Distinct timescales dissociate spontaneous thought dimensions
Bibliographic record
Abstract
Our spontaneous thoughts encompass various dimensions, such as task-relatedness (off vs. on task), and thought orientation (internal vs. external). However, their distinction remains unclear. Our study addresses this issue by focusing on their timescales at both the behavioral level (using fast and slow finger tapping) and the neural level (using EEG) using two independent datasets (N = 84 and 35). Behavioral results revealed a double dissociation: Task-relatedness was linked to fast tapping only, whereas thought orientation was associated with slow tapping only. At the neural level, we assessed topographic similarity in EEG to quantify the temporal influence of past neural activity on current ones. Task-relatedness modulated topographic similarity only during fast tapping, while thought orientation did so only during slow tapping. Critically, topographic similarity was phase-based, as shown by its correlation with phase-locking value and the loss of associations with thought after phase-shuffling. This indicates that the neural signatures of both thought dimensions are strongly phase-dependent. Finally, we demonstrate that nonlinearity plays a distinct role mediating the impact of different timescales (slow and fast finger tapping) on spontaneous thoughts at both behavioral (precision error) and neural levels (topographic similarity). Overall, these results demonstrate that task-relatedness is associated with short timescales, whereas thought orientation is associated with long timescales. This highlights how distinct temporal dynamics shape different spontaneous thought dimensions on both their behavioral and neural features.
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".