Does word frequency influence judgments of learning (JOLs)? A meta-analytic review.
Bibliographic record
Abstract
Although decades of research have identified robust effects of word frequency (WF) on memory performance, the comparatively smaller body of research examining the impact of WF on judgments of learning (JOLs) has yielded inconsistent findings. The purpose of this brief meta-analytic review is to synthesize the existing literature examining WF effects on JOLs with the aim of clarifying the extent to which such judgments are influenced by WF, and to identify some potential moderators of this effect. In analysing 17 experiments across 6 published and 1 unpublished studies, a small, but reliable effect of WF on JOLs was found (g = .23), with high frequency (HF) words afforded higher JOLs than low frequency (LF) words. There was, however, extensive heterogeneity among the effect sizes, implying that the WF effect on JOLs is subject to the influence of potentially many different moderator variables. The potential implications of this finding for understanding the sources of information that guide JOLs are discussed. In addition, speculation as to potential moderators contributing to the observed heterogeneity is offered, and emphasis is placed on the importance of considering item-level variability when items are nested within the conditions to be contrasted. (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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".