The function of episodic memory in animals
Bibliographic record
Abstract
The best-known example of episodic memory in animals came from food-storing birds. One of the beauties of the food-storing system was that inherent in the behaviour were the elements that (at the time) made up episodic memory: what, where and when. While there were then already plenty of data on animals' ability to put together what and where, the addition of the time element in animals' memory and its testing was one that was both new and experimentally challenging. It has, however, led to an increasing variety of examples showing that animals can put together all three informational components. If episodic memories can be described as those memories that make any one of us who we are, why should non-human animals have such memories? Here, we argue that episodic memories play a significant functional role in the lives of real animals, in particular, enabling them to make decisions about how they might or should act in their future. We support our argument with data from a range of examples, focussing on data from the field.This article is part of the theme issue 'Elements of episodic memory: lessons from 40 years of research'.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".