Cognitive tasks could be biased towards generalists: a lesson from wild non-eusocial bees
Bibliographic record
Abstract
Ecological niches are closely intertwined with cognition in many animal lineages. For example, diet breadth is linked with performance on tasks measuring learning and exploration in several vertebrates, with generalists often exhibiting faster learning and more exploratory behavior than specialists. We compared associative learning performance and exploratory tendencies between dietary specialist and generalist bee (Anthophila) species using a closed-environment task with free-moving bees called the free-moving proboscis-extension response (FMPER). We found lower participation rates than expected, especially among specialist species, which hindered our ability to answer our primary question. Because participation rates of specialist species were so low, we combined our data with another published dataset that reported results from the same learning task but for several different bee species (again including specialists and generalists) to investigate the relation of diet breadth with associative learning and exploration across a broader species assemblage. Phylogeny-informed generalized linear mixed models indicate that neither specialists nor generalists increased accuracy throughout the task, although bees of both diet breadths became faster at drinking from the rewarding strip. Bees decreased their drinking latency-a measure of exploration-throughout the experiment, with no effect of diet breadth. However, specialists became less likely to participate over the course of the task compared to generalists. Our results suggest that specialist and generalist bees have experienced similar selection for associative learning abilities, and that specialists are hesitant to continue interacting with novel stimuli. Our study highlights the importance of developing cognitive tasks that measure abilities equally across the full range of life history traits.
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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) | 0.001 | 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 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".