Testing a Strategy-Disruption Account of the List-Strength Effect
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it