Rapid eye movements are reduced in blind individuals
Bibliographic record
Abstract
There is ongoing controversy regarding the role of rapid eye movements (EMs) during rapid eye movement (REM) sleep. One prevailing hypothesis is that EMs during REM sleep are indicative of the presence of visual imagery in dreams. We tested the validity of this hypothesis by measuring EMs in blind subjects and correlating these with visual dream content. Eleven blind subjects, of whom five were congenitally blind (CB) and six late blind (LB), and 11 matched sighted control (SC) subjects participated in this study. All participants underwent full-night polysomnography (PSG) recordings that were staged manually following American Academy of Sleep Medicine (AASM) criteria. Nocturnal EMs were detected automatically using a validated EM detector, and EM activity was represented as "EM coverage" computed as percentage of time with EM in each sleep stage. Frequency of sensory dream elements was measured in dream recall questionnaires over a 30-day period. Both blind groups showed less EM coverage during wakefulness, N1, N2 and REM sleep than did controls. CB and LB subjects did not differ in EM activity. Validation of the detector applied to blind subjects revealed an overall accuracy of 95.6 ± 3.6%. Analysis of dream reports revealed that LB subjects reported significantly more visual dream elements than did CB. Although no specific mechanisms can be revealed in the current study, the quasi absence of nocturnal EMs in LB subjects despite preserved visual dream content does not support the visual scanning of dreams hypothesis. Specifically, results suggest a dissociation between EMs and visual dream content in blind individuals.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".