When pigeons in motion lose sight of their food: behaviour on visible displacement tasks revisited
Bibliographic record
Abstract
In traditional visible displacement tasks, animals view an object as it is moved out of their sight, but either the object moves behind an occluder or an occluder moves in front of it. Here we present a more ecologically realistic visible displacement task for pigeons ( Columba livia (Gmelin, 1789)) in which it was the animal that occluded the object, a food dish in this case, by virtue of its own motion. In a branched maze, pigeons had visual access to food, which they would lose from view as they moved through the maze. A within-subject design was used whereby the task was presented first in descending order of difficulty (i.e., decreasing memory load owing to the opening of (i) gaps and (ii) windows in the walls of the maze), and followed 10 months later by an ascending order. When the food was visible at all times through windows and gaps, pigeons would make turns in the maze that would bring them closer to the food (i.e., they chose the shortest route above chance levels). In general, they failed to do so when the food was lost from view, but there was one exception at the end of the study (the second time that there were gaps but no windows): there was a significant tendency for the last two turns that brought the bird out of the maze to be the shortest route to the food. The pigeons may have learned to take advantage of opportunities to lighten their memory load.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".