On the Cost of Detection Response Task Performance on Cognitive Load
Bibliographic record
Abstract
OBJECTIVE: This study investigates the cost of detection response task performance on cognitive load. BACKGROUND: Measuring system operator's cognitive load is a foremost challenge in human factors and ergonomics. The detection response task is a standardized measure of cognitive load. It is hypothesized that, given its simple reaction time structure, it has no cost on cognitive load. We set out to test this hypothesis by utilizing pupil diameter as an alternative metric of cognitive load. METHOD: Twenty-eight volunteers completed one of four experimental tasks with increasing levels of cognitive demand (control, 0-back, 1-back, and 2-back) with or without concurrent DRT performance. Pupil diameter was selected as nonintrusive metric of cognitive load. Self-reported workload was also recorded. RESULTS: -back performance. CONCLUSION: Results indicate that DRT performance produced an added cost on cognitive load. The magnitude of the change in pupil diameter was comparable to that observed when transitioning from a condition of low task load to one where the 2-back was performed. The significant increase in cognitive load accompanying DRT performance was also reflected in higher self-reported workload. APPLICATION: DRT is a valuable tool to measure operator's cognitive load. However, these results advise caution when discounting it as cost-free metric with no added burden on operator's cognitive resources.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.005 | 0.001 |
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".