Infra-slow scale-free dynamics modulate the connection of neural and behavioral variability during attention
Bibliographic record
Abstract
The activities of the human brain vary across different timescales, exhibiting scale-free dynamics. Previous research has highlighted the psychological and physiological significance of brain dynamical fluctuations across the Delta to Gamma bands. However, there has been less focus on infra-slow scale-free dynamics, e.g. power law exponent (PLE), and neural variability, e.g. standard deviation (SD), and sample entropy (SE), in mediating brain-behavior connection during attention. In this study, we recruited 49 participants and recorded functional magnetic resonance imaging (fMRI) resting-state and task data during a sustained attention task paradigm to investigate how the three measures-SD, SE, and PLE-modulate the dynamics of behavioral performance. Our findings demonstrate the following: (i) PLE, SD, and SE exhibit differential topographic distribution with a hierarchical structure from sensory to associative networks, during their rest-task modulation. (ii) PLE, SD, and SE show different topographic extensions from visual cortex to default-mode network in their relationship with behavioral variability. (iii) The relationship between SD and SE is mediated by PLE in the empirical data, which (iv) is further confirmed in simulation. Collectively, our results highlight the topographically- and dynamically-layered mechanisms of distinct neurodynamical features during attention processing: scale-free dynamics modulate neural and behavioral variability.
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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) | 0.001 | 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".