Recovering and preventing loss of detailed memory: differential rates of forgetting for detail types in episodic memory
Bibliographic record
Abstract
Episodic memories undergo qualitative changes with time, but little is known about how different aspects of memory are affected. Different types of information in a memory, such as perceptual detail, and central themes, may be lost at different rates. In patients with medial temporal lobe damage, memory for perceptual details is severely impaired, while memory for central details is relatively spared. Given the sensitivity of memory to loss of details, the present study sought to investigate factors that mediate the forgetting of different types of information from naturalistic episodic memories in young healthy adults. The study investigated (1) time-dependent loss of "central" and "peripheral" details from episodic memories, (2) the effectiveness of cuing with reminders to reinstate memory details, and (3) the role of retrieval in preventing forgetting. Over the course of 7 d, memory for naturalistic events (film clips) underwent a time-dependent loss of peripheral details, while memory for central details (the core or gist of events) showed significantly less loss. Giving brief reminders of the clips just before retrieval reinstated memory for peripheral details, suggesting that loss of details is not always permanent, and may reflect both a storage and retrieval deficit. Furthermore, retrieving a memory shortly after it was encoded prevented loss of both central and peripheral details, thereby promoting retention over time. We consider the implications of these results for behavioral and neurobiological models of retention and forgetting.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".