Author response: Enhancement of fear memory by retrieval through reconsolidation
Bibliographic record
Abstract
Video cameras allow us to record events as they happen. When we look back at a video clip, what we see is an exact replica of what was originally recorded. We tend to assume that our memories work in a similar manner. However, recent research suggests that our memories may be more malleable than we realize. Once a memory has been reactivated, it goes through a process known as reconsolidation that can make it stronger or weaker, or that can change its content. Now, Fukushima et al. have carried out a series of experiments which shed light on the process of memory reconsolidation. Mice were trained to remember a negative event, and later tested on their memory of this event. Some of the mice were also given a ‘reactivation’ session, during which they were reminded of the original memory. These mice were more fearful of the event during the memory test than those who had not been reminded of it. This suggests that the process of reconsolidating the memory after it had been retrieved had the effect of making the memory stronger. Fukushima et al. then demonstrated that this enhancement depended on the synthesis of proteins in particular regions of the brain. When the mice were given an injection to block protein synthesis immediately after reactivation of the memory, their memory of the negative event was weakened. Crucially, this effect only happened when the injection was given immediately after reactivation of the memory; if the memory had not been reactivated, the injection did not change its strength. Fukushima et al. went on to show that three regions of the brain—the amygdala, the hippocampus, and the medial prefrontal cortex—are involved in memory enhancement. However, only one of them, the amygdala, is involved in the other aspects of reconsolidation. This research could support clinical work by elucidating the potential role of reconsolidation in conditions such as post-traumatic stress disorder.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.313 | 0.123 |
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".