A processing approach to the working memory/long-term memory distinction: Evidence from the levels-of-processing span task.
Bibliographic record
Abstract
Recent theories suggest that performance on working memory (WM) tasks involves retrieval from long-term memory (LTM). To examine whether WM and LTM tests have common principles, Craik and Tulving's (1975) levels-of-processing paradigm, which is known to affect LTM, was administered as a WM task: Participants made uppercase, rhyme, or category-membership judgments about words, and immediate recall of the words was required after every 3 or 8 processing judgments. In Experiment 1, immediate recall did not demonstrate a levels-of-processing effect, but a subsequent LTM test (delayed recognition) of the same words did show a benefit of deeper processing. Experiment 2 showed that surprise immediate recall of 8-item lists did demonstrate a levels-of-processing effect, however. A processing account of the conditions in which levels-of-processing effects are and are not found in WM tasks was advanced, suggesting that the extent to which levels-of-processing effects are similar between WM and LTM tests largely depends on the amount of disruption to active maintenance processes.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".