Disentangling the roles of frequency-vs. state-dependence in generating individual differences in behavioural plasticity
Bibliographic record
Abstract
Theoretical work suggests that both negative frequency-dependent payoffs and state-dependent payoffs can lead to individual variation in behavioural plasticity. We investigated the roles of both frequency- and state-dependence on the occurrence of individual variation in behavioural plasticity in a series of experiments where we manipulated perceived predation danger for red knots (Calidris canutus islandica). We found individual variation in plasticity in a trait with negative frequency-dependent payoffs (vigilance), but not in a trait with positive frequency-dependent payoffs (escape flights). Furthermore, there was no correlation between the average level of vigilance under low predation danger and the magnitude of response to increased predation danger, as would be expected under state-dependence. Thus, our results provide support for the hypothesis that negative-frequency dependence favours individual variation in plasticity. However, negative-frequency dependence alone cannot explain why plasticity would be consistent within individuals, and future studies should address the factors that might favour individual consistency.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".