Experimentally induced arousals during sleep: a cross‐modality matching paradigm
Bibliographic record
Abstract
Micro-arousals occur spontaneously or in response to exogenous and endogenous sensory input during sleep. The function of micro-arousals remains unclear, for example, whether it reflects a disturbance or a preparatory response to environmental changes. The goal of this study was to assess arousal responsiveness when two types of sensory stimulations were used: auditory (AD) alone and the addition of a vibrotactile (VT) sensation. Ten normal sleepers participated in three nights of polygraphic recordings. The first night was for habituation and to rule out sleep disorders, and the second to collect baseline sleep data. During the third night, AD and VT + AD stimuli, with three levels of intensities for auditory and vibratory signals, were randomly given to induce arousal responses in sleep stages 2, 3 and 4 and rapid eye movement (REM). The frequency of the arousal responses increased with stimulus intensity for all sleep stages and was lowest in stages 3 and 4. In non-REM (NREM) sleep, combined VT + AD stimulation induced more frequent and more intense arousal responses than AD alone. In REM sleep, more frequent micro-arousals rather than awakenings were triggered by combined stimulations. In stage 2, the response rate of total induced K-complexes did not differ between both types of stimulations while more K-complexes followed by arousals were evoked by the combined VT + AD stimulation than by the AD alone. The induced arousals were associated with an increase in heart rate in all sleep stages. An increase in suprahyoid muscle tone was observed in NREM sleep only, REM being not associated with a rise in muscle tone following experimental stimulation. Most leg and body movements occurred in response to induced awakenings. These results suggest that the cross-modality sensory stimuli triggered more arousal responses in comparison with single-modality stimuli. In an attempt to wake a sleeping subject, the addition of a tactile stimulation, such as shaking the shoulder, is an effective strategy that increases the arousal probability.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".