Workload Assessment of Operators: Correlation Between NASA-TLX and Pupillary Responses
Bibliographic record
Abstract
Operators in high-stress environments often face significant cognitive demands that can impair their performance, underscoring the need for comprehensive workload assessment. This study aims to study the relationship between subjective self-reported measures, the NASA task load index (NASA-TLX), objective bio-signal measures, and pupillary responses. The participants engaged in either a visual tracking task or a laparoscopic visuomotor task while their eye movements were recorded using a Tobii Pro Nano eye tracker (Tobii Technology Inc., Stockholm, Sweden). Immediately after completing the tasks, participants provided NASA-TLX scores to assess their perceived workload. The study tested three hypotheses: first, whether increased pupil dilation correlates with higher NASA-TLX scores; second, whether task type affects workload; and third, whether task repetition influences workload. The results showed a moderate positive correlation between pupil size and NASA-TLX scores (r = 0.513, p < 0.001). The laparoscopic surgery task, which requires visuomotor coordination, resulted in significantly higher NASA-TLX scores (t = –6.23, p < 0.001), larger original pupil sizes (t = –22.57, p < 0.001), and more adjusted pupil sizes (t = –22.57, p < 0.001) than the purely visual task. Additionally, task repetition led to a significant reduction in the NASA-TLX scores (t = 2.86, p = 0.005), the original mean pupil size (t = 5.50, p < 0.001), and the adjusted pupil size (t = 6.34, p < 0.001). In conclusion, the study confirms a positive correlation between NASA-TLX scores and pupillary responses. Task type and repetition were found to influence workload and pupillary responses. The findings demonstrate the value of using both subjective and objective measures for workload assessments.
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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 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".