Searching high and low: Domestic dogs’ understanding of solidity
Bibliographic record
Abstract
Physical reasoning appears central to understanding how the world works, suggesting adaptive function across the animal kingdom. However, conclusive evidence for inferential reasoning about physical objects is limited to primates. We systematically tested a central feature—understanding of solidity—in domestic dogs, by adapting a validated procedure (the shelf task) previously used to test children and non-human primates. Dogs watched a treat dropped into an apparatus with a shelf either present (treat landing on the shelf) or absent (treat landing on the bottom surface) and chose where to search for it (above or below the shelf). Across four studies (N=64), we manipulated visual access to the treat trajectory and apparatus interior. Dogs correctly inferred the location of treats using physical cues when the shelf was present (Study 1), and learned rapidly when visual cues of continuity were limited (Study 2), and when the shelf was absent (Study 3). Dogs were at chance when the apparatus was fully occluded, and the presence and absence of the shelf varied across trials within subjects, and showed no evidence of learning (Study 4). The findings of these four studies suggest that dogs may be able to make some inferences using solidity and continuity and do not exhibit proximity or gravity biases. However, dogs did not always search correctly from Trial 1, and failed to search correctly when the rewarded location varied within-subjects, suggesting a role for learning, and possible limits to their ability to make inferences about physical objects.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".