Stimulus Complexity and Dual Tasking Effects on Sustained Auditory Attention in Noise
Bibliographic record
Abstract
INTRODUCTION: The effect on sustained auditory attention of a constellation of factors that characterize aviation, maritime, and land environments was investigated. The ability to detect infrequent spoken messages while performing an unrelated visual motor tracking task was studied in the presence of background noise. METHOD: Six subjects each were assigned to four conditions defined by the presence/absence of 80-dBA pink noise and presence/absence of the secondary task. Subjects were given three detection tests in which the 75-dB SPL critical signal was comprised of one, two, or three components. A button press with the left hand signified detection of the critical signal. For each test there were six consecutive 20-min vigils comprised of 150 trials. The critical signal was presented in 10 of these. For the tracking task subjects used a computer mouse controlled by the right hand to follow the sinusoidal movement of a 3-cm vertical line across a computer monitor. RESULTS AND CONCLUSIONS: Background noise resulted in a significant decrease in hits, and significant increases in false alarms and response time. Since the signal and noise were both auditory, the noise was likely effective as a masker rather than a stressor. The secondary task did not impact performance, possibly because the auditory task was too easy and the event rate too slow. As in previous studies using alphanumeric events, there was no attention decrement over time. However, response time decreased as the number of components in the critical signal increased. Possible explanations are guided attention through priming or the provision of a temporal foreperiod.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".