P042 Does sleep inertia affect how we perceive time? – Implications for insomnia
Bibliographic record
Abstract
Introduction Sleep inertia (SI) can negatively affect cognitive functions including time perception. Accurate time perception is important for evaluating wakefulness at night. Overestimating wakefulness can be anxiety-provoking for individuals with insomnia. Here we present data from three studies testing the impact of SI on time perception after waking from sleep versus after a wake period. Methods Participants were required to complete a time estimation task after waking from sleep and after a wakeful period. In study 1 and 2 (n=18), sleep occurred as part of a daytime nap with polysomnography. In study 3 (n=9) sleep occurred at night in a laboratory. Study 1 and 3 included good sleepers and study 2 included poor sleepers. The time estimation task was the same across all studies asking participants to state when they believed 15 minutes had passed, see figure 1. Results Data from study 1 and 2 were pooled. Participants overestimated time, however there was a greater overestimation after waking from a nap compared to the wake condition, t(17)=-2.089, p=0.052, d=0.7. For those individuals who reached stage 3 sleep during the nap (when waking with SI is more likely) the difference was significant, t (9)=-3.22,p<0.05, d=1.1). There was no main effect of sleep status (poor sleeper vs. good sleeper) on these differences. In study 3 there was no difference (p>0.05) between the two conditions (wake vs. sleep), see figure 2 for a summary of the findings. Conclusion Overestimation of time awake was more pronounced after waking from a nap condition compared to after a wake period, especially when waking from stage 3 sleep. The same effects were not present when waking from sleep at night, perhaps due to different study designs. Further research in larger samples is needed to understand the impact of SI on time perception.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".