Testing a Strategy-Disruption Account of the List-Strength Effect
Bibliographic record
Abstract
Presenting items multiple times on a study list increases their memorability, a process known as item strengthening. The list-strength effect (LSE) refers to the finding that, compared to unstrengthened (pure) lists, lists for which a subset of the items have been strengthened produce enhanced memory for the strengthened items and depressed memory for the unstrengthened items. Although the LSE is found in free recall (Tulving & Hastie, 1972), it does not occur in recognition (Ratcliff et al., 1990). In free recall, the LSE in mixed lists is attributed to a sampling bias promoting priority recall of strong items and consequent output interference affecting weak items. We suggest that, in recognition, the disruption of this pattern through the randomization of test probes is responsible for the null LSE. We present several pilot experiments consistent with this account; however, the registered experiment, which had more statistical power, did not support this account.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".