Word length, set size, and lexical factors: Re-examining what causes the word length effect.
Bibliographic record
Abstract
The word length effect, better recall of lists of short (fewer syllables) than long (more syllables) words has been termed a benchmark effect of working memory. Despite this, experiments on the word length effect can yield quite different results depending on set size and stimulus properties. Seven experiments are reported that address these 2 issues. Experiment 1 replicated the finding of a preserved word length effect under concurrent articulation for large stimulus sets, which contrasts with the abolition of the word length effect by concurrent articulation for small stimulus sets. Experiment 2, however, demonstrated that when the short and long words are equated on more dimensions, concurrent articulation abolishes the word length effect for large stimulus sets. Experiment 3 shows a standard word length effect when output time is equated, but Experiments 4-6 show no word length effect when short and long words are equated on increasingly more dimensions that previous demonstrations have overlooked. Finally, Experiment 7 compared recall of a small and large neighborhood words that were equated on all the dimensions used in Experiment 6 (except for those directly related to neighborhood size) and a neighborhood size effect was still observed. We conclude that lexical factors, rather than word length per se, are better predictors of when the word length effect will occur. (PsycINFO Database Record (c) 2018 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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".