Are Poor Sleepers Afraid of the Dark? A Preliminary Investigation
Bibliographic record
Abstract
No studies have investigated whether those with poor sleep are aware of being uncomfortable in the dark via subjective inquiry, and no study has evaluated whether poor sleepers have increased fear in the dark using objective indices (e.g., a validated startle paradigm). Good and poor sleepers (N = 108) completed questionnaires about their level of discomfort with the dark and were evaluated for an increased startle reflex by measuring eyeblink latency via electrooculogram in response to unexpected noise in the dark and the light. Participants listened to bursts of unexpected white noise, while in counterbalanced light/dark conditions. Relative to good sleepers, more poor sleepers reported increased discomfort in the dark. There was a significant lighting × time × sleeper status interaction for eyeblink latency. Relative to the first trial in the dark, eyeblink latency in good sleepers increased in the second dark exposure; suggesting habituation in the dark. Eyeblink latency in poor sleepers did not decrease. Thus, poor sleepers reported being uncomfortable in the dark and they remained more easily startled in the dark over the course of the study. It is unclear if the dark may predispose people to sleep problems, or if sleep problems sensitize poor sleepers to fear darkness.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".