3D flightpaths reveal the development of spatial memory in wild hummingbirds
Bibliographic record
Abstract
ABSTRACT Many animals learn to relocate important places and reflect this spatial knowledge in their behaviour. Traditionally evidence for learning is examined experimentally by studying spatial memory. However, tools developed for analysing tracking data from widely ranging animals allow a more holistic analysis of behaviour. Here we use the two together in novel combination of experimental and modelling approaches to analyse how patterns of hummingbird movements change as birds learn to find a reward in a location indicated by a pair of landmarks. Using hidden Markov models (HMMs) we identified two movement states which we interpret as Search and Travel and compared these to experimental behavioural measures of spatial memory. When birds had a single training trial to learn a flower’s location, both the behavioural measures and HMMs showed that hummingbirds relied on landmarks to guide search. Hummingbirds focussed hovering around the rewarded location and were more likely to be in the Search state, and more likely to switch from Travel to Search, when closer to the rewarded location, but only when the landmarks were present. When birds had had 12 additional training trials, however, the HMMs and behavioural measures showed differences in how reliant birds were on landmarks. While behaviours like hovering were still strongly affected by removing landmarks, the likelihood of being in or entered the Search state was the same regardless of whether the landmarks were present or removed. These results suggests that hummingbirds rapidly learn to use nearby landmarks to structure where they search, but as birds gain experience the role of these landmarks changes. While familiar local landmarks were still essential for precise search, experienced birds were able to use alternative cues to guide broad-scale transitions between behaviour. HMMs and traditional behavioural measures each capture a different aspect of this learning, with neither approach alone accurately described the role of landmarks in spatial 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 | 0.001 | 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) | 0.002 | 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".