Bibliographic record
Abstract
A model of early development of motivational states is proposed. The states are modeled in terms of modern concepts of state space and are physically realized by long-term-potentiation (LTP)-based neural circuits. The basic idea is to assume existence of libido and aggression instincts that would receive single sensory stimulus and induce capabilities for fight-or-flight, freeze or run, etc. The libido state may lead to happiness, contentment, or activities such as dance or play, imitation of observed behavior and action of others, and engagement in learning by trial and error.Enhancement of two motor skills are presented: responding more quickly in time and delivering a larger force of contact. This is a simple example of how the perception system, the motor system and the motivation system interact. A one-degree-of-freedom second-order mechanical system is modified by a first-order neural facilitator or compensator.The tit-for-tat phenomenon in force escalation is also modeled. The model includes tactile sensors for the measurement of a known force applied to a human finger, afferent transmission of the sensed force to the brain, storage of the perceived force, recovery of the stored force from memory, and efferent transmission of the force to the finger. The situation may change based on perception of more adversaries discouraging retaliation and encouraging resort to withdrawal and / or retreat.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".