Sustained Attention in Auditory and Visual Monitoring Tasks
Bibliographic record
Abstract
OBJECTIVE: Performance and mental workload were observed for the administration of a rest break or exogenous vibrotactile signals in auditory and visual monitoring tasks. BACKGROUND: Sustained attention is mentally demanding. Techniques are required to improve observer performance in vigilance tasks. METHOD: Participants (N = 150) monitored an auditory or a visual display for changes in signal duration in a 40-min watch. During the watch, participants were administered a rest break or exogenous vibrotactile signals. RESULTS: Detection accuracy was significantly greater in the auditory than in the visual modality. A short rest break restored detection accuracy in both sensory modalities following deterioration in performance. Participants experienced significantly lower mental workload when monitoring auditory than visual signals, and a rest break significantly reduced mental workload in both sensory modalities. Exogenous vibrotactile signals had no beneficial effects on performance, or mental workload. CONCLUSION: A rest break can restore performance in auditory and visual vigilance tasks. Although sensory differences in vigilance tasks have been studied, this study is the initial effort to investigate the effects of a rest break countermeasure in both auditory and visual vigilance tasks, and it is also the initial effort to explore the effects of the intervention of a rest break on the perceived mental workload of auditory and visual vigilance tasks. Further research is warranted to determine exact characteristics of effective exogenous vibrotactile signals in vigilance tasks. APPLICATION: Potential applications of this research include procedures for decreasing the temporal decline in observer performance and the high mental workload imposed by vigilance tasks.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 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".