The effect of lexical factors on recall from working memory: Generalizing the neighborhood size effect.
Bibliographic record
Abstract
The word-length effect, the finding that lists of short words are better recalled than lists of long words, is 1 of the 4 benchmark phenomena that guided development of the phonological loop component of working memory. However, previous work has noted a confound in word-length studies: The short words used had more orthographic neighbors (valid words that can be made by changing a single letter in the target word) than long words. The confound is that words with more neighbors are better recalled than otherwise comparable words with fewer neighbors. Two experiments are reported that address criticisms of the neighborhood-size account of the word-length effect by (1) testing 2 new stimulus sets, (2) using open rather than closed pools of words, and (3) using stimuli from a language other than English. In both experiments, words from large neighborhoods were better recalled than words from small neighborhoods. The results add to the growing number of studies demonstrating the substantial contribution of long-term memory to what have traditionally been identified as working memory tasks. The data are more easily explained by models incorporating the concept of redintegration rather than by frameworks such as the phonological loop that posit decay offset by rehearsal. (PsycINFO Database Record
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".