The impact of familiarization strategies on the missing-letter effect
Bibliographic record
Abstract
When reading a text and searching for a target letter, readers make more omissions of the target letter if it is embedded in frequent function words than if it is in rare content words. While word frequency effects are consistently found, few studies have examined the impacts of passage familiarity on the missing-letter effect and studies that have present conflicting evidence. The present study examines the effects of passage familiarity, as well as the impacts of passage familiarization strategy promoting surface or deep encoding, on the missing-letter effect. Participants were familiarized with a passage by retyping a text, replacing all common nouns with synonyms, or generating a text on the same topic as that of the original text, and then completed a letter search task on the familiar passage as well as an unfamiliar passage. In Experiment 1, when both familiar and unfamiliar passages use the same words, results revealed fewer omissions for the retyping and synonyms conditions. However, in Experiment 2, when different words are used in both types of texts, no effect of familiarization strategy was observed. Furthermore, the missing-letter effect is maintained in all conditions, adding support to the robustness of the effect regardless of familiarity with the text.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.004 | 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".