The Intrinsic Hierarchy of Self – Converging Topography and Dynamics
Bibliographic record
Abstract
Abstract The brain can be characterized by an intrinsic hierarchy in its topography which, as recently shown for the uni-transmodal distinction of core and periphery, converges with its dynamics. Does such intrinsic hierarchical organization in both topography and dynamic also apply to the brain’s inner core itself and its higher-order cognitive functions like self? Applying multiple fMRI data sets, we show how the recently established three-layer topography of self (internal, external, mental) is already present during the resting state and carried over to task states including both task-specific and -unspecific effects. Moreover, the topographic hierarchy converges with corresponding dynamic changes (measured by power-law exponent, autocorrelation window, median frequency, sample entropy, complexity) during both rest and task states. Finally, analogous to the topographic hierarchy, we also demonstrate hierarchy among the different dynamic measures themselves according to background and foreground. Finally, we show task-specific- and un-specific effects in the hierarchies of both dynamics and topography. Together, we demonstrate the existence of an intrinsic topographic hierarchy of self and its convergence with dynamics.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".