Dynamics of neural recruitment surrounding the spontaneous arising of thoughts in experienced mindfulness practitioners
Bibliographic record
Abstract
Thoughts arise spontaneously in our minds with remarkable frequency, but tracking the brain systems associated with the early inception of a thought has proved challenging. Here we addressed this issue by taking advantage of the heightened introspective ability of experienced mindfulness practitioners to observe the onset of their spontaneously arising thoughts. We found subtle differences in timing among the many regions typically recruited by spontaneous thought. In some of these regions, fMRI signal peaked prior to the spontaneous arising of a thought - most notably in the medial temporal lobe and inferior parietal lobule. In contrast, activation in the medial prefrontal, temporopolar, mid-insular, lateral prefrontal, and dorsal anterior cingulate cortices peaked together with or immediately following the arising of spontaneous thought. We propose that brain regions that show antecedent recruitment may be preferentially involved in the initial inception of spontaneous thoughts, while those that show later recruitment may be preferentially involved in the subsequent elaboration and metacognitive processing of spontaneous thoughts. Our findings highlight the temporal dynamics of neural recruitment surrounding the emergence of spontaneous thoughts and may help account for some of spontaneous thought's peculiar qualities, including its wild diversity of content and its links to memory and attention.
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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 | 0.000 | 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".