Encoding of local and global cues in domestic dogs’ spatial working memory
Bibliographic record
Abstract
The current study investigated whether domestic dogs encode local and/or global cues in spatial working memory. Seven dogs were trained to use a source of allocentric information (local and/or global cues) to locate an attractive object they saw move and disappear behind one of the three opaque boxes arrayed in front of them. To do so, after the disappearance of the target object and out of the dogs’ knowledge, all sources of allocentric information were simultaneously shifted to a new spatial position and the dogs were forced to follow a U-shaped pathway leading to the hiding box. Out of the seven dogs that were trained in the detour problem, only three dogs learned to use the cues that were moved from trial to trial. On tests, local (boxes and experimenter) and/or global cues (walls of the room) were systematically and drastically shifted to a new position in the testing chamber. Although they easily succeeded the control trials, the three dogs failed to use a specific source of allocentric information when local and global cues were put in conflict. In discussion, we explore several hypotheses to explain why dogs have difficulties to use allocentric cues to locate a hidden object in a detour problem and why they do not differentiate the local and global cues in this particular experimental setting.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".