Bumblebees Learn to Forage like Bayesians
Bibliographic record
Abstract
Bayesian foraging in patchy environments requires that foragers have information about the distribution of resources among patches (prior information), either set by natural selection or learned from past experience. We test the hypothesis that bumblebee foragers can rapidly learn prior information from past experience in two very different experimental environments. In the high-variance environment (patches of low and high quality), stochastic optimality models predicted that finding rewards should sometimes sharply increase an optimal forager's tendency to stay in a patch (an incremental response), whereas in the uniform environment, finding rewards should always decrease the tendency to stay (a decremental response). We use Cox regression models to show that, in a matter of hours, bees learned to match both predicted responses, resulting in a reward intake rate that averaged 80% of the predicted maximum. Following training in either environment, bees' adaptive behavior carried over to a common test environment, thus confirming the influence of memorized prior information. Although Bayesian foraging by learning is often presumed, this study is the first to clearly isolate the adaptive use of a learned prior expectation. More generally, it highlights the remarkable adaptive plasticity of an important generalist pollinator and agent of selection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".