Sleepers Selectively Suppress Informative Inputs during Rapid Eye Movements
Bibliographic record
Abstract
Sleep leads to a disconnection from the external world. Even when sleepers regain consciousness during rapid eye movement (REM) sleep, little, if any, external information is incorporated into dream content [1-3]. While gating mechanisms might be at play to avoid interference on dreaming activity [4], a total disconnection from an ever-changing environment may prevent the sleeper from promptly responding to informative events (e.g., threat signals). In fact, a whole range of neural responses to external events turns out to be preserved during REM sleep [5-9]. Thus, it remains unclear whether external inputs are either processed or, conversely, gated during REM sleep. One way to resolve this issue is to consider the specific impact of eye movements (EMs) characterizing REM sleep. EMs are a reliable predictor of reporting a dream upon awakening [10, 11], and their absence is associated with a lower arousal threshold to external stimuli [12]. We thus hypothesized that the presence of EMs would selectively prevent the processing of informative stimuli, whereas periods of REM sleep devoid of EMs would be associated with the monitoring of external signals. By reconstructing speech in a multi-talker environment from electrophysiological responses, we show that informative speech is amplified over meaningless speech during REM sleep. Yet, at the precise timing of EMs, informative speech is, on the contrary, selectively suppressed. These results demonstrate the flexible amplification and suppression of sensory information during REM sleep and reveal the impact of EMs on the selective gating of informative stimuli during sleep.
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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) | 0.004 | 0.001 |
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".