Brain meta-state transitions demarcate thoughts across task contexts, exposing the mental noise of trait neuroticism
Bibliographic record
Abstract
Summary Using new methods to capture streams of neural meta-state transitions in single participants, we characterize the psychological meaning of these regular neural events. Similar to past group-average-based analyses, participants’ individual transition streams aligned to meaningful events during movie-viewing. However, our individual-based approach also afforded observation of participants’ idiosyncratic transition timing at rest. Across these two dramatically different task contexts, transitions featured similar trait-like frequency, concurrence with activation of regions associated with spontaneous thought, and suppression by attention regions. Based on this generalization, as well as the centrality of semantics to thought, we argue transitions serve as a general, implicit neurobiological marker of new thoughts, and that their frequency therefore approximates participants’ mentation rate. Finally, to contribute convergent validity and illustrate the utility of our approach for thought dynamics, we regressed resting transition rate and movie-viewing group temporal conformity against trait neuroticism, yielding a first neural confirmation of the “mental noise” theory.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".