Is play a behavior system, and, if so, what kind?
Bibliographic record
Abstract
Given that many behavior patterns cluster together in sequences that are organized to solve specific problems (e.g., foraging), a fruitful perspective within which to study behaviors is as distinct 'behavior systems'. Unlike many behavior systems that are widespread (e.g., anti-predator behavior, foraging, reproduction), behavior that can be relegated as playful is diverse, involving behavior patterns that are typically present in other behavior systems, sporadic in its phylogenetic distribution and relatively rare, suggesting that play is not a distinct behavior system. Yet the most striking and complex forms of play have the organizational integrity that suggests that it is a behavior system. One model that we develop in this paper, involves three stages of evolutionary transition to account for how the former can evolve into the latter. First, play-like behavior emerges from the incomplete development of other, functional behavior systems in some lineages. Second, in some of those lineages, the behavior patterns typical of particular behavior systems (e.g., foraging) are reorganized, leading to the evolution of specific 'play behavior systems'. Third, some lineages that have independently evolved more than one such play behavior system, coalesce these into a 'super system', allowing some animals to combine behavior patterns from different behavior systems during play. Alternative models are considered, but irrespective of the model, the overall message from this paper is that the conceptual framework of the behavior system approach can provide some new insights into the organization and diversity of play present in the animal kingdom.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".