Niche-specific cognitive strategies: object memory interferes with spatial memory in the predatory bat, <i>Myotis nattereri</i>
Bibliographic record
Abstract
Related species with different diets are predicted to rely on different cognitive strategies: those best suited for locating available and appropriate foods. Here we tested two predictions of the niche-specific cognitive strategies hypothesis in bats, which suggests that predatory species should rely more on object memory than on spatial memory for finding food and that the opposite is true of frugivorous and nectivorous species. Specifically, we predicted that: (1) predatory bats would readily learn to associate shapes with palatable prey and (2) once bats had made such associations, these would interfere with their subsequent learning of a spatial memory task. We trained free-flying Myotis nattereri to approach palatable and unpalatable insect prey suspended below polystyrene objects. Experimentally naïve bats learned to associate different objects with palatable and unpalatable prey but performed no better than chance in a subsequent spatial memory experiment. Because experimental sequence was predicted to be of consequence, we introduced a second group of bats first to the spatial memory experiment. These bats learned to associate prey position with palatability. Control trials indicated that bats made their decisions based on information acquired through echolocation. Previous studies have shown that bat species that eat mainly nectar and fruit rely heavily on spatial memory, reflecting the relative consistency of distribution of fruit and nectar compared with insects. Our results support the niche-specific cognitive strategies hypothesis and suggest that for gleaning and clutter-resistant aerial hawking bats, learning to associate shape with food interferes with subsequent spatial memory learning.
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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 | 0.000 | 0.000 |
| 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".