Bibliographic record
Abstract
From a stimulus-response (S-R) point of view, or even with an intermediate step, involving cognition (S-O-R), the existence of behavioral variablity in organisms, even under tightly controlled experimental conditions, suggests that 1) the relevant inputs to the system have not been fully characterized, 2) even the most minute difference in system inputs can produce vastly variable behavioral output, or 3) that behavior is fundamentally variable. Any of these possibilities leads to the conclusion that precise behavioral prediction, at any given moment, is virtually impossible. One can, however, re-conceptualize the challenge of understanding behavior such that it involves not what the organism will do from moment to moment, but what the characteristics of the system that governs the behavior of the organism are. In this paper, I outline a closed-loop cybernetic approach to understanding behavior, for which behavioral variability is actually a requirement. Findings are presented from a series of experiments across species, and using computer simulations, that support a cybernetic interpretation of behavior. I argue that behavioral variability provides adaptive advantages to organisms – regardless of whether that variability is produced by noise, or is actively generated by nervous systems. Finally, I discuss some ideas from embodied cognition that impose constraints on the variability of behavior.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".