The mitigating effect of repeated memory reactivations on forgetting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Memory reactivation is a process whereby cueing or recalling a long-term memory makes it enter a new active and labile state. Substantial evidence suggests that during this state the memory can be updated (e.g., adding information) and can become more vulnerable to disruption (e.g., brain insult). Memory reactivations can also prevent memory decay or forgetting. However, it is unclear whether cueing recall of a feature or component of the memory can benefit retention similarly to promoting recall of the entire memory. We examined this possibility by having participants view a series of neutral images and then randomly assigning them to one of four reactivation groups: control (no reactivation), distractor (reactivation of experimental procedures), component (image category reactivation), and descriptive (effortful description of the images). The experiment also included three retention intervals: 1 h, 9 days, and 28 days. Importantly, the participants received three reactivations equally spaced within their respective retention interval. At the end of the interval, all the participants were given an in-lab free-recall test in which they were asked to write down each image they remembered with as many details as possible. The data revealed that both the participants in the descriptive reactivation and component reactivation groups remembered significantly more than the participants in the control groups, with the effect being most pronounced in the 28-day retention interval condition. These findings suggest that memory reactivation, even component reactivation of a memory, makes memories more resistant to decay.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it