Anticipatory attractors, functional neurochemistry and “Throw & Catch” mechanisms as illustrations of constructivism
Bibliographic record
Abstract
Abstract This review explores several rarely discussed examples illustrating constructivism principles, generative and selective features of neuronal regulation of behaviour. First, the review highlights Walter Freeman’s experiments and mathematical analysis that uncovered the existence of anticipatory attractors, i.e. non-random dynamical patterns in neurodynamics. Since Freeman’s work did not extend to neurochemistry, this paper then points to the proposed earlier neurochemical framework summarizing the managerial roles of monoaminergic, cholinergic and opioid receptor systems likely contributing to anticipatory attractors in line with functional constructivism. As a third example, neurochemistry’s evidence points to the “Throw & Catch” (T&C) principle in neurodynamics. This principle refers to the pro-active, neurochemically expensive, massive but topical increase of potentials (“Throw”) within electrodynamics and neurotransmission in the brain whenever there is an uncertainty in selection of degrees of freedom (DFs). The T&C also underlines the relay-like processes during the selection of DFs. The “Throw” works as an internally generated “flashlight” that, contrarily to the expectations of entropy reduction, increases entropy and variance observed in processes related to orientation and action-formation. The discussed examples highlight the deficiency of structures-oriented projects and excitation-inhibition concepts in neuroscience. The neural regulation of behaviour appears to be a fluid, constructive process, constantly upgrading the choice of behavioural DFs, to ensure the compatibility between the environmental and individual’s individuals’ needs and capacities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".