Short- and long-term memory tasks predict working memory performance, and vice versa.
Bibliographic record
Abstract
The Brown-Peterson, operation span, and continual distractor tasks all require people to retain information while performing a distractor task. Scale Independent Memory, Perception, and Learning (SIMPLE), a local relative distinctiveness model, has been fit to aspects of each task and offers the same explanation for each: the distractor task serves to space the items out in time and memory performance depends on the relative distinctiveness of the target item at the time of recall. If this is correct, it follows that performance on all three tasks should correlate, even though the tasks have, at various times, been ascribed to different memory systems, short-term memory, working memory, and long-term memory, respectively. We tested 190 subjects on all three tasks and found that performance on all three tasks is significantly correlated. We then fit the data from each task using SIMPLE. We argue that these results support the relative distinctiveness principle (Surprenant & Neath, 2009). We contrast SIMPLE with other models of the same tasks. (PsycINFO Database Record (c) 2019 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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".