Dissociation of memory systems: The story unfolds.
Bibliographic record
Abstract
In this article we describe the ideas and circumstances that led to the experiment demonstrating a triple dissociation of memory systems. We then move on to discuss the results of 20 years of investigation of those ideas. First, evidence is described from animal studies consistent with the ideas that memory for different kinds of information is stored in different brain systems, and that the hippocampus, amygdala, and dorsal striatum are each central structures in one of the systems. We then focus on the 3 tasks used in the original triple dissociation: win-stay learning, conditioned cue preference, and win-shift learning. Each of these tasks is specific to behavior resulting from the type of information stored in one of the systems, but the use of other behavioral tests that are sensitive to the types of information stored in other systems has revealed that, in each case, other types of information are acquired in parallel. Next, evidence consistent with the idea that the outputs of the systems compete for control of behavior is discussed together with alternative forms of more direct interactions among the systems. Finally, some evidence that many of these ideas about multiple parallel memory systems may apply to humans is reviewed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".