Bibliographic record
Abstract
The first major laboratory studies of vigilance by Mackworth in 1948 and later revealed a decline in the probability of detecting brief targets as the time on task increases. Whether referred to as a vigilance decrement or something else (e.g., a failure of sustained attention), because such failures have great applied significance (e.g., in road safety, radiology, air-traffic control, civil defense, etc.), understanding the vigilance decrement and discovering ways to avoid it are important goals for psychological science. The purpose of this historical review is to provide a picture of the extensive scientific literature exploring the nature(s) of the vigilance decrement, with an emphasis, but not exclusionary focus, on the signal detection theory framework. Beginning in the early 1960s, researchers started to interpret this decline in target detections using signal detection theory, wherein a decrease in detections can be attributed to a decrease in sensitivity of the observer to the difference between targets and non-targets, a conservative shift in the observer's response criterion, or, of course, both. Some early investigators suggested that which of these two causes of the decline in detections is operating may depend on the rate at which events (targets and non-targets combined) are presented: When the event rate is slow, criterion shifts dominate detection failures, whereas declines in sensitivity become more pronounced as event rates increase. Nevertheless, the contribution of sensitivity declines has been recently challenged. One source of the challenge is the relatively low false-alarm rate in so many studies on the vigilance decrement. Another is the possibility that for a variety of reasons, the observer in a relatively long vigil may stop attending to the source of the task-relevant signals. Some recommendations are offered based on our reading of the ~75 years of vigilance research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".