Brain meta-state transitions demarcate thoughts across task contexts exposing the mental noise of trait neuroticism
Bibliographic record
Abstract
Researchers have observed large-scale neural meta-state transitions that align to narrative events during movie-viewing. However, group or training-derived priors have been needed to detect them. Here, we introduce methods to sample transitions without any priors. Transitions detected by our methods predict narrative events, are similar across task and rest, and are correlated with activation of regions associated with spontaneous thought. Based on the centrality of semantics to thought, we argue these transitions serve as general, implicit neurobiological markers of new thoughts, and that their frequency, which is stable across contexts, approximates participants' mentation rate. By enabling observation of idiosyncratic transitions, our approach supports many applications, including phenomenological access to the black box of resting cognition. To illustrate the utility of this access, we regress resting fMRI transition rate and movie-viewing transition conformity against trait neuroticism, thereby providing a first neural confirmation of 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 | 0.004 |
| 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) | 0.002 | 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".