Spatial cognition in three dimensions
Bibliographic record
Abstract
To date, most studies of spatial learning have been conducted in the horizontal plane, \nwith few addressing the vertical dimension. I aimed to investigate learning of 3-D \nlocations by wild, free-living hummingbirds and compare them with rats. In my first \nexperiment, I found that hummingbirds can encode a 3-D rewarded location after a \nsingle visit. Using a one-dimensional array, I then found that the birds more readily \nlearned a location in a horizontal than in a vertical linear array. However, the ease of \nlearning was a product not only of the orientation of the array but also of its spacing \nscale. By the end of training, hummingbirds visited the central rewarded flower and \nthe two adjacent flowers more than they visited the distal flowers for all arrays. \nHowever, when the array was horizontal and the flowers spaced 30 cm apart, they \nlearned the absolute location of the rewarded flower. In a diagonal array birds \nlearned the 2-D reward location but they chose at random when tested on a vertically \nor horizontally oriented array. However, when birds trained in the diagonal array \nwere tested on a 180° rotated diagonal array they chose the flower with the same \nhorizontal component as the rewarded flower rather than with the flower with the \nsame vertical component. Finally in order to compare the spatial learning of animals \nthat move in volumes with those who move in two dimensions I trained \nhummingbirds and rats to a rewarded location in a cubic maze. Although both \nhummingbirds and rats learned a 3-D location within a cubic maze, hummingbirds \nappeared to learn the rewarded location as a 3-D coordinate while rats seemed to \nlearn the vertical and horizontal component of the 3-D location independently. In \naddition, hummingbirds were more accurate in the vertical and rats in the horizontal, \nwhich is consistent with their type of locomotion. More experiments in volumetric, terrestrial and climbing animals are needed in order to determine whether the \ncontrasting search strategies and learning accuracies constitute adaptations to \nparticular spatial niches.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".