Evolving a Playful Brain: A Levels of Control Approach
Bibliographic record
Abstract
Play is rare in the Animal Kingdom, but relatively common in the larger brained vertebrate taxa. Comparisons at the level of classes, orders, and, in some cases, families, suggest that larger brained taxa are more likely to contain playful species. However, at the species level, such relationships generally disappear. In some well documented mammalian taxa, such as Rodentia, it is clear that there are species which do not play at all, some where the play is quite complex and some showing all grades in between. Comparative methods are used here to supplement proximal analyses of the content of one particular form of play, play fighting, so as to identify the neurobehavioral mechanisms that are needed in rodents to evolve complex play from simpler antecedents. At least five independent neural mechanisms are shown to be necessary to produce the most complex example of play fighting in rodents. The identification of such levels of control provides a new method for systematizing the diversity of play present in mammals. Furthermore, this approach sets the stage for re-evaluating the relationship between brain size and play. That is, the issue can be reconceptualized in terms of whether species with larger brains are more likely to have a greater number of control mechanisms. It is not that larger brained species are more likely to play, but rather, that when they do play, the content of their play is more flexible. Suitable comparative data sets are needed to test these possibilities.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".