Increased reliance on long-term memory when anticipating attentional guidance
Bibliographic record
Abstract
Imagine attempting to locate your keys on a cluttered desk. This everyday scenario exemplifies how memories shape our attention. However, how long-term memories (LTMs) guide our attention remains a puzzle. Traditional models of memory and attention attribute an essential role to working memory (WM) to bias ongoing perception towards attentional goals. Accordingly, we hypothesized that long-term memories (LTMs) stored for guiding attention should be strongly represented in WM. To explore this, we used contralateral delay activity (CDA), an electrophysiological index of working memory storage, to assess WM recruitment to store LTMs when preparing for both a search task and a recognition task. Unexpectedly, the CDA was higher for the recognition task than for the search task, indicating that humans rely more on LTM than on WM in anticipation of attentional guidance. This finding suggests an exciting strategy: humans may delegate task goals to LTM to free up WM resources for more demanding tasks, such as visual search. Our results challenge prevailing models of memory and attention, revealing the unexpected preference for LTM in guiding attention.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".