate the sensitivity of these measures to predicted differences in intrinsic load arising from variations in task complexity and learner expertise during simulation-based surgical skills training. METHODS: We randomly assigned 28 novice medical students to simulation training on a simple or complex surgical knot-tying task. Participants completed 13 practice trials, interspersed with computer-based video instruction. On trials 1, 5, 9 and 13, knot-tying performance was assessed using time and movement efficiency measures, and cognitive load was assessed using subjective rating of mental effort (SRME) and simple reaction time (SRT) on a vibrotactile stimulus-monitoring secondary task. RESULTS: Significant improvements in knot-tying performance (F(1.04,24.95) = 41.1, p < 0.001 for movements; F(1.04,25.90) = 49.9, p < 0.001 for time) and reduced cognitive load (F(2.3,58.5) = 57.7, p < 0.001 for SRME; F(1.8,47.3) = 10.5, p < 0.001 for SRT) were observed in both groups during training. The simple-task group demonstrated superior knot tying (F(1,24) = 5.2, p = 0.031 for movements; F(1,24) = 6.5, p = 0.017 for time) and a faster decline in SRME over the first five trials (F(1,26) = 6.45, p = 0.017) compared with their peers. Although SRT followed a similar pattern, group differences were not statistically significant. CONCLUSIONS: Both secondary task performance and mental effort ratings are sensitive to changes in intrinsic load among novices engaged in simulation-based learning. These measures can be used to track cognitive load during skills training. Mental effort ratings are also sensitive to small differences in intrinsic load arising from variations in the physical complexity of a simulation task. The complementary nature of these subjective and objective measures suggests their combined use is advantageous in simulation instructional design research.
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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 | 0.002 | 0.018 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".