Systems consolidation, transformation and reorganization: Multiple Trace Theory, Trace Transformation Theory and their Competitors
Bibliographic record
Abstract
We review the literature on systems consolidation by providing a brief history of the field to place the current research in proper perspective. We cover the literature on both humans and non-humans, which are highly related despite the differences in techniques and tasks that are used. We argue that understanding the interactions between hippocampus and neocortex (and other structures) that underlie systems consolidation, depend on appreciating the close correspondence between psychological and neural representations of memory, as postulated by Multiple Trace Theory and Trace Transformation Theory. We end by evaluating different theories of systems consolidation in light of the evidence we reviewed and suggest that the concept of systems consolidation, with its central concern with the time-limited role the hippocampus plays in memory, may have outlived its usefulness. We suggest replacing it with a program of research on the psychological processes and neural mechanisms that underlie changes in memory across the lifetime – a natural history of memory change.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".