The tradeoff between item and order information in short-term memory does not depend on encoding time.
Bibliographic record
Abstract
Participants can optimize encoding of an immediate verbal memory test for item or for order information, or they can try to be ready for either type of test. Dividing encoding between both kinds of information, however, comes at a cost. Recently, it has been shown that the cost is more severe for order information compared to item information (Guitard et al., 2022). Here, for the first time, we evaluated which factor can better account for this asymmetry by contrasting two hypotheses. According to a rate hypothesis, divided attention affects the rate of encoding more for order than for items. According to an alternative, asymptote hypothesis, divided attention does not affect the rates but diminishes the endpoint, or asymptotic level, of order encoding more than item encoding. In three experiments to distinguish these hypotheses, participants prepared for an item fragment completion test, an order reconstruction test, or both types of tests, in trials with different durations of presentation. Overall, our results were better accounted for by a model which assumes that dividing attention between preparation for item and order testing affects the asymptote of encoding more for order than for items, with no effects on the rates of order or item encoding compared to preparation for a single test. The findings not only replicate our prior results, but also demonstrate that the allocation of attention to item or order processing can be disentangled from the time on task. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".