Multiple trace theory of human memory: Computational, neuroimaging, and neuropsychological results
Bibliographic record
Abstract
Hippocampal-neocortical interactions in memory have typically been characterized within the "standard model" of memory consolidation. In this view, memory storage initially requires hippocampal linking of dispersed neocortical storage sites, but over time this need dissipates, and the hippocampal component is rendered unnecessary. This change in function over time is held to account for the retrograde amnesia (RA) gradients often seen in patients with hippocampal damage. Recent evidence, however, calls this standard model into question, and we have recently proposed a new approach, the "multiple memory trace" (MMT) theory. In this view, hippocampal ensembles are always involved in storage and retrieval of episodic information, but semantic (gist) information can be established in neocortex, and will survive damage to the hippocampal system if enough time has elapsed. This approach accounts more readily for the very long RA gradients often observed in amnesia. We report the results of analytic and connectionist simulations that demonstrate the feasibility of MMT. We also report a neuroimaging study showing that retrieval of very remote (25-year-old) memories elicits as much activation in hippocampus as retrieval of quite recent memories. Finally, we report new data from the study of patients with temporal lobe damage, using more sensitive measures than previously the case, showing that deficits in both episodic and spatial detail can be observed even for very remote memories. Overall, these findings indicate that the standard model of memory consolidation, which views the hippocampus as having only a temporary role in memory, is wrong. Instead, the data support the view that for episodic and spatial detail the hippocampal system is always necessary.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".