Evolving the tactics of play fighting: insights from simulating the “keep away game” in rats
Bibliographic record
Abstract
Play fighting in many animals consists of a complex choreography of somewhat stereotypical behaviors involving attack and defense—typically of particular body areas—that are differentially generated under specific conditions. In most domains where behavior is considered, including the study of social play, the prevailing explanatory theories rest on the assumptions that: (1) behavior is the result of “programs” that can be strictly or loosely specified, located somewhere in the central nervous system, and (2) the behavior an organism produces in a certain circumstance is the result of a choice between all (or many) of the available options, assumed to be arrived at by considering internally generated predictions about the consequences of actions. To test these assumptions, we used sets of parameters generated by our previous work with rats and crickets to create an agent-based model of a game of “keep-away.” We demonstrate that the agents need to possess neither behavioral programs (e.g., fixed or modal action patterns), nor any predictive capacity, in order to reproduce the tactics commonly used when organisms protect an object of interest from conspecifics. The results are presented in terms of the evolution of social play, which can be seen as a variation of the game of keep away.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".