Has multiple trace theory been refuted?
Bibliographic record
Abstract
Multiple trace theory (Nadel & Moscovitch, Current Opinion in Neurobiology, 1997, 7, 217-227) has proven to be one of the most novel and influential recent memory theories, and played an essential role in shifting perspective on systems-level memory consolidation. Here, we briefly review its impact and testable predictions and focus our discussion primarily on nonhuman animal experiments. Perhaps, the most often supported claim is that episodic memory tasks should exhibit comparable severity of retrograde amnesia (RA) for recent and remote memories after extensive damage to the hippocampus (HPC). By contrast, there appears to be little or no experimental support for other core predictions, such as temporally limited RA after extensive HPC damage in semantic memory tasks, temporally limited RA for episodic memories after partial HPC damage, or the existence of storage of multiple HPC traces with repeated reactivations. Despite these shortcomings, it continues to be a highly cited HPC memory theory.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".