Exposing the behavioral gambit: the evolution of learning and decision rules
Bibliographic record
Abstract
Behavioral ecologists have long been comfortable assuming that genetic architecture does not constrain which phenotypescan evolve (the “phenotypic gambit”). For flexible behavioral traits, however, solutions to adaptive problems are reached not only by genetic evolution but also by behavioral changes within an individual’s lifetime, via psychological mechanisms such as learning. Standard optimality approaches ignore these mechanisms, implicitly assuming that they do not constrain the expression of adaptive behavior. This assumption, which we dub the behavioral gambit, is sometimes wrong: evolved psychological mechanisms can prevent animals from behaving optimally in specific situations. To understand the functional basis of behavior, we would do better by considering the underlying mechanisms, rather than the behavioral outcomes they produce, as the target of selection. This change of focus yields new, testable predictions about evolutionary equilibria, the development of behavior, and the properties of cognitive systems. Studies on the evolution of learning rules hint at the potential insights to be gained, but such mechanism-based approaches are underexploited. We highlight three future research priorities: (1) systematic theoretical analysis of the evolutionary properties of learning rules; (2) detailed empirical study of how animals learn in nonforaging contexts;and (3) analysis of individual differences in learning rules and their associated fitness consequences.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".