Re-stepping into the same river: competition problem rather than a reconsolidation failure in an established motor skill
Bibliographic record
Abstract
Animal models suggest that consolidated memories return to their labile state when reactivated and need to be restabilized through reconsolidation processes to persist. Consistent with this notion, post-reactivation pharmacological protein synthesis blockage results in mnemonic failure in hippocampus-dependent memories. It has been proposed that, in humans, post-reactivation experience with a competitive task can also interfere with memory restabilization. However, several studies failed to induce performance deficit implementing this approach. Moreover, even upon effective post-reactivation interference, hindered performance may rapidly recover, raising the possibility of a retrieval rather than a storage deficit. Here, to address these issues in procedural memory domain, we used new learning to interfere with restabilization of motor memory acquired through training on a sequence of finger movements. Only immediate post-reactivation interference was associated with the loss of post-training delayed gains in performance, a hallmark of motor sequence memory consolidation. We also demonstrate that such performance deficit more likely indicates a genuine memory impairment rather than a retrieval failure. However, the reconsolidation view on a reactivation-induced plasticity is not supported. Instead, our results are in line with the integration model according to which new knowledge acquired during the interfering experience, is integrated through its consolidation creating memory competition.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".