The similarities (and familiarities) of pseudowords and extremely high-frequency words: Examining a familiarity-based explanation of the pseudoword effect.
Bibliographic record
Abstract
The pseudoword effect is the finding that pseudowords (i.e., rare words or pronounceable nonwords) give rise to more hits and false alarms than words. Using the retrieving effectively from memory (REM) model of recognition memory, we tested a familiarity-based account of the pseudoword effect: Specifically, the pseudoword effect arises because pseudowords lack distinctive semantic meanings. Because semantics can differentiate orthographically similar words (e.g., horse vs. house), by lacking distinctive semantics, pseudowords have greater interitem similarity than words, and hence more familiarity, which gives rise to the pseudoword effect. Across two sets of simulations, we demonstrate that this account explains the pseudoword effect in addition to accounting for why the pseudoword effect is absent when irregular nonwords are compared with words. Furthermore, our modeling efforts suggest a novel experiment that leads us to the discovery of a new concordant effect. Namely, extremely high-frequency words behave like pseudowords (giving rise to more hits and false alarms than high-frequency words) and also have less distinctive semantics than high-frequency words. We conclude that our work provides strong evidence in favor of the familiarity-based accounts of the pseudoword effect. We discuss the implications of our research with regard to various issues surrounding the pseudoword effect and REM model.
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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 | 0.002 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".