A critical examination of the evidence for sensitivity loss in modern vigilance tasks.
Bibliographic record
Abstract
It is well known that when human observers must monitor for rare but critical events, probability of detection tends to wane over time, a phenomenon known as the "vigilance decrement." Over 60 years of empirical study on this topic has culminated in the general consensus that performance suffers due to a loss in observers' ability to distinguish signal from noise (a loss in sensitivity) provided that the task loads memory and stimuli are presented at a relatively high rate. We challenge this assertion on 2 fronts: First, we contend on a theoretical level that the metrics employed to measure observer sensitivity in modern vigilance tasks (derived from signal detection theory) are inappropriate and largely uninterpretable. This contention is supported by an evaluation of recent empirical work in the vigilance domain. Second, we present the results of an experiment that demonstrates that shifts in response bias (the observer's "willingness to respond") over time can masquerade as a loss in sensitivity. Consequently, the basic underlying cause of the vigilance decrement is actually unclear, and may simply reflect a shift in response criterion rather than sensitivity. The theoretical, as well as practical implications of these conclusions are discussed with respect to sustained attention in general, and vigilance in particular.
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 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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".