The reactivation of task rules triggers the reactivation of task-relevant items
Bibliographic record
Abstract
Working memory (WM) describes the temporary storage of task-relevant items and procedural rules to guide action. In spite of its central importance for goal-directed behavior, the interplay between WM and long-term memory (LTM) remains poorly understood. Recent studies have shown that repeated use of the same task-relevant item in WM results in a hand-off of the storage of that item to LTM, and switching to a new item reactivates WM. To further elucidate the rules governing WM-LTM interactions, we here planned to probe whether a change in task rules, independent of a switch in task-relevant items, would also lead to WM reactivation of maintained items. To this end, we used scalp-recorded electroencephalogram (EEG) data, specifically the contralateral delay activity (CDA), to track WM item storage while manipulating repetitions and changes in task rules and task-relevant items across trials in a visual WM task. We tested two rival hypotheses: If changes in task rules result in a reactivation of the target item representation, then the CDA should increase when a task change is cued even when the same target has been repeated across trials. However, if the reactivation of a task-relevant item only depends on the mnemonic availability of the item itself instead of the task it is used for, then only the changes in task-relevant items should reactivate the representations. Accordingly, the CDA amplitude should decrease for repeated task-relevant items independently of a task change. We found a larger CDA on task-switch compared to task-repeat trials, suggesting that reactivation of task rules triggers the reactivation of task-relevant items in WM. By demonstrating that WM reactivation of LTM is interdependent for task rules and task-relevant items, this study informs our understanding of visual WM and its interplay with LTM.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".