Using Signal-to-Noise Ratio to Explore The Cognitive Cost of The Detection Response Task
Bibliographic record
Abstract
The Detection Response Task (DRT) is a standardized measure of cognitive load requiring manual responses to intermittent stimuli. Given its simplicity, it is hypothesized that its completion will not interfere with the primary task. However, recent studies challenge this assumption showing a definite cost of DRT performance. In this study we adopt signal-to-noise ratio (SNR), a measure commonly used in communication engineering: 1) to explore the cognitive cost of DRT 2) to compare the sensitivity of DRT performance and pupil size in measuring cognitive load. SNR was calculated using the data from a study wherein DRT performance and pupil size were recorded while participants completed increasingly difficult mental tasks. We conclude that DRT completion interfered with the overall cognitive task demand and showed pupil size’s greater sensitivity to changes in cognitive load. Though exploratory, our study advances using SNR as a powerful tool for data integration in HF/E 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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".