Is the failure to detect stimulus deviance during sleep due to a rapid fading of sensory memory or a degradation of stimulus encoding?
Bibliographic record
Abstract
The mismatch negativity (MMN) is thought to reflect the outcome of a system responsible for the detection of change in an otherwise repetitive, homogenous acoustic environment. This process depends on the storage and maintenance of a sensory representation of the frequently presented stimulus to which the deviant stimulus is compared. Few studies have been able to record the MMN in non-rapid eye movement (NREM) sleep. This pattern of results might be explained by either a rapid fading of sensory memory or an inhibition of stimulus input prior to entry into the cortical MMN generator site. The present study used a very rapid rate of presentation in an attempt to capture mismatch-related negativity prior to the fading of sensory memory. Auditory event-related potentials were recorded from 12 subjects during a single sleep period. A 1000 Hz standard stimulus was presented every 150 ms. At random, on 6.6% of the trials, the standard was changed to either a large 2000 Hz or a small 1100 Hz deviant. In wakefulness, the large deviant elicited an extended negativity that was reduced in amplitude following the presentation of the small deviant. This negativity was also apparent during REM sleep following the presentation of the large deviant. These deviant-related negativities (DRNs) were probably a composite of N1 and MMN activity. During NREM sleep (stage 2 and slow-wave sleep), only the large deviant continued to elicit a DRN. However this DRN might be overlapped by the initial activity of a component that is unique to sleep, the N350. There was little evidence of the DRN or the MMN during sleep following the presentation of the small deviant. A rapid rate of presentation, therefore, does not preserve the MMN following small deviance within sleep. It is possible that inhibition of sensory input occurs before entry into the MMN generating system in the temporal cortex.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".