Dogs (Canis familiaris) use odor cues to show episodic-like memory for what, where, and when.
Bibliographic record
Abstract
Episodic-like memory is a personal memory that contains what happened, where it happened, and when it happened. Although episodic-like memory in nonhuman animals has been shown using what-where-when memory paradigms, it has not previously been shown in dogs. Dogs are an excellent candidate for developing translational models of neurodegenerative disorders related to episodic memory, including Alzheimer's disease. Dogs were tested in experiments that involved spatially and temporally unique sequences of odor stimuli to see if they remembered the odors, their locations, and their times of presentation. By choosing the earlier exposed odor on two-choice tests, dogs showed the ability to encode what-when, where-when, or what-where-when memory. Further tests revealed that dogs performed optimally when all three components of what-where-when memory were available for encoding and could flexibly use this information on unpredictable tests. Although the experiments reported here show that dogs remembered what, where, and when, they did not indicate whether these components were part of an integrated single memory or were retrieved from separate files. Evidence on the question of integrated memory requires trials on which all three components are tested. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".