The list length effect in short-term memory
Bibliographic record
Abstract
In free recall, the list length effect (LLE) refers to the finding that the proportion of correctly recalled items decreases as set-size increases but at the same time the total number of recalled items continues to increase with set-size (Murdock, 1962). Oberauer et al. (2018) proposed that a decrease in memory accuracy as a function of increasing set-size was fundamental to conceptualizations of short-term and working memory. Evidence of a LLE in short-term/working memory would contradict this benchmark. Beaman (2006) observed a LLE in serial recall whereas Unsworth and Engle (2006) observed no such effect in either serial recall or complex span. In the current research, we sought to reconcile these conflicting results. Six experiments were conducted, examining the relationship between the number and proportion of words recalled in both serial recall and complex span. No LLE was observed in either task. Instead, the proportion of words recalled decreased as a function of list length, while the number of words recalled initially increased, before either reaching a plateau or decreasing. The results suggest that recall accuracy decreases as a function of increasing list length due to increased interference and decreased positional and temporal distinctiveness in longer lists.
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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 | 0.008 | 0.043 |
| 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 | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| 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".