The Development of the Missing-Letter Effect Revisited
Bibliographic record
Abstract
When participants read a text for comprehension while identifying a target letter, the letter is more often missed in a frequent function word than in a less frequent content word. This is the missing-letter effect. Studies have shown the importance of both frequency and word function. The role of each of these factors in development is less understood. The goal of this study was to revisit the influence of frequency and word function in the missing-letter effect in development with better-controlled stimuli. Two hundred sixteen participants took part in this study and were divided into five groups (6-7 years, 8 years, 9 years, 10-11 years, and university students). They were asked to read four experimental texts for comprehension and to circle a target letter. The results showed a basic missing-letter effect with more omissions for a frequent function word than a less frequent content word for every group. When frequency was controlled, we found a word function effect as early as 6-7 years of age, with more omissions for a function word than a content word. In contrast, when word function was controlled, an effect of frequency was only significant for adults and 8-year-olds. These results clarify discrepancies in the literature and support the importance of rigorous stimuli control.
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".