Wireless Sensors Measure the Neural Effects of Sleep Debt on Prospective Memory for Pilots
Bibliographic record
Abstract
Wireless sensing of frontal lobe brain activity during flight offers an opportunity to measure the impact of sleep debt on the neural processes that support prospective memory in pilots. Pilots rely on prospective memory to remember to complete planned (future) tasks while flying. Yet, prospective memory is particularly vulnerable to sleep debt because of its reliance on a network of mental processes, such as the detection of details from the environment needed to “cue” the task. Therefore, pilots assume high risk for prospective memory failures due to their accumulation of sleep debt arising from work schedules and circadian disruptions associated with frequent time-zone changes. Because of its ability to sense rapid changes in brain activity non-obtrusively, wireless encephalography was used to investigate the effects of sleep debt on scalp electrical potentials and spectral power time-locked to prospective memory responses. During simulated automated flight segments 36 non-pilots completed a series of prospective memory tasks. Event-related potentials and spectral perturbation associated with the prospective memory responses showed that those with sleep debt accumulated over the previous week had weaker monitoring activity prior to the response, and excessive neural activity after the response. These results show the application of wireless sensors located on the scalp over the frontal lobes were sufficient to measure sleep debt related inefficiency in brain activity associated with prospective memory during automated flight.
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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 | 0.000 | 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 | 0.000 |
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".