Sometimes slower is better: slow-exploring birds are more sensitive to changes in a vocal discrimination task
Bibliographic record
Abstract
Animal personality, defined as consistent individual differences across context and time, has attracted much recent research interest in the study of animal behaviour. More recently, this field has begun to examine how such variation arose and is maintained within populations. The habitat-dependent selection hypothesis, which posits that animals with differing personality types may fare better (i.e. have a fitness advantage) in different habitats, suggests one possible mechanism. In the current experiment, we tested whether slow- and fast-exploring black-capped chickadees (Poecile atricapillus), determined by performance in a novel environment exploration task, perform differentially when the demands of an acoustic operant discrimination (cognitive) task were altered following successful task acquisition. We found that slow-exploring birds learn to reverse previously learned natural category rules more quickly than faster exploring conspecifics. In accordance with the habitat-dependent selection hypothesis, and previous work with great tits (Parus major), a close relative of the black-capped chickadee, our results suggest that fast-exploring birds may perform better in stable, predictable environments where forming a routine is advantageous, while slow-exploring birds are favoured in unstable, unpredictable environments, where task demands often change. Our results also support a hypothesis derived from previous work with great tits; slow-exploring birds may be generally more flexible (i.e. able to modify their behaviour in accordance with changes in environmental stimuli) in some learning tasks.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".