Attention allocation between item and order information in short-term memory
Bibliographic record
Abstract
In immediate memory for verbal lists, recently it has been shown that participants can choose to carry out encoding that prioritises readiness for an item test at some cost to order information or, conversely, that prioritises readiness for an order test at a cost to item information. Here, we ask whether participants can control attention to items and order in a graded fashion. We examined this issue by manipulating the percentage of order or item test trials participants would receive in a block (for each type of test, 25%, 50%, 75%, or 100% of the trials in a block). Overall, the results revealed that participants were able to allocate their attention in a fine-grained manner that took into account the trial distribution within the block. However, there was a difference between the effects of allocating attention to item versus order. Divided attention, compared with full attention to one attribute, had an asymmetry, such that divided attention impaired order performance more than item performance. The exact point at which this asymmetry could be seen differed between two experiments, which included different item tests (fragment completion vs. free recall). The results suggest a common resource for item and order encoding and/or retention in working memory, which can be voluntarily allocated to different mixtures of these two attributes.
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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 | 0.001 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".