Short Article: The interaction of word frequency and word class: A test of the GO model's account of the missing-letter effect
Why this work is in the frame
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Bibliographic record
Abstract
When asked to detect target letters while reading a text, participants miss more letters in frequent function words than in less frequent content words. In this phenomenon, known as the missing-letter effect, two factors covary: word frequency and word class. According to the GO model, there should be an interaction between word class and word frequency with more omissions for function than for content words only among high-frequency words. This pattern would be due to the fact that function words could only assume a structure-supporting role if they are identified rapidly, which is only possible for high-frequency words. These predictions were tested by assessing omission rate for frequent and rare function and content words. Results lend support to the GO model with more omissions for frequent than for rare words, and more omissions for the function than for the content word among high-frequency words, but not among low-frequency words. These results were observed both in English (Experiment 1) and in French (Experiment 2).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it