Cognitive control and semantic thought variability across sleep and wakefulness
Bibliographic record
Abstract
The flow of thought is persistent, and at times merciless. Mental content is generated throughout the day and into the night, moving forward predictably at times but surprisingly at others. Understanding what influences the trajectory of thought—how thoughts continuously unfold over time—has important implications for the diagnosis and treatment of thought disorders like schizophrenia and recurrent nightmares. Here, we examine whether cognitive control restricts moment-to-moment content shifts across sleep and wakefulness, thus acting as a fundamental constraint on thought variability. Thought variability was measured as the semantic incoherence between sequential thought phrases and was applied to independent datasets of dreaming and waking reports. Our results show that within both sleeping and waking reports, conditions typically marked by higher levels of cognitive control were associated with decreased thought variability (i.e., semantic incoherence). During wakefulness, on-task conditions were associated with reduced levels of thought variability compared to off-task conditions, and thought variability was greater when thoughts wandered around more freely. During sleep, lucid dreams, marked by higher levels of cognitive control, were associated with reduced levels of thought variability compared to non-lucid dreams. Together, these results suggest that cognitive control may limit thought variability across the 24-hour cycle of thought generation. Such findings are consistent with the Dynamic Framework of Thought, where mental states are expected to vary on a continuum of deliberate constraints, with lower cognitive control leading to a categorical cluster of spontaneous thought processes that includes both mind-wandering during wakefulness and non-lucid dreams during sleep. This observation has broad implications for models of cognition, specifically highlighting the continuity of cognitive processes throughout the circadian cycle and the importance of considering varying levels of thought constraint in both waking and dreaming states.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".