The processing of infrequently-presented low-intensity stimuli during natural sleep: An event-related potential study
Bibliographic record
Abstract
Event-related potentials (ERPs) provide an exquisite means to measure the extent of processing of external stimuli during the sleep period. This study examines ERPs elicited by stimuli with physical characteristics akin to environmental noise encountered during sleep. Brief duration 40, 60 or 80 dB sound pressure level (SPL) tones were presented either rapidly (on average every two seconds) or slowly (on average every 10 seconds). The rates of presentation and intensity of the stimuli were similar to those observed in environmental studies of noise. ERPs were recorded from nine young adults during sleep and wakefulness. During wakefulness, the amplitude of an early negative ERP, N1, systematically increased as intensity level increased. A later positivity, the P3a, was apparent following the loudest 80 dB stimulus regardless of the rate of stimulus presentation; it was also apparent following the 60 dB stimulus, when stimuli were presented slowly. The appearance of the N1-P3a deflections suggests that operations of the central executive controlling ongoing cognitive activity was interrupted, forcing subjects to become aware of the obtrusive task-irrelevant stimuli. The auditory stimuli elicited very different ERP patterns during sleep. During non-rapid eye movement (NREM) sleep, the ERP was characterized by an enhanced (relative to wakefulness) early positivity, P2, followed by a very prominent negativity, the N350. Both deflections systematically varied in amplitude with stimulus intensity level; in addition, N350 was much larger when stimuli were presented at slow rates. The N350, a sleep-specific ERP, is thought to reflect the inhibition of processing of potentially sleep-disrupting stimulus input. During rapid eye movement (REM) sleep, a small amplitude N1 was apparent in the ERP, but only for the loudest, 80 dB stimulus. A small (nonsignificant) P3a-like deflection was also visible following the 80 dB stimulus, but only when stimuli were presented slowly. The findings of the present study offer, on one hand, an explanation of the means by which consciousness is prevented during sleep but also, on the other hand, an explanation of how sleep can be disrupted and possibly reversed, leading to an awakening.
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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 | 0.001 |
| 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".