Adaptive episodic memory: how multiple memory representations drive behavior in humans and nonhumans
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Episodic memory is a declarative long-term memory of a specific past experience. As such, it is multifaceted, encompassing both the objective and subjective components of that experience. These components can be flexibly represented at different levels of granularity, from precise, context-specific details to generalized, gistlike representations. In this review, we suggest that 1) multiple representations of an episodic memory at different levels of granularity are simultaneously encoded into a memory trace and 2) the relative weighting of these representations determines the extent to which a memory is reconstructed or reproduced at retrieval. We propose that this representational flexibility drives adaptive behavior by prioritizing reconstruction or reproduction depending on the age of the memory, its relationship to prior knowledge, current attentional goals or task demands, and individual differences. Drawing on research in humans and nonhuman animals, we show a close correspondence between psychological and neural representations of a memory across encoding, consolidation, and retrieval. Specifically, we discuss how hippocampal activity in humans and engram formation and activation in rodents support the reproduction of detailed memory representations, whereas schema formation across species, mediated by the medial prefrontal cortex, facilitates reconstruction and generalization to guide behavior. Finally, we consider how species- and individual-level differences shape episodic memory representations. By integrating findings across species, we illustrate how the correspondence between neural and psychological representations enables multiple memory representations to balance stability and flexibility, ultimately driving adaptive behavior.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it