Proceedings of the 5th Neurological Disorders Summit (NDS-2019)
Bibliographic record
Abstract
This talk provides a self-contained summary of neural models of normal and abnormal learning and memory consolidation in which the hippocampus plays an important role.As heuristically described in the Multiple Trace Theory of Moscovitch and Nadel, the role of the hippocampus in some learning processes is time-limited, but in others more enduring.This theme raises the question of why and how several different kinds of learning processes all include hippocampal resources.The talk will describe neural models of cognitive, adaptively-timed cognitive-emotional, and spatial navigational processes that all involve the hippocampus in learning and memory consolidation processes, but which differ in the extent of hippocampal involvement as memory consolidation proceeds.It hereby provides mechanistic explanations of the differences that have been experimentally reported about hippocampal involvement.Many psychological and neurobiological data are explained in a unified way by these models, including data about clinical disorders like medial temporal amnesia and problems with allocentric navigation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.135 | 0.070 |
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".