Bibliographic record
Abstract
ABSTRACT Do animals have episodic memory—the kind of memory which gives us rich details about particular past events—or is this uniquely human? This might look like an empirical question, but is attracting increasing philosophical attention. We review relevant behavioural evidence, as well as drawing attention to neuroscientific and computational evidence which has been less discussed in philosophy. Next, we distinguish and evaluate reasons for scepticism about episodic memory in animals. In the process, we articulate three pressing philosophical issues underlying these sceptical arguments, which should be the focus of future work. The Problem of Interspecific Variation asks which differences between humans and animal memory mean that an animal has a variant of episodic memory, and which mean that it has a different kind of memory altogether. The Problem of Functional Variation asks how we should conceptualise the functions of episodic memory and other capacities across species and across evolutionary time. Finally, the Problem of Alternatives asks what, besides episodic memory, might explain the evidence—and how we should evaluate competing explanations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".