Individual differences in working memory reactivation of long-term memories predict protection against anticipated interference
Bibliographic record
Abstract
Most daily tasks require frequent information exchange between working memory (WM) and long-term memory (LTM). However, the factors that modulate the reactivation of LTMs in WM remain to be explored. Here, we tested the effects of anticipated perceptual interference on reactivation using contralateral delay activity (CDA) in the EEG. On each trial, participants saw a previously studied object that was tested after a brief retention interval. In half of the blocks, the retention contained perceptual distractors. Half of the participants had larger CDA on interference blocks (WM preparers) and others on no interference blocks (LTM preparers). WM preparers showed smaller interference costs in accuracy suggesting that preparing against interference via reactivating LTMs in WM is a more effective strategy than relying on passive LTMs. Moreover, in interference blocks, contralateral alpha suppression, an index of spatial attention, disappeared during retention in anticipation of interference, mostly in WM preparers. These results indicate that individuals stopped attending to reactivated memories when anticipating interference, presumably to prevent the involuntary encoding of perceptual distractors that appear at attended locations. Together, these results highlight individual differences in preparing for anticipated interference in recruiting WM to store LTMs, and their effects on proneness to interference.
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 | 0.003 |
| 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) | 0.002 | 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".