Organizing memories for generalization in complementary learning systems
Bibliographic record
Abstract
ABSTRACT Memorization and generalization are complementary cognitive processes that jointly promote adaptive behavior. For example, animals should memorize a safe route to a water source and generalize to features that allow them to find new water sources, without expecting new paths to exactly resemble previous ones. Memory aids generalization by allowing the brain to extract general patterns from specific instances that were spread across time, such as when humans progressively build semantic knowledge from episodic memories. This cognitive process depends on the neural mechanisms of systems consolidation, whereby hippocampal-neocortical interactions gradually construct neocortical memory traces by consolidating hippocampal precursors. However, recent data suggest that systems consolidation only applies to a subset of hippocampal memories; why certain memories consolidate more than others remains unclear. Here we introduce a novel neural network formalization of systems consolidation that highlights an overlooked tension between neocortical memory transfer and generalization, and we resolve this tension by postulating that memories only consolidate when it aids generalization. We specifically show that unregulated memory transfer can be detrimental to generalization in unpredictable environments, whereas optimizing systems consolidation for generalization generates a high-fidelity, dual-system network supporting both memory and generalization. This theory of generalization-optimized systems consolidation produces a neural network that transfers some memory components to the neocortex and leaves others dependent on the hippocampus. It thus provides a normative principle for reconceptualizing numerous puzzling observations in the field and provides new insight into how adaptive behavior benefits from complementary learning systems specialized for memorization and generalization.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".