The list-length effect occurs in cued recall with the retroactive design but not the proactive design.
Bibliographic record
Abstract
An ongoing debate in the memory literature concerns whether the list-length effect (better memory for short lists compared with long lists) exists in item recognition (Annis, Lenes, Westfall, Criss, & Malmberg, 2015; Dennis, Lee, & Kinnell, 2008). This debate was initiated when Dennis and Humphreys (2001) showed that, when confounds present in earlier list-length experiments were controlled, the list-length effect disappeared. The issue has yet to be settled. Interestingly, the same confounds present in recognition experiments exist in cued-recall experiments. Here, we implemented Dennis and Humphreys' (2001) methodological controls to test for the list-length effect in cued recall. In Experiment 1, we found a robust list-length effect when start-of-study items from the long list were tested. However, no list-length effect was found in Experiments 2 and 3 when end-of-study items from the long list were tested. These results are consistent with the view that cued recall is susceptible to retroactive interference but not proactive interference, a position supported by early interference work (e.g., Lindauer, 1968; Melton & von Lackum, 1941). (PsycINFO Database Record (c) 2020 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 | 0.027 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".