Intrusions in episodic memory: reconsolidation or interference?
Bibliographic record
Abstract
It would be profoundly important if reconsolidation research in animals and other memory domains generalized to human episodic memory. A 3-d-list-discrimination procedure, based on free recall of objects, with a contextual reminder cue (the testing room), has been thought to demonstrate reconsolidation of human episodic memory (as noted in a previous study). Our goal was to replicate the central result, a high intrusion rate during recall of the target list, and evaluate the reconsolidation account relative to an alternative account, based on state-dependent learning and interference. First, replication was not straightforward (Experiment 1). Second, using a very unique, highly salient context (Experiment 2), the method produced a qualitative replication, but it was small in magnitude. A critical assumption of the reconsolidation account, that the target list is reactivated and destabilized during re-exposure to the study context, was not supported (Experiment 3). Although troubling for the reconsolidation account, the findings can be easily accommodated by an alternative account that does not assume additional neurobiological processes underlying the destabilization of consolidated memories, instead explaining intrusion rates simply in terms of well-established cognitive effects, such as item-to-context binding and interference during retrieval.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".