Grand Unified Theories of the brain need better understanding of behavior: the two-tiered emergence of function.
Bibliographic record
Abstract
Over the last few decades, neuroscience, and various associated disciples, has expanded enormously in terms of output, tools, methods, concepts and large-scale projects. In spite of these developments, the principles underlying brain function and behavior are of yet only partially understood. We claim that brain functioning requires the elucidation of the rules associated with all possible task realizations, rather than targeting the activity underlying a specific realization. A first step into that direction was taken by approaches focusing on dynamical structures underlying task performances, as exemplified by Coordination Dynamics. Theoretically, this approach is founded on Haken’s Synergetics, which provides a mechanism through which the degrees of freedom associated with high-dimensional systems may be effectively reduced to one or a few functional ones. This dimensionality reduction, however, is only valid in the vicinity of phase transitions, which severely limits the framework’s domain of explanation. This limitation does not hold for the recently advanced framework of Structured Flows on Manifolds (SFM), which is similar in spirit yet complementary to Synergetics. Following novel theoretical work on the onset, propagation, and offset of epileptic seizures, we expand the SFM framework, and propose that the resulting two-tiered fast-slow dynamics may be a generic mathematical organization underlying and linking brain and behavior.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".