Elevation-related difference in serial reversal learning ability in a nonscatter hoarding passerine
Bibliographic record
Abstract
Environments characterized by scarce and variable food supply, termed “harsh environments,” have been hypothesized to favor cognitive abilities that aid an animal in finding food, remembering where it is located, or predicting its availability. Most studies of the “harsh environment” hypothesis have found that scatter hoarders from harsher environments have better spatial memory abilities, but few studies have looked at this hypothesis in nonscatter hoarders. Here, we present the first comparison of performance on a serial reversal learning task in a nonscatter hoarder from 2 elevations that differ in harshness. Serial reversal learning tasks measure a suite of cognitive abilities that are believed to allow an animal to adjust its foraging behavior to match changes in the availability of food over time. Therefore, performance on this task is predicted to increase with elevation. There was no significant difference between the high and low elevation great tits in initial reversal learning accuracy. While both high and low elevation birds were able to improve their reversal learning accuracy, they did not differ in their rate of improvement over reversals. However, we found that lower elevation birds had higher accuracy across all reversals. Contrary to the “harsh environment” hypothesis, our findings suggest that birds from the less harsh environment at low elevation performed more accurately on the reversal learning task. Overall, our results suggest that the study of the relationship between harshness and cognition in nonhoarders would benefit from taking into account other environmental factors, and trade-offs with other cognitive abilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".