When does length cause the word length effect?
Bibliographic record
Abstract
The word length effect, the finding that lists of short words are better recalled than lists of long words, has been termed one of the benchmark findings that any theory of immediate memory must account for. Indeed, the effect led directly to the development of working memory and the phonological loop, and it is viewed as the best remaining evidence for time-based decay. However, previous studies investigating this effect have confounded length with orthographic neighborhood size. In the present study, Experiments 1A and 1B revealed typical effects of length when short and long words were equated on all relevant dimensions previously identified in the literature except for neighborhood size. In Experiment 2, consonant-vowel-consonant (CVC) words with a large orthographic neighborhood were better recalled than were CVC words with a small orthographic neighborhood. In Experiments 3 and 4, using two different sets of stimuli, we showed that when short (1-syllable) and long (3-syllable) items were equated for neighborhood size, the word length effect disappeared. Experiment 5 replicated this with spoken recall. We suggest that the word length effect may be better explained by the differences in linguistic and lexical properties of short and long words rather than by length per se. These results add to the growing literature showing problems for theories of memory that include decay offset by rehearsal as a central feature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".